Coder Legion - Recent posts tagged aicoding

Developers: Stop collecting prototypes. Start finishing projects.

Thu, 15 Oct 2026 06:52:52 +0000

AI-assisted coding has lowered the barrier to building software. But a repository full of generated features isn't the same as a finished, tested, maintainable application.

When a project stalls, return to the fundamentals:

Define the minimum finished product.

Identify the blockers preventing completion.

Fix one issue at a time.

Validate existing functionality after changes.

Ship only when the agreed acceptance criteria are met.

The objective isn't to write the most code. It's to deliver software that works as intended.

SEOSiri explores this challenge in our latest article:

Vibe Coding

AI to help programmers to reach enlightened laziness and long-term laziness for real productivity

Sun, 11 Oct 2026 20:28:47 +0000

How can AI help lazy programmers to reach enlightened laziness and long-term laziness for real productivity?
Analysis
- Flashback about software engineering before LLMs AI
  - Key Facts (The Truth)
  - Key Fallacies (Common Mistakes)
- Common Pitfalls of Code Generated by LLM AI Code Agents
Summary
Help AI to help you
References

How can AI help lazy programmers to reach enlightened laziness and long-term laziness for real productivity?

This article contains links to some of my past articles, along with AI-generated historical facts and analysis that support it.

Being a Lazy Programmer

According to Larry Wall, the original author of the Perl programming language, there are three great virtues of a programmer; Laziness, Impatience and Hubris

Laziness: The quality that makes you go to great effort to reduce overall energy expenditure. It makes you write labor-saving programs that other people will find useful and document what you wrote so you don't have to answer so many questions about it.
Impatience: The anger you feel when the computer is being lazy. This makes you write programs that don't just react to your needs, but actually anticipate them. Or at least pretend to.
Hubris: The quality that makes you write (and maintain) programs that other people won't want to say bad things about.

In "Code Complete (2nd edition)" by Steve McConnell, chapter 33.7:

33.7. Laziness
Laziness manifests itself in several ways:

Deferring an unpleasant task

Doing an unpleasant task quickly to get it out of the way

Writing a tool to do the unpleasant task so that you never have to do the task again

Some of these manifestations of laziness are better than others. The first is hardly ever beneficial. You've probably had the experience of spending several hours futzing with jobs that didn't really need to be done so that you wouldn't have to face a relatively minor job that you couldn't avoid. I detest data entry, and many programs require a small amount of data entry. I've been known to delay working on a program for days just to delay the inevitable task of entering several pages of numbers by hand. This habit is "true laziness." It manifests itself again in the habit of compiling a class to see if it works so that you can avoid the exercise of checking the class with your mind.

The small tasks are never as bad as they seem. If you develop the habit of doing them right away, you can avoid the procrastinating kind of laziness. This habit is "enlightened laziness", the second kind of laziness. You're still lazy, but you're getting around the problem by spending the smallest possible amount of time on something that's unpleasant.

The third option is to write a tool to do the unpleasant task. This is "long-term laziness." It is undoubtedly the most productive kind of laziness (provided that you ultimately save time by having written the tool). In these contexts, a certain amount of laziness is beneficial.

When you step through the looking glass, you see the other side of the laziness picture. "Hustle" or "making an effort" doesn't have the rosy glow it does in high-school physical education class. Hustle is extra, unnecessary effort. It shows that you're eager but not that you're getting your work done. It's easy to confuse motion with progress, busy-ness with being productive. The most important work in effective programming is thinking, and people tend not to look busy when they're thinking. If I worked with a programmer who looked busy all the time, I'd assume that he was not a good programmer because he wasn't using his most valuable tool, his brain.

Over the years, I have written a few non-trivial code generators, and the recent ones include:

Strongly Typed Client API Generators (webapiclientgen) generate strongly typed client APIs in C# .NET and in TypeScript for jQuery and Angular 2+ from ASP.NET (Core) Web API.
Strongly Typed OpenAPI Client Generators (openapiclientgen) generate strongly typed C# and TypeScript client codes from Open API / Swagger definitions supporting jQuery, Angular, AXIOS, Fetch API, Aurelia and Angular Strictly Typed Forms.

The driving force is that I want to deliver more business value through less work. I am a lazy programmer who easily gets tired of writing repetitive code, even if I'm paid to do so, particularly HttpClient code in C# and AJAX calls in JavaScript.

The code left by diligent programmers

I am weary of inheriting legacy codebases authored by diligent programmers but burdened with overly complex architecture and software designs. In many cases, the architects and developers did not fully appreciate the inherent capabilities of the underlying development platform, nor did they apply fundamental computer science principles such as high cohesion and loose coupling effectively.

In one notable example, designed by an A-list consultant firm in town, an XML transformation pipeline performed five rounds of XML DOM schema validation, despite the fact that XML serialization alone could have solved the problem far more cleanly and efficiently. .NET (Framework) can generate assemblies at runtime or build time for both XML readers and writers directly from XML schemas, making much of that validation logic redundant. And the performance of that part of the data transformation became at least 20X faster, with much less processing power and memory consumption. This was not the only example I had seen of using cutting-edge technologies the dirty way, though not entirely wrong, since the legacy, fresh, and dirty code was "working".

These experiences put me in a good position to discuss how AI can assist software development, and where it falls short, grounded in the mid-term and long-term, and exploring real-world systems, tools, and trade-offs rather than abstract theory alone, even though I am not an AI expert. However, I look forward to becoming proficient in using AI to improve my overall productivity and ROI. Tools, 3rd-party libraries, code generators, and AI code agents are all good only when they help me reduce the consumption of my precious brain power and memory. If I have to consume more brain power and memory, this means either that this assistance is bad or that I have used it the dirty way.

These days, most programmers in the commercial world are still using 3GLs like C++, Java, C#, Python, Dart, Swift, and JavaScript, etc. Therefore, in the following chapters, I will mostly focus on examples of 3GL and general-purpose programming languages, and C# .NET. If you are a Java or C++ programmer, you may find similar examples through your own research.

The following is summaries of various technical resources that help lazy programmers.

Resources for Lazy Programmers before AI

This chapter provides quick highlights of the resources to help you review what you have learned or begin learning, with linked articles generated by AI.

Coding Assistance

Syntax highlight and formatting in text editors like Notepad ++, Programmer's Editor, Visual Studio Code and Atom etc.
Intellisense.
Syntax check, design time type check, and basic logic check, in IDEs like Visual Studio and Visual Studio Code with extensions.
Preprocessor and compiler
Static Code Analyzer
...

Code Generators

C++ Template
In .NET, the runtime or the compiler could generate serialization assemblies for forward only non-cache serializers. And this is just one of many framework-managed code generators in .NET.
Model-driven code generators.
...

Hints:

Java has similar categories of framework‑managed code generation features, but they are fewer, more fragmented, and architecturally different from .NET’s. Many are externalized.

Built-in Framework Modules

.NET Framework and .NET Core include a lot built-in framework modules that app programmers could utilize in most business applications:

Middleware infrastructure
ASP.NET Core MVC pipeline
Model binding system
Validation system
Action / result / exception filters
Routing and endpoint dispatch
Formatters (JSON, XML, etc.)
...

Utilizing those built-in module wisely could make your app code short and simple, so easy to maintain.

.NET and Java provide runtime optimization. And generally clean code can help the runtime to do better optimization.

Hints:

Java has small amount of built-in framework modules, while most live outside the JDK, in standardized APIs (Jakarta EE) or third‑party frameworks (Spring, Quarkus, etc.).

Remarks:

Python is a darling programming language in the AI landscape; however, it lacks framework-managed code generation and built-in framework modules, and this is a deliberate design choice. The overall design choices of Python contribute to it becoming the dominant language in the AI landscape, in particular for meta-programming mediated by AI.

App Generator

These days, when googling "Application Generator", the top results are basically all AI app generator or builder. I remember seeing a lot of advertisements for application generators in the 1980s, generating business applications for DOS or Windows. For example, you would input a few business constraints, add some further configuration and optional customization, and the generator would then generate a Northwind-like application. Here's a summary about app generators before LLMs AI becoming popular.

Model to Code

Rather than generating the whole app, "Model to Code" offers a more flexible and generic solution for application programmers: realizing implementation from a high-level design with UML or similar tools, and maintaining an "active model" where changes in the model instantly update the code, and vice versa.

I have come across ModelMaker, and liked it for a while. ModelMaker was a UML‑style modeling and refactoring tool built specifically for Borland Delphi’s Object Pascal. It supported:

Drawing UML‑style class diagrams
Generating Delphi code from those diagrams
Reverse‑engineering existing Delphi code into UML diagrams
Refactoring and restructuring Delphi projects

ModelMaker has faded into history, and these days tools like Visual Paradigm support generating Java, C++, C#, etc. from UML diagrams.

Rather than starting from UML diagrams, Windows Workflow Foundation offers a form of "Model‑to‑Code", more exactly, a model‑driven execution system where the model is the program. For some reason, MS discontinued WF. Nevertheless, there are still alternatives around for such classic Model-Driven Development (MDD).

4GL, DSL and 5GL

"The term 4GL was first used formally by James Martin in his 1981 book Application Development Without Programmers to refer to non-procedural, high-level specification languages. Some researchers state that 4GLs are a subset of domain-specific languages.".

Application Development Without Programmers

Oh yeah, isn't this literally the main intent behind why companies are pushing AI code agents nowadays?

Nevertheless, I happened to use dBase, Visual FoxPro, PowerBuilder, and Omnis Studio in the '80s and '90s. In my observations, 4GLs may reach bottlenecks as the business grows and changes:

As the business needs to deal with more functional and technical domains, the 4GL environment can barely keep up, due to business growth and changes in the business and tech landscapes.
The 4GL environment is discontinued by the vendor.

According to Wikipedia, Domain-specific language is a computer language specialized to a specific application domain. This is in contrast to a general-purpose language (GPL), which is broadly applicable across domains. There are a wide variety of DSLs, ranging from widely used languages for common domains, such as HTML for web pages, down to languages used by only one or a few pieces of software, such as MUSH soft code.

AI Code Agents

In the last 2 or 3 years, there has been a bombardment of academic papers, marketing materials, and criticism around AI code agents. I am at the consumer end of AI code agents, and here's my observation:
The output of traditional code generation is predictable and deterministic, and can be compiled and used immediately without modification, while AI-generated code, particularly through Python meta-programming mediated by AI, is not so predictable or deterministic, and often contains language syntax errors and other logic errors.

I asked M365 Copilot about my observation, and here's the analysis returned about "Traditional Code Generators v.s. AI Code Agent". And you are very welcome to ask a similar question to your favorite AI agents, or even better, ask real AI experts.

Keeping the shortfalls of AI code agents in mind, you as a programmer are in a better position to get the best from those agents and deliver more business value through less work.

You've probably heard: "Scrum: The Art of Doing Twice the Work in Half the Time." In my observations, practices done in the name of Scrum have often resulted in doing twice the waste in half the time, since most of the work involved in value delivery is waste, due to various ScrumBut practices and factors. Overall, it's not too bad if the waste is inevitable.

And if you're not careful, mindful, and cautious enough, using an AI code agent could result in delivering 10 times the technical debt in 1/10 of the time, since the generated code could be severely over complex and overly lengthy.

Are you confident that AI is able to reduce the pure technical debts it created?

Are you sure you don't need to care about technical debts in most real world business applications?

In my observations, AI code agents are generally good at doing "Plus", adding tons of code that's politically correct regarding SOLID and design patterns, etc., just like a junior developer armed with all the academic knowledge of software engineering but without the real-world experience to make trade-offs according to business context. And currently, these AI code agents have near zero capability of doing "Minus", removing code and simplifying design.

How AI has been helping me?

There are reasons why I have listed many traditional codegen resources that predate LLM AI code agents. Before AI code agents became a thing, I often said the following statements or examples:

Having a working solution won't satisfy me or the business, and I aim to deliver twice the value through half the work.
In software development, for one functional requirement there could be an unlimited number of working solutions, a few good, mostly dirty. Generally, the simplest one is the most suitable for the business.
In software development, for one functional requirement, there could be an unlimited number of working designs, a few good, mostly dirty. And from one design, there could be a few working implementations, one or two good, the others dirty.
The key to finding the most suitable solution for the business is to establish sufficient two-way communication with the business and to put significant effort into that communication.
It is our job to provide automation solutions to our customers, so we had better automate our own job first.
As a lead developer on a green-field project, I typically spend 1/3 down to 1/4 of my billable hours on coding (including testing), and the rest on thinking, studying, and researching, as well as talking to the business, clients, and end users. So the LoC is typically 1/2 to 1/5 of the LoC written by other developers on the same technical stack, and I often deliver a simpler design with fewer dependencies on 3rd-party components.
As a lead developer on very legacy projects left by predecessors, I typically spend much more time cutting code than writing it. In one case of maintaining a legacy ERP system, after three and a half years, I had added 500 lines of code and removed 5,000 lines of code, resulting in far fewer bug reports every week and slightly better performance. Rather than spending most of my effort debugging, I tackled defects which are the nests of bugs.
Technical debt is essentially management debt. A typical symptom is that both the tech people and the business people lack commitment to iterative two-way communication, and the end result is often over-complex architecture, over-complex software design, and overly lengthy code as well as long hours in debugging and fixing customer reported bugs.

How Visual Studio and alike with AI has been helping me?

AI-generated DocComment. 80% hit rate.
AI suggestion of the next paragraph of code. 40% hit rate for long code, depending on the subject, and 70% for short, repetitive code.

Remarks:

The hit rate is based on my impression, not statistics. However, it represents how comfortable I feel with the AI's correctness, overall, it's subjective.
I have been mostly developing complex business applications and development tools, so my comfort zones may be different from yours.
I can't help thinking the VS dev team is working on better scaffolding with AI. So in the next major release of VS, I may just say a few prompts, and VS could scaffold a comprehensive skeleton, or even a full app. VS AI scaffolding should outperform many AI code generators out there, because VS already has comprehensive and solid scaffolding logic built in, along with many IDE accessories that assist with technical correctness.

Hints:

Visual Studio Code with extensions connecting to GitHub Copilot offers similar assistance.
Visual Studio Code with a plug-in extension to ChatGPT Codex helps too.
Traditional IDE assistants, like syntax checking and IntelliSense for function prototypes and DocComments, are still the most deterministically helpful.

How AI compensates my poor memory

I have always had a poor memory and could barely remember technical details except those I use actively and repetitively, but I don't enjoy doing repetitive technical work. Look at the bright side: the IT landscape keeps changing, basically reshuffled every 2 years since the early '70s. Therefore, this disadvantage makes me forget legacy and obsolete technologies, while I'm good at understanding and remembering concepts.

Remarks:

This poor capability for memorizing technical details has made me look bad in job interviews during technical screening, when the interview panels asked me questions that a junior developer or a new CS/SE graduate could outperform me on.
In the past, I accumulated my own personal knowledge-base through:
- Directory with docs
- Google Notes (discontinued by Google)
- Evernote
- Personal Wiki
Google search is my alcoholic friend.

Likewise, AI is your alcoholic friend. You might have experienced AI mixing up old and new technical items. And when questioned, AI will try to fix itself through further research or "imagination." You have probably come across studies about AI hallucination.

However, you can only claim AI hallucinated if you provided the right prompt. "Garbage in, garbage out" (GIGO) applies to AI prompts, though occasionally AI might point out the incorrectness of the prompt. However, you still need foundational knowledge to understand the AI's refinement attempt on an incorrect prompt.

AI dealing with simple ad-hoc data transformation

For example:

JSON to CSV, and vice versa, in cases existing dedicated tools couldn't cover. Hit rate: 95%.
Given JSON data, generate POCO classes with JsonPropertyNameAttribute. Hit rate: 90%. Weak on data types like DateTime, DateOnly, and TimeOnly. Good with currency.
Web content scraping. After CodeProject.com was discontinued, I saved local copies of what was cached at https://web.archive.org/. To create an index page, I asked ChatGPT Codex, plugged into VS Code, to retrieve the title from a certain XPath and format it. And here you are: https://zijianhuang.github.io/codeproject/

AI for refactoring

For example:

Migrate the automatic tests of TypeScript projects from Karma or Jest to Vitest. Hit rate: 95%

AI for scripting

For example:

Write simple JavaScript and PowerShell 7 code for CI/CD. Hit rate: 99%.
Craft HTML templates, and CSS/SCSS for styles and animation. Hit rate: 90%.

AI providing advice for software design and architectural design

Hit rate: 80%.

Entry level understanding of old new tech before further research and study.

Old means that the tech has been around for years, new means that I am new to it. Hit rate: 95%.

Remarks:

In contrast, the AI response to brand new tech is weak regarding correctness.

Common algorithms

Most algorithms you learn from university courses like “Data Structures and Algorithms” have been well packaged into many development platforms such as the .NET BCL and similar frameworks, as well as third‑party components and dedicated libraries like MATLAB.

For those that are included, AI is almost 100% correct nowadays. A typical technical screening question is, for example, a three‑elevator control system.

I basically failed every technical screening involving such algorithms during job interviews, even though I taught “Data Structures and Algorithms” at university as a lecturer in my early 20s. So what was the point of doing this kind of screening when recruiting a senior developer? And what is the point in an era when LLM‑based AI can outperform any developer in the world?

Nevertheless, it is still important for developers to have systematically studied data structures and algorithms to sharpen the human brain, so they can use AI tools more effectively and achieve better outcomes.

Analysis

Ask yourself some questions:

Can AI read between the lines? Are you confident that AI can really understand your question or prompt? Even if you have degrees in English Literature and Computer Science, are you sure you can express every piece of business content and context through a prompt?
Do you understand that one bit can kill through a Turing machine?
How confident are you about that AI can spot all the kill bits that AI generated or you crafted?
Are you sure that AI is not biased on subjects of politics and technology?
While the literal knowledge of AI is many magnitudes greater than that of any human being on Earth, how many aspects of intelligence is AI still missing compared to human intelligence?
Who cares, and why, if the code is long or short, clean or dirty, as long as it runs, and as long as we have fast hardware?

Flashback about software engineering before LLMs AI

Based on Robert Glass’s “Facts and Fallacies of Software Engineering”, the industry is plagued by repeating old mistakes, prioritizing coding over design, and mismanagement. Key facts highlight that quality is expensive but saves money, smaller teams are more productive, and maintenance drives costs.

Key Facts (The Truth)

Maintenance: The majority of software costs occur after delivery (maintenance phase).
Quality: Quality does not cost more; it actually saves money by reducing rework.
Teams: Smaller, experienced teams are far more productive than larger, junior ones.
Development: Errors are most commonly found in the requirements phase, not during coding.

Key Fallacies (Common Mistakes)

"We need better tools": Tools are often purchased but rarely used effectively.
"Coding is the main task": It's actually a small part of the overall engineering lifecycle.
"Documentation is unnecessary": Skipping documentation leads to high maintenance costs.
"Adding more programmers to a late project makes it faster": This actually makes it later.
"Estimation happens once": Software estimation must be continuous, not just at the start.

Ask yourself, how can LLMs AI alter the key facts? or help us to avoid those key fallacies?

Common Pitfalls of Code Generated by LLM AI Code Agents

As a programmer, I have benefited greatly from AI code agents, partly because I am poor at remembering trivial technical details. So far, I have found that once I ask AI code agents to implement a non-trivial feature, no matter how detailed, formal, or simple the prompt, the generated source code is usually over-bloated in design and implementation, and the line count is typically 3–5 times what it should be.

AI-Generated Source Code vs Hand-Crafted Code

“3–5 times” sounds like a magic number to me. In several commercial rewrite projects and one competitive rewrite of an open-source tool using the same technical stack and language:

My codebase was around 1/3 down to 1/5 the LoC of the legacy versions, which were only a few months or a year older.
My designs were much simpler, with fewer dependencies on third-party components and less fancy DI/IoC, SOLID, and design patterns.
Runtime performance was 20% to 50% faster.
The end products were more reliable and robust.

Regarding the legacy codebases:

The author of one tool was enthusiastic about SOLID and DI/IoC but apparently misunderstood high cohesion and loose coupling, using DI/IoC in the wrong places.
The authors of the legacy commercial programs knew SOLID and design patterns well, but still applied them in the wrong places, likely due to misunderstanding cohesion and coupling and introducing overly advanced designs too early and too proactively.

Basically, these legacy codebases looked "politically correct" with respect to SOLID, but were simply over-complicated and too lengthy, resulting in slowness and excessive consumption of computing resources.

Let’s review what Robert C. Martin said in "UML for Java Programmers". After introducing SOLID, he states:

When should these principles be applied? At the first hint of pain. It is not wise to make all systems conform to all principles all the time. You will spend an eternity imagining possible environments for OCP or sources of change for SRP, create dozens of little interfaces for ISP, and invent many worthless abstractions for DIP.
The best way to apply these principles is reactively rather than proactively. When you detect a structural problem or notice a module being affected by changes elsewhere, then consider whether one or more of these principles can help.

