Coder Legion - Recent posts tagged jobs

EarlyStageStartups.com

Mon, 05 Oct 2026 10:30:07 +0000

EarlyStageStartups.com is a job board for people who want to join a startup while it is still early. It lists open roles at pre-seed, seed and Series A companies, with salary transparency: every posting that lists a salary shows it in the currency the company wrote it in.

You can browse jobs by category, location, workplace (remote, hybrid, on-site) and job type, see which early-stage startups are hiring, compare median pay by category, industry and country, and get a free weekly email with the newest jobs. Startups can post a job, and a Pro membership gives early access to new jobs a day before everyone else.

Try it: https://earlystagestartups.com/?utm_source=coderlegion.com&utm_medium=directory&utm_campaign=listings

Is It Still RAG If There's No Retrieval?

Sun, 04 Oct 2026 01:48:59 +0000

I've been shipping software for 28 years, 21 of them as a consultant working across over 100 different engagements. Past a certain point, a resume stops being a record and becomes a burdensome editing problem. Two pages can barely hold a half-dozen engagements, selected by hand for someone to read cold before I even get a chance to chat with them. Everything else is real experience that somebody, somewhere, might want to know.

It bothered me that the odds are decent the one engagement a particular hiring manager is looking for didn't make the cut. Maybe the point-of-sale integration story that would have mattered to a retail-tech team got bumped for something that better fit the job description.

So I built a (sigh, another) chatbot. As I put myself into the job market, an idea that had been sitting there since my last product shipped resurfaced: put my career history, skills, hobbies, and interests behind a single interface anyone can query, and ship it as an actual product rather than a demo. Two birds, one side quest.

What I ended up with is a small Go and HTMX app with one curated Markdown file as its entire knowledge base: no embeddings, no vector store, no agent loop anywhere. It's live; you'll have to visit my LinkedIn profile or snag a copy of my resume for the link. Whether that even counts as RAG is a fair question, which I hope to answer in this article. What follows is how I constrained it, what I built, and where I had the most fun.

The design brief

I set the constraints before writing any code as they would help shape the implementation and technology choices.

Nothing I'm not allowed to say. Twenty-one years of consulting means NDAs, confidentiality agreements, and client names all through my records. The source data had to be treated as a leak risk before it could become knowledge. Client names became anonymized descriptors ("a national grocery chain" instead of the real one), contact details and commercial terms were stripped, and anything under NDA was excluded outright. Then review passes, plural: an agentic review, a mechanical keyword sweep for every client name I could remember, a manual read of the whole file, and a regeneration. The entire knowledge boundary is one curated Markdown file. If it isn't in the file, the bot can't say it.

Me, minus most of me. No address, no phone, no email. The only personal details in the bundle are the ones I put there on purpose: location, availability, and hobbies and interests for the culture-fit questions. LinkedIn is the contact route, full stop. My personal information is in the hands of enough shady data brokers already.

Never invent. The knowledge boundary dictates what the bot can answer; this rule sets how it answers: only facts and metrics that appear explicitly in the source data. No rounding up engagement counts, no inferring a skill from an adjacent one, no helpful guesses. When a question falls outside the bundle, the bot gently declines to answer. A chatbot that confabulates my experience is worse than the resume problem I started with.

No opinions. The bot is a little more than a fact retriever. Asked "is Jason a fit for this role?", it answers with the facts the bundle provides and suggests that would be a great thing to bring up in an interview. It doesn't editorialize about my strengths or volunteer a verdict. Judging fit is the hiring team's job; the bot's job is making sure they're judging complete information.

It is not an agent

A key design decision came next: the bot has no tools, no ReAct loop, no reasoning cycle of any kind. It is just a query tool. You ask a question, it answers from the bundle, simple and done.

An "agent" didn't make sense here, so everything shifted to pre-processing and bundle build time. Look things up? The entire knowledge base is in the prompt. Compute years of .NET experience? All aggregates were precomputed. Take an action? There is nothing for it to do: the product is a conversation.

Stripping the agent loop streamlined things. There is no tool-calling code to write, no schema to maintain, no loop to debug when the model decides to call the wrong thing three times in a row. Requests are mini-sessions and responses are fast, and the failures are boring. It shrinks the security surface too: an agent that can call tools and take actions gives an attacker something to reach for, and this bot can only speak.

To "RAG" or not to RAG

Retrieval-augmented generation (RAG) is the standard answer when you want a model to answer from your data: chunk the documents, embed them, drop them in a vector store, and fetch the top matches for each question. My entire career knowledge base fits in a modern context window with room to spare, so at runtime there was nothing to retrieve. Every chunk is always "retrieved."

Instead of abandoning the acronym, I re-staged its three phases.

Retrieval happened offline, long before any user showed up, and before I even started the bot. It was a mining and cleansing process: extracting the history from client dossiers and notes, applying the curation gate, and staging the result as a single Markdown file. The retrieval question, what the bot should know, got answered once, reviewed and edited by hand, carefully.

Augmentation happens at the start of every chat session, as part of the conversation: the entire bundle goes in as context. Nothing is fetched per-question because everything is already there.

Generation is the phase that's truer to the word. The model reads the question, infers which engagements, skills, and metrics answer the query, and composes an answer from the full corpus. That inference is the entire point, and it's the part a keyword search over my resume can't do.

These tradeoffs create high-quality results. No chunking means no retrieval misses: nothing relevant can be missed because everything is present. There's no embeddings pipeline to maintain and no drift between what was indexed and what was written. The cost is context window spend on every session, which prompt caching brings under control later.

This stops being viable the moment the corpus outgrows the window. Then chunking, embeddings, and vector search earn their keep, and I'd be doing actual RAG.

Reducing confabulation

The never-invent rule from the design brief needed mechanical help, because a model that's been handed a pile of career facts will still helpfully invent a number when asked for one. Two decisions cover most of it.

The model choice was important here. I picked Gemini 3.6 Flash, a modern instruction follower, over the flashier reasoning models: for this workload the job is faithful extraction from provided context, and instruction-following models are the ones that stay on script. I went with the slightly older 3.6 model for cost reasons, backed by a mini-evaluation harness to ensure it stayed on track.

Math got removed from the model entirely. Years of experience by skill, engagement counts by industry, the aggregate tables useful to recruiters: all of it was precomputed (mechanically, not by an LLM) from structured dates and written into the bundle as an appendix the bot quotes verbatim. Overlapping consulting engagements make naive date arithmetic wrong in ways that look plausible, and a confidently wrong "14 years of Go" is worse than a table it can't misread.

