Coder Legion - Recent posts tagged devrel

What even is a developer experience audit?

Wed, 28 Oct 2026 15:20:55 +0000

Since getting into the Developer Relations space, I take onboarding to new companies as an opportunity to evaluate what the overall product experience is like. This comes with all sorts of fun things like identifying broken links to fully broken internal processes. And everything in between

I am a strong believer that everyone is doing their best. I am also rational in understanding that priorities and focus change regularly. I want to make sure I approach this with curiosity and enthusiasm to propose a solution and fix it myself, if that's possible. After all, I probably have less meetings and less responsibilities than everyone else, simply by being new.

If I can't fix it myself, I propose a recommendation. A former manager of mine would be thrilled to see me bringing solutions, not problems. (To be fair to both of us, the platform I supported at the time didn't have a lot of solutions but certainly had a lot of problems.)

So with a use case in hand, I set off to see what the developer experience was like for Apryse.

But wait, where does Developer Experience fit in Developer Relations?

Before we get into the audit details, let's address the DevEx vs. the DevRel elephant in the room.

For me, Developer Relations is the broader umbrella that encompasses awareness (advocacy), enablement (developer experience), and engagement (community).

Some companies will bucket this a little different, including the one I'm at now, where Developer Experience is the umbrella but the core focus areas are still largely covered. At Arpyse, we also include AEO under Developer Experience which I think fits nicely in this era of AI-enabled or AI-assisted builders.

Now let's talk about audit process in terms of awareness, enablement, and engagement.

The process

I want to try to replicate, to the best of my ability, what the public facing developer experience is like. How do developers find the product, how can they try it out, and then how can they (or someone else in the company) start the purchasing and procurement process.

Doing this during onboarding should be the easiest time to ever do this because you probably have little to no deep product knowledge. you can literally approach it like an external developer because you are one. Unless your onboarding includes product training, which is cheating if it's only available to internals.

Discovery (awareness)

Can I find the product when I'm looking for it? Is it positioned well against competitors?

In today's world, discovery includes both a traditional search and AI tools, including LLMs like Claude, but also includes AI search summaries, like the ones you see at the top of an otherwise traditional Google search.

I approach this like any research project I did in school - lots of keywords, questions, and use case based researching. Then I see what kind of results I get.

If you think only your marketing website or your docs are your entry points for a developer audience, you have some work to do.

Consider other entry points, like if your product is available on a cloud or platform marketplace, as developers participating in that ecosystem may start there first - think a Salesforce or Appian developer starting in their marketplace rather than accidentally finding something not in the marketplace already and having to build it full custom. Is this information up-to-date? Well maintained and linked back to more granular information across your website and docs? Does it show up on the marketplace search with vague terms or only the company or product name?

Beyond marketplaces think of entry points like GitHub, package managers, or other places libraries and dependencies already live. Unless I'm starting from scratch or doing a re-platforming effort, the closer you are to where I'm already working, the easier it will be for me to pick it up. And here again, is this information up-to-date, well maintained, and linked back to more granular information? It's not enough to just be listed, it needs to look safe and maintained, often with very limited context.

Outside the scope of this audit, you can also evaluate broad, general awareness activities typically managed by marketing like events, webinars, blogs, etc. If I go to a developer conference, is Apryse there and how quickly can I understand what the product does in a conference setting?

All of this is part of the discovery process.

One thing I should mention before moving on - if your product doesn't come up when and where you expect it, that's a notable gap. Congrats, you have action items following your audit. To me, awareness gaps are the most critical to address immediately because if people (and AI) can't find you, they can't use you. Fix that!

Evaluation & trial (enablement)

I found the product I think I want to use, but I need to check on the features and eventually build a proof of concept (PoC).

You've been found, either by humans or AI, and they are moving forward with understanding and using your product, often in some kind of trial capacity.

Before they start building, they need to know if your features and capabilities will solve their problems. Maybe they've seen some marketing pages or blog describing this, but this is where they will start digging for more details.

This is probably where my process has changed the most in recent years. Using AI tools I can go from discovery to building really fast, sometimes even in the same chat.

And I did, with several LLMs on basic or free tiers with as little access to memories or prior knowledge as I could possibly give them. I didn't want them to taint my results by knowing I worked for Apryse because I wanted to experience what an external developer would experience, and a bias to Apryse because I was an employee was going to harm that.