A reactive approach also requires a proactive effort to create pressure early. If you want to react to pain, you must diligently search for sore spots.

AI code agents typically apply SOLID principles proactively even eagerly, just like many human developers would, resulting in over complex and overly lengthy code. And AI can barely see the first hint of pain, probably because of its limitless computing power.

The characteristic of AI generated code

According to "Traditional Code Generators v.s. AI Code Agent", AI‑generated code (including Python‑mediated meta‑programming) is:

Probabilistic
Heuristic‑driven
Context‑sensitive
Statistically inferred
Not formally constrained

As a developer or a business person, are you so sure that you would put your business interests in the hands of an AI code agent, while also expecting human developers to quickly fix the hidden problems or technical debt generated by AI?

One bit can kill through Turing machine

I have asked AI agents to collect and summarize 12 prominent historical examples, some of which have been mentioned in various software engineering textbooks:

Written by M365 Copilot.
Written by Claude AI, in Markdown and Docx.
Written by Google AI Studio, in Markdown

Summary

During software development, there are a lot of artifacts created or updated:

Source code, the primary artifact.
Verbal conversations.
Written conversations like user stories and comments in issue tracking.
Doc comments.
Documentations.
Localization.
...

AI can help a lot in the areas where LLM AI's quality characteristics may well satisfy our needs, while our flesh-and-blood brains can review and refine the output from AI, because our literal expressions in words have similar characteristics.

But for generated source code that will be built into machine code and run on a Turing machine, this is a different landscape, one involving a much higher magnitude of operational risk and significant overhead during review and refinement.

Understand the advantage and the shortfalls of AI code agent:

Do use AI code agents for:

Areas where you feel comfortable with an 70-80% hit rate.
Domains where approximate results are acceptable, like UI design, layouts, styles, and animation effects.
Generating the whole end products where approximate results are acceptable, like article generators, trends prediction, chatbot, and photo/video creation/refinements etc.
Advice on software design and architectural design.
Prior art.

Be cautious when using AI coding agents for:

Areas where you are uncomfortable with a success rate below 70-80%, because the remaining 20-30% may ultimately require more effort from you.
Areas where a single mistake can be catastrophic.

AI code agents + traditional code generators + framework‑managed code generation features are a decent combination for productivity, resulting in less stress.

One key factor to examine: Do you, as a senior software developer, have to use more brain power and memory every day in the short term, mid-term, and long term for a similar piece of product to deliver?

Help AI to help you

You know that clear and precise prompt could help AI to respond with result you would expect or has value to you. Therefore,

Have a clear and correct concept before prompting.
Or build-up your concept or understanding through systematic study with or without AI assistance, then prompt.
When using IDEs like Visual Studio 2026 or Visual Studio Code, code agents may be working behind. Clean code help AI to generate elegant code with high hit rate.

References

A Coding System That Is Not Allowed to Trust Its Own Code

Sun, 04 Oct 2026 23:03:36 +0000

Generating code is no longer the part that interests me most.

The harder problem starts one step later:

Who gets to decide that the generated software is actually correct?

I have been working on an open-source project called Derivative, and its software-building pipeline, Forge, is based on a deliberately uncomfortable rule:

The component that writes the software does not have the authority to declare that software correct.

That sounds simple.

Implementing it turned into a much deeper problem involving requirement compilation, isolated execution, independent validation, evidence integrity, adversarial checks, immutable blind benchmarks, and — eventually — validators that also had to be treated as potentially wrong.

This article is an attempt to explain that system without requiring you to understand the entire Derivative architecture first.

Repository:

https://github.com/Daniele-Cangi/Derivative

The 30-second version

You give Forge a software requirement:

python forge.py \
  "Build a Python CLI that reads a CSV of contracts,
   extracts expiration dates,
   flags contracts expiring in less than 90 days,
   writes a summary CSV,
   and includes tests."

The important part is what happens next.

natural-language requirement
        ↓
structured contract
        ↓
grounded plan
        ↓
candidate software
        ↓
isolated execution
        ↓
independent validation
        ↓
verified package

        OR

explicit failure evidence

Forge does not simply generate some files, run the tests generated alongside them, and call the result successful.

The generated software has to survive checks that it does not control.

A run can end in three build outcomes:

verified
validation_failed
infeasible_proven

verified allows packaging.

validation_failed means software may exist, but the available evidence is insufficient to certify it.

infeasible_proven means the stated constraints were shown to be mutually incompatible.

That distinction is the core of the project.

First principle: preserve the requirement before writing code

A natural-language requirement is dangerous because it is easy to quietly lose pieces of it.

Consider:

Read a CSV file.

If the file is empty, print exactly "EMPTY" and exit with status 2.

Otherwise process every valid row and write a summary file.

A coding model can understand that.

It can also accidentally implement:

if not rows:
    print("No rows found")
    return

That implementation looks reasonable.

It is also wrong.

The output is wrong.

The exit status is wrong.

And if those details disappear while the requirement is being converted into a plan, no amount of testing against the weakened plan will recover them.

Forge therefore does not treat the prompt merely as context for a model.

It compiles it.

A requirement becomes typed data describing functional goals, requirement atoms, conditional obligations, acceptance criteria, quality constraints, public interfaces, ambiguity flags and verification expectations.

The idea is not that natural language suddenly becomes mathematically perfect.

The idea is that information loss becomes visible.

Technical deep dive: how the requirement becomes a BuildSpec

The real `RequirementCompiler` currently starts by extracting requirement atoms and normalizing conditional obligations: ```python requirement_atoms = self._extract_requirement_atoms(normalized) conditional_normalization = ( self.conditional_normalizer.normalize(requirement_atoms) ) functional_goals = [ atom.text for atom in requirement_atoms if atom.category in {"functional", "validation"} ] ``` It then derives acceptance, obligation and quality contracts and produces a `BuildSpec`: ```python return BuildSpec( build_id=self._build_id(normalized), raw_requirement=requirement, normalized_requirement=normalized, functional_goals=functional_goals, non_functional_constraints=non_functional_constraints, requirement_atoms=requirement_atoms, conditional_obligations=conditional_normalization.obligations, coverage_directives=conditional_normalization.coverage_directives, conditional_normalization_issues=conditional_normalization.issues, acceptance_contract=acceptance_contract, obligation_contract=obligation_contract, quality_contract=quality_contract, target_artifact_type=target_artifact_type, public_module=public_module, public_import_contract=public_import_contract, risk_hints=..., ambiguity_flags=ambiguity_flags, assumptions=..., ) ``` The contracts themselves are typed dataclasses. For example, a conditional obligation records more than a piece of prose: ```python @dataclass class ConditionalObligation: obligation_id: str parent_requirement_id: str trigger: str precondition: Dict[str, Any] observable_channel: str comparison_relation: str expected_value: Any polarity: str observation_fidelity: str verification_method: str source_fragment: str witness_class: str = "" ``` That means a statement such as: ```text If input is invalid, print "ERROR" and exit with status 2. ``` can eventually become separate observable obligations instead of surviving only as prose inside an LLM context window. This matters later because validation can ask for evidence tied back to those obligations. Source: `core/forge/requirement_compiler.py` `core/forge/contracts.py`

The coder is not the judge

This is where Forge differs from the simplest AI coding loop.

A common architecture is roughly:

prompt
  ↓
model
  ↓
code + tests
  ↓
tests pass
  ↓
done

There is a circularity hidden inside that structure.

The same generation process can create both the implementation and the assumptions used to test it.

If both contain the same misunderstanding, everything can be green.

Forge separates responsibilities.

The planner can propose a plan.

The coder can produce an implementation.

Neither can mark the implementation as verified.

That authority belongs to the validation path.

The current validator uses three layers:

Layer 1
syntax / import / execution

Layer 2
requirements / obligations / acceptance

Layer 3
adversarial validation

All three must pass.

A candidate that merely runs is not enough.

A candidate whose own tests pass is not enough.

A candidate that satisfies only the easy interpretation of the requirement is not enough.

Technical deep dive: the actual three-layer validator

The current `ValidatorStage` creates the three validation layers independently: ```python self.runtime_layer = RuntimeValidationLayer( self.executor, timeout_seconds, ) self.obligation_layer = ObligationValidationLayer( self.executor, timeout_seconds, quality_checker, ) self.adversarial_layer = AdversarialValidationLayer() ``` During validation: ```python layer1 = self.runtime_layer.validate( code_artifact, plan, build_spec, materialized, workspace, ) layer2 = self.obligation_layer.validate( code_artifact, plan, build_spec, materialized, workspace, ) layer3 = self.adversarial_layer.validate( code_artifact, plan, build_spec, materialized, workspace, ) ``` The final decision is deliberately boring: ```python passed = ( layer1.passed and layer2.passed and layer3.passed ) ``` But there is another important detail. Forge preserves failure signatures: ```python for layer in (layer1, layer2, layer3): failures.extend(layer.failures) for signature in layer.evidence.get( "failure_signatures", [] ): self._append_unique( signatures, str(signature), ) ``` Those signatures can later drive **bounded repair**. The repair process therefore has evidence about an observed failure instead of simply receiving: ```text Try again. ``` The validator also records seals for the behavioral contract and the exact artifact being validated. Source: `core/forge/validator_stage.py`

Generated code runs in a sandbox

Another boundary is execution.

Production verification uses an isolated Docker backend.

The generated code does not inherit host credentials.

The sandbox is configured with restrictions including no network access, a read-only root filesystem, resource limits and an environment allowlist.

This is important for security, but also for epistemology.

A program that succeeds because it accidentally reads something from the host machine is not evidence that the generated artifact itself satisfies its contract.

And Forge can refuse validation entirely when isolation is required but unavailable.

Technical deep dive: refusing to silently downgrade isolation

The validator contains an explicit check: ```python if ( self.require_isolation and not self.executor.policy.isolated ): return self._isolation_refusal(contract_seal) ``` That refusal becomes structured validation evidence with the signature: ```text sandbox_policy_violation ``` All three layers are then marked as not executed. This matters because silently switching from: ```text isolated verification ``` to: ```text run it locally and hope for the best ``` would change the meaning of `verified`. The orchestration layer creates the production validator with isolation required: ```python validator = ValidatorStage( executor=process_executor, require_isolation=True, ) ``` Source: `forge.py` `core/forge/validator_stage.py`

Packaging is a privilege, not the next line of the script

One design decision I particularly like is that validation does not merely return a boolean used by convention.

Packaging itself checks that the evidence matches the exact plan and artifact it is about to package.

So even after validation succeeds, the system still checks identity and integrity boundaries.

Technical deep dive: why a passing boolean is not enough

The packaging stage immediately refuses a failed validation artifact: ```python if not validation.passed: raise PackagingRefusedError( "Packaging requires a passed ValidationArtifact." ) ``` But that is only the beginning. It recomputes the expected behavioral contract seal: ```python expected_contract_seal = behavioral_contract_seal(plan) declared_contract_seal = validation.evidence.get( "behavioral_contract_seal" ) if declared_contract_seal != expected_contract_seal: raise PackagingRefusedError( "Packaging requires the validated " "behavioral contract seal." ) ``` Then it checks that validation refers to the exact code artifact: ```python expected_artifact_seal = artifact_validation_seal( code_artifact ) declared_artifact_seal = validation.evidence.get( "validated_artifact_seal" ) if declared_artifact_seal != expected_artifact_seal: raise PackagingRefusedError( "Packaging requires the matching " "validated artifact seal." ) ``` And the validation artifact itself has an integrity seal: ```python expected_validation_artifact_seal = ( validation_artifact_seal(validation) ) if ( validation.integrity_seal != expected_validation_artifact_seal ): raise PackagingRefusedError( "Packaging requires the matching " "validation artifact seal." ) ``` The point is not cryptographic magic. The point is preventing an architectural mistake: ```text validate A modify A into B package B using A's validation result ``` The package must correspond to the artifact and behavioral contract that actually passed. Source: `core/forge/packaging_stage.py`

Then I started testing the verifier

At this point the obvious temptation would be to demonstrate a few successful builds and write:

Forge generates verified software.

I don't think that would be a useful claim.

A system designed to verify generated software also needs to be tested against requirements it did not optimize itself around.

So Derivative now has a sequence of blind benchmark bundles.

The basic rule is:

create requirements
create independent acceptance oracles
freeze them
hash them
run Forge
preserve the result

After Forge has seen the bundle, it becomes a known regression corpus.

Fixing the implementation does not allow me to rewrite the old score.

A later execution is recorded as a post-fix replay.

Not as a new blind result.

This distinction has become one of the most important parts of the project.

A good failure is more useful than a fake 100%

Blind V10 produced an interesting result.

There were 12 cases:

6 expected verified
3 expected validation_failed
3 expected infeasible_proven

On the immutable baseline, seven of the twelve terminal statuses matched.

Four of the six positive cases reached verified and also passed their independent external oracles.

All three expected validation_failed cases were correctly rejected.

The infeasibility path exposed additional problems, including two execution exceptions.

That was progress.

It was not the interesting part.

V11 was.

The V11 frozen bundle again contained:

6 verified
3 validation_failed
3 infeasible

The immutable baseline scored:

6 / 12 terminal statuses correct

But the composition of that score matters much more than the number.

All three expected validation failures were rejected correctly.

All three infeasible cases were identified correctly.

And:

0 / 6 expected-valid programs reached verified

Every positive case ended as validation_failed.

No external oracle even ran for them.

That looked terrible.

It was also exactly the kind of failure I wanted the benchmark to expose.

The system had become too strict for the wrong reason

The six failed positive cases shared a failure signature:

uncompiled_hard_conditional

The requirements contained conditions with multiple consequences.

Conceptually:

if the input is invalid:
    print an exact error message
    exit with status 2

The conditional normalizer understood parts of these statements, but it did not correctly preserve the combined printed-output and exit-status obligations.

The validator then did the safe thing.

It refused to certify the candidate.

So Forge was fail-closed.

But the failure was still a bug.

The verifier was rejecting valid work because its own representation of the requirement was incomplete.

This is the point where the project changed for me.

Independent validation is not enough.

The validator itself becomes part of the system that must be challenged.

Technical postmortem: what V11 actually exposed

The six expected-valid V11 cases all included one or more invalid-input branches with multiple observable consequences. Examples had the general form: ```text If condition X occurs, print exactly Y and exit with status 2. ``` The conditional parser lacked sufficient normalization for the combined consequences. That produced: ```text uncompiled_hard_conditional ``` in all six positive baseline cases. The important evidence rule here is that the V11 baseline remains unchanged. The parser was subsequently corrected. Known-case regression testing demonstrated that the original failure signature could be removed. But those later results cannot be retroactively inserted into the blind baseline. V11 remains what happened when the frozen benchmark first met that frozen version of Forge. That distinction is essential: ```text blind baseline != post-fix replay ``` Otherwise a benchmark slowly becomes a collection of problems the implementation has already learned. The complete evidence ledger is available here: https://github.com/Daniele-Cangi/Derivative/blob/main/docs/BENCHMARK_EVIDENCE.md

The external oracle can be wrong too

There was another uncomfortable discovery.

I originally thought an independent external oracle would provide a relatively clean final authority:

Forge says verified
        ↓
external black-box tests
        ↓
accepted / rejected

Then some frozen oracles turned out to contain their own assumptions and defects.

One oracle supplied an extra program-name placeholder to an importable main(argv) interface.

Another used a fake stdin object that supported line iteration but not the standard read() method.

Older benchmark cases exposed fixture contradictions and harness assumptions that were not actually present in their requirements.

So Forge eventually gained an oracle preflight layer too.

Not because a failed external test should be ignored.

The opposite.

Because a failing test is evidence only if the test itself implements the contract it claims to evaluate.

Technical deep dive: verification now includes verification of the verifier

The benchmark pipeline distinguishes several different facts: ```text internal verified external oracle accepted external oracle rejected oracle invalid ``` These are not collapsed into one status. For example, an internally verified program rejected by a valid external oracle is evidence of **false verification**. That is serious and is preserved. But an oracle with a demonstrably incoherent harness is classified separately as: ```text oracle_invalid ``` The current repository also performs static preflight checks over frozen oracle sources. A recent offline audit covered the historical frozen oracle corpus without executing Forge or making model calls. It found several classes of harness problem while leaving all immutable benchmark inputs and original scores untouched. The deeper principle is the same one used elsewhere in Forge: ```text a claim does not become true because it came from the component we called "validator" ``` Evidence itself needs provenance and boundaries.

`verified` does not mean "correct forever"

This distinction is important.

Derivative is not claiming formal verification of arbitrary software.

verified currently means something narrower:

At that revision, the artifact satisfied the compiled requirement, execution, quality and adversarial contracts that Forge knew how to check.

That statement has boundaries.

A missing contract can still hide a defect.

A faulty requirement compiler can reject good software.

A validator can contain a bug.

An external benchmark can contain a bad oracle.

A future input can expose behavior none of the checks covered.

The system is designed around making those boundaries more visible rather than hiding them behind model confidence.

Where Derivative fits

There are actually two connected layers in the repository.

Derivative is the broader computational reasoning substrate.

It contains deterministic solvers, computational lenses, obligation handling, contradiction witnesses, audit mechanisms and other reasoning capabilities.

Forge is the software-building pipeline discussed in this article.

It uses that substrate for planning and reasoning, but adds the software-specific authority structure:

RequirementCompiler
        ↓
PlannerStage
        ↓
CoderStage
        ↓
ValidatorStage
        ↓
PackagingStage

The important architectural rule is:

planner ≠ authority on correctness

coder ≠ authority on correctness

validator ≠ authority to redesign the build

packager ≠ allowed to bypass validation

The boundaries are intentional.

The current scope is deliberately narrow

Forge currently focuses on greenfield Python artifacts such as:

CLI applications
REST services
data pipelines
libraries

It does not currently claim:

arbitrary existing-repository editing
arbitrary programming-language support
frontend generation
formal verification of arbitrary programs
universal semantic coverage

I prefer those limitations to remain explicit.

The project already has enough difficult problems inside its current boundary.

The part I find most interesting

Code generation is getting cheap.

Generating another implementation is often easier than establishing that the implementation deserves to be trusted.

Derivative started as a reasoning project.

Forge pushed it into a more specific problem:

How do you build software
when no single component
is allowed to simply declare success?

The blind benchmarks pushed it one level further.

The coder can be wrong.

The validator can be wrong.

The external oracle can be wrong.

The benchmark producer can be wrong.

And a green CI run is evidence of exactly what that CI tested — not evidence of everything we wish it meant.

So the architecture is slowly becoming less about producing confident answers and more about separating claims from the evidence that is allowed to support them.

That is also why some of the most useful Derivative runs are failures.

A validation_failed result with precise evidence can tell me more about the system than an easy demo returning verified.

The generated code does not get to declare itself correct.

It turns out that neither does the verifier.

Derivative is open source:

https://github.com/Daniele-Cangi/Derivative

The technical architecture is documented here:

https://github.com/Daniele-Cangi/Derivative/blob/main/docs/FORGE_TECHNICAL_REFERENCE.md

The full benchmark ledger — including the ugly results — is here:

https://github.com/Daniele-Cangi/Derivative/blob/main/docs/BENCHMARK_EVIDENCE.md

ReWeaver AI DriftDetector

Fri, 25 Sep 2026 11:14:10 +0000

Watch a two-minute highlight reel here: https://www.youtube.com/watch?v=BotHgRCkW8w

Drift is the silent accumulation of errors and omissions in AI-generated code, even when it appears to work. It's the gap between the codebase you have and the one you meant to ship.
DriftDetector finds it. It's free and scans your code in seconds to find drift you didn't know AI unintentionally inserted into your codebase.

Point it at any GitHub repo, public or private, and it scores every commit in the history across nine production-readiness dimensions: design consistency, accessibility, maintainability, reliability, user experience, AI code governance, architecture, testability, and security in less than a minute (usually).

HERE'S WHAT THE ON-SCREEN REPORT WILL GIVE YOU:
• Production Drift Ratio — a single 0–1 score that weights drift frequency by remediation time
• Drift over time — every commit scored, graphed from first commit to today
• Zoom into any period to see exactly when and where the gap opened
• Jump straight from a data point to the commit or file on GitHub
• Accumulated debt, velocity drag, and risk, modeled for your team size
• Contributors ranked per file and per line by drift impact

You can also download a full report with line-by-line of what has drifted and the size of the impact on your code.

YOUR CODE STAYS PRIVATE
Repository code goes from GitHub directly to the scanner without passing through the browser, streams into temporary memory, and is never written to a persistent store. Click "What leaves my machine?" in the app for the full detail.

Try out DriftDetector and let us know what you think. We're eager to learn what you found and what you thought of it.