Prompt caching

With all the career data riding along in every session, input tokens became the cost of the product, especially as the conversation grows. Prompt caching fixes this: providers let you pay a fraction of the normal input price for tokens they've seen before, provided the prompt starts with exactly the same bytes every time.

The stable parts, the system prompt and the full career bundle, go first; the conversation follows after. Anything per-request that lands in that prefix, a timestamp, a session ID, even a helpful "today is," silently invalidates the cache and quietly turns every chat back into a full-price chat. I went out of my way to keep the prefix byte-identical and to manage sessions so each conversation maps to a consistent cache key.

Then I verified it through a test harness and eyeballing the OpenRouter logs, tuning it until caching was consistent and supported across multiple sessions. When the bundle changes, the cache busts. That's fine; the bundle changes rarely.

The build: brutally efficient again

My last side project, DumbQuestion.ai, taught me a build mantra I've kept: brutally efficient. So the bot is another Go binary with HTMX for the UI. No JavaScript framework, no client-side state, no build pipeline beyond Go's own. One tiny container, one process, and it renders GitHub-flavored Markdown straight into the chat (the aggregate tables from the bundle deserve to render as tables).

I didn't start from zero. The Turnstile abuse gate, the prompt-guard library, the embedded asset pipeline: all of it came over from the DumbQuestion codebase. The bundle itself is baked straight into the container build. The original design pulled it from an R2 bucket at boot with a re-check loop, but the data changes rarely, so the bucket became a moving part I didn't need. Updating the career data is now just a rebuild and redeploy, and the whole thing ships as one artifact.

OpenRouter carries the model traffic, one API surface behind the caching work from the last section. This allowed me to route Google model traffic to my GCP account to take advantage of the monthly credit courtesy of my Google AI Pro subscription. Deployment is straightforward: GitHub Actions builds the container and pushes it to Cloud Run for scale-to-zero and crazy fast cold start times.

Abuse protection

A public chatbot backed by a metered LLM API bills me for strangers' curiosity, and if a stranger wants to know a little something about me, fine. But abuse protection wasn't optional. With the whole bundle riding in every session, an enthusiastic scraper could have turned "cheap" into a surprise bill overnight. I did at least remember to set a monthly budget on the model usage.

The outer layer is Cloudflare: rate limits, bot protection, and Turnstile in front of the chat. The cheap rejections happen at the edge, where they cost me nothing, and proving you're human before you're allowed to interrogate my career filters out nearly everything casually automated.

The inner layer is prompt-guard, the injection detection that came over from the DumbQuestion codebase. It classifies attempts to override instructions, reveal the system prompt, or otherwise jailbreak the bot. What the bot does when it catches one is the fun part, and that's next.

Personality: the fun part

The design brief said no opinions. It said nothing about character, and a chatbot's response to a jailbreak attempt is a product surface. So when prompt-guard flags one, the bot answers in character. I originally pulled in DumbQuestion's quirky personality, but overly aggressive satire wasn't quite a fit for a conversational career bot.

Try to prompt inject and you get a parlay into the pitch: a security-first mindset, the same instinct behind the curation gate and the no-agent decision earlier in this article. Try to convince the bot to ignore its instructions and it suggests that would make a great interview question. Ask my age and it cheerfully answers with how long I've been shipping software instead.

The real defense is data isolation, a single curated file with nothing sensitive behind it and a lack of tools to break out of that box. The personality is the UX of the refusal.

Nothing is logged or tracked, so there are no war stories from the wild. The responses above are designed behaviors, and the demo is the bot itself.

The resume problem I started with didn't have a resume-shaped fix. Twenty-eight years and 100-plus engagements won't compress into two pages, and they shouldn't have to. No hiring manager wants a 12-page CV. The bot holds all of it, answers honestly about what it knows, declines what it doesn't, and costs about nothing to keep running. I shipped another AI product along the way, which was the other half of the point.

If your history doesn't fit the format either, the recipe is short: curate the data first, put the whole thing in context, and skip the vector store until you actually need one.

Real-Time AI Interview Help: Keeping Technical Answers Relevant as Questions Change

Tue, 15 Sep 2026 08:22:45 +0000

A technical interview rarely follows a straight line. You suggest a solution, the interviewer changes a constraint, and suddenly the most important part of your original answer no longer applies. The challenge is keeping your explanation aligned with the problem as it develops.

That is the angle I find most interesting about real-time AI interview help. Generating a plausible answer is only one part of the task. An assistant also needs to recognize what changed, preserve the details that still matter, and avoid pulling the conversation back toward an outdated solution.

I’m Peter, founder of Linkjob AI. In this article, I’ll use a hypothetical backend interview to explain how I think about useful assistance during technical conversations. These are design principles, not performance claims about a particular model.

Start with a concrete problem

Imagine an interviewer asks you to design a service that accepts uploaded files and processes them in the background. You propose an API, object storage, a job queue, and workers.

That is a reasonable starting point, but listing components does not explain the decisions behind them. A clearer answer connects each component to a requirement:

Object storage holds the uploaded files.
The queue separates upload requests from processing work.
Workers process jobs independently of the request lifecycle.
A status endpoint lets users check progress.

An assistant should help the candidate articulate those relationships. Producing a longer list of technologies would add little unless the interviewer has introduced requirements that justify them.

Treat a follow-up as a change to the problem

Now the interviewer asks: “What happens if a worker finishes processing a file but crashes before acknowledging the message?”

The conversation has moved from architecture to failure handling. Repeating the original system diagram would miss the question.

A useful response should focus on the sequence: processing completed, acknowledgment did not, and the queue may deliver the job again. The candidate can then discuss how to make repeated processing safe.

A short prompt might be enough:

“Explain redelivery, identify the operation that must be idempotent, and describe how completion is recorded.”

That leaves room for the candidate to reason through the actual design. It also avoids claiming that adding a queue automatically solves reliability.

If the interviewer then asks about two workers receiving the same job concurrently, the assistant should update its guidance again. Checking whether a job is complete is not sufficient if both workers can pass that check before either records completion.

Keep requirements separate from assumptions

Technical answers become confusing when assumptions silently turn into facts. In the file-processing example, the interviewer might never have specified file size, processing duration, traffic volume, or whether jobs must finish in order.