During this evaluation phase is also where I check competitors and even PoC them too. You want to be choosy about this step because it's either time consuming or compute intensive (tokens), but it's a very realistic step for developers evaluating multiple options.

I'm evaluating the features, the ease of implementation, the clarity of documentation or other assets available for reference, and ultimately how much debugging did I have to do to stand up a working PoC. If this goes smoothly, I will literally go tell all my tech friends about it. (And this should be your goal, to have people outside the company talking about how great it is.)

Trial keys, licenses, and more

If your product has a trial process, it should be as frictionless as possible. I get that you want leads. You know what I, as an external developer, want? To figure out how many PoCs I have to do before I recommend a solution to my manager on a tight timeline. And know that when I build a PoC, I've timed it so it still works when I have to do a demo.

Your trial process should be super clear. How many days, weeks, credits, runs is the trial valid for? What features are included or not? Will I see uninformative error messages when using a trial key vs. a production key?

It does not matter if the friction experienced in this phase is 100% because of how trial keys or licenses are implemented. You need to fix that. External developers shouldn't have to absorb that.

A curated developer journey

This is the hill I'm willing to die on, sometimes daily. You cannot risk developers or AI defining their own developer journey. You need to own and nurture this. This stage of the audit is where this becomes alarmingly clear.

Taking a use case, I usually pick a programming language or framework along with it. In this case, I went Javascript (React + Node, with some Vite and Angular sprinkled in along the way. Remember, I ended up doing like 5 or 6 PoCs across Apryse and competitors.)

With both my own eyeballs and mouse pointer AND, separately, my crew of LLMs, I wanted to see if there was a clear, defined journey through Apryse for a Javascript developer. This was a well supported journey, but not necessarily a curated one depending on where I went or what questions I asked. For example, landing on the marketing website, we don't really link to a developer portal. In fact, we have about 4 pages I would consider someone external to Apryse calling a "developer portal," so we'll need to fix that.

A north star goal here would be for my various programming languages or frameworks, a developer in that space can move from discovery to building with clearly identified resources that we nurture, specific to their needs and use cases. For example, a Java developer would probably have different needs and use cases simply by living in the server space (Apryse Server SDK) compared to a developer using WebViewer (Web SDK). This could end up being the same developer, but they are solving different aspects of the problem and need an intro and understanding of our different SDKs.

Up and running, and beyond (engagement)

Is there anyone out there to support me? How can I get deeper knowledge?

Once I've successfully built a PoC, or like 5? 6...? in my case, I need to help whoever is actually going to do the purchasing make a final decision to make the purchase happen.

And in parallel, I need to make sure I know how and where to get support, either paid or community. Ideally, this is open and searchable because I'm both lazy and antisocial.

This is where I look for community support platforms, forums like Discourse, messaging platforms like Slack or Discord, GitHub communities, etc. I also evaluate their community health - is anyone actually posting? Are the only responses from employees or contributors? Does it look like a place I would feel safe asking a question?

If there are hundreds or thousands of people silently hanging out, this is a red flag. If only employees or contributors are responding, this is probably a yellow flag or maybe even green if you were looking for free (community) support. As a community grows, the community support should scale with it. If it doesn't, this might be something to look in to.

I also look at GitHub and forums outside of the direct control of the project or company. What's the sentiment here? Do issues remain open with no responses? Are PRs stale and neglected? Is there a whole subreddit bashing the project?

At this point, I also start looking for any signs of a champions program or certified developers in this space. This might come up on LinkedIn or a community support forum. These are usually really healthy indicators, that someone external to the company cares enough to go the extra mile with the technology. They may speak at conferences and share their experience too, which loops back to the general awareness activities.

With this information I can usually wrap up my recommendation and feel confident we are making a good choice.

The caveat

It's super important to stay as public and as focused on exactly what a given resource says. If you have access to internal experts or secret resources, this isn't a good replication of what an external user would have access to. If you overcome any and all friction with extensive research, that's not realistic with someone on a development team with a tight sprint timeline.

The AI caveat

I took this particular audit as an opportunity to use every LLM and AI chatbot I could reasonably get my hands on - Claude, Microsoft Copilot, GitHub Copilot, Gemini, ChatGPT + Codex. Occasionally I would use them a bit adversarially quoting one to the other. I also wanted to see what kind of project structure they delivered, what kind of experience I had pair programming or debugging with them, and what kind of AI-enabled or assisted experience they delivered.