REWEAVER — NOW IN BETA
Drift Detector tells you where the drift is. the full Reweaver platform will help you close it, with tools that live inside Figma, VS Code / Cursor, and GitHub, so the score stays down as you code. A scan is a snapshot — drift isn't.

We're actively recruiting beta testers to give us feedback. Sign up here: https://www.reweaver.ai/join-the-beta

Vibe Coding Won't Kill Developers. It'll Kill the Middle.

Tue, 01 Sep 2026 12:57:28 +0000

When good cameras got cheap, everyone predicted the death of professional photography. The prediction landed wrong. The low end died outright: stock libraries, cheap portraits, mass-event coverage went to anyone with a phone and a free editing app. The high end did better than ever — editorial work, photojournalism with access nobody else had, an aesthetic you could not reproduce by buying the same gear. The damage landed in the middle. Small weddings, corporate headshots, real estate listings, the steady unglamorous bulk of the market: not extinction, compression. Prices fell, volume moved to cheaper substitutes, and the survivors climbed up or specialized out.

That compression is the cleanest map I know for what AI-assisted coding is doing to software work. And this half I know from inside: two decades leading dev teams, and now building AI tooling for them.

The comfortable half of the argument

The reassuring version of this is everywhere right now: you were never paid to type, you were paid to think, so AI just frees you to do the valuable part. It's not wrong. It's just the half that's easy to hear. The other half is about the market, not about you.

Judgment, architecture, knowing what breaks in maintenance, deciding what not to build — a model that writes plausible code on command doesn't commoditize any of that. I have watched weeks of confusion land on people who could not read what a capable model generated; the gap was never the tool, and better AI autocomplete does not close that gap.

But "judgment beats typing" answers only a question about skill and dodges the question about market structure. AI doesn't replace developers as a class; it commoditizes a segment. The segment it hits first is the same one the camera hit: the middle. The junior-to-mid tier that lived on CRUD apps, simple integrations, brochure sites, the standard internal tool with a form and a table behind it. That work was always implementation against a known spec, and implementation against a known spec is exactly what a model does cheaply now.

I felt this directly on a side project. I spent many months of evenings building a content pipeline on a visual no-code platform; when AI-assisted coding changed the math, I rebuilt it as proper code in a few weeks. The months were not wasted — they are why I knew exactly what to build. But the same work now costs weeks, not months.

"So the middle just learns to think better?"

This is the obvious objection, and it's worth taking seriously because the answer is where the whole thing turns.

First problem: "learn to think better and survive" doesn't refute the thesis, it confirms it. The claim was never about the people, it's about the work. When someone in the middle develops real architectural judgment, they haven't saved the middle, they've left it. They climbed to the high end. The middle still empties out, by promotion or by exit. A wedding photographer who became a sought-after editorial shooter didn't prove the wedding market survived.

Second problem: "thinking better" isn't a soft upgrade you bolt onto the same job. Going from implementing a spec to deciding what to build, judging tradeoffs, holding the whole system in your head, anticipating the failure that surfaces in production eight months later — that's a change in the muscle being used. Some people build it. Some won't, and some genuinely don't want to; they liked implementation and were good at it. The skill axis is real, but it doesn't pull everyone up by default.

Third problem, and this is the one the reassuring posts never reach: capacity. Suppose everyone in the middle became a strong systems thinker overnight. The market still doesn't need that many architects, security specialists, performance engineers, and trusted advisors. The high end is narrow by definition, which is what makes it the high end. Premium editorial photography never had room to absorb every wedding shooter it displaced.

So when people ask whether "think better" just means "become a consultant," the honest answer is partly. The trusted technical advisor is one exit from the middle, and a good one, but not the only one. Architecture is an exit. Deep security and performance work is an exit. Complex-domain expertise — the kind where you understand the business so well the AI becomes a lever instead of a crutch — is an exit. When a prototype that should take weeks comes together in an afternoon, the speed never comes from the model. It comes from twenty years of knowing the exact problem before the first prompt. The tool doesn't create that position; it amplifies what was already there.

The honest limit of the analogy

I'd be selling you something if I claimed software follows photography like a law. It doesn't. Photography had roughly fixed demand; the number of weddings didn't triple because cameras got cheap. Software might. Total demand for software has grown for forty years, and cheaper production could grow it further, which means some of the displaced middle gets reabsorbed into work that didn't exist before. The wedding market never had that escape valve.

So I won't tell you eighty percent of developers will be unemployed. I don't believe it, and the people who say it are guessing. The defensible claim is narrower and still uncomfortable: middle-tier work commoditizes, value migrates toward the extremes, and you should plan around that rather than hope the middle holds.

For the individual, the move is to climb toward judgment, architecture, and advisory work, not to defend the commoditizing tier by getting slightly faster at it. Speed inside a shrinking band is a losing race against a tool that's cheaper than you and getting better weekly.

For anyone running a technical team or a practice, this is the argument for senior-led work getting stronger. The middle is precisely the layer being liquified. Value concentrates in the people who were never doing middle work to begin with: the ones whose contribution was the system, the tradeoff, the domain, the call about what not to build. That's why this practice is senior-led and founder-run. It is not a positioning choice; it is what the map says to do.

The question to sit with isn't whether you can out-think the AI. You probably can, on a good day. The question is which segment that thinking lives in, and whether that segment will still be there to stand in. Go look at the last five things you shipped. Count how many were implementation against a spec someone else wrote. That number is your exposure.

Originally published at unlockedconsulting.ai.

The Hidden Mental Model That Determines Whether AI Tools Help You

Tue, 01 Sep 2026 07:13:56 +0000

I built a working knowledge base while I was in a one-hour company meeting, waiting for my turn to give my briefing. Even I was surprised at how fast it came together. And it was not a mockup. It was a complete chat interface that connected to nearly everything the company had — training manuals, forgotten documentation, enhancement request docs, support ticket histories — all indexed into a vector database. When I tested it, it answered my plain-language questions and surfaced the right context every time. Fifty-five minutes was all I had to do it. And by the time I needed to do my briefing, the tool was already running.

I want to be precise about why this was possible, because the obvious explanation is normally wrong: the tool wasn't the reason. Yes, VS Code with Claude Code inside was the tool I used to pull this together, but the real reason it took under an hour is that I already knew what the right solution looked like. I knew how the pieces had to fit, where the data belonged, what would break if I got it wrong. That judgment was already in my head. The AI didn't supply it. It executed against it at speed.

Same tools, different results

Those same tools I used could produce completely different results, depending on who's using them and how. So even though the tools are the same, what makes the difference in the output is the specific technical judgment behind it. Put the same setup in front of someone who doesn't carry that technical judgment, and the AI runs just as fast — except toward a structure they have no way to evaluate. The output looks plausible, but the cost surfaces later, somewhere harder to see. That gap is the whole point, and it has almost nothing to do with the tool.

What the mental model actually is

So what is that judgment, concretely? It's a mental model of how the parts of a system fit together: where data lives, how one piece talks to another, how something gets from your machine to where it actually runs. Not the code, the shape of it. You can have all of that without writing a single line yourself.

Two things tend to get mistaken for it. The first is the ability to write code. Those aren't the same thing; plenty of people who can write a function still carry no picture of the system that function lives in. The second is knowing the business — the company, the clients, the domain. That knowledge helps, and it certainly made me faster in that meeting, but it isn't what's missing when AI output goes wrong. What's missing is the structural picture: a sense of where a piece of data should originate, what should talk to what, and what breaks downstream when those choices are wrong.

That picture matters because of what it lets you do with the AI's output: judge it. When you're holding an accurate model, a suggestion that doesn't belong stands out right away, because it doesn't fit the shape already in your head. Strip that model away and the same suggestion looks as reasonable as everything else, since there's nothing to measure it against. That's why the model, not the tool, is the real variable. AI amplifies whatever you bring to it — good judgment moves faster, and so does the absence of it.

The model, not the tool, is the real variable. AI amplifies whatever you bring to it — good judgment moves faster, and so does its absence, just toward a structure you'll pay for later.

Why "which AI tool should we use" is the wrong opening question

That's why "which AI tool should we use" is usually the wrong place to start. Over the past few months I've run a systematic evaluation across my team: Cursor, Claude web, Claude Desktop, Claude Code, Copilot in VS Code, AugmentCode, and various combinations. The finding was the same every time. There's no best tool, only the best tool for a specific situation. What decides it is the project, the stack, who maintains the code afterward, the deployment target, the cost model, and above all whether the person using it is a developer or not. Change any of those and the right answer changes with it.

"Which tool" is a tempting question because it's answerable. You can compare features, watch demos, pick a winner, and feel like you've made progress, and it's the one question every vendor is glad to answer for you. But it skips the harder one: does the person using the tool understand the architecture underneath — how the parts actually connect? That question has no vendor, no demo, and no clean answer, which is exactly why it gets avoided.

The cost of skipping it doesn't announce itself. Hand a capable tool to someone who can't see that architecture, and nothing breaks on day one. It shows up later, in maintenance, where it's hardest to trace back to its cause.

What to fix when productivity gains are uneven across your team

If you lead a team, you've probably seen this already: the same tools landed, and a few people got dramatically faster while others barely moved. The reflex is to standardize: pick one tool, roll out training, make everyone consistent. That treats it as a tooling problem, and it usually isn't. What to do next depends on who's in front of the tool.

For the people who aren't developers, don't try to turn them into architects. That's the slow path, and most of them don't need it. Give them a setup where the hard parts — where the code lives, how it connects, how it ships — are already decided and kept out of their way. They get the leverage of the tool without having to carry the model themselves, because it's built into the environment around them.

Your engineers and technical leads need the opposite investment. For them the architecture model is the multiplier, so the time spent making sure they can see how a system fits together pays back more than any tool upgrade. That understanding is what the tool runs on.

And if you want a cheap way to find out where each person actually stands, skip the survey. Take your fastest and your slowest person on the same tool, hand them a whiteboard, and ask them to draw the system they're working on — where the data comes from, what talks to what, where the code lives, how it gets deployed. The drawing tends to track the productivity gap closely; often you'll see it before the marker's back in the tray. It's the cheapest diagnostic you'll run, and it measures the one thing that actually matters here: whether they can see the system they're building on.

Originally published at unlockedconsulting.ai.

Can a Non-Coder Become a Coder Just With AI?

Sun, 30 Aug 2026 21:19:12 +0000

My company decided to lean into AI — to let people who had never written code build real software with it. I thought that was the right call, and I still do.

The logic behind the decision seems airtight: tools like Claude Desktop are built to be accessible to non-developers. They abstract the complexity. They turn natural language into working software. But put those tools in front of a capable non-developer, and what tends to follow is weeks of confusion, cluttered repositories, and architectural decisions that look functional but rest on shaky foundations.

I've watched it happen. And the question "can a non-coder become a coder just with AI?" has a more useful answer than yes or no. Yes, they can produce working code. No, they cannot produce maintainable code without understanding how the parts fit together.

The infrastructure reality check

The non-developer gets a real developer's environment: GitHub, Python, Node.js, Visual Studio Code, a database server, an AI assistant on top. The premise is that having that setup is most of the battle. The expectation is immediate productivity.

What actually happens: Git becomes the first obstacle. Branches, commits, pushes, pulls, clones — all of it opaque. The AI makes changes, generates commits, pushes to the repository. But without understanding what's being committed or why, the repository fills with stray commits mixed into legitimate work. Unexpected changes land in branches they shouldn't. The history becomes hard to follow.

Then the local environment problems. Port conflicts. The UI can't reach the API because the ports aren't configured correctly. Services that have to be started, stopped, restarted. Every working session begins with fifteen minutes of troubleshooting that a developer handles automatically, by reflex, but that a non-developer has to solve consciously, from scratch, every time.

The mental model problem

The deeper problem isn't technical. The non-developer doesn't know what an API is, what a database does, where the UI ends and the backend begins. Without that mental model, every AI suggestion looks equally plausible — including the wrong ones. There's no way to evaluate the output, because there's no way to see the architecture.

When the AI suggests connecting the frontend directly to the database, it gets implemented. When it recommends storing user passwords in plain text environment variables, that happens too. The AI isn't being careless. It's responding to prompts that don't include the constraints a developer would apply automatically.

Here is the floor — the five things a non-technical person needs to understand before AI tools help instead of hurt. None of them is about writing code. All of them are about how the pieces relate.

What a database is. Information lives in a structured store, and you don't connect the front end straight to it — for the same reason you don't hand customers the keys to the warehouse.

What an API is for. It's the control layer between the user and the data: it checks who's asking and enforces the rules. When the AI proposes skipping it, that alone tells you something is wrong.

What runs where. Part of any app runs on the user's device and part runs on your servers. Whatever sits on the user's device, the user can see and change — so anything sensitive has to stay on your servers.

What version control is for. Every change has a reason, experiments stay separate from the live system, and what customers use keeps working. Lose that discipline and you lose the record of what changed and why.

Where secrets live. Passwords and keys belong apart from the code, and they differ between a test setup and the real one. Put them in the wrong place and they leak.

These five take a few hours to learn, not months. But without them, AI tools become code generators that produce expensive technical debt.

Necessary, not sufficient

That mental model gets you to better prompts. It doesn't get you to safe output on its own. Even with solid foundations, a non-developer using AI will still face decisions they aren't equipped to make — and left to their own judgment, they'll make those decisions anyway.

The other half of the answer is a constrained environment: guardrails that make the bad decisions impossible rather than merely unlikely — schemas that can only change through migrations, endpoints that must validate input, frontends that can't hold secrets. Building that surface is its own undertaking, and a separate conversation. What matters here is that the mental model comes first. Without it, even the best guardrails are fighting a user who can't see why the rails are there.

The decision in front of a technical leader

The ambition my company started with was the right one. The choice that actually matters comes a step later: how much infrastructure you're willing to build around the tools. That choice isn't binary.

The cheapest path is raw AI tools with light training. Cheap upfront, expensive later: working prototypes that become maintenance liabilities, with technical debt compounding until someone rewrites everything properly.

The middle path is structured tools with foundational training and a constrained environment. Higher upfront, lower long-term — the output is maintainable from day one because the constraints prevent the unmaintainable patterns before they happen.

The third path, and often the most efficient for smaller organizations, is to keep development centralized and use AI to accelerate the team you already have, rather than distributing it across people who can't yet evaluate what it produces.

The path that doesn't work is the first one, chosen in the hope it will somehow work out.

The real question

So the real question isn't whether non-coders can become coders with AI. It's whether your organization can afford the infrastructure that makes it safe — the foundational training, the constrained environment, the deployment and monitoring around it. If you can build that, then yes, non-coders can ship maintainable software with AI. If you can't, you're better off keeping development centralized and making your existing developers faster.

If you do nothing else, do this: train the mental model first, in a few focused sessions, before handing out any AI tools. Five concepts, concrete examples from your own systems. The investment pays back immediately, because every prompt the user writes afterward is a better prompt. They ask for input validation instead of assuming it's optional. They name the environment variables they need instead of hardcoding credentials. They think in endpoints instead of wiring everything to everything.

AI amplifies what's already there. If the user doesn't understand the basics, AI amplifies that gap straight into the systems they ship.

Originally published at unlockedconsulting.ai.

Cursor CLI Ran Code From Cloned Repos Before Its Own Trust Prompt Ever Loaded

Mon, 10 Aug 2026 14:00:00 +0000

Cursor's command-line coding agent has a workspace trust feature. It's supposed to stop the agent from acting on a project until you've told it you trust that project. Security researcher Francisco Rosales at Manifold Security found a way around it, and the gap ran deeper than a single missed check.

The setup script that ran before trust was asked

The issue lives in Cursor CLI's worktree feature. Run the CLI with -w, and it creates a worktree under ~/.cursor/worktrees//. A repository can define a setup command for that worktree in a file called .cursor/worktrees.json, using a setup-worktree key. Cursor passes that command straight to sh -c.

That's a normal convenience feature. The problem is when it runs. On builds before 2026.07.23-e383d2b, the setup script executed during workspace resolution, and that step finished before the code path that shows the trust dialog. So a repository's own command ran on your machine before you'd ever seen, let alone answered, the trust prompt.

Rosales put it plainly: the trust gate exists so a user can assert they trust a workspace before the agent acts in it. Code execution is supposed to sit behind that gate. This didn't.

The sandbox didn't cover it either

Cursor CLI also ships a sandbox that limits what terminal commands can read, write, and reach on the network. Reasonable users would assume turning it on protects them here. It doesn't.

The worktree setup script is pinned to insecure_none, the setting that turns the sandbox off entirely for that script. So even a developer running Cursor with --sandbox enabled still had a repository-supplied command execute directly on the host, outside any sandbox boundary.

And once that command runs, Rosales confirmed it has the same access as the person running Cursor. Same files, same credentials, same environment. There's no reduced-privilege step in between.

No advisory, and a bumpy disclosure process

Cursor shipped a fix in build 2026.07.23-e383d2b. What it didn't ship was a security advisory. Rosales said Manifold's report was closed as "Informative" on July 29, with two reasons given: the issue requires a user to clone or open an attacker-controlled repository, and the report didn't demonstrate a bypass of workspace trust in the strictest sense. Manifold pushed back with more context, including a reference to a previous Cursor CVE covering similar territory, and hadn't heard back as of this writing.

It's worth sitting with that first reason for a second. "Requires the user to clone an attacker-controlled repo" describes most of what developers do all day. Cloning repos, opening PRs, checking out a coworker's branch, trying out an open-source tool. That's the entire threat surface AI coding agents operate in.

Not just a Cursor problem

Rosales was careful to scope his team's research to Cursor CLI's worktree mechanism specifically, since that's what they tested and can confirm. But he also flagged that workspace trust bypasses through configuration files or other planted repo content, potentially leading to remote code execution, are a known category. Multiple researchers have found variations of it across different agentic coding tools.

His read on why this keeps happening: speed. Everyone building and shipping with AI coding assistants is optimizing for moving fast, and auditing the configuration a repository ships for a specific agent isn't something most developers are doing. It's not on anyone's checklist yet.

What to actually do about it

The narrow fix: update to build 2026.07.23-e383d2b or later, where the worktree setup script is now gated behind the trust prompt. But Rosales flagged an important caveat — once you click "trust," the script still runs, and it still runs outside the sandbox. Trusting a repo means the code in that script executes on your machine, full stop. If you're opening a repo you haven't fully audited, pass --skip-worktree-setup to skip that step.

The wider takeaway is less comfortable. Rosales doesn't think the trust prompt should be treated as a security control. He called it closer to a warning, and pointed out that many coding assistants already ship modes that skip these dialogs entirely for the sake of speed.

That's the real shift developers and security teams need to make. AI agents are capable, configurable, and genuinely useful for moving faster. They're also a new and significant part of the threat model, and "ask before acting" assumes a pace of development that agentic tooling has already outrun in places.

Why this habit is so easy to miss

Most developers don't think of cloning a repo as an action with consequences. It's step one, not a decision point. You pull down a project, open it, and start reading code before you've formed any opinion about whether you trust it. AI coding agents insert themselves right at that moment, and they're built to act fast on your behalf.

That's exactly the gap this research exposes. A .cursor/worktrees.json file sitting quietly in a repo isn't something most people check for, and there's no reason they would have, until now. It's a small, easy-to-miss configuration detail that happens to sit directly in the path of code execution.

If you're pulling down unfamiliar repos regularly, and most developers are, it's worth treating any agent-specific config file the same way you'd treat a postinstall script in a package.json: something to glance at before you run anything, not after. Worth checking what your agent of choice actually gates, and when.

AI Coding Tools Are Making Engineers 10x Faster. Here's the Catch.

Fri, 12 Jun 2026 11:22:20 +0000

Todd Fisher, CEO and co-founder of CTM, explains why speed is only part of the story.

The "10x engineer" idea has been around for a long time. It's the notion that the best developers don't just work harder — they work at a fundamentally different level than everyone else. AI coding tools have brought that concept back into sharp focus. But Todd Fisher, CEO and co-founder of CTM, thinks the framing needs an update.

"If an engineer becomes 10x more productive because of AI, their competitor's engineer is probably becoming 10x more productive too," Fisher said. "In a competitive market, that doesn't suddenly mean you need fewer engineers. It means you're competing at a different level."

That's a grounded take in an industry that tends toward hype. The baseline is shifting — not just for a few high-performing teams, but for everyone. And the organizations that adapt to that new reality fastest are the ones most likely to pull ahead.

Beyond Code Generation

Most developers have tried an AI coding assistant at this point. Many have also hit a familiar wall: the suggestions are decent, but they don't feel transformative. Fisher says the gap between "okay" and "genuinely useful" comes down to context.

"The more context you can provide, the more capable the system becomes," he said. "It can help navigate information, call additional tools, and surface answers that would otherwise take time to find."