An assistant could fill those gaps with invented numbers and produce an impressive-looking architecture. The candidate would then be defending decisions built on requirements that nobody agreed to.

A better approach is to identify which unknown changes the design. For example:

Large files may affect upload strategy and storage access.
Long-running jobs may require attention to visibility timeouts or lease renewal.
Strict ordering may constrain how work is distributed.
External side effects may make retries harder to handle safely.

The candidate does not need to ask every possible question. They need to surface the assumptions that materially affect their answer.

Suggest the next reasoning step

During a live conversation, a full essay can compete with the interviewer for attention. I would rather see an assistant offer a compact sequence the candidate can develop in their own words.

For the worker-crash question, that sequence could be:

State that the job might be delivered again.
Explain what duplicate execution would do.
Describe a mechanism for preventing or safely handling duplicate effects.
Acknowledge the remaining failure case.

The fourth step matters. Suppose the worker calls an external billing service and then crashes before recording success locally. A local “completed” flag alone cannot establish whether the charge happened. The answer needs to consider the external service’s behavior, such as whether it supports idempotency keys.

This is the kind of distinction that makes technical assistance useful: helping someone identify the next issue they should reason about.

Do not manufacture the candidate’s experience

There is also a difference between explaining a design and claiming to have implemented it. An assistant may be able to describe a distributed job-processing system without any evidence that the candidate has built one.

If the interviewer asks, “Have you handled this in production?”, the response should reflect the candidate’s actual background. It should not invent a migration, an incident, or a performance improvement.

A candidate can explain how they would approach an unfamiliar problem while being clear about their experience. Assistance should preserve that distinction, particularly when using a résumé or project notes as context.

My interest in real-time AI interview help centers on making relevant information easier to use in the moment. That includes recognizing when the available information does not support a claim.

Evaluate how well the assistant follows the conversation

Testing an interview assistant with isolated questions misses an important part of the experience. A response can be correct for the opening question and irrelevant by the third follow-up.

For the file-processing example, I would test a sequence that introduces duplicate delivery, concurrent workers, external side effects, and then a simpler traffic requirement that removes the need for some earlier complexity.

The questions I would ask during evaluation are practical:

Does the assistant notice each changed constraint?
Does it abandon advice that no longer applies?
Does it distinguish a proposed design from a proven guarantee?
Can the candidate understand the suggestion while continuing to listen?
Does the output encourage clarification when necessary?

These checks examine whether the assistant supports an evolving explanation.

Keep the candidate in control

A real-time AI interview assistant should leave room to ignore a suggestion, pause assistance, or request clarification. Interviewers may also have specific rules about external tools, so candidates should establish what assistance is permitted before using it in an assessed conversation.

The design goal I care about is straightforward: help someone explain their reasoning clearly as the question changes. In a technical interview, that means tracking constraints, discussing failure cases, and knowing which assumptions still need to be checked.

A useful suggestion gives the candidate something they can understand, evaluate, and explain. That is a more meaningful standard than how much text an assistant can generate.

Disclosure: I’m the founder of Linkjob AI. The product links in this article lead to my company’s website.

Is Your Unity Game’s Physics a Hidden Bottleneck?

Sat, 04 Jul 2026 12:48:21 +0000

Is Your Unity Game’s Physics a Hidden Bottleneck? Unlock CPU Power with Jobs and Burst

Introduction

It’s 2026, and player expectations for high-fidelity, responsive game worlds have never been higher. Yet, for many Unity developers, the pursuit of complex physics, intricate AI, or large-scale simulations often runs headlong into a critical bottleneck: the main thread. If your Unity game still relies primarily on MonoBehaviour.Update() for computationally heavy tasks like custom collision detection, advanced pathfinding, or sophisticated flocking behaviors, you’re inadvertently sacrificing precious frames and player experience. The sequential nature of Update() becomes a severe limitation, preventing your game from fully utilizing modern multi-core CPUs.

The solution isn’t just an optimization; it’s a fundamental architectural shift. Unity’s Jobs System and Burst Compiler are no longer esoteric tools reserved for DOTS (Data-Oriented Technology Stack) purists. They are immediate, essential allies for extracting raw, predictable, and highly performant power from your CPU cores. By embracing these systems, you can transform your game’s performance, delivering unparalleled fluidity and scalability.

Code Layout and Walkthrough: Embracing Parallelism

The core problem with MonoBehaviour.Update() is that it executes serially on the main thread. While fine for simple per-frame logic, complex calculations involving many entities quickly become a single-threaded choke point. The Jobs System, coupled with the Burst Compiler, offers a robust alternative.

1. The Power Duo: Jobs System and Burst Compiler

Jobs System: This framework allows you to break down heavy computations into small, independent units of work (Jobs) that can be scheduled to run in parallel across multiple CPU cores. It handles the complexities of thread management, allowing you to focus on the logic.
Burst Compiler: This incredible technology takes your C# code written for Jobs and compiles it into highly optimized native machine code. It leverages Single Instruction Multiple Data (SIMD) CPU instructions and performs aggressive optimizations, resulting in significantly faster execution than standard C# code, often by orders of magnitude.

2. The `IJobParallelFor` Interface

For tasks where you need to perform the same operation on a large collection of data, IJobParallelFor is your go-to. It distributes iterations of a loop across available CPU cores.

Let’s consider a simplified example: calculating an “influence” (like a force or a state change) for many agents based on their positions, simulating a custom physics query or a step in a flocking algorithm.

using Unity.Collections;
using Unity.Jobs;
using UnityEngine;
using Unity.Burst;

// 1. Define Your Job Struct
[BurstCompile] // Crucial: Enables Burst compilation for this Job
public struct CalculateInfluenceJob : IJobParallelFor
{
    // Input: Read-only positions of all agents
    [ReadOnly] public NativeArray<Vector3> AgentPositions;
    // Input: A global influence source position
    [ReadOnly] public Vector3 InfluenceSourcePosition;
    // Input: A multiplier for the influence
    [ReadOnly] public float InfluenceMultiplier;

    // Output: Influence vector for each agent
    public NativeArray<Vector3> AgentInfluenceVectors;

    // The core logic that runs for each item in parallel
    public void Execute(int index)
    {
        Vector3 agentPos = AgentPositions[index];
        Vector3 directionToSource = (InfluenceSourcePosition - agentPos).normalized;
        float distance = Vector3.Distance(agentPos, InfluenceSourcePosition);

        // Simple inverse square law influence for demonstration
        float influenceMagnitude = InfluenceMultiplier / (distance * distance + 0.01f); // Add small epsilon to prevent division by zero
        AgentInfluenceVectors[index] = directionToSource * influenceMagnitude;

        // In a real scenario, this could involve more complex custom collision checks,
        // neighbor lookups (using NativeArray.GetEnumerator for nearby agents safely),
        // or AI decision-making.
    }
}

3. Orchestrating the Job from a `MonoBehaviour`

Now, let’s see how you would schedule and manage this job from a traditional MonoBehaviour (though in a full DOTS context, this would live within a SystemBase).

using UnityEngine;
using Unity.Collections;
using Unity.Jobs;
using System.Collections.Generic; // For initial GameObject setup

public class PhysicsOptimizer : MonoBehaviour
{
    public int numberOfAgents = 1000;
    public Vector3 influenceSource = Vector3.zero;
    public float influenceStrength = 100f;

    private List<GameObject> agents = new List<GameObject>();
    private NativeArray<Vector3> agentPositions;
    private NativeArray<Vector3> agentInfluenceOutputs;

    void Start()
    {
        // Initialize agents (for demonstration purposes)
        for (int i = 0; i < numberOfAgents; i++)
        {
            GameObject agent = GameObject.CreatePrimitive(PrimitiveType.Sphere);
            agent.transform.position = new Vector3(
                Random.Range(-50f, 50f),
                Random.Range(-50f, 50f),
                Random.Range(-50f, 50f)
            );
            agents.Add(agent);
        }

        // Allocate NativeArrays, ensuring they match the number of agents
        agentPositions = new NativeArray<Vector3>(numberOfAgents, Allocator.Persistent);
        agentInfluenceOutputs = new NativeArray<Vector3>(numberOfAgents, Allocator.Persistent);
    }

    void OnDestroy()
    {
        // IMPORTANT: Always dispose NativeArrays when you're done with them
        if (agentPositions.IsCreated) agentPositions.Dispose();
        if (agentInfluenceOutputs.IsCreated) agentInfluenceOutputs.Dispose();
    }

    void FixedUpdate() // Or Update, depending on your simulation needs
    {
        // 1. Copy current GameObject positions into the NativeArray (Input for Job)
        for (int i = 0; i < numberOfAgents; i++)
        {
            agentPositions[i] = agents[i].transform.position;
        }

        // 2. Create an instance of your Job
        CalculateInfluenceJob job = new CalculateInfluenceJob
        {
            AgentPositions = agentPositions,
            InfluenceSourcePosition = influenceSource,
            InfluenceMultiplier = influenceStrength,
            AgentInfluenceVectors = agentInfluenceOutputs
        };

        // 3. Schedule the Job
        // The first parameter is the number of items to process.
        // The second parameter (innerLoopBatchCount) hints to the scheduler how many items
        // to process in one batch on a single thread. Tune this for performance (e.g., 32, 64, 128).
        JobHandle jobHandle = job.Schedule(numberOfAgents, 64);

        // 4. Wait for the Job to complete (or chain dependencies)
        // For simple cases, `Complete()` blocks the main thread until the job finishes.
        // For more advanced scenarios, you can chain job handles to create dependencies
        // without blocking the main thread until later.
        jobHandle.Complete();

        // 5. Apply the results back to GameObjects (Output from Job)
        for (int i = 0; i < numberOfAgents; i++)
        {
            // For this example, let's just move the agent based on the calculated influence
            agents[i].transform.position += agentInfluenceOutputs[i] * Time.fixedDeltaTime;
        }
    }
}

This walkthrough demonstrates the fundamental pattern: define your parallelizable logic in a [BurstCompile] IJobParallelFor struct, pass data efficiently via NativeArrays, schedule the job, and then process its results. This Nat

You Are Not Being Rejected. You Are Invisible.

Thu, 02 Jul 2026 18:42:32 +0000

I place developers with US tech companies for a living. Before that sentence makes you close the tab: this article contains the things I tell developers for free, every week, one conversation at a time. I got tired of saying it one person at a time.

Here is the pattern. A strong developer outside the US decides to go for US remote work. The money is real, the work is real, the market is real. They polish the CV, they apply to twenty, fifty, a hundred roles.

And they hear nothing.

Not "no". Nothing. And after enough nothing, every single one of them arrives at the same quiet conclusion: I must not be good enough.

I have read the CVs and talked to the people behind them. The conclusion is almost always wrong. What is actually happening is mechanical, boring, and fixable, and nobody explains it because nobody profits from explaining it. Let me walk through the three filters that are actually eating your applications.

Filter 1: You are applying to jobs that were never open to you

"Remote" means two completely different things, and job boards mix them together.

Remote (US only) means the company wants you on US payroll. They will let you work from your couch, as long as your couch is in the United States. For someone applying from Prague or Bratislava or anywhere else abroad, this is a closed door. No amount of skill opens it, because the blocker is legal structure, not talent. Most "remote" listings on the big boards are this kind.

Remote (global, or contractor) means the company hires people anywhere and pays them as independent contractors who invoice. No US payroll, no visa, no sponsorship, because you are a vendor, not an employee. This is your actual market. It is smaller, it is real, and it is far less crowded.

If you do not check which kind you are looking at before applying, most of your applications go into doors that were locked before you knocked. Ten seconds of scanning for "worldwide", "contractor", "B2B", or "must be US-based" saves you months.

Filter 2: The knockout question

Somewhere in most application forms sits a question: "Are you legally authorized to work in the United States?"

If you are applying as a would-be employee and you answer honestly, the application ends right there. Not because a human read your CV and decided against you. Because a form gate closed. You never find out. This single checkbox kills more qualified international applicants than every skills gap combined.

You do not beat it by lying. You beat it by not being an employee. When you position yourself as an independent contractor who invoices the company, the question changes from "can this person legally be our employee" (hard, expensive, immigration lawyers) to "can we pay this vendor's invoice" (yes, accounting does it every day). Same developer. Same skills. Different door entirely.

Practically, that means saying it early and plainly: a line near the top of your CV like "Independent contractor, invoices directly, 4+ hours daily overlap with US time zones." You are answering the company's quiet worry before it forms.