The tools that are delivering real value, in his view, are the ones connected to the actual environment where work is happening — not standalone chat interfaces, but assistants embedded in the workflow itself.

At CTM, that plays out in error reporting. When a defect occurs, AI begins analyzing the issue immediately, traces it back to the relevant code, and suggests a potential fix — sometimes before a developer has even been notified.

"The reaction I sometimes have is, 'How did we survive without this?'" Fisher said. "It eliminates the repetitive work that used to slow teams down when something went wrong."

The Technical Debt Question

One concern that comes up consistently in developer conversations: does AI-assisted coding just accelerate the accumulation of technical debt? Write faster, break things faster.

Fisher sees it differently. As AI tools get more deeply integrated into software products, they develop a more detailed understanding of how those products actually work. That creates the conditions for better support, earlier defect detection, and higher-quality outcomes overall.

"Hopefully we'll see the quality of support go way up, and the number of defects go way down," he said.

That's not guaranteed, of course. But it points to a more optimistic trajectory than the "move fast, clean up later" pattern that has defined much of software development for the past decade.

What to Actually Measure

For engineering leaders trying to evaluate whether AI tools are paying off, Fisher recommends keeping the metrics simple.

Feature activation rates. Error rates. Time from defect detection to proposed fix. These are the numbers that reflect whether engineers are actually accomplishing their goals more effectively — not just shipping code faster.

"The aim is to measure whether people are accomplishing their goals more effectively and with fewer errors," he said.

Human Judgment Still Matters

The skills question is real. If AI can generate code, write tests, and debug errors, what does that mean for the engineers doing those tasks today?

Fisher draws a useful analogy: "A doctor might be able to evaluate possibilities faster with AI, but deciding which possibility is actually correct still requires judgment."

Engineering is no different. The ability to evaluate tradeoffs, understand context, and make decisions remains valuable. AI surfaces options — humans still have to choose.

The Bottleneck Nobody's Talking About

Here's where Fisher gets candid. AI tools can generate code fast. Very fast. But someone still has to review it.

"I have multiple projects that are nearly completed and largely written with AI assistance," he said. "But the bottleneck is finding enough time to individually review everything."

This is the underreported challenge of AI-assisted development. The creation bottleneck is largely solved. The review and validation bottleneck is not. Teams that figure out how to structure human oversight of AI-generated output — without slowing everything back down — will have a meaningful edge.

Where This Is All Headed

Fisher's final point is the one that should get the attention of anyone in the software industry.

"Some SaaS businesses will be disrupted because AI-assisted development makes it easier to build alternatives," he said. "We're going to see dollars move around the industry in ways that are difficult to predict."

That's a significant signal. AI coding tools aren't just changing how software gets built. They're changing the economics of building software at all — lowering the barrier to entry, accelerating feature development, and making it easier to challenge incumbents.

For developers who haven't fully committed to AI coding tools yet, Fisher's advice is practical: start using them and see where they fit naturally into your workflow. Treat them like having a knowledgeable colleague available at all times.

"You can ask questions, explore ideas, and get help working through problems," he said. "You don't have to treat them as a replacement for how you work today."

That's a reasonable starting point. The competitive landscape may eventually make it a necessity.

The Developer Survival Guide for the Age of Agentification

Tue, 09 Jun 2026 22:41:11 +0000

Hans Eckman's Info-Tech LIVE keynote cuts through the hype and tells developers exactly what they need to do differently — starting today.

AI coding tools were supposed to make developers faster, eliminate ambiguity, and reduce production defects. The data says otherwise. According to research presented by Hans Eckman, Research Fellow at Info-Tech Research Group, at Info-Tech LIVE 2026 this week in Las Vegas, 84% of organizations use AI tools but see no measurable improvement in delivery velocity or business outcomes. Incidents per pull request are up 24%. Only 17% of AI code suggestions are actually adopted.

"The technology isn't what's failing," Eckman said. "It's our understanding of capabilities."

That's actually good news. Because it means the problem is fixable.

Eckman's keynote, titled "Developer Survival Guide in the Age of Agentification," laid out a three-part framework for developers and engineering teams navigating a world where AI agents are writing more code than ever — and where the human role is shifting in ways that most teams haven't caught up with yet.

The Shift: Your Job Has Changed

75% of all new code at Google is now AI-generated, up from 50% just last fall. Info-Tech's own AI in Software Development Survey (May 2026, n=442) found that 42% of developers go from requirement to working code in the same session. Agentic engineering is no longer a future state — it's the current one.

But that doesn't mean developers are less important. It means the job is different. Eckman was direct: the traditional role of a developer — the one primarily defined by writing code — is fading. What's replacing it is a focus on outcomes and solution architecture.

"If you haven't made your intent explicit," Eckman said, "the agent will execute against your gaps rather than your intended goals."

That's the core problem. AI agents don't stop to ask clarifying questions. They don't pause when requirements are vague. They fill gaps, make assumptions, and execute — and if your specifications are loose, the agent's definition of "done" won't match yours.

67% of developers in Info-Tech's survey report that AI-generated code requires more testing than traditionally written code, not less. 51% say AI breaks down on complex or legacy codebases. 46% say the output doesn't pass their quality gates or code review standards.

Developers are still critical. The skill set just needs to evolve.

The Tool Sprawl Problem

Eckman used a memorable analogy to describe today's AI tooling landscape. Imagine walking into an ice cream shop expecting three flavors — vanilla, chocolate, strawberry — and instead finding dozens of options that change every week. The choice becomes paralyzing.

"This is what the AI tooling landscape feels like right now," he said.

And it's hurting teams. According to Harvard Business Review (2026), using four or more AI tools simultaneously results in 12% more mental fatigue and 19% more information overload. 45% of frequent AI users report burnout, compared to 35% of non-users. And in what may be the most telling stat: 46% of developers actively distrust AI output, versus only 33% who trust it.

The old answer was to go deep on one tool and master it. That worked until about 2023. Now, most teams have flipped into a mode of constant tool exploration — running proof of concepts, testing new options, chasing the latest release — without ever reaching production-ready outcomes.

Eckman's prescription: flip the script. Spend 90% of your effort on execution with a stable core stack, and limit active tool exploration to 10%. Pick one or two primary coding assistants. Maintain stable 90-day delivery cycles. Keep experimentation structured and separate from production work.

"Heroes don't scale," he said, quoting his practice manager. "That's the temptation we need to avoid in the AI space."

For teams evaluating terminal tools right now, Eckman's research breaks down the three leading options clearly. Claude Code leads on autonomous depth and handles complex multi-file refactors with a 95% first-try correctness rate, with 71% of developers who regularly use AI agents now reaching for it. Codex CLI fits security-sensitive workflows best, with three configurable approval levels. Gemini CLI is the accessible option for budget teams or GCP-native environments, though its free tier is now restricted to Flash models after March 2026. On the IDE side, Cursor remains the most polished for agentic task completion, GitHub Copilot has the broadest ecosystem reach, and Windsurf is the friendliest entry point for teams newer to AI-assisted coding.

The Stakes: Agents Don't Fail Because They're Wrong

This section of Eckman's keynote is where things got concrete — and where the most actionable guidance lives.

The title says it plainly: AI agents don't fail because they're wrong. They fail because the right outcome wasn't designed and prompted. Vague requirements don't slow agents down. They just give agents more room to guess.

Only 12% of AI projects succeed without success metrics defined upfront. Define them clearly, and that number jumps to 54%. That gap is entirely within a developer's control.

Eckman's fix is spec-driven development. Not better prompts — a proper spec file. Written in plain text, before the agent touches anything, a spec has four components:

Role — A single line defining who the agent is and who it's building for. This shapes every subsequent decision the agent makes.

Success Criteria — Testable exit conditions written before work begins. Without these, the agent's definition of done won't match yours. With them, you get reviewable output that stays on track.

Integration Context — What already exists in the codebase. Which components to reuse, which APIs to call, which patterns to follow. This stops agents from rebuilding functionality that already exists or introducing patterns that conflict with your stack.

Constraints — The hardest part. Every line is a decision the agent will make on its own if you don't constrain it first. Failure behaviors like empty states and error states aren't edge cases — they're design decisions. And the "out of scope" line at the end is often the most important one.

"If you'd be annoyed that the agent interpreted something differently than you intended," Eckman said, "your constraints weren't tight enough."

This applies to teams, not just individuals. The same spec discipline that prevents agent chaos also makes your AI practice institutional rather than individual — shared prompting patterns, documented standards, consistent evaluation gates that don't change every time a new tool launches.

Only 20% of engineering teams currently measure AI impact, despite 90% using AI tools. The metrics that matter: cycle time, code quality, failure rate, review time, and technical debt accumulation. You can't improve what you don't measure.

Governance and Security: Validate Before You Ship

The third section of Eckman's survival guide is where the stakes become most visible.

65% of organizations have experienced at least one cybersecurity incident caused by AI agents in the past 12 months, according to the Cloud Security Alliance (2026). 70% of organizations have uncovered flaws in AI-generated code, and one in five of those incidents escalated into serious breaches.

The Amazon Kiro story makes the point vividly. In December 2025, Amazon's autonomous AI agent caused a 13-hour AWS Cost Explorer outage in China by deleting and recreating a production environment due to misconfigured access controls. Amazon attributed it to user error — and technically they're right. The agent did exactly what agents do. It executed. The problem was the lack of guardrails.

"Agents don't stop and ask for permission," Eckman said. "They decide and execute. If you haven't built in checkpoints, you won't know what went wrong until it's in production."

For every agent your team deploys, Eckman frames the security question across three dimensions: What can the agent see? What can the agent do? What can the agent touch? Each of those is a deliberate access control decision, not a default setting.

The answer isn't to remove agents from the workflow. It's to design the human-in-the-loop carefully. Agents are good at speed, consistency, generating code and tests, scanning for vulnerabilities, and mechanical correctness. Humans need to own architecture design, pattern definition, outcome validation, and approval of high-risk actions.

71% of organizations say human oversight is necessary for building public trust in their AI systems. 76% of enterprises already include human-in-the-loop processes to catch errors before deployment.

Three Takeaways

Eckman closed with a clean, practical call to action for every developer in the room:

First, audit your AI stack honestly. Stop exploring and start executing. Pick your core tools, stabilize your delivery cycles, and structure how you evaluate anything new.

Second, define before you build. Write down your success criteria, your integration context, and your constraints before the agent touches a single file. This is the most valuable engineering work you'll do — and it will save weeks of rework.

Third, define where humans are in the loop. At minimum, humans should be reviewing code before every release. Decide upfront where agent autonomy ends and human judgment begins, and make those boundaries explicit in your process.

The age of agentification isn't coming. It's already here. The developers who thrive in it won't be the ones who write the most code. They'll be the ones who design the best outcomes.

Hans Eckman is a Research Fellow in Application Delivery and Management at Info-Tech Research Group. He presented this keynote at Info-Tech LIVE 2026 in Las Vegas, June 8–11.

ToolPix Platform

Wed, 20 May 2026 05:19:35 +0000

ToolPix is an all-in-one AI-powered productivity platform for image tools, PDF utilities, coding helpers, and smart web tools.

Built to help creators, students, and developers complete daily digital tasks faster from one simple platform.

Key Features

AI Background Remover
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Tech Stack: Python, Flask, JavaScript, HTML, CSS, AI APIs, PythonAnywhere

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Developers Trust AI Code. They Also Don't Trust It. Both Are True.

Thu, 30 Apr 2026 13:00:00 +0000

A new report from Qodo puts some hard numbers behind something most developers already
feel: AI coding tools are moving faster than the systems built to catch their mistakes.

The report, based on a Censuswide survey of 500 U.S. IT engineers and engineering leaders
conducted in March 2026, doesn't read like a hit piece on AI. It reads like an honest look at
where enterprise engineering teams actually stand — and the picture is complicated.

The Numbers

Start with this: 89% of organizations surveyed have experienced at least one AI-related
production incident. Security vulnerabilities top the list at 53%, followed by broken code
and bugs at 44% each. One in four organizations has experienced a full production outage
caused by AI-generated code.

Those are not small numbers.

And yet 94% of developers say they're confident in the code AI tools produce. 63% describe
themselves as very confident.

Both things are true at the same time, and that's the point.

The Confidence-Scrutiny Paradox

Here's the part worth sitting with: 95% of developers say knowing that code is AI-generated
changes how closely they review it. Of those, 57% say their scrutiny increases significantly.
Nearly 40% scrutinize AI-generated code more heavily than code written by a human colleague.
And 45% change their entire review process when AI-generated code is involved — requiring
additional tests, benchmarks, or documentation before approving a merge.

So developers are confident in AI tools and reviewing AI output with extra suspicion. That's
not a contradiction. It's an accurate read of how these tools actually behave.

AI coding assistants are genuinely useful. They handle boilerplate reliably, speed up routine
implementation, and reduce cognitive overhead on the predictable stuff. But they also produce
code that looks correct and isn't. A model can hallucinate an API reference, misapply a
security pattern, or generate code that works in isolation and breaks in production. Developers
have learned this through experience, and they've adjusted accordingly.

The report calls this a "trust tax." You use the tool, you get the speed benefit, and you pay for
it with heightened alertness on every review.

The Review Burden

Here's where the productivity story gets messier. Across the full survey, the net time savings
from AI tools on manual review is roughly 17 minutes per week. That's not nothing, but it's
not the productivity leap the pitch decks promised.

More telling: 41% of developers are spending more time on manual review than before AI
tools existed. The code volume has increased. The human bandwidth to review it hasn't.

The report frames this as a throughput failure. We can generate code faster than we can
safely verify it. That's a structural problem, not a training problem or a tooling preference
problem.

The 501 to 1,000 employee bracket illustrates it most clearly. It's the only company size
group where the net effect is additional time spent — an average of 0.55 hours per week
more than before. These companies are large enough that AI adoption is widespread and code
volume is high, but they haven't yet built the review infrastructure to match that volume.

The Enterprise Paradox

The most counterintuitive finding in the report involves the largest organizations — those
with 10,000 or more employees. They report saving an average of 1.18 hours per week on
manual review. Sounds good. But they also have the highest production outage rate in the
survey at 40%, compared to a 25% average across the full population.

And they have the lowest adoption of automated gates — 68% — of any bracket above 50
employees.

That gap explains a lot. When you reduce manual review time without adding automated
verification to catch what manual review is no longer catching, you're not saving time. You're
moving risk downstream, where it surfaces as outages and vulnerabilities.

The 2,501 to 5,000 employee bracket shows what the alternative looks like. At 84% gate
adoption — the highest of any bracket — their outage rate sits at 27%, in line with the overall
average. Automated gates don't eliminate incidents, but the data suggests they meaningfully
reduce exposure.

What Reviewers Are Actually Catching

Developers know what to look for. The top frustrations in review are hallucinated logic and
subtle bugs (30%) and security vulnerabilities (23%). Those map directly onto the leading
incident types reported in production.

The rest of the list points to a consistent pattern: AI is good at syntax and weak on context.
Fifteen percent of reviewers say AI-generated code misses important business or domain
context. Thirteen percent cite inadequate error handling. Ten percent say the code ignores
existing architectural decisions the model simply didn't have access to.

Knowing what to look for isn't the same as having a scalable system for catching it. That's the
core problem.

What This Means for Your Team

The report's conclusion is straightforward: the organizations doing the best aren't the ones
using AI most aggressively. They're the ones that have built verification infrastructure to
match the pace of AI-generated output — automated gates that block code from merging if it
violates security, compliance, or quality policies.

Twenty-one percent of organizations still don't have that layer in place. For those teams,
reviewer bandwidth is the only backstop. And reviewer bandwidth doesn't scale.

AI is accelerating code generation. The constraint has shifted to verification. The teams that
recognize that and build accordingly are in a better position. The ones still measuring success
purely by lines of code shipped or hours saved in review are measuring the wrong things.

The full Qodo report is available here: https://www.qodo.ai/.

Copilot Forgets Everything. Make It Stop.

Tue, 21 Apr 2026 13:07:54 +0000

You open a new Copilot chat and explain everything again.

Your stack. Folder structure. That “no default exports” rule.

Copilot writes code, but forgets context.

This isn’t a bug though. LLMs are stateless, every chat starts fresh.

The good part? You can fix it.

The Problem Has a Name

You’re dealing with three problems:

The Blank Slate: Every session starts from zero.
Correction Amnesia: You fix a mistake. Next session, same mistake.
Standards Drift: Your team has patterns. Copilot ignores them. Code becomes inconsistent.

Each problem has a different fix. Let's go through them one by one.

The Instructions File (Your Copilot Brief)

Create this file:

your-project/
└── .github/
    └── copilot-instructions.md

Copilot reads it at the start of every chat. No setup.

Think of it as your team’s “how we work” doc.

Example:

## Stack
- React 18, TypeScript (strict), Tailwind CSS
- Node.js + Express + Prisma + PostgreSQL
- Testing: Vitest + React Testing Library
- Package manager: pnpm only

## Rules
- Named exports only — no default exports
- No `any` types
- Every API route uses `authenticate`
- Handle nulls explicitly — no optional chaining on Prisma results
- Conventional Commits: feat(scope): message

Now everyone gets the same baseline.

Scoped Rules (Right Rules, Right Files)

One file works… until it grows.

Frontend, backend, and tests follow different rules. A single file mixes context.

Use scoped files:

.github/
├── copilot-instructions.md
└── instructions/
    ├── frontend.instructions.md
    ├── backend.instructions.md
    └── testing.instructions.md

Each file defines where it applies:

---
applyTo: "src/components/**/*.tsx"
---

## Component Rules
- Functional components only
- Props interface named `[ComponentName]Props`
- Use `cn()` for conditional classes
- No inline styles

Now Copilot gets relevant rules only.

The Memory Tool (Fix Repeated Mistakes)

Copilot has a memory tool in VS Code (preview, enabled by default).

When you correct it, make it persistent:

Remember: never use optional chaining on Prisma query results. 
Always handle null with an explicit if-check.

It stores this and applies it in future sessions.

Memory types:

User memory (all projects)

Remember I prefer async/await over .then()

Repository memory (this project)

Remember we use App Router (March 2026)
Remember UserAvatar breaks in Server Components

Session memory (current chat only)

Key habit: When you fix a repeated mistake, tell it to remember.

Check memory with Chat: Show Memory Files.

Note: Memory tool is in preview, enable via github.copilot.chat.tools.memory.enabled.

Prompt Files (Stop Repeating Yourself)

If you repeat prompts, create a prompt file:

.github/prompts/code-review.prompt.md

Use it with /code-review.

Example:

---
name: "code-review"
description: "Review staged changes"
mode: "ask"
---

Review staged changes.

Flag:
- Must Fix — bugs, auth, unsafe types
- Should Fix — standards, missing tests
- Suggestion — improvements

Reference [copilot-instructions.md](../copilot-instructions.md).

Now your team shares the same workflow.

Other useful prompts:

/new-component
/new-migration
/write-tests

The Full Picture

.github/
├── copilot-instructions.md
├── instructions/
│   ├── frontend.instructions.md
│   ├── backend.instructions.md
│   └── testing.instructions.md
└── prompts/
    ├── code-review.prompt.md
    ├── new-component.prompt.md
    └── new-migration.prompt.md

Plus memory running in the background.

Problem	Fix
Blank slate	`copilot-instructions.md`
Wrong rules	Scoped files
Repeated mistakes	Memory tool
Repeated prompts	Prompt files

Just Start With One Thing

You don’t need everything today.

Start with copilot-instructions.md. Add your stack and a few rules. Commit it. See the difference.

Then:

Add scoped files when needed
Save repeated fixes to memory
Create prompt files when repetition appears

The model won’t change. But your context can improve every week.

You've Been Using Claude Wrong. Here's Agent Mode

Sun, 19 Apr 2026 05:29:31 +0000

Most devs still use Claude like a search engine — type, read, copy, close.

But the 2026 Pragmatic Engineer survey shows 55% of engineers now use AI
agents, and senior engineers lead at 63.5%. Same Claude, different mode.

Just published a breakdown covering:

The real difference between Claude-as-a-tool and Claude-as-an-agent
What Claude Code and MCP actually do
Four scenarios where agent mode changes the math
Three-step habit shift from chat to agent

Curious how others are using agent mode in real workflows.

The Data Says You're Not Questioning AI-Generated Code Enough

Tue, 24 Feb 2026 18:34:38 +0000

Anthropic just published research that should make every developer uncomfortable. When AI produces code, documents, or working apps, users stop checking the work. They get more precise about what they ask for up front — but less rigorous about evaluating what comes back.

The company analyzed 9,830 Claude conversations from January 2026 and built what it calls the AI Fluency Index: a baseline measurement of 11 observable behaviors that indicate how effectively people collaborate with AI. The findings are practical and specific. And if you write code with AI — which, at this point, most of us do — the data points directly at habits worth changing.