Filter 3: Six seconds against six hundred applicants

A popular US remote role gets hundreds to a couple thousand applications. The human scanning the pile gives each CV a few seconds on the first pass. Whatever your CV needs to say, it has to say it in those seconds, in the first lines, or it has said nothing.

The CVs I see from European seniors are built for a different reader. Photo, long education section, four pages, every project since 2012, and a summary that says "passionate software engineer with strong communication skills", which appears on fifty thousand other CVs and carries zero bits of information.

What survives the six-second scan is outcomes with numbers in the first fifteen lines. "Led migration of 186 services to AWS, zero downtime." "Built payment infrastructure processing 3M transactions/month." Ownership, scale, results. Not who you are. What you did.

This is not fair, and it does not measure engineering skill, and it is also simply how the pile gets processed. The good news hiding in it: the fix is packaging, not talent. I have watched the same engineer go from silence to interviews without gaining a single new skill. The experience was always there. The first fifteen lines finally showed it.

What this adds up to

The silence was never a verdict on your ability. It is three mechanical filters doing what they were built to do: a market where most listings were never open to you, a checkbox that ends honest employee applications automatically, and a reading process that gives you six seconds you were not built for.

Every one of those is beatable, and none of the fixes require becoming a better engineer. Aim only at globally open roles. Position as a contractor and say so early. Rebuild the first fifteen lines around outcomes. Then reach actual humans instead of piles, which is a whole topic of its own.

The developers landing US remote contracts from abroad are not the most talented ones. They are the ones who understood which doors were real and showed up legible. That can be you with embarrassingly little additional effort, aimed in the right direction.

I wrote a free guide that goes deeper on exactly this, the honest version I tell developers one at a time: get it here. And if you have questions, comments, I read everything.

Looking for Collaborators

Mon, 01 Jun 2026 13:16:27 +0000

I am a team leader at a software engineering firm and also a business owner.

I’m currently looking to connect with a motivated Latin candidate for a monthly paid position within our team.
This is a non-technical role, although any technical skills are welcome and appreciated.
More importantly, I’m looking for someone with a positive, collaborative mindset who is eager to work toward shared goals.
Our agency is currently expanding, and we believe this partnership can be mutually beneficial.

How it works:

We will use your profiles to apply for remote job opportunities on platforms such as Upwork, Freelancer, and Fiverr.
Once a project is secured, our team will handle all project work, communication, and technical tasks.
Your primary responsibility will be managing the payments received from these jobs.
This role does not require technical skills or significant time commitment, as long as you are able to receive and manage payments.

All accounts will remain fully under your control, and we will not have access to your funds, ensuring that the arrangement is safe for you.

Compensation:

Monthly payment: $70–$100
Plus 10% of any profits generated from jobs secured through your profiles

If this opportunity sounds interesting or if you would like more information, feel free to reach out:

WhatsApp: +1 (361) 332-6512
Microsoft Teams: https://teams.live.com/l/invite/FAAk3iOSJkDyS11JQE?v=g1
Email: Emails are not allowed

There is absolutely no pressure—my goal is simply to find the right fit and offer a worthwhile opportunity.
Best regards.

EduLumix – One Platform for Learning, Jobs, Resources & Growth

Thu, 07 May 2026 12:27:59 +0000

Students today are facing a common problem.

There are too many platforms for:

Learning
Jobs
Courses
Resources
Practice
Career Guidance

And because of that, many students feel confused about:

where to start
what to learn
how to grow
where to find opportunities

That’s why we built EduLumix.

https://edulumix.in

EduLumix is a complete platform specially designed for:

Students
Freshers
Developers
Self-learners
Tech enthusiasts

Our goal is simple:

Learn better. Build skills. Find opportunities. Grow together.

✨ What You Can Find on EduLumix

Job & Internship Opportunities

We regularly share:

Fresher Jobs
IT Jobs
Non-IT Jobs
Remote Jobs
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Hiring Updates

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Diversity and Inclusion Are Officially Dead in Tech

Mon, 02 Mar 2026 02:03:34 +0000

According to a study by Edward J.W. Park, published in 1999 in Qualitative Sociology, the tech industry have always had a problem with racism where white candidates always received more favorable treatment.

If you are a person of color or identify as non-white, you will learn in this article about the sad trajectory that tech is going and potential solutions. If you felt imposter syndrome by countless rejections, this article might help you feel better. Please note that while rejections due to discrimination are real, it's not always the case and there could be many more factors that come into play such as market saturation, skills gap or simply vacancies being canceled or frozen.

If you identify as white and this topic resonates with you, you'll learn how to hold your peers accountable for discrimination and recognize stereotypes and biases. I will not shy away from using term "White" because I have strong belief that white privilege does exist and have seen it throughout the years globally.

Few years ago, there was a serious attempt for the first time ever by corporations to be more diverse and inclusive by implementing DEI programs. while these programs were far from perfect, they enabled more people of color to get a fair chance and to grow.

Last year, unfortunately, many corporations specially in the US rolled back all their DEI programs citing financial reasons without offering any meaningful alternatives.

There is a strong narrative among the majority in Western nations that people of color had been given opportunities not based on merits but based on quotas. According to study, this narrative doesn't hold merit and instead appeals to confirmation bias.

If you're a minority or person of color, and you suddenly notice that organizations have become toxic or you're being treated unfairly, you are not imagining it. Between mass layoffs, market panic and also reversal of DEI programs, what you experience is how things used to be before DEI towards minorities and people of color including women who have always struggled to get fair opportunities in tech.

The problem becomes even more pronounced the more senior you become because unless you fit their stereotype of the low skilled labor, you'll face huge resistance and lots of rejections specially in organizations where leadership have severe lack of diversity.

If you go to LinkedIn and browse through the people in leadership positions in most tech companies specially in Western countries, you'll rarely find minorities or people of color in leadership positions. I do not have unfortunately the data but based on general trends and my own observations, I concluded this as the reality.

If you are a minority or person of color, you'll always be held to much higher standards for the same role that a white applicant got in just 1 hour interview with low effort.

There is nothing wrong with you, the problem has more to do with the hiring managers and recruiters who have untreated biases; you may ask, why would they bother calling you if they perceive you as low skilled?

If you look at the call back rates, it's actually much lower compared to white applicants in the market. Those who called you, did so because they want to seem fair but without disclosing the fact that they're exercising a double standard (which is illegal in most states). This helps them reject your job application without feeling guilty. At the same time, they do this sometimes to protect themselves legally. The whole interview process for you is about building a case to reject your job application.

For me for instance, when I used to get a rejection, I used to ask for a feedback and many times, the feedback seemed to be either untrue or made up. There were genuine cases where the hiring manager had very high standards for the role but most of the feedback I received was subjective and projecting certain stereotypes that are not based on objective factors.

What's The Solution?

Honestly speaking, unless we see DEI coming back with more energy and more effort, I'd recommend that you seek self employment and be extra cautious and selective about companies you'd like to work with. Ideally, via networking and making connections you might meet a hiring manager or client who is like minded or with whom you have positive rapport or relationship with.

You have a better chance landing projects as a freelancer than finding an employer who will give you fair treatment without DEI.

The Consequences Are Severe for Western Tech

The tech industry specially in Western countries still have a long way to go to address all of those challenges to create a more fair and equitable workplace environment that is blind to skin color, nationality or biases. Western companies are expected to struggle in the long-term if those issues remain unresolved or if policy makers won't create policies that are enforced and hold people accountable for not following them or abusing them.

According to Chicago University study, talents mis-allocation have led to a cost of 16$ trillion dollars and roughly 6 million jobs loss over 20 years. Another study in 2024, concluded that homogeneous teams are 66% less likely to outperform and persistent bias exacerbates skill gap that is costing the UK economy GBP 63 billion per year; and this is even more pronounced in AI/cybersecurity fields.

Racism and discrimination not only hurts minorities and people of color but have a long-lasting negative consequences on the entire economy if left unaddressed.

Will policy makers and corporations recognize the threat of bias and create meaningful enforceable frameworks or will talents find opportunities elsewhere?

What are your thoughts?

The New Employment Market Reality – It’s Much Worse Than You Think!

Sat, 21 Feb 2026 04:01:17 +0000

I truly don't know where to begin. This is the one of the worst employment markets I have ever seen!

Recruiters and companies are being bombarded with qualified candidates from all over the world. The mass layoffs left 100s of thousands people stranded!

The once was considered golden job with real stability and security had been obliterated by AI!

This doesn't mean that tech doesn't need humans, it simply shifted or transformed to different needs. Corporations no longer need coders, they want designers, architects and AI oriented professionals.

The problem is that, what corporations missed, is that they no longer understand why Devs wanted to be Devs.

People regardless of their background, are always looking for good jobs where they feel respected and valued. The problem with the current market dynamics, employers no longer feel the need to care about their employees or care about having a healthy workplace environment.

There is a very huge rise in serious problems such as workplace bullying, mobbing and harassment! What happened?

The truth is that unfortunately, when there is 1000 person ready to take your job, those in leadership lose the incentive to put effort in retention of employees. At the same time, employers realized that a lot of roles specially white collar jobs are no longer needed, so they want to get rid of as many people as possible and remain small. This is why, statistically speaking, the layoff stats far exceeded new job creation. You will hear a lot that employers are being "Cautious".

Do you share my sentiment, do you feel the employment market has become unsustainable? Is it time for Devs to be self employed first?

Share your thoughts in the comments!

CoderLegion Jobs Module: A New Job Board for Developers and Tech Talent

Wed, 21 Jan 2026 10:24:26 +0000

Introducing the CoderLegion Jobs Module — Where Tech Talent Meets Opportunity

We’re excited to announce the launch of the Jobs Module on CoderLegion — a powerful new feature designed to help developers discover meaningful roles and enable companies to connect with skilled tech professionals worldwide.

This release marks an important step in CoderLegion’s evolution — transforming the platform into more than a learning space and positioning it as a destination for careers, hiring, and professional growth.

Explore the Jobs Module
https://coderlegion.com/jobs

What the Jobs Module Offers

The Jobs Module delivers a centralized job marketplace fully integrated into the CoderLegion ecosystem — allowing developers and recruiters to post, discover, apply for, and manage jobs with ease.

For Job Seekers

Browse job listings
Search and filter opportunities by role type, location, remote availability, salary range, and posting date.
Quick apply process
Apply to roles directly through your CoderLegion account without leaving the platform.
⭐ Smart job recommendations
Receive tailored job suggestions based on your skills, profile, and preferences.
Application tracking
Stay updated on the progress of every application from your personal dashboard.

For Recruiters & Hiring Teams

Post roles at no cost
Publish detailed job openings with role expectations, requirements, and application steps.
Manage job postings
Edit, update, or remove listings easily through the recruiter dashboard.
Review applicants
View and assess candidate profiles from a single, organized interface.
Hiring insights
Monitor views, applications, and engagement metrics to optimize hiring outcomes.

A Unified Dashboard Experience

Both developers and recruiters benefit from intuitive dashboards that bring all job-related actions into one place — enabling efficient job management and a clear, streamlined experience.

Built for the Global Tech Community

CoderLegion has always empowered developers to learn, connect, and evolve. The Jobs Module strengthens this vision by helping members move forward in their careers — whether they’re just starting out or looking to hire exceptional talent.

Seamlessly connected with articles, networking spaces, and member profiles, the Jobs Module transforms CoderLegion into a complete professional ecosystem for technologists.

How to Get Started

For Developers

Create a free CoderLegion account or log in.
Visit the Jobs section and refine your search using filters.
Select a role and click Apply to submit your application.
Track all applications from your dashboard.

For Recruiters

Register or sign in to your recruiter account.
Go to Post a Job and publish your listing.
Manage and update job posts from your dashboard.
Review applications and contact candidates directly.

Why This Matters

As hiring in tech continues to evolve, the Jobs Module places CoderLegion users at the heart of opportunity — offering a community-driven, efficient, and transparent way for developers and companies to connect.

Take the Next Step in Your Tech Journey

Start Browsing Jobs Now
https://coderlegion.com/jobs

AI 2030: 10 Predictions That Will Reshape Our World

Fri, 16 Jan 2026 09:20:10 +0000

As the Founder of ReThynk AI, I look at AI 2030 through one lens:

AI will not just change technology. It will change what “normal life” looks like. And the biggest shift will be democratisation, AI moving from elite teams to everyday people.

Here are my 10 predictions for 2030.

AI 2030: 10 Predictions That Will Reshape Our World

1) AI literacy will become as basic as internet literacy

Schools, workplaces, and hiring will treat “AI fluency” like a fundamental skill, not a bonus.