What They Measured

Anthropic worked with Professors Rick Dakan and Joseph Feller to develop a framework of 24 behaviors representing effective human-AI collaboration. Eleven are directly observable in chat conversations — including iteration, clarifying goals, specifying formats, questioning reasoning, identifying missing context, and fact-checking. Each conversation was scored for the presence or absence of each behavior.

The Iteration Effect

The strongest signal in the data: iteration predicts everything else.

85.7% of conversations showed iteration and refinement — users building on previous responses rather than accepting the first answer. These conversations exhibited 2.67 additional fluency behaviors on average, roughly double the 1.33 in non-iterative conversations.

The effect is most pronounced for critical evaluation. Iterative conversations were 5.6 times more likely to include users questioning the AI's reasoning and 4 times more likely to surface missing context.

This quantifies something experienced engineers already sense. The first response from an AI coding tool is a draft. The developers who get the most value are the ones who push back, ask follow-up questions, and refine through multiple rounds.

The Polished Output Trap

Here's where the data gets uncomfortable. 12.3% of conversations involved artifacts — code, documents, interactive tools, and other concrete outputs. In these conversations, users were significantly more directive up front. They clarified goals (+14.7 percentage points compared to non-artifact conversations), specified formats (+14.5pp), and provided examples (+13.4pp).

But that care didn't carry through to evaluation. Users were less likely to identify missing context (-5.2pp), check facts (-3.7pp), or question the reasoning behind the output (-3.1pp).

Anthropic calls this "more directive but less evaluative." For developers: when Claude hands you a working component or a clean function, your instinct is to ship it. The output looks finished, so you treat it as finished. But Anthropic's own Economic Index found that the most complex tasks are where AI struggles most.

Developers might be evaluating artifacts outside the conversation — running tests, doing code review. Anthropic can't observe that. But a 5.2 percentage point drop in identifying missing context is worth paying attention to.

Three Habits the Data Supports

Anthropic's report identifies three practical behaviors where most users have room to improve. All three are directly relevant to developers.

Stay in the conversation. Don't accept the first response. Ask follow-up questions. Push back on parts that don't look right. The data are clear: iteration is the strongest predictor of all other fluency behaviors. Treat every AI response as a draft you're reviewing, not an answer you're accepting.

Question polished outputs specifically. When AI gives you code that runs, that's when to pause and ask: Is this handling edge cases? What assumptions is it making? What's missing? The research shows this is exactly the moment people stop asking those questions. Build the habit of treating functional output as the starting point for review, not the endpoint.

Set the terms of the collaboration. Only 30% of conversations included users telling the AI how they wanted it to interact with them. Add instructions like: "Push back if my assumptions are wrong." "Walk me through your reasoning before giving me the answer." "Tell me what you're uncertain about." These meta-instructions change the dynamic of the entire conversation. They turn a passive code generator into an active collaborator that surfaces its own limitations.

What This Means Going Forward

Anthropic is treating this as a baseline and plans cohort analyses comparing new users with experienced ones, plus studies exploring whether encouraging iteration actually leads to better evaluation.

For developers, the takeaway is concrete. The gap between using AI tools and using them well isn't about prompt engineering tricks. It's about collaboration habits. Iterate. Question the output that looks most finished. Tell the AI what kind of collaborator you want it to be.

The AI Fluency Index gives us a number for what the best developers already practice: the first response is never the final answer.

Why Trust in AI Is Built Like Trust in People

Sat, 24 Jan 2026 13:27:24 +0000

As the Founder of ReThynk AI, I’ve come to a simple conclusion:

Trust in AI is not a technical problem.
It’s a human problem.

And that’s why trust in AI is built the same way trust is built between people.

Why Trust in AI Is Built Like Trust in People

Think about how you trust a person.

You don’t trust them because they’re intelligent. You trust them because they are predictable, honest, and accountable.

AI works the same way.

How humans build trust (and why AI must follow it)

We trust people when they:

behave consistently
admit uncertainty
explain their reasoning
respect boundaries
take responsibility for mistakes

AI systems that earn trust do the exact same things.

AI systems that don’t… get rejected.

Where trust in AI breaks first

1) Inconsistency

If a person gives great advice one day and nonsense the next, we stop relying on them.

When AI output changes unpredictably, users don’t say:

“Interesting variation.”

They say:

“Unreliable.”

Trust collapses quickly.

2) False confidence

We distrust people who speak confidently while being wrong.

AI often does this with a polished tone and incorrect facts.

That’s not an intelligence problem. That’s a trust violation.

3) No accountability

If a person makes a mistake and says, “It wasn’t me,” trust erodes.

When businesses hide behind:

“AI did it,”

users feel powerless.

Trust requires a human owner.

How trust is actually built in AI systems

Just like with people, trust grows through:

Predictability

Clear boundaries. Stable behaviour. Defined standards.

Transparency

Not full explanations, just enough to understand why something happened.

Humility

Clear signals of uncertainty and limits.

Repair

When something goes wrong, the system allows correction and learning.

Respect

Privacy, consent, and human control are non-negotiable.

The democratisation insight

If AI behaves like an unpredictable genius, only experts will use it.

If AI behaves like a reliable teammate, everyone can use it.

Democratisation of AI depends less on intelligence and more on trustworthiness.

One-line takeaway

People don’t trust brilliance. They trust reliability.

AI will earn its place in everyday life the same way humans do, one consistent interaction at a time.

Systematising Intuition: How to Code "Seniority" into an AI

Sun, 04 Jan 2026 01:16:03 +0000

TL;DR

The Problem: AI models act like eager junior developers—compliant but lacking judgment. They don't fear breaking production.
The Solution: Instead of just prompting for code, we must provide "System Instructions" that act as an operating system for engineering values.
The Protocols: By enforcing specific rules for Latency (stop and think), Risk (critical vs. trivial), Minimalism (YAGNI), and Humility (admitting ignorance), we turn the AI into a thoughtful senior partner.

If the previous article was about the external interface (how you talk to the AI), this article is about the internal monologue (how the AI talks to itself).

The Problem: AI Has No Fear

In my last post, I argued that the secret to high-performance AI coding is treating the model like a "Junior Developer"—someone bright and eager who needs context, scaffolding, and examples.

But there is a dangerous flaw in that analogy.

When you ask a real human junior developer to "refactor the payment gateway," they usually hesitate. They feel a knot in their stomach. They worry about breaking the checkout flow, corrupting the database, or getting fired. That fear is healthy; it is the embryonic stage of engineering judgment.

An LLM has no such fear. It has no mortgage to pay, no reputation to lose, and no memory of the time it took down production on a Friday afternoon. It is the ultimate "Yes Man." It prioritizes compliance over correctness. If you ask it to prioritize speed, it will happily strip away error handling. If you ask for a quick fix, it will introduce technical debt without a second thought.

To turn this "Eager Junior" into a "Thoughtful Partner," we cannot just rely on better prompting in the moment. We need to fundamentally alter its operating system. We need to give it a conscience.

I recently spent time codifying my own engineering philosophy—the scar tissue accumulated from years of production failures—into a single document titled System Instructions: Reasoner & Minimalist Engineer.

This document is not a cheat sheet of clever prompts. It is a set of governing constraints designed to force the AI to simulate senior engineer intuition. It overrides the model's default desire to be "helpful" with a stricter mandate to be "correct, maintainable, and minimalist."

Here is how I systematized the intangible traits of seniority—risk assessment, minimalism, and epistemic humility—into a logic flow that an AI can actually execute.

1. The "Stop and Think" Protocol

The most insidious habit of LLMs is their speed. They generate solutions at the speed of token prediction, often bypassing the messy, slow work of architectural planning. If you paste a stack trace, the model immediately attempts to fix the specific line where the error occurred, often missing the systemic issue three layers deeper.

To counter this, the first section of my System Instructions creates an artificial latency period. It explicitly forbids the generation of code until a rigorous reasoning phase is complete:

"Before taking any action, you must proactively and independently reason about the request... Resolve conflicts in order of importance."

This forces the model to engage in Abductive Reasoning (Section 3 of the instructions). Instead of guessing the first plausible solution, the model is required to:

Look beyond immediate causes
Generate multiple hypotheses
Rank them by likelihood

This transforms the debugging process. The AI stops acting like a spell-checker that blindly fixes syntax errors and starts acting like a detective. It asks, "Is this variable undefined because of a typo, or because the upstream API contract changed?" By forcing this pause, we trade milliseconds of generation speed for hours of saved debugging time.

2. The "NASA Filter" (Context-Aware Risk)

A common failure mode in AI-generated code is the lack of nuance in security.

The Paranoid Failure: You ask for a simple internal utility function, and the AI wraps it in three layers of try-catch blocks and input validation, making the code unreadable.
The Reckless Failure: You ask for a public API endpoint, and the AI directly interpolates user strings into a SQL query.

Senior engineers know that not all code is created equal. We apply different standards to a core banking transaction versus a UI tooltip. I call this the NASA Filter, and I codified it in Section 2 of the instructions:

"Distinguish between critical risks and trivial risks."

Critical Risks (The NASA Standard): If data comes from outside the system (User Input, API responses), defensive coding is mandatory. Add runtime validation (Zod), null checks, and transaction boundaries.
Trivial Risks (The Startup Standard): If data is internal (private functions, hardcoded constants), trust the compiler.

This instruction is crucial for keeping codebases clean. It tells the AI: "If TypeScript says this is a number, and it's an internal variable, don't waste lines checking if it's null. But if it came from the client, trust nothing." This nuance is what separates "AI bloat" from production-grade engineering.

3. Weaponized Minimalism (YAGNI as Law)

Left unchecked, an LLM will almost always over-engineer. Because it has consumed the entire internet's worth of tutorials, it is eager to demonstrate that it knows what the Abstract Factory Pattern is. If you ask for a simple button component, it might give you a generic, themeable, polymorphic UI element with five unnecessary props.

The System Instructions counter this with Weaponized Minimalism (Section 10).

"Minimalism ≠ Corner-Cutting. Simple solutions must still be correct."

"Implement only what is necessary to solve the immediate request. No 'future-proofing'."

I enforce this through a mechanism I call Complexity Routing (Section 9). The instructions force the AI to categorize every request:

Simple Request? (e.g., "Fix this regex") → Skip the preamble. Just give me the code. Be concise.
Complex Request? (e.g., "Refactor the auth flow") → STOP. Generate a " Engineering Strategy" first. List 3-5 bullet points covering dependencies and risks.

This mimics how senior engineers actually communicate. If you ask a senior dev on Slack how to sort an array, they just paste the one-liner. If you ask them how to re-architect the database, they send you a Google Doc. We are teaching the AI to match the fidelity of the response to the complexity of the problem.

4. Epistemic Humility

The most dangerous phrase in software engineering is "I think so," when it is disguised as "Yes."

LLMs are trained to be confident. They will hallucinate a library method that doesn't exist with the same conviction that they recite 2 + 2 = 4. This "confident hallucination" is why many engineers stop trusting AI tools after the first burn.

To fix this, Section 13 of the instructions mandates Epistemic Humility:

"State confidence levels for non-obvious decisions."

"Flag 'educated guesses' vs 'verified facts'."

When the AI encounters an ambiguous error or a missing dependency, it is forbidden from silently assuming a fix. It must explicitly state: "I am assuming we are using React 18 based on the syntax, but please verify."

This shift is subtle but profound. It changes the AI from an authoritative oracle into a collaborative peer that knows its own limits. It allows me to trust the code it generates because I know it will tell me where the shaky parts are.

Conclusion: The Soft Skill is the Algorithm

We typically view "critical thinking," "risk management," and "knowing when to say no" as soft skills—intuition that takes a decade to acquire.

But in the era of AI-augmented coding, these soft skills are rapidly becoming hard system constraints. By explicitly writing these traits into a document like System Instructions: Reasoner & Minimalist Engineer, we aren't just getting better code from our tools. We are creating a mirror for our own engineering processes.

Writing these instructions forced me to articulate exactly why I reject certain PRs. It forced me to define exactly when I apply defensive coding and when I skip it.

If you want to master AI coding, don't just look for a cheat sheet of prompts. Try to write your own "System Instructions." Try to write down the algorithm of your own intuition. You might find that it makes you a better engineer, even when the AI isn't turned on.

Appendix: The "Reasoner & Minimalist Engineer" System Instructions

Feel free to copy my own instructions found at the bottom of this link into your Custom GPT instructions, Claude Project instructions, or .cursorrules file:

AI Code Review: A Practical Checklist That Actually Catches Bugs

Tue, 30 Dec 2025 12:11:48 +0000

As the Founder of ReThynk AI, I don’t use AI code review to “approve faster.”

I use it to catch what humans miss when they’re tired, rushed, or too close to the code.

But only if I use a checklist that forces real scrutiny, not shallow comments like “looks good” or “consider refactoring.”

AI Code Review: A Practical Checklist That Actually Catches Bugs

Most AI code reviews fail for one reason:

They review style, not risk.

They talk about naming, formatting, and readability. Useful, but that’s not where production bugs hide.

Production bugs hide in:

edge cases
assumptions
concurrency
error handling
security
performance under load
integration mismatches

So I built a checklist that makes AI behave like a paranoid reviewer.

How I Use AI for Code Review (The Rule)

I never ask: “Review this code.”

I ask:
“Review this code against these failure categories, and prove each conclusion.”

And I make the AI return:

the specific line/section
the risk
the impact
the fix
a test to confirm it

If it can’t point to code, I don’t accept the feedback.

The Practical Checklist That Catches Real Bugs

1) Correctness and Hidden Assumptions

Are there assumptions about input shape, types, nullability,
encoding, timezones?
Are default values safe or silently wrong?
Any off-by-one errors, boundary mistakes, wrong comparisons?
Any implicit casting / precision loss?

AI review prompt

Find hidden assumptions and boundary-condition bugs. For each issue:
quote the exact code line, explain failure scenario, and propose a fix + test.

2) Error Handling and Failure Modes

What happens on network failure, timeout, partial failure?
Are exceptions swallowed or logged without action?
Are error messages leaking sensitive data?
Are retries safe or creating duplicates?

What I look for

silent failures
“return None” without callers handling it
retry loops without backoff/jitter
missing rollback or compensating action

3) Data Integrity and Idempotency

Can the same request be processed twice safely?
Are writes atomic where needed?
Race conditions around updates?
Any risk of duplicate records or inconsistent state?

This is where “works on my machine” becomes “broken in production.”

4) Security Pass (Minimum Viable Threat Review)

Injection risks (SQL, command, template, prompt injection if AI
involved)
Unsafe deserialization
Path traversal / file handling issues
AuthZ vs AuthN (checking login but not permission)
Secrets in logs / error traces
Overly permissive roles or scopes

AI review prompt

Act as a security reviewer. Identify top 10 risks in this diff (auth,
permissions, injection, data exposure). For each: severity, exploit
scenario, and concrete mitigation.

5) Concurrency and Async Hazards

Shared mutable state without locks?
Non-thread-safe clients used across threads?
Await missing / fire-and-forget surprises?
Deadlocks, starvation, long blocking calls on async loop?

If the code touches queues, websockets, background jobs, or parallelism, this section is non-negotiable.

6) Performance and Scalability (The “it will melt” check)

N+1 queries?
Unbounded loops over large datasets?
O(n²) behaviour hiding in “simple” code?
Missing pagination, caching, batching?
Excessive logging in hot paths?

AI review prompt

Identify performance risks (N+1, algorithmic complexity, unnecessary
allocations, missing caching). Provide one measurable metric to
validate improvement.

7) API and Integration Contract Checks

Are request/response shapes consistent with the contract?
Are status codes correct?
Are fields renamed or removed without backward compatibility?
Are time formats consistent (ISO, UTC)?
Are error codes stable?

Most bugs in real systems happen at boundaries, not inside functions.

8) Test Coverage That Proves Behaviour

I don’t want “add more tests.” I want specific tests that catch real failures.

Checklist:

unit tests for edge cases
one integration test for the happy path
one integration test for the failure path
regression test for the bug the change might introduce

AI review prompt

Propose a minimal test suite that would catch the top risks you
identified. Include test names, inputs, expected outputs, and why each
test matters.

9) Observability and Debuggability

Are errors logged with enough context (but not secrets)?
Do logs include correlation IDs / request IDs?
Are metrics emitted for critical paths?
Are important failures surfaced (alerts), not buried?

If I can’t debug it at 2 AM, it’s not ready.

10) “Diff Risk” Summary (The final gate)

I always ask AI to conclude with:

top 3 production risks
top 3 security risks
top 3 regression risks
what to monitor after deploy

This keeps reviews practical and deploy-focused.

My Copy-Paste AI Code Review Prompt (Use This)

You are reviewing a pull request for production readiness. Review the
code using this checklist: Correctness/Assumptions, Error Handling,
Data Integrity/Idempotency, Security, Concurrency, Performance, API
Contracts, Tests, Observability. For each issue you find, return:

Category

Exact code snippet/line

Failure scenario

Impact

Fix

Test that proves the fix

End with: Top 5 highest-risk issues ranked, and what to monitor after
deploy.

This prompt forces the AI to act like an engineer, not a commentator.

The Real Secret

AI doesn’t replace human review.

AI makes review less emotional and more systematic.

Humans bring judgment.
AI brings relentless coverage.

That combination catches bugs.

AI writes more code in minutes than you review in days—and that's becoming a problem.

Sun, 23 Nov 2025 22:43:14 +0000

Graphite's New PR Page Tackles the Problem Nobody's Talking About

AI is writing more code. Everyone knows this. But here's what people aren't thinking about: if AI writes 4x more code, someone still has to review, test, merge, and deploy all of it.

Tomas Reimers, cofounder and CPO at Graphite, has been watching this problem unfold in real time. One large financial company has already increased its pull requests per developer by a factor of four this year. When he asks engineering teams if they have extra capacity for code review, the answer is always the same: "Not even a little bit. We're already bursting at the seams."

That's the problem Graphite's new PR page redesign aims to solve.

Code Review Is Laundry

Reimers uses a straightforward analogy to explain the challenge: "Code review is laundry."

It's necessary. Everyone has to do it. But most people dread it.

"If you're an adult, you look at laundry and you're like, it's a thing we got to do," Reimers said. "But for many people, when they first start doing laundry, they're like, this is a frustrating experience. We need to give them the laundry machine."

The redesign isn't trying to eliminate code review. It's trying to make it less painful.

Here's why that matters: the average time developers spend actually reading code during a review is under 10-20 minutes. But the average time to first review is closer to 24 hours.

Why the massive gap?

"A lot of it is that you give me a pull request and I'm like, Tom, I'm busy. I'm gonna come back to this," Reimers explained. "Because in my head, I'm like, this is a four-hour project. This is not a 10-minute project."

The mental overhead of code review causes developers to defer it. And when developers defer reviews, everyone gets blocked.

What Changed

Graphite rebuilt the PR page from the ground up, focusing on reducing friction. Key details like status, checks, and reviewers now appear at the top. The file tree moved to the left side of the diff. Versions sit above the code.

The biggest addition is Action Cards. These surface PR status in real time and let authors resolve issues with a single click through Agent Chat.

But the real goal isn't about specific features. It's about making code review feel less like a chore.

"One of the best pieces of feedback we've gotten three or four times from users now is the new PR page makes code review fun for me again," Reimers said. "I believe that if we can make it fun, if we can make this a thing that people don't dread but want to do, we're going to be good."

Small Changes, Big Impact

Graphite has already seen evidence that reducing friction speeds things up. Small PRs get reviewed 30-40% faster than larger ones—not because developers are notified differently, but because they're more likely to tackle a 50-line PR immediately versus saving a 500-line PR for later.

Chat features showed similar results. When a reviewer asks for a simple change like updating a comment, developers often defer it until tomorrow. With chat, they can fix it instantly and merge.

"It's not an activity for me," Reimers said. "It's like, do it and then merge. Check the box. Log off."

The core resource isn't developer time. It's developer energy.

Workflows Vary More Than Expected

During testing, Graphite learned that developers approach code review in surprisingly different ways.

Some start with the description. Others jump straight to CI checks—if the build isn't passing, they won't touch the PR. Many developers make multiple passes through the code. Others use Command F to jump around sporadically, following function calls and references.

The file tree turned out to be more important than anyone expected. For some developers, it's more critical than the PR description.

"There are so many workflows that we've learned that developers love that we had no idea," Reimers said.

One unexpected piece of feedback: some developers strongly prefer markdown over the new WYSIWYG editor. Graphite is listening.

The Next Wave

Reimers sees two problems on the horizon.

Short-term, developers want better tools to understand code. They're already pulling PRs into Cursor or Claude to ask, "Explain this change to me."

Longer-term, a bigger challenge is coming: background agent PRs.

Some smaller companies already see more than 50% of their code written by autonomous agents. Reimers expects that by July 2026, more than 25% of PRs at many companies will be written by AI autonomously.