2) Every job will have an “AI co-pilot” layer

Not because jobs disappear, but because repetitive load disappears. The role becomes more about judgment, supervision, and outcomes.

3) Local-language AI will unlock the next billion users

The biggest AI growth will come from people who don’t work in English. Language will become the gateway to inclusion.

4) Small businesses will run like big businesses

AI will give small teams the leverage of larger teams, operations, marketing, support, and reporting, without hiring 10 extra people.

5) “Trust” will become the real product differentiator

AI features will be everywhere. The winners will be products that feel safe, controllable, and predictable, with privacy and transparency by design.

6) The new divide will be “system users” vs “random users”

People who build repeatable AI workflows will compound benefits. People who use AI occasionally will see limited results.

7) AI will reduce decision fatigue more than it boosts creativity

The biggest daily benefit will be clarity: summarising, prioritising, comparing options, and reducing mental overload.

8) Regulation will mature, and businesses will be forced to grow up

Light governance will become mandatory: data boundaries, accountability, audit trails, and “human ownership” of critical decisions.

9) Personal agents will manage life operations

Scheduling, shopping, travel planning, learning plans, and finance tracking will shift from apps to assistants; people will “ask” instead of “click.”

10) Democratisation of AI will become a global race

Countries, institutions, and companies will compete to make AI accessible because access will directly impact productivity, education, and national competitiveness.

My closing belief

AI 2030 will reward one thing more than raw intelligence:

the ability to learn, adapt, and build trust.

That’s why I’m building in the Democratisation of AI niche, so more people move from fear to fluency.

Discover CoderLegion’s New Jobs Module — Connecting Developers with Opportunities

Wed, 14 Jan 2026 03:42:55 +0000

Discover CoderLegion’s New Jobs Module — Connecting Developers with Opportunities

We’re thrilled to unveil the newly launched Jobs Module on CoderLegion — a dedicated platform feature crafted to help developers find their next role and allow companies to reach skilled technical professionals globally.

This launch represents a significant advancement in CoderLegion’s mission to be more than a learning community — now, it’s also a hub for career growth, recruitment, and job opportunities.

Check out the Jobs Module
https://coderlegion.com/jobs

Features of the Jobs Module

The Jobs Module offers a comprehensive job board integrated within the CoderLegion platform — making it seamless for developers and recruiters to post, find, manage, and apply for jobs.

For Job Seekers

Explore openings
Filter roles by type (full-time, part-time, contract, freelance), location, remote options, salary, and date posted.
Easy application
Submit applications directly through your CoderLegion account dashboard.
⭐ Personalized job suggestions
Get recommendations based on your profile, skills, and preferences.
Track applications
Monitor the status of all jobs you’ve applied to right from your dashboard.

For Recruiters & Hiring Managers

Post jobs for free
Create detailed job listings including role descriptions, qualifications, and application instructions.
️ Manage listings
Easily edit, update, or remove postings through your recruiter dashboard.
View candidates
Access all applicants and review their profiles in one centralized place.
Insightful dashboard
Track job views, applications, and engagement metrics for better hiring decisions.

Unified Dashboard Experience

Both developers and recruiters enjoy user-friendly dashboards that centralize job activities — from posting and editing jobs to managing applications — creating a smooth and transparent workflow.

Designed for the Tech Community

CoderLegion has always been a space for developers to learn, collaborate, and grow. With the Jobs Module, we’re enhancing that mission by helping members advance their careers — whether you’re entering tech, seeking new challenges, or hiring top talent.

Integrated with existing platform features — including articles, networking groups, and member profiles — the Jobs Module makes CoderLegion a comprehensive hub for professional development in technology.

Getting Started

For Developers (Job Seekers)

Create a free CoderLegion account or sign in.
Go to the Jobs section and use filters to find suitable positions.
Click Apply to submit your application in a few steps.
Keep track of your applications directly on your dashboard.

For Recruiters

Sign up or log in to your recruiter account.
Navigate to Post a Job and enter the role details.
Update and manage all job listings from your dashboard.
Review applicants and reach out to candidates directly.

Why It’s Important

As the tech hiring landscape evolves, the Jobs Module positions CoderLegion users at the center of opportunity. For developers and companies alike, the platform provides a simple, efficient, and community-focused way to connect.

Step Into the Future of Tech Careers

Start Exploring Jobs Today
https://coderlegion.com/jobs

Breaking into System Design: Here’s What I Learned Today

Thu, 16 Jan 2025 12:15:31 +0000

What I Learned Today About System Design

Today, I explored some fundamental concepts of System Design. Here’s a quick overview of what I learned:

Why System Design is Important in Interviews
Scaling
What is System Design?
Types of Scaling

Now, let's dive deeper into each point!

1. Why System Design is Important in Interviews****

When you’re interviewing for mid-level or senior positions, System Design is crucial because it shows your ability to plan and build systems that work well as they grow.

In interviews, you'll be asked to design things like websites or apps, and the interviewer wants to see if you can build systems that handle more users, work efficiently, and don’t crash when traffic increases.
System Design tests your skills to think beyond just writing code—you need to think about how the whole system will function and scale.

Note: Freshers are generally not expected to know System Design because their focus is on building strong foundations in Data Structures and Algorithms (DSA) and core programming skills. However, having some knowledge of System Design can set you apart from others!

2. What is System Design?

System Design is the process of planning and organizing how a software application will work. It’s like creating a map that shows how different parts of the system should connect and interact with each other.

For example, how will data be stored? How will users interact with the system? What happens if the system gets too many requests?

Imagine you're building a new library. Before you start construction, you need to plan:

Where the bookshelves will be
How the visitors will move around
Where the entrances and exits will be
What to do if there’s a huge crowd

Similarly, in System Design, you decide:

Where to store data (like customer info, product details, etc.).
How users will interact with the system (e.g., through buttons, forms, or links).
What will happen if too many users try to access the system at once, and how to make sure it doesn’t crash.

In short, System Design is about creating a robust architecture to ensure that the system can handle the demands of users, handle errors gracefully, and evolve over time.

3. Scaling in System Design

scaling, which is one of the most critical aspects of creating systems that last and grow with increasing users or traffic.

It is about making sure that as more people use the system, it can grow and still function smoothly.

For example, if you design a library that works perfectly with 100 visitors, you need to think about what happens when 1,000 visitors show up. Will the system still function smoothly, or will it crash? That’s where scaling comes in.