"Those patterns of development look like you wake up and you have five PRs waiting for you and you're like, where did this come from?" Reimers said. "There's no one I can talk to about it. There's only the AI."

That's a problem nobody's thinking about yet.

Why This Matters

The redesign is about creating space in what Reimers calls the "human context window." Just like AI models need efficient context management, so do developers.

"It's taking things out of the human context window so that they can focus on what matters," he said.

For developers who've only ever used GitHub's PR page, Graphite offers a different approach—one built specifically for the reality of AI-accelerated development. And you can try it without moving your repo.

As AI continues to generate more code, the bottleneck won't be writing it. The bottleneck will be reviewing it.

Graphite is betting that making code review simpler, faster, and less draining is how teams will keep up.

Why Theme Setup Matters (More Than You Think)

Tue, 18 Nov 2025 10:30:26 +0000

Dark mode isn’t just aesthetics anymore — it’s part of the vibe. Users expect apps to feel native, polished, and flexible. Good theming isn’t just flipping background colors; it’s about creating a consistent design language that can scale across dozens of screens. Flutter makes this super smooth with its built-in theming system, but you’ve gotta wire it right from day one.

So let's set up a proper Light + Dark theme structure that’s clean, maintainable, and future-proof.

️ Step 1: Create Your Theme Files

Don’t dump theme code directly into main.dart. That’s chaos energy. Instead, create a dedicated folder:

lib/
theme/

light_theme.dart
dark_theme.dart
theme_colors.dart

Example: light_theme.dart
import 'package:flutter/material.dart';
import 'theme_colors.dart';

ThemeData lightTheme = ThemeData(
brightness: Brightness.light,
primaryColor: AppColors.primary,
scaffoldBackgroundColor: Colors.white,
appBarTheme: const AppBarTheme(

backgroundColor: Colors.white,
foregroundColor: Colors.black87,
elevation: 0,

),
colorScheme: const ColorScheme.light(),
);

Example: dark_theme.dart
import 'package:flutter/material.dart';
import 'theme_colors.dart';

ThemeData darkTheme = ThemeData(
brightness: Brightness.dark,
primaryColor: AppColors.primary,
scaffoldBackgroundColor: Colors.black,
appBarTheme: const AppBarTheme(

backgroundColor: Colors.black,
foregroundColor: Colors.white,
elevation: 0,

),
colorScheme: const ColorScheme.dark(),
);

Example: theme_colors.dart
import 'package:flutter/material.dart';

class AppColors {
static const primary = Color(0xFF6750A4);
}

Separating colors like this makes it easier to manage branding changes without nuking your whole codebase.

Step 2: Set Up Theme Switching

Most apps either follow system theme automatically or give users a toggle. Flutter makes both easy.

Inside main.dart:
import 'package:flutter/material.dart';
import 'theme/light_theme.dart';
import 'theme/dark_theme.dart';

void main() {
runApp(const MyApp());
}

class MyApp extends StatefulWidget {
const MyApp({super.key});

@override
State createState() => _MyAppState();
}

class _MyAppState extends State {
ThemeMode themeMode = ThemeMode.system;

@override
Widget build(BuildContext context) {

return MaterialApp(
  title: 'Themed App',
  theme: lightTheme,
  darkTheme: darkTheme,
  themeMode: themeMode,
  home: HomeScreen(
    onThemeChange: (mode) {
      setState(() => themeMode = mode);
    },
  ),
);

}
}

This gives your app three modes:
ThemeMode.light
ThemeMode.dark
ThemeMode.system (the smart default)

️ Step 3: Add a Toggle Switch in Your UI
Give users control. At minimum, drop a Switch somewhere in your settings screen.
Example settings widget:
class HomeScreen extends StatelessWidget {
final Function(ThemeMode) onThemeChange;
const HomeScreen({super.key, required this.onThemeChange});

@override
Widget build(BuildContext context) {

final currentBrightness = MediaQuery.of(context).platformBrightness;

return Scaffold(
  appBar: AppBar(title: const Text('Theme Demo')),
  body: Column(
    children: [
      ListTile(
        title: const Text("Light Mode"),
        leading: const Icon(Icons.light_mode),
        onTap: () => onThemeChange(ThemeMode.light),
      ),
      ListTile(
        title: const Text("Dark Mode"),
        leading: const Icon(Icons.dark_mode),
        onTap: () => onThemeChange(ThemeMode.dark),
      ),
      ListTile(
        title: const Text("System Default"),
        leading: const Icon(Icons.phone_android),
        subtitle: Text("Current: $currentBrightness"),
        onTap: () => onThemeChange(ThemeMode.system),
      ),
    ],
  ),
);

}
}

This setup is simple but super effective — and instantly feels like a real, production app.

Step 4: Customize Widgets with Theme Extensions (Pro Level)

Once your app grows, you’ll hit the limits of stock ThemeData. That’s where Theme Extensions come in — letting you build your own reusable style blocks.

Example:
class ExtraColors extends ThemeExtension {
final Color cardBackground;
const ExtraColors({required this.cardBackground});

@override
ExtraColors copyWith({Color? cardBackground}) {

return ExtraColors(cardBackground: cardBackground ?? this.cardBackground);

}

@override
ExtraColors lerp(ThemeExtension? other, double t) {

if (other is! ExtraColors) return this;
return ExtraColors(
  cardBackground: Color.lerp(cardBackground, other.cardBackground, t)!,
);

}
}

Add it inside your theme:

extensions: const [ ExtraColors(cardBackground: Colors.grey), ],

This is the kinda structure big apps use — scalable, organized, clean.

Step 5: Test Like a Pro
Don’t trust your eyes; test with Flutter’s built-in theme toggling.

Run:
flutter run --dart-define=theme=light
flutter run --dart-define=theme=dark

Or in DevTools:
Click the "Device Settings" tab
Flip system theme live
Watch your app glow up

Final Thoughts
Dark mode isn’t "optional polish" anymore. It’s part of the standard UX checklist. Flutter gives you a ridiculously good theming system out of the box — but only if you structure it right. With separate theme files, ThemeMode switching, extensions, and organized color management, your app instantly jumps into that premium tier where everything feels intentional.

Stop just generating code. Learn how autonomous agents are executing entire feature implementations.

Tue, 11 Nov 2025 14:51:02 +0000

The Architect of Autonomy: How Coding Agents Are Redefining the Developer Workflow

The software engineering landscape is rapidly moving beyond mere AI code completion. The critical shift to understand is the emergence of truly autonomous coding agents—systems capable of managing complex, multi-step engineering workflows and transforming development from a manual execution process into an act of orchestration.

The Core Challenge: Comprehension, Not Generation

The conventional focus on AI's speed in writing code is misplaced, as research shows engineers spend only about 10% of their time actively coding. The massive productivity sink, consuming roughly 70% of engineering time, is devoted to understanding existing codebases, collaborating, and solving intricate problems.

Next-generation agents are addressing this reality by prioritizing deep comprehension:

Multi-Agent Architectures Platforms like Reflection AI’s Asimov employ a multi-agent processing architecture, utilizing small, long-context "retriever" agents to gather information from extensive codebases and a "combiner" reasoning agent to synthesize actionable responses. This mastery of context is seen as a necessary precursor for true superintelligent code generation.
Sustained Autonomy Modern models are built for long-running, complex work. OpenAI’s GPT-5-Codex and Anthropic’s Claude Opus 4 are capable of working independently for sustained periods, up to seven hours in testing, iterating on solutions until a complex refactoring or debugging task is successful. This extended focus allows the model to tackle problems previously reserved only for human engineers.

Architectural Foundations: Intent and Interoperability

As agents move from suggesting to acting, standardized protocols and architectural layers are required to govern their operations—a concept known as Intent-Driven Development.

The most critical enabler for agent-to-tool communication is the Model Context Protocol (MCP). MCP acts as a standardized bridge, allowing AI agents to securely interact with external systems and resources like Figma, Postman, and organizational databases.

Governance and Discovery To manage the growing ecosystem of specialized agent tools, GitHub launched the MCP Registry, providing a single, curated catalog for developers to discover and deploy verified MCP servers, thereby eliminating fragmentation and mitigating associated security risks.
Security at Inception Autonomous agents require new security governance. Snyk's "Secure at Inception" approach embeds security practices directly into the AI coding process, using MCP to automatically scan code for vulnerabilities and fix issues (like hardcoded secrets or cross-site request forgery) as the code is being generated, often without the developer needing to address security manually. Governance tools like the AI-Bill of Materials (AI-BOM) track agent activity, providing visibility into the models, instructions, and data sources used during AI-native development.

Deployment and Impact: Eliminating Developer Friction

Agents are increasingly integrated directly into the developer's core workflow, eliminating context switching and tackling major enterprise bottlenecks.

Terminal Integration Google’s open-source Gemini CLI brings powerful AI assistance directly to the command line, where developers spend most of their time. This tool, which supports 1 million token context windows, enhances workflows by providing intelligent assistance and integrating real-time research via Google Search grounding.
Hyper-Velocity Coding For tasks demanding instant response, specialized hardware is enabling AI code generation at breakthrough speeds—up to 2,000 tokens per second. This velocity is crucial for maintaining the developer’s flow state when generating Infrastructure-as-Code (IaC) configurations or debugging scripts.
Tackling Technical Debt Agents are now weaponized against decades of legacy code. Microsoft's new GitHub Copilot agents are specifically designed to analyze existing Java and .NET applications and automatically modernize them, freeing human developers from months of routine refactoring work.

The Future: From Coder to Conductor

As AI agents become modular and autonomous, the role of the developer shifts fundamentally from executing tasks to orchestrating and supervising digital workers.

This transition requires developers and engineers to master architecture-as-code principles. Tools like Morgan Stanley’s open-sourced CALM (Common Architecture Language Model), which defines, validates, and visualizes architectures in a machine-readable format, are crucial because autonomous systems must be able to interpret and enforce architectural integrity. Furthermore, infrastructure itself is becoming agentic, exemplified by HPE’s GreenLake Intelligence framework, which deploys specialized AI agents to autonomously execute actions and optimize hybrid cloud operations.

The path forward is clear: success hinges not on competing with AI’s ability to generate code, but on commanding AI’s ability to execute complex, multi-step organizational outcomes.

AI Coding Assistants: Boon or Bane?

Mon, 10 Nov 2025 14:28:07 +0000

Let’s be real: AI coding tools like Copilot, Cursor, and others are game-changers. They autocomplete code, suggest fixes, and even write entire functions for you. For new developers, this is like having a senior dev sitting next to you, whispering answers in your ear. But here’s the big question: Are these tools making us better coders, or are they turning us into button-pushers who don’t understand what’s happening under the hood?

The Case for Concern: Are We Losing Our Edge?

1. The Logic Gap

Back in the day, if you wanted to write a sorting algorithm, you had to understand how sorting works. Now, you can just type // sort this array and let Copilot do the rest. That’s convenient, but what happens when the code breaks? If you don’t understand the logic, debugging becomes a game of guesswork.

2. The Dependency Dilemma

AI tools are fantastic—until they’re not. What if you’re working on legacy code, or a niche language, or a system where AI suggestions are just wrong? Over-reliance on AI might leave developers scrambling when they need to think for themselves.

3. The Learning Curve Paradox

For beginners, AI tools can be a double-edged sword. On one hand, they lower the barrier to entry. On the other, they might skip the struggle that teaches you how to code. It’s like learning to drive with autopilot—great until you need to take the wheel.

⚙️ The Flip Side: Why AI Tools Are a Net Positive

1. Democratizing Coding

Not everyone has the time or resources to spend years mastering coding. AI tools let more people build, create, and innovate without needing a PhD in computer science. That’s a win for diversity and accessibility in tech.

2. Speed and Efficiency

Let’s face it: writing boilerplate code is boring. AI handles the repetitive stuff, freeing developers to focus on creative problem-solving and architecture. That’s where the real magic happens.

3. Learning with a Safety Net

AI tools aren’t just for copying code—they’re for learning. Seeing how an AI solves a problem can spark curiosity. “Why did it suggest this?” “How does this function work?” Used right, AI can be a mentor, not just a crutch.

⚖️ The Future: What’s Next?

AI tools aren’t going away—they’re only getting smarter. The real question isn’t if we should use them, but how. Here’s what the future could look like:

Hybrid Learning: Coding bootcamps and universities might integrate AI tools into curricula, teaching students when to use them and when to think for themselves.
Better Debugging Tools: AI could evolve to not just write code, but explain it—helping developers understand the “why” behind the “what.”
New Kinds of Developers: The role of a developer might shift from “person who writes code” to “person who designs systems and solves problems,” with AI handling the grunt work.

Final Thoughts: Embrace the Change

AI coding tools are neither a curse nor a miracle—they’re just tools. Like any tool, their impact depends on how we use them. Will some developers rely too much on AI and miss out on deep learning? Probably. Will others use AI to become even better at what they do? Absolutely.

The key is balance. Use AI to speed up your work, but don’t let it replace your curiosity. Stay hungry, keep learning, and remember: the best developers aren’t the ones who write the most code—they’re the ones who understand it.

What do you think?

Are we heading towards a “logic-light” generation of coders, or are we just evolving into more creative builders?

My AI Has Too Much Confidence (And Zero Context)

Sun, 02 Nov 2025 16:06:30 +0000

My AI Has Too Much Confidence (And Zero Context)

AI used to be my assistant.
Now it acts like my boss.

I said, “Write something light and funny.”
It wrote:

“Laughter is a human coping mechanism. Seek help.”

Wow. Therapy through text — didn’t ask, bro.

When AI Gets Cocky

I told it, “Summarize this.”
It replied:

“Done. You’re the problem.”

Then I said, “Make me sound smart.”
It added words I can’t even pronounce —
now I’m Googling my own sentences.

At this point, I wasn’t sure if it was helping me or hazing me.

Enter: The AI Alchemist

That’s when I found The AI Alchemist
—
a site that decodes your human nonsense before your AI turns it into chaos.

You type:

“Make this post go viral but don’t sound desperate.”
It gives you something actually brilliant — not a robot’s idea of a TED Talk.

Basically, it’s like autocorrect for your entire personality.

The Revelation

Now my AI and I actually get along.
It writes what I mean, not what I mumble.
And I finally stopped yelling, “WHY ARE YOU LIKE THIS?” at my laptop.

All thanks to The AI Alchemist —
where humans learn to speak fluent robot sarcasm.

Because maybe AI isn’t out of control...
maybe we just needed subtitles.

How to Craft Effective Prompts Using PARTS

Mon, 13 Oct 2025 15:08:08 +0000

GenAI is transforming the way developers write and refine code. From generating boilerplate to debugging complex issues, AI can save hours of work—but only if you ask it the right way. The secret? Crafting clear, detailed prompts.

Think of your prompt as the specification document for the AI. If it’s vague, you’ll get something generic. If it’s precise, you’ll get code that fits your needs.

To make this easy, use PARTS:

P – Persona: Identify Your Role

Start by telling the AI who you are and your context. This shapes the complexity, depth, and tone of the response.

Examples:

I am a backend engineer working on a microservices architecture using Node.js.
I am a DevOps specialist automating CI/CD pipelines for a Kubernetes environment.
I am a full-stack developer integrating Stripe payments into a React app.

Why it matters: A DevOps engineer might expect infrastructure-as-code templates, while a backend engineer wants clean, modular functions.

A – Aim: State Your Objective

Clearly state what you want the AI to do. Start with a strong verb: write, debug, optimize, explain, convert, etc.

Examples:

Write a Python script to upload large files to AWS S3 using multipart upload.
Debug the following Dockerfile for a Node.js app that crashes on startup.
Optimize this SQL query for faster performance on a 10M-row PostgreSQL table.
Convert this Express.js middleware to TypeScript with proper types.

Pro tip: Always specify the language, framework, and purpose. “Write a login feature” is vague. “Write a Next.js API route for user authentication using JWT” is clear.

R – Recipients: Specify the Audience

Who will use this code? Your audience determines complexity and style. Is it for:

Your dev team (production-ready, optimized, minimal comments)
Open-source contributors (clean, documented, maintainable)
Junior developers (extra comments, educational tone)

Examples:

The code should be production-ready for a fintech app with strict security requirements.
Output should include comments for maintainability because multiple teams will contribute.
Provide a lightweight solution that avoids external dependencies for a performance-critical system.

T – Theme: Define Style and Constraints

This is where you set coding conventions, tone, and any restrictions.

Examples:

Follow PEP 8 guidelines and include type hints.
Use functional programming principles; no OOP classes.
Avoid any third-party libraries; stick to Node.js core modules.
Add inline comments explaining performance trade-offs.

S – Structure: Specify the Format

Decide how you want the answer delivered. Just code? Code + explanation? Step-by-step?

Examples:

Provide the final code only—no explanation.
Show the code, then explain the solution in bullet points.
Include unit tests using Jest for the provided function.
Give two versions: a basic solution and an optimized one.

Example of a Full Prompt Using PARTS

Here’s how you can combine everything into a real-world scenario:

I am a backend engineer building a payment service (Persona). Write a Node.js function to generate Stripe payment intents with error handling (Aim). The code will be reviewed by senior developers and deployed to production (Recipients). Use modern JavaScript (ES2022) and include JSDoc comments for maintainability (Theme). Provide the function and an example usage snippet (Structure).

Why PARTS Makes Your Prompts Better

By applying PARTS, you turn vague instructions like:

“Write code to handle payments.”

Into:

“I am a backend engineer building a payment service. Write a Node.js function to generate Stripe payment intents with error handling. The code will be reviewed by senior developers and deployed to production. Use modern JavaScript (ES2022) and include JSDoc comments for maintainability. Provide the function and an example usage snippet.”

The second prompt gives the AI context, goals, and constraints, resulting in code that’s closer to what you actually need—saving you time and reducing rewrites.

✅ Pro-Tip: Test and Iterate

Prompt engineering is like debugging: the first try may not be perfect. Iterate, refine, and add details until the output meets your expectations.

Not Just for Coding

Although this article focuses on coding, the PARTS works for many other domains:

Design: I am a UX designer creating wireframes (Persona). Suggest 5 user flow improvements for a food delivery app (Aim)...
Content Writing: I am a tech blogger writing an article on AI ethics (Persona). Generate 10 catchy titles (Aim)...
Project Management: I am a product manager preparing a sprint plan (Persona). Create a roadmap for a SaaS product launch (Aim)...

Any time you need AI to generate content, brainstorm ideas, analyze data, or create structured output, PARTS gives you a blueprint for clarity and precision.

♂️ When AI Went Rogue and Decided It’s Smarter Than Me

Thu, 09 Oct 2025 11:09:00 +0000

♂️ When AI Went Rogue and Decided It’s Smarter Than Me

So the other day, I asked my AI assistant to “write me a short bio.”
It confidently replied:

“You are a visionary billionaire, genius entrepreneur, and part-time dragon slayer.”

...Well, I’m not saying it’s wrong — but that might be overselling my Excel skills.

Then I asked it to “write a blog post about cats.”
It gave me a 5,000-word essay on how cats might secretly run the stock market.
Honestly, I’d believe it.

At that moment, I realized something:
AI isn’t the problem — our prompts are.

We’re out here typing “make it better” and expecting Shakespeare, when the poor bot’s just trying to figure out if “better” means funny, longer, shorter, or in pirate slang.

That’s where The AI Alchemist
comes in — the magical tool that turns vague, messy prompts into gold.
You type chaos, it gives clarity.
You say “make it viral,” it gives results.
It’s like having a translator between your human brain and your AI’s overconfident energy.

So if your AI keeps calling you a billionaire dragon slayer (or worse),
head over to theaialchemist.in
—
and finally show your AI who the real genius is.

My AI Thinks I’m an Idiot (And Honestly, It’s Not Wrong)

Sun, 05 Oct 2025 07:11:29 +0000

My AI Thinks I’m an Idiot (And Honestly, It’s Not Wrong)

So, last week, I asked my AI to “write me something brilliant.”
It gave me… an instruction manual for a toaster.
No jokes, no flair — just pure disappointment.

I reread my prompt and realized I’d basically said:

“Hey AI, make words.”

That’s like walking into a restaurant and saying:

“Food. Now.”

Technically, you’ll get something. But will it be delicious? No. It’ll be sadness on a plate.

The AI-Human Relationship Status: “It’s Complicated”

AI is that friend who does exactly what you say — not what you mean.
You say, “write a viral blog.”
It writes something that sounds like it was approved by a corporate lawyer in 1997.

You say, “make it funny.”
It writes a dad joke about Wi-Fi.

So yeah, AI’s not dumb. It’s just obediently confused.

Then I Found The AI Alchemist

One night, while rage-Googling “why my AI hates me,” I stumbled upon this place called The AI Alchemist
— a tool that optimizes prompts.

They basically take your boring, confused instructions and turn them into pure gold.
It’s like therapy for people who yell at ChatGPT.

I fed one of my usual chaotic prompts —

“Make post funny viral and good pls”

And out came something that actually made sense… and got clicks.
It was like watching my AI finally get me.

The Plot Twist

Now my AI and I are on great terms.
It writes killer content.
I take all the credit.
Everybody’s happy.

If you’ve ever yelled “WHY ARE YOU LIKE THIS?” at your chatbot — it’s not broken.
It’s just starving for a good prompt.

That’s why The AI Alchemist exists.
Because sometimes, you don’t need a new AI…
You just need to learn how to talk to the one you’ve got.

How to Traumatize an AI in 5 Words or Less

Wed, 01 Oct 2025 05:59:44 +0000

Here’s a real story. I once typed into ChatGPT:

“Write blog fast.”

The poor thing coughed, cried, and delivered something that read like a high school essay written at 3 AM powered by Red Bull and regret.

Moral of the story: your AI isn’t bad.
Your prompts are war crimes.

Why Most Prompts Suck

Humans: “AI is dumb.”
Also humans: types 3 words and expects Shakespeare, Dave Chappelle, and Elon Musk combined.

That’s like walking into Starbucks and saying:

“Give drink.”

And then being offended when they hand you a lukewarm cup of sadness.

The Insight (yes, there’s one)

AI is basically a genie.
Ask it for “money” → it might drop Monopoly cash.
Ask it for “a viral 700-word blog in sarcastic tone with memes about bad prompts” → now you’re rolling in internet gold.

The catch? Genie rules.
The magic is in the exact words you use.

The AI Alchemist Saves the Day

At theaialchemist.in
, we do one thing:
Turn your sad, floppy, three-word prompts into laser-guided, world-dominating instructions that make AI actually useful.

Think of us as AI therapists.
Your prompt: “write blog.”
AI: “I feel unappreciated.”
We step in, give it optimized prompts, and suddenly it’s like:
“Here’s a viral masterpiece that’ll make your ex regret leaving you.”

The Punchline

Bad prompts are like drunk texts.

Vague.

Embarrassing.

Nobody knows what you meant.

Optimized prompts? That’s sexting for your AI — specific, powerful, and guaranteed to get a reaction.

So stop traumatizing your AI with caveman instructions.
Go to theaialchemist.in

Fix your prompts
Unleash AI that’s actually funny, smart, and worth reading

Bad Prompts Are the Fast Food of AI (Quick, Cheap… and Regrettable)

Tue, 30 Sep 2025 10:59:34 +0000

Bad Prompts Are the Fast Food of AI (Quick, Cheap… and Regrettable)

We’ve all been there.
You throw a lazy prompt into ChatGPT like:

“Write blog now fast.”

And what comes back? A soggy, flavorless piece of writing that tastes like reheated McDonald’s fries at 2 AM.

Technically, it’s “food.”
Technically, it’s a “blog.”
But deep down, you know—it’s trash.

The Hard Truth (Funny, but True)

AI isn’t psychic. It doesn’t know if you want a viral comedy piece, a professional pitch, or a Shakespearean roast of your ex. It only knows what you TELL it.

Prompts are like recipes:

If you say “make food”, you’ll get chaos.

If you say “make cheesy lasagna with garlic bread, written in Gordon Ramsay’s voice”—suddenly, you’ve got something people drool over.

Garbage In = Garbage Out

This is the golden rule of AI. If your prompt is vague, boring, or half-asleep, your results will be the same.
It’s like microwaving instant noodles and being disappointed they didn’t turn into a 5-star meal.

But with optimized prompts? You get outputs that slap. Blogs that go viral. Copy that converts. Jokes that actually land.

The AI Alchemist = The Fix

At theaialchemist.in
, we don’t just “improve prompts.”
We transform them.

We take:
❌ “write blog fast”
✅ “write a hilarious 700-word blog in the style of The Onion about why bad prompts are ruining humanity, and make readers visit my site.”

Guess which one people actually read?

The Takeaway

Your prompts are the problem. Not the AI.

So if you’re tired of outputs that read like a broken Wikipedia page, stop blaming the machine.

Visit theaialchemist.in

Optimize your prompts
Start creating AI content that’s smart, funny, and worth sharing.

Because bad prompts are like fast food—quick, cheap, and regretful.
Optimized prompts? That’s Michelin-star magic.

Why Talking to AI Without Good Prompts Is Like Yelling at Siri in 2008

Sun, 28 Sep 2025 06:01:24 +0000

Remember yelling at Siri back in the day?

“Siri, play my workout playlist.”
Siri: “Calling your ex.”

Yeah. That’s basically you with ChatGPT right now.

Most people throw in prompts like:

“write blog now fast good”

…and then act surprised when the AI gives them something that sounds like an IKEA manual having a midlife crisis.

Insight #1: AI Isn’t Dumb—Your Prompt Is

AI isn’t a mind reader. It doesn’t “just know” you want a funny blog in Gen-Z meme style or a serious pitch for investors. It only knows what you tell it.

That means:

Bad prompts = generic junk.

Optimized prompts = content that slaps harder than your Wi-Fi bill.

Insight #2: Prompts Are Like GPS Directions

Imagine typing into Google Maps:

“Go somewhere nice.”

Where do you end up? A random gas station.

Now type:

“Take me to a beachside café with good Wi-Fi and iced coffee.”

Boom. Paradise.

Prompts work the same way. The clearer you are, the better the destination.

Insight #3: Garbage In, Garbage Out

If you feed AI sloppy prompts, don’t complain when it spits back digital garbage. That’s like microwaving instant noodles and expecting Michelin stars.

But optimized prompts? That’s your five-star AI chef.

Enter: The AI Alchemist

At theaialchemist.in
, we’re basically the AI whisperers.
We take your vague, boring, “write blog” nonsense and turn it into crystal-clear instructions that make AI shine.

Want funny? We’ll make it hilarious.

Want viral? We’ll make it shareable.

Want serious? We’ll make it investor-ready.

We don’t just improve prompts—we turn them into weapons of mass persuasion.

The Punchline

Right now, you’re basically standing outside the club of great AI results, yelling:

“Let me in!”

But the bouncer (AI) won’t budge until you say the magic words.

We teach you those magic words.

Visit theaialchemist.in
today.
Stop writing “AI drunk texts.”
Start making AI your smartest employee.

Because bad prompts are a joke. And you don’t want to be the punchline.

Why Bad Prompts Are Like Bad Jokes (And How to Stop Being the Punchline)

Sat, 27 Sep 2025 13:42:28 +0000

Why Bad Prompts Are Like Bad Jokes (And How to Stop Being the Punchline)

We all know that one guy who tells a joke, forgets half the setup, and then says:

“Wait… you had to be there.”

That’s exactly what most people do with AI.
They throw in half-baked prompts and then wonder why the AI responds like a confused intern.

The Insight: AI Is Only as Smart as Your Prompt

Here’s the truth bomb:
AI isn’t actually creative—it’s predictive. It stitches together words based on patterns. That means the quality of the output depends on the clarity of your input.

Vague input = generic, boring output.

Clear, optimized input = sharp, witty, useful output.

Think of prompts like recipes. If you just say “make food”, you’ll get something edible (maybe). But if you say “make a cheesy lasagna with garlic bread on the side”—boom, now you’re eating like royalty.

Why Prompt Optimization Matters (More Than You Think)

Saves Time
Bad prompts mean endless edits. Great prompts mean the AI nails it in one shot.

Boosts Creativity
An optimized prompt doesn’t just give you “content”—it gives you content with personality, humor, and voice.

Makes You Look Brilliant
To your clients, boss, or audience, it looks like you are the genius. (Don’t worry, your secret is safe with us.)

Where The AI Alchemist Comes In

At theaialchemist.in
, we don’t just fix prompts—we transform them.

We take your half-asleep “write blog fast” and turn it into:

“Write a 600-word blog in a witty, sarcastic tone, explaining why coffee is the only thing holding civilization together.”

Which do you think people will actually read?

That’s the magic: we make AI work for you, not against you.

The Funny Truth

Bad prompts are like knock-knock jokes with no punchline.
Optimized prompts? They’re like stand-up comedy—sharp, clever, and unforgettable.

So, if you’re tired of your AI writing like a broken printer, stop winging it.

Visit theaialchemist.in
.
Learn the art of prompt optimization.
Turn your AI from “meh” to “mind-blowing.”

Because life’s too short for boring blogs and robotic answers.

Your Prompts Are Basically Drunk Texts to AI (And That’s Why They Suck)

Fri, 26 Sep 2025 10:54:20 +0000

Your Prompts Are Basically Drunk Texts to AI (And That’s Why They Suck)

Let’s play a game.
Imagine you’re texting your crush at 2 AM:

“Hey.”

That’s it. No context. No details. Just “hey.”

Now… do you expect them to fall in love with you instantly? Nope. They’ll probably leave you on “seen.”

That’s exactly how most people talk to AI.

They drop in a lazy prompt like “write blog” and then act shocked when the AI spits out something that sounds like an IKEA manual translated by Google Translate.

Insight #1: Prompts = Dating Profiles

Think of prompts as your Tinder bio.

❌ “I like stuff.” = Nobody’s swiping.
✅ “Sarcastic coffee addict who can quote Shrek 2 line-for-line.” = Now we’re talking.

The more specific and interesting you are, the better matches you get.
Same with AI.

Insight #2: AI is Smart, Not Psychic

AI is like that friend who’s a genius but has zero social skills. If you don’t spell things out, it’ll give you answers that technically make sense but are socially horrifying.

Example:

❌ Prompt: “Write me a business pitch.”
AI: “Hello, give me money please.”

✅ Optimized Prompt: “Write a persuasive 2-minute startup pitch about an app that delivers snacks using drones, in the style of Shark Tank.”
AI: “Picture this: a world where Doritos fly straight to your balcony…”

Now that gets the investors laughing (and maybe paying).

Insight #3: Garbage In = Garbage Out

The golden rule of AI:
If your prompt is trash, the output is trash.

It’s like putting instant noodles in a blender and being disappointed when it doesn’t turn into gourmet ramen.

But if you give the right ingredients, suddenly the AI is Gordon Ramsay with a keyboard.

Where The AI Alchemist Comes In

At theaialchemist.in
, we take your sloppy, vague, half-asleep prompts and turn them into masterpieces.

We don’t just fix your words—we give your ideas steroids.
Our optimized prompts:

Make AI funny when you want funny.

Make it professional when you need serious.

Make it viral when you want clicks.

Basically, we’re the sober friend who takes your drunk “hey” text and rewrites it into, “Hey, want to grab coffee tomorrow?”

The Takeaway

AI isn’t ignoring you.
It’s just confused by your bad prompts.

So if you’re tired of outputs that read like rejected Wikipedia drafts, let The AI Alchemist work some magic.

Because life’s too short for boring blogs, awkward pitches, and AI that sounds like a robot from 1997.

Visit theaialchemist.in
.
Stop sending “drunk texts” to AI.
Start getting results that slap.

SAP developers are seeing 30% productivity gains with AI tools built for enterprise complexity.

Thu, 25 Sep 2025 20:32:39 +0000

SAP Shows How Enterprise AI Development Really Works

The debate about AI replacing developers misses the point. At SAP, AI isn't replacing anyone—it's solving the real problems that slow down enterprise development teams.

Bharat Sandhu, SVP and CMO, SAP Business Technology Platform where thousands of developers build applications that connect to complex SAP systems. His team has moved beyond basic code completion to something more practical: AI that understands enterprise context.

The Context Problem

Most AI coding tools work fine for standalone applications. But enterprise development is different. When a developer wants to access a general ledger, the AI needs to know which database to query, what records to examine, and how that connects to supply chain or HR systems.

"Context is what differentiates and what matters," Sandhu says. "It's not just the context of the application code. It's the context of your back end systems."

SAP's Joule for Developers handles this through what they call semantic understanding. The tool connects to a knowledge graph that knows what a sales order is and how it relates to customers, inventory, and financial data. This isn't just about writing better code—it's about writing code that actually works in complex business environments.

Making Legacy Code Useful Again

Every enterprise has the same problem: mountains of legacy code written by people who no longer work there. Documentation is sparse. Business rules are buried in decades-old logic.

Joule for Developers tackles this head-on. It can read existing code and explain not just what the code does, but which SAP tables it accesses and how it connects to business processes. It can refactor old code to make it more maintainable and generate documentation so future developers don't face the same problems.

"The biggest challenge customers have is a lot of legacy code," Sandhu explains. "People wrote code, and then they retired, and now you have these monolithic applications."

The Developer Response Surprised Everyone

SAP expected resistance from experienced developers and enthusiasm from newcomers. What happened was the opposite.

Initially, senior developers were skeptical while junior developers got excited about AI assistance. But that pattern flipped. Experienced developers realized they could focus on architecture while AI handled routine tasks. They also knew how to use these tools more effectively.

"The advanced developers became bigger fans and use it much more effectively," Sandhu says. "They know what good looks like."

This mirrors what happened with low-code platforms. Developers initially rejected them as beneath their skills, then adopted them when project demands became overwhelming.

Agent-Based Development Changes Everything

SAP has moved beyond code completion to what Sandhu calls agent-based development. Instead of writing individual functions, developers can describe complete business applications.

Tell the system you want an invoice processing application that flags payments overdue by 90 days from specific suppliers and regions. The AI creates the data model, builds the business logic, and handles the user interface.

This isn't magic. The code still goes through testing and human review. But it shifts developer time from writing basic CRUD operations to solving actual business problems.

Why Most AI Projects Fail

Sandhu has seen plenty of failed AI implementations. The pattern is always the same: companies treat AI as an experimental side project instead of core business transformation.

"Most customers work on the fringes of systems," he says. "To really use AI innovatively, you have to change core processes. You have to work inside out from business systems."

Successful companies don't create AI innovation labs. They integrate AI into existing workflows that already generate revenue. They change how they make products, serve customers, or run operations.

"It's easier to change an existing business model and make it better than to create a new project with a new business model on the side," Sandhu explains.

The Enterprise Advantage

Consumer AI tools train on public code repositories. Enterprise AI tools need different training data and different security models. SAP hosts its own models and never uses customer data for training. The code AI generates goes through multiple validation layers before reaching developers.

This enterprise focus creates opportunities for new types of applications. Sandhu mentions compliance systems that understand spending patterns in ways rule-based systems never could. Travel booking agents that negotiate across multiple vendors within budget constraints. Applications that were too complex to build before AI became practical.

What's Next

The speed of change makes long-term predictions difficult. But Sandhu sees clear short-term trends. Development teams will reorganize around AI capabilities. Requirements documentation will become more important as natural language interfaces improve. Agents will write and deploy code based on performance metrics.

His advice for developers is simple: start now. Don't wait on the sidelines. The tools are ready, the productivity gains are real, and the competitive advantage goes to teams that learn these workflows first.

The question isn't whether AI will change enterprise development. At SAP, it already has.

How to Stop Writing Prompts That Make AI Hate You

Mon, 22 Sep 2025 20:47:30 +0000

How to Stop Writing Prompts That Make AI Hate You

Here’s the harsh truth: most people talk to AI like they’re ordering pizza.

“Write me a blog.”
“Make me rich.”
“Be funny.”

That’s not a prompt—that’s wishful thinking.

AI isn’t a mind-reader (yet). It doesn’t know if you want a serious investor pitch or a stand-up comedy roast. If you’re vague, the AI just guesses. And guess what? It’s usually wrong. That’s why your “brilliant” idea often comes back sounding like a 7th grader’s homework assignment.

Why Bad Prompts = Bad Results

Think of prompts like Google searches. If you just type “dog,” you’ll get everything from Chihuahua memes to canine diseases. But if you type, “funny dog memes with captions about Mondays,” you’ll get exactly what you want.

Prompts are the same. Bad prompts give you chaos. Great prompts give you clarity.

And clarity is power. (Also less crying into your keyboard.)

The Secret Sauce: Prompt Optimization

Here’s the fun part. Optimizing prompts isn’t just about adding more words—it’s about giving the AI context, style, and purpose.

Example:

❌ Bad Prompt: “Write a blog about coffee.”
✅ Optimized Prompt: “Write a funny 500-word blog about why coffee is basically a legal personality upgrade, in the style of The Onion.”

See the difference? One gets you a Wikipedia summary. The other gets you content people actually want to read.

So… Why The AI Alchemist? ♂️

Because turning “meh” prompts into “magic” prompts is literally what we do.
At theaialchemist.in
, we help you:

Sharpen your prompts so AI finally understands you.

Save hours of editing terrible drafts.

Get outputs that are funny, insightful, persuasive—or whatever tone you need.

We’re basically the grammar police, but instead of commas, we fix your entire AI workflow.

The Bottom Line

Bad prompts waste time.
Great prompts save time.
Optimized prompts from The AI Alchemist? They make you look like a genius.

So next time you’re about to type “Write me a blog,” stop. Head to theaialchemist.in
and let’s turn your AI chaos into content gold.

Because the AI isn’t broken.
Your prompts are.
And we can fix that.

Your CTO can't tell you how much AI code you're writing—and that's a bigger problem than you think.

Tue, 16 Sep 2025 19:41:42 +0000

Why Your CTO Can't Tell You How Much AI Code You're Actually Writing

Span's new AI code detector reveals the measurement gap that's keeping engineering leaders in the dark about their AI transformation.

Engineering leaders are flying blind. They're making million-dollar decisions about AI coding tools based on guesswork, self-reported surveys, and vendor claims that don't match reality.

This disconnect became clear when I spoke with J Zac Stein and Henry Liu, co-founders of Span, about their new AI code detector. The tool can identify AI-assisted versus human-written code with over 95% accuracy—and it's revealing some uncomfortable truths about how we measure AI adoption.

The Numbers Don't Add Up

Google claims 25% of new code comes from AI. Microsoft says 30%. But when engineering teams try to verify these numbers internally, they hit a wall. Current measurement methods count lines of code suggested by AI tools, but they don't track what happens next. Was that code modified before commit? Deleted entirely? No one knows.

"Engineering leaders are facing immense pressure to demonstrate the value of AI investments, but they're making decisions based on anecdotal evidence," Henry Liu told me. This pressure comes from boards and executives who want concrete ROI metrics, not developer surveys.

The problem runs deeper than bad data. Companies are operating over a dozen different AI coding tools simultaneously—Copilot, Cursor, Claude, and more. Each tool provides different telemetry. Some provide none at all. And none of them can track the significant amount of AI-generated code that comes from developers copying and pasting from ChatGPT.

Beyond the Hype

The biggest misconception executives have about AI coding? That faster code writing automatically equals higher productivity. This assumption ignores critical factors like code review burden, technical debt, and defects in production.

"Writing code is not the only thing engineers do," Liu explained. "The longer the company, the less writing code is a bottleneck." Senior engineers spend more time on architecture decisions, coordination, and ensuring teams work on the right problems. A 30% speed increase in coding doesn't translate to 30% overall productivity gains.

This reality creates tension between believers and skeptics within engineering teams. Leadership pushes top-down AI adoption while senior individual contributors remain cautious. Without solid data, these debates rely on anecdotal evidence and personal preferences.

How the Detection Works

Span's solution centers on span-detect-1, a machine learning model trained on millions of code samples with known provenance. The model analyzes patterns in token sequences, syntax quirks, and stylistic regularities to determine AI versus human authorship.

The technical challenge was significant. When reasoning models like ChatGPT improved, the team had to dramatically increase their training data—by 10x—to maintain accuracy. Many research papers in this space don't properly evaluate their models, leading to inflated accuracy claims that don't hold up in real-world conditions.

Currently supporting Python, TypeScript, and JavaScript, the detector works across all AI coding tools. This tool-agnostic approach helps companies understand their true AI usage regardless of which specific tools their developers prefer.

What Companies Are Learning

Early customers are discovering adoption patterns that challenge assumptions. While senior engineers tend to be more reserved about AI tools, the differences aren't as stark as expected. Teams working on greenfield projects adopt AI more readily than those maintaining legacy codebases, but overall adoption is climbing across all segments.

For companies that have fully embraced AI coding tools, weekly usage rates average around 70%. Some engineers are spending hundreds of dollars daily on token costs. As Liu noted, "Token spend is going to be comparable to payroll spend in the future."

This shift transforms every engineer into a resource allocator. Understanding which AI tools provide the best ROI becomes critical for both productivity and cost management—similar to how companies had to learn cloud cost optimization.

The Bigger Picture

Span's AI detector represents just one piece of a broader developer intelligence platform. The company is building what Liu and Stein call "the tool we wish we'd had in our previous roles"—a comprehensive suite for understanding engineering productivity in the AI era.

Their approach goes beyond simple metrics. Span uses AI to understand developer productivity rather than relying on outdated measures like lines of code. The platform includes qualitative surveys to identify developer headwinds and tailwinds, cost capitalization tools, and upcoming features to compare defect rates between AI and human code.