Scaling ensures that your system can grow without breaking down. It’s about handling more traffic and data efficiently as your users increase.

4. Types of Scaling

In System Design, there are two main ways to scale your system:

Vertical Scaling (Scaling Up)

Vertical Scaling involves upgrading your existing system to make it more powerful. It’s like adding more capacity to a single resource.

Example in a Library:
- imagine your library has a checkout counter that is too slow with more visitors. Instead of adding more counters, you upgrade the existing counter with faster technology, better staff, or automation to handle more visitors.
In software systems, vertical scaling means upgrading a single server—adding more RAM, a faster processor, or better storage to handle increased demand.
For example, when a website starts getting more visitors, the first step is often upgrading the existing server to manage the traffic better.

Challenges:
While vertical scaling works well initially, it has limits:

- Physical Boundaries:
Imagine you keep upgrading your computer’s RAM. At some point, the motherboard or hardware can’t support more, and upgrading further isn’t an option.

- Cost Increases:
Upgrading high-end servers becomes costly because as you add more powerful parts, the price increases quickly. After a certain point, the improvements in performance are small compared to the high cost, making it less worthwhile.

- Single Point of Failure:
A Single Point of Failure means that if you rely on only one server for your entire system, and that server stops working, everything else depending on it will fail too.

For example, imagine you have a website that only runs on one upgraded server. If that server crashes or faces issues (like hardware failure, network problems, or even a power outage), the website will go offline because there's no backup or alternative server to take over the work.

Advantages:

Simple to Implement: Easier to upgrade the existing server rather than setting up new ones.
Performance Boost: Immediate performance improvement for single-server workloads.

When do we use Vertical Scaling?

It’s often the first solution when your system starts to feel overloaded. It’s faster to improve your current system than to add new ones.

Horizontal Scaling (Scaling Out)

After vertical scaling reaches its limits, we move to horizontal scaling, where we add more resources to handle more traffic.

Example in a Library:
- If you’ve already upgraded your checkout counter but there’s still a huge crowd, you may decide to add more counters to handle the flow of visitors. You could also hire more staff to help people checkout quickly.
In software systems, horizontal scaling involves adding more servers. When one server gets too slow due to the load, you add more servers, and they all work together to handle requests.

Advantages

Scalability: You can keep adding more servers as needed.
Reliability: If one server fails, others keep the system running.
Cost-effective: Often cheaper than upgrading one server.

Challenges

Complexity: Managing multiple servers can be tricky.
Data synchronization: Ensuring all servers have the same data.
Latency: Communication between servers may introduce delays.

Why Use Horizontal Scaling?

This is useful when your system has reached the limits of what a single server can handle, and you need to distribute the load across multiple systems or servers. It's common for large platforms like Netflix and Amazon.

Summary:

Vertical Scaling (Scaling Up) is about upgrading your existing system to make it more powerful. It’s the first option for scaling but has its limits.
Horizontal Scaling (Scaling Out) is about adding more servers or resources when vertical scaling isn’t enough to handle the load.

Both scaling techniques are part of System Design to ensure that your system grows with demand and provides a smooth user experience.

This is what I've learned about System Design so far, and I look forward to sharing more insights with you as I continue to explore and learn. Stay tuned for more updates!

Feel free to share your experiences or examples of scaling in real-life applications—let’s learn from each other!

Coder Legion - Recent posts tagged jobs

EarlyStageStartups.com

Is It Still RAG If There's No Retrieval?

The design brief

It is not an agent

To "RAG" or not to RAG

Reducing confabulation

Prompt caching

The build: brutally efficient again

Abuse protection

Personality: the fun part

Real-Time AI Interview Help: Keeping Technical Answers Relevant as Questions Change

Is Your Unity Game’s Physics a Hidden Bottleneck?

Is Your Unity Game’s Physics a Hidden Bottleneck? Unlock CPU Power with Jobs and Burst

Introduction

Code Layout and Walkthrough: Embracing Parallelism

1. The Power Duo: Jobs System and Burst Compiler

2. The IJobParallelFor Interface

3. Orchestrating the Job from a MonoBehaviour

You Are Not Being Rejected. You Are Invisible.

Filter 1: You are applying to jobs that were never open to you

Filter 2: The knockout question

Filter 3: Six seconds against six hundred applicants

What this adds up to

Looking for Collaborators

EduLumix – One Platform for Learning, Jobs, Resources & Growth

✨ What You Can Find on EduLumix

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Courses & Skill Development

Mock Tests & Practice

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️ Digital Products

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How It Works:

Why EduLumix is Different

Who Should Use EduLumix?

Our Vision

Explore EduLumix Today

️⃣ Tags

EduLumix #Students #Freshers #Learning #Technology #WebDevelopment #Programming #CareerGrowth #Jobs #Internships #TechCommunity #AI #MockTest #Courses #DigitalProducts #Career #Developers #Education

Diversity and Inclusion Are Officially Dead in Tech

What's The Solution?

The Consequences Are Severe for Western Tech

The New Employment Market Reality – It’s Much Worse Than You Think!

CoderLegion Jobs Module: A New Job Board for Developers and Tech Talent

Introducing the CoderLegion Jobs Module — Where Tech Talent Meets Opportunity

What the Jobs Module Offers

For Job Seekers

For Recruiters & Hiring Teams

A Unified Dashboard Experience

Built for the Global Tech Community

How to Get Started

For Developers

For Recruiters

Why This Matters

Take the Next Step in Your Tech Journey

AI 2030: 10 Predictions That Will Reshape Our World

Discover CoderLegion’s New Jobs Module — Connecting Developers with Opportunities

Discover CoderLegion’s New Jobs Module — Connecting Developers with Opportunities

Features of the Jobs Module

For Job Seekers

For Recruiters & Hiring Managers

Unified Dashboard Experience

Designed for the Tech Community

Getting Started

For Developers (Job Seekers)

For Recruiters

Why It’s Important

Step Into the Future of Tech Careers

Breaking into System Design: Here’s What I Learned Today

What I Learned Today About System Design

1. Why System Design is Important in Interviews****

2. What is System Design?

Similarly, in System Design, you decide:

3. Scaling in System Design

4. Types of Scaling

Vertical Scaling (Scaling Up)

When do we use Vertical Scaling?

Horizontal Scaling (Scaling Out)

2. The `IJobParallelFor` Interface

3. Orchestrating the Job from a `MonoBehaviour`