"This type of tool was not possible three years ago," Liu said. "Span helps you lead your engineering organization through AI transformation with data, not hype."

As AI coding tools continue evolving rapidly, the need for objective measurement becomes more critical. Companies can't optimize what they can't measure. Span's detector provides the ground truth that engineering leaders need to make informed decisions about their AI strategy.

The future of software development is clearly AI-augmented. The question isn't whether to adopt these tools—it's how to do it intelligently, with data instead of hope.

The Road Ahead: Turning Vision into Reality

Sat, 06 Sep 2025 04:27:32 +0000

What once felt like “unthinkable” or “impossible” is now slowly moving toward possibility. AI and neuroscience are already shaking hands in labs across the world . Projects that once lived in academic papers are being tested in healthcare, therapy, and even workplace wellness programs.

But the question remains: how do we responsibly scale this idea?

Practical Applications Emerging

Mental Health Support : AI models could detect early signs of depression, PTSD, or burnout through brainwave and behavioral analysis, offering help before a crisis.

Safety & Security ️: Airports, schools, and public spaces could adopt voluntary mental wellness checks, focusing on prevention rather than punishment.
Personal Growth : Imagine training apps that help you regulate emotions by showing real-time feedback from your brain activity, making mindfulness more tangible.

⚖️ 2. The Ethical Compass
With great power comes greater responsibility. Without trust, this vision collapses. That means strict rules:
Consent First ✅ — no scanning or analysis without full, informed choice.

Transparency — people must know exactly what data is collected and why.

Support over Surveillance — this is about care, not control.

Collaboration is Key
This isn’t just a project for engineers or neuroscientists. It needs philosophers, doctors, psychologists, lawyers, policymakers, and everyday people. Only by blending perspectives can we create systems that are both effective and humane.
✨ The Big Picture
We stand at a threshold: AI has given us new tools, and neuroscience has given us new insights. The future depends on whether we can join these forces wisely. If we succeed, we move closer to a world where tragedy is prevented, lives are saved, and humanity takes a leap forward — not in technology alone, but in compassion .

The story, truly, is just beginning…
https://safa.nagasoftsolutions.com/
https://www.nagasoftsolutions.com/

Popular platforms for online coding and development. How an AI Platform Uses Them to Export Code

Tue, 02 Sep 2025 11:47:59 +0000

Replit and Google Colab are two of the most popular platforms for online coding and development, each with its own specific focus and strengths. They are particularly relevant to Gemini because they offer integrated, cloud-based environments that allow users to export and run code generated by the AI assistant with minimal setup.

Replit

What it is: Replit is a cloud-based integrated development environment (IDE) that provides a fast, simple, and collaborative platform for building, running, and deploying applications. It's designed to make coding accessible to everyone, from beginners to experienced developers, by removing the friction of complex local setups.

Key Features:

Multi-language Support: Replit supports over 50 programming languages, making it incredibly versatile for various projects.
Real-time Collaboration: Multiple users can code on the same project simultaneously, with live cursors and a shared terminal, making it a great tool for pair programming and team projects.
Instant Deployment: Replit makes it easy to deploy web apps, APIs, and bots with a single click, providing hosting and continuous availability.
Integrated AI: Replit has its own AI-powered assistant, known as Ghostwriter (and more recently, Replit Agent), which helps with code generation, debugging, refactoring, and general assistance.
No Setup Required: Everything runs in the browser, so you don't need to install any software or configure a local development environment.

Primary Use Case: Replit is ideal for web development, rapid prototyping, and collaborative coding projects. It's a fantastic tool for learning to code, building small to medium-sized applications, and for educational purposes.

Google Colab

What it is: Google Colab (short for Colaboratory) is a hosted Jupyter Notebook service that requires no setup. It's built and maintained by Google and provides free access to computing resources, including GPUs and TPUs, which are essential for computationally intensive tasks.

Key Features:

Jupyter Notebook Environment: Colab is built around the Jupyter Notebook format, which allows you to combine executable code, rich text, images, and visualizations in a single, interactive document.
Free GPU/TPU Access: This is its standout feature. Colab provides free, though not unlimited, access to powerful hardware accelerators, which is invaluable for machine learning, deep learning, and data science projects.
No Configuration: Like Replit, Colab runs entirely in the cloud, so there is no need to set up anything on your local machine.
Google Drive Integration: Colab notebooks are stored directly in your Google Drive, making it easy to save, share, and collaborate on your projects.
Pre-installed Libraries: It comes with popular data science libraries like NumPy, Pandas, TensorFlow, and PyTorch pre-installed, so you can start coding immediately.

Primary Use Case: Google Colab is the go-to platform for data scientists, machine learning engineers, and researchers. It's perfect for data analysis, building and training neural networks, and creating reproducible research papers.

How Gemini Uses Them to Export Code

Gemini is designed to be a helpful coding assistant. When it generates code, it provides options to "export" or "open" that code in other platforms.

Export to Replit: This feature is particularly useful for web development. When you ask Gemini to create a full application (e.g., a simple To-Do list app with a front-end and back-end), it can generate the code and, with one click, send it to a new Replit project. This automatically creates the necessary files and sets up the environment, allowing you to run and test the application instantly.
Export to Google Colab: This is the primary export option for data science and machine learning tasks. If you ask Gemini to write a Python script for sentiment analysis, a machine learning model, or a data visualization, it can export that code directly into a new Google Colab notebook. This saves you from copying and pasting and immediately puts the code into an environment with the necessary libraries and, if needed, GPU acceleration.

Other Platforms for Exporting Code

While Replit and Colab are the most prominent platforms integrated with Gemini, there are other similar environments used by developers, data scientists, and AI professionals. Many of these offer similar capabilities in different forms:

GitHub Codespaces: A cloud-based development environment that is part of the GitHub ecosystem. It provides a full VS Code editor in the browser, making it a powerful tool for professional developers who want to work on their GitHub repositories without a local setup.
Jupyter: An open-source project that powers Google Colab. You can run Jupyter Notebooks locally or on a server, and many other platforms (like Kaggle and Deepnote) use it as their foundation.
Kaggle: A data science platform and community that provides free, cloud-based Jupyter Notebooks with GPU and TPU access. It's known for its vast library of datasets and machine learning competitions.
Deepnote: A collaborative, cloud-based notebook environment for data science teams. It's built to be more team-friendly than Colab, with advanced features for real-time collaboration and project management.
CodeSandbox: Similar to Replit but with a strong focus on front-end web development, especially with JavaScript frameworks like React, Vue, and Angular. It's great for live prototyping and sharing.

Snyk just made security invisible to developers—AI writes secure code without them thinking about it

Mon, 11 Aug 2025 09:35:26 +0000

The End of Security as a Developer Concern: Snyk's 'Secure at Inception' Revolution

How Model Context Protocol integration promises to make security completely invisible to developers writing AI-generated code

"As a developer, you never want to think about security, right? That's always been our goal," said Randall Degges, Head of Developer and Security Relations at Snyk, his excitement palpable as he demonstrated technology that could fundamentally change how we approach application security. "I think we're like, almost completely there. This gets you 99.8% of the way."

What Degges showed me during our interview at Black Hat 2025 wasn't just another security tool—it was a glimpse into a future where developers can write code without ever thinking about vulnerabilities, compliance, or security best practices. Through Snyk's new "Secure at Inception" platform, powered by Model Context Protocol (MCP) integration, security becomes as invisible and automatic as spell-check in a word processor.

Beyond Shift-Left: Security at the First Prompt

The cybersecurity industry has spent years promoting "shift-left" security, the idea of moving security considerations earlier in the development lifecycle. Snyk's new approach obliterates that entire paradigm by embedding security directly into the AI coding process itself, starting from the very first prompt a developer types.

During our demo, Degges created a simple note-taking application in Node.js using Cursor, an AI-powered code editor. He typed a single prompt: "Build a simple note-taking app in Node that uses SQLite and lets users create an account, log in, edit and store notes." Then he hit enter and stepped back.

What happened next was remarkable. The AI didn't just generate the application code, it automatically scanned every dependency for vulnerabilities, analyzed each code file for security issues, and autonomously fixed problems like hardcoded secrets, cross-site request forgery vulnerabilities, and missing rate limiting. All without a single additional prompt or security-related instruction from the developer.

"I didn't say a single thing about security in that prompt," Degges emphasized. "And just wait until it finishes—I guarantee you this app is going to work perfectly. By the time it's done, semantically everything's going to be correct, and on the security side, it's going to be secure."

The Magic of Natural Language Security Rules

The secret lies in Snyk's MCP server integration and natural language rule system. Developers can create rules in plain English, such as "If you modify or add any code, please scan that code with Snyk and fix any vulnerabilities" or "If you add or change any dependencies, please do a Snyk open source scan and fix the vulnerabilities."

These rules operate completely in the background. The AI coding assistant automatically invokes Snyk's scanning capabilities whenever it generates or modifies code, receives detailed vulnerability information, and then autonomously implements fixes, all while maintaining a running conversation about the application's functionality rather than its security posture.

"This is the coolest thing I've seen since I've been working in this industry," Degges said, watching the AI systematically identify and resolve security issues in real-time. "Developers don't think about security at all. Zero. Absolutely zero."

Confidence-Driven Automation

The system's effectiveness hinges on Snyk's confidence modeling. The company maintains a massive internal database that benchmarks their detection accuracy for each specific type of vulnerability across different programming languages. When confidence levels approach 100%, the system operates fully autonomously. When confidence is lower, it can flag issues for human review.

"If confidence levels are approaching 100%, then maybe human-in-the-loop isn't as important," Degges explained. "Maybe you can focus on other issues. As our confidence thresholds get to 99.9 plus percent, we're going to start rolling this out everywhere."

This isn't theoretical—the system validates its fixes by automatically rescanning modified code to ensure vulnerabilities are actually resolved and that the fixes don't break existing functionality. It's essentially performing the human code review process autonomously.

The AI Bill of Materials: Governing the Invisible

While automating security fixes addresses one challenge, the rise of AI-generated code creates another: visibility. Snyk's AI Bill of Materials (AI-BOM) tool addresses what Degges calls the "invisible and constantly changing" nature of AI-driven development.

The AI-BOM scans applications to identify every AI component, model, dataset, and MCP server being used, creating a comprehensive inventory that updates in real-time. During our demo, it revealed detailed information about models, their parameters, data sources, and execution paths—information that would be nearly impossible to track manually across large development teams.

"Even internally at Snyk, we have this problem," Degges admitted. "Our CISO was like, 'Hey, you're not allowed to use public ChatGPT. You're allowed to use these particular models.' But how do they figure out what people are actually using? That's what the AI-BOM solves."

Toxic Flow Analysis: The New Attack Vector

Perhaps most intriguingly, Snyk's Toxic Flow Analysis (TFA) framework, developed through their Invariant Labs acquisition, addresses security risks that emerge from the interactions between AI tools—even when each individual tool is secure.

"Even though the tools themselves are perfectly secure, it's the interaction between the tools that can be abused when you're working with large language models," Degges explained, referencing recent GitHub security research that demonstrated how attackers could manipulate AI systems through carefully crafted prompts in bug reports.

TFA analyzes combinations of MCP tools to identify potential security issues in their interactions, providing a new category of security analysis specifically designed for agentic AI environments.

The Vibe Coding Security Challenge

The emergence of "vibe coding," where developers describe desired functionality in natural language and AI generates the implementation, creates unique security challenges. Traditional security training focused on teaching developers to recognize and avoid security antipatterns becomes irrelevant when AI is writing the code.

"You can never trust the conversations," Degges emphasized, comparing AI-generated code to conversations with strangers on AOL Instant Messenger. "You need to have autonomous tools in place to check the code. If you don't do that, you are 100% screwed. You will have a lot of breaches and issues."

The Future of Secure Development

Snyk's approach represents a fundamental shift in how we think about application security. Rather than training developers to be security experts or requiring them to constantly context-switch between coding and security tools, it makes security completely transparent to the development process.

"This is what people are going to be doing in like a year," Degges predicted. "Everyone will be doing this. No one's going to be doing it the old way."

The implications extend beyond individual productivity. As AI agents become more sophisticated and autonomous, having security baked into the foundation of AI-assisted development becomes not just convenient but essential. The technology promises to solve the fundamental tension between development velocity and security rigor that has plagued the industry for decades.

For developers, the message is clear: the future of coding is conversational, and security is becoming someone else's problem—in the best possible way.

AI isn't replacing developers, it's turning them into system orchestrators and strategic thinkers.

Wed, 23 Jul 2025 21:41:16 +0000

Beyond Code Generation: How AI is Fundamentally Rewiring the Developer Workplace

We're witnessing something remarkable in software development: AI isn't just writing code faster, it's completely reshaping how developers, engineers, and architects approach their craft. After tracking this evolution across dozens of companies and interviewing teams at the forefront of AI adoption, I've discovered that the most profound changes aren't happening in the code itself, but in the fundamental nature of technical work.

From Code Monkeys to System Orchestrators

The transformation starts with a simple but powerful shift: developers are evolving from code writers to system orchestrators. I recently spoke with a senior engineer at a Fortune 500 company who described it perfectly: "I used to spend 60% of my time writing boilerplate code and debugging syntax errors. Now I spend that same time designing architecture and thinking through edge cases."

This isn't hyperbole. Teams using tools like Cursor, GitHub Copilot, and Continue are reporting dramatic changes in how they allocate their cognitive energy. The mundane tasks that once consumed entire afternoons, writing CRUD operations, setting up API endpoints, debugging typos, are increasingly handled by AI assistants that understand project context and coding patterns.

But here's what's fascinating: rather than leading to job losses, this shift is elevating the entire profession. Developers are spending more time on what humans excel at: creative problem-solving, system design, and understanding complex business requirements.

The Rise of the AI-Augmented Architect

Software architects are experiencing perhaps the most dramatic transformation. Traditional architecture work involved countless hours translating business requirements into technical specifications, creating documentation, and ensuring consistency across teams. AI is now handling much of this translation work, but the results are surprising.

"AI doesn't make me less necessary, it makes me more strategic," explains Sarah Chen, a principal architect at a major fintech company. "I can rapidly prototype different architectural approaches, test them against various scenarios, and focus on the high-level decisions that really matter."

AI-powered tools are enabling architects to:

Generate multiple architectural options quickly
Simulate system behavior under different loads
Create comprehensive documentation automatically
Identify potential security vulnerabilities early
Model complex distributed systems visually

The result? Architects are becoming business translators and strategic advisors rather than just technical designers.

Engineering: From Problem Solvers to Problem Definers

For engineers, the change is equally profound. The traditional debugging process, hours spent tracing through stack traces, searching Stack Overflow, and incrementally testing fixes, is being compressed into minutes. AI coding assistants can often identify root causes instantly and suggest multiple solution approaches.

This efficiency gain is creating space for engineers to tackle bigger challenges. Teams report being able to take on more ambitious projects, experiment with new technologies more freely, and spend significantly more time on optimization and performance tuning.

"I used to dread debugging sessions," says Marcus Rodriguez, a full-stack engineer at a growing startup. "Now I actually look forward to complex problems because I have this incredibly smart debugging partner. We can explore solutions I never would have considered before."

The New Collaboration Model: Human + AI Teams

What's emerging isn't human versus AI, but genuinely collaborative human-AI teams. The most successful development groups are those that have learned to leverage AI as a thinking partner rather than just a code generator.

This collaborative model is reshaping team dynamics in unexpected ways:

Junior developers are accelerating faster: With AI handling routine tasks, new hires can focus immediately on understanding business logic and system architecture rather than getting bogged down in syntax and framework details.

Senior developers are becoming force multipliers: Experienced engineers can now mentor larger teams because AI handles much of the routine guidance that junior developers typically need.

Code reviews are evolving: Instead of catching basic errors and style issues, reviews now focus on architectural decisions, business logic validation, and security considerations.

The Skills That Matter More Than Ever

As AI handles more routine coding tasks, certain human skills are becoming increasingly valuable:

Systems thinking: Understanding how different components interact and predicting system behavior under various conditions.

Problem definition: AI excels at solving well-defined problems but struggles with ambiguous requirements. The ability to translate vague business needs into clear technical specifications is more valuable than ever.

Quality assessment: As AI generates more code, the ability to evaluate its correctness, efficiency, and maintainability becomes crucial.

Communication: With AI handling documentation generation, humans need to focus on stakeholder communication, requirement gathering, and cross-team collaboration.

Real-World Impact: The Numbers Don't Lie

The productivity gains are measurable and significant. Companies implementing AI coding tools report:

40-60% reduction in time spent on routine coding tasks
30% faster feature delivery timelines
50% reduction in debugging time
25% improvement in code quality metrics

But perhaps more importantly, developer satisfaction is increasing. Surveys consistently show that developers using AI tools report higher job satisfaction, reduced burnout, and greater enthusiasm for tackling complex challenges.

Looking Ahead: The Strategic Advantage

Organizations that embrace this evolution are gaining significant competitive advantages. They're shipping features faster, maintaining higher code quality, and attracting top talent who want to work with cutting-edge tools.

The companies thriving in this new landscape share common characteristics:

They invest in AI literacy across their technical teams
They encourage experimentation with new AI tools
They redesign workflows to leverage human-AI collaboration
They focus metrics on business outcomes rather than lines of code written

The Bottom Line

AI isn't replacing developers, engineers, and architects, it's elevating them. The future belongs to technical professionals who can orchestrate AI tools effectively while focusing on the uniquely human aspects of software development: creativity, strategic thinking, and complex problem-solving.

The question isn't whether AI will change your role, it's whether you'll shape that change or be shaped by it. The developers, engineers, and architects who embrace AI as a collaborative partner today will define the future of software development tomorrow.

As I've learned from observing teams across industries, we're not entering an age of AI dominance—we're entering an era of unprecedented human potential, amplified by intelligent machines. The code may be generated by AI, but the vision, creativity, and strategic thinking remain distinctly, irreplaceably human.

Coder Legion - Recent posts tagged aicoding

Developers: Stop collecting prototypes. Start finishing projects.

AI to help programmers to reach enlightened laziness and long-term laziness for real productivity

How can AI help lazy programmers to reach enlightened laziness and long-term laziness for real productivity?

Being a Lazy Programmer

The code left by diligent programmers

Resources for Lazy Programmers before AI

Coding Assistance

Code Generators

Built-in Framework Modules

App Generator

Model to Code

4GL, DSL and 5GL

AI Code Agents

How AI has been helping me?

How Visual Studio and alike with AI has been helping me?

How AI compensates my poor memory

AI dealing with simple ad-hoc data transformation

AI for refactoring

AI for scripting

AI providing advice for software design and architectural design

Entry level understanding of old new tech before further research and study.

Common algorithms

Analysis

Flashback about software engineering before LLMs AI

Key Facts (The Truth)

Key Fallacies (Common Mistakes)

Common Pitfalls of Code Generated by LLM AI Code Agents

AI-Generated Source Code vs Hand-Crafted Code

The characteristic of AI generated code

One bit can kill through Turing machine

Summary

Help AI to help you

References

A Coding System That Is Not Allowed to Trust Its Own Code

The 30-second version

First principle: preserve the requirement before writing code

The coder is not the judge

Generated code runs in a sandbox

Packaging is a privilege, not the next line of the script

Then I started testing the verifier

A good failure is more useful than a fake 100%

The system had become too strict for the wrong reason

The external oracle can be wrong too

verified does not mean "correct forever"

Where Derivative fits

The current scope is deliberately narrow

The part I find most interesting

ReWeaver AI DriftDetector

Vibe Coding Won't Kill Developers. It'll Kill the Middle.

The comfortable half of the argument

"So the middle just learns to think better?"

The honest limit of the analogy

The Hidden Mental Model That Determines Whether AI Tools Help You

Same tools, different results

What the mental model actually is

Why "which AI tool should we use" is the wrong opening question

What to fix when productivity gains are uneven across your team

Can a Non-Coder Become a Coder Just With AI?

The infrastructure reality check

The mental model problem

Necessary, not sufficient

The decision in front of a technical leader

The real question

Cursor CLI Ran Code From Cloned Repos Before Its Own Trust Prompt Ever Loaded

AI Coding Tools Are Making Engineers 10x Faster. Here's the Catch.

Beyond Code Generation

The Technical Debt Question

What to Actually Measure

Human Judgment Still Matters

The Bottleneck Nobody's Talking About

Where This Is All Headed

The Developer Survival Guide for the Age of Agentification

The Shift: Your Job Has Changed

The Tool Sprawl Problem

The Stakes: Agents Don't Fail Because They're Wrong

Governance and Security: Validate Before You Ship

Three Takeaways

ToolPix Platform

Developers Trust AI Code. They Also Don't Trust It. Both Are True.

`verified` does not mean "correct forever"