Several times I had to reign them in because they could see my email in my directory file tree or path and infer Apryse as my employer. I get that they are being "smart" but this is not what I wanted. Microsoft Copilot had broad internal access and I actually ended up abandoning it because it would go find internal resources after I would explicitly tell it to only look on the public internet.

Speaking of public internet, GitHub Copilot out-of-the-box didn't have access to the public internet and neglected to share that with me. I wrote a whole thing about that, but TL;DR it was GUESSING old npm versions before I was finally like CITE YOUR SOURCES and it couldn't... because it didn't have access to the internet.

Wrapping up

So this was more info into my developer experience audit process. Have you done something similar? What does your process look like?

Again, and I cannot stress this enough, this isn't a finger pointing exercise. Some things may be accidentally neglected or forgotten, just fix them or recommend a fix and move on. Some things may be missing because it just wasn't the right time to do them, and maybe it's still not the right time.

The point is you now have a starting point for how and where to improve the developer experience, a great set of talking points to meet folks across the company and start building those cross-functional relationships, and you'll be able to directly impact the external developers trying to build a solution with your product. Lots of points. I think you get the point. 😉

_Photo by Agence Olloweb on Unsplash_

Is DevRel Dead or Just Disaggregated?

Thu, 07 May 2026 17:39:00 +0000

The "USB-C Moment" for Developer Experience

We’ve entered what I call the "USB-C Momentstrong text" for AI systems—a period of rapid standardization where the Model Context Protocol (MCP) is doing for AI tools what universal ports did for hardware. But this shift raises an existential question for those of us in leadership: If an LLM can write the boilerplate, explain the API, and debug the integration, is Developer Relations still relevant?

The short answer: The traditional "Tutorial-as-a-Service" model of DevRel is dead. The new model is just beginning.

From Documentation to Context-Provisioning

Historically, DevRel was the bridge between a complex product and a developer’s terminal. We spent our cycles on documentation, "Hello World" samples, and answering syntax questions. Today, developers are increasingly using agentic workflows to handle that "Glue Code".

In this era, DevRel's value prop shifts from content to context:

Context-First Engineering: Our job is no longer just writing docs for humans; it’s ensuring our systems are "model-ready." This means architecting MCP servers so that an AI agent can navigate our product's capabilities with high-reasoning synthesis.
The Auditor Role: We are moving into a "Forensic" era of advocacy. DevRel must act as the "Guardian," providing human-in-the-loop governance to ensure that synthetic code generated for our platforms is secure, ethical, and performant.
Systems Over Syntax: We aren't teaching people where the semicolon goes; we are teaching them how our tool fits into their "Enterprise Agentic Memory".

The Disaggregation of the Role

DevRel isn't disappearing; it’s being absorbed into the very fabric of the product.

Builders need us to provide high-integrity digital twins of our systems.
Technical Leaders need us to prove scalability and governance—the "Final Boss" of enterprise adoption.
Domain Operators need us to translate technical capabilities into resilient, real-world systems, like an "Agile Harvest" for agriculture or a "Sovereign Vault" for data.

The Verdict

If your DevRel strategy is still measured by the number of blog posts published, you’re trailing the curve. In the AI era, we are the Context Engineers. We provide the structured reality that prevents AI agents from hallucinating our product’s value.

What do you think?

Is your team still focused on "Developer" outreach, or have you pivoted to "Builder" relations? I’d love to hear how you're retooling your advocacy for 2026.

Your Launch Post Got 4 Likes. Your Product Deserved Better

Wed, 01 Apr 2026 20:34:43 +0000

I will intro this article with my experience, but retold.

You've spent six weeks building something real. You merged the final PR at 11pm on a Thursday. You pushed to production. You watched the deployment logs scroll clean. And then you did what every builder does: you opened Twitter, typed something like "Just shipped [thing]. Super excited to share this with everyone ", hit post, and went to bed.

You woke up to four likes. Two of them were your teammates.

The product was solid. The problem it solved was real. But the post? The post was invisible.

Here's the thing nobody tells you when you're deep in the build:

>shipping is only half the work.

The other half is making people care. And most technical founders, developers, and DevRel professionals are running that half on empty.

The Gap Between Building and Being Seen

There's a particular kind of frustration that lives in technical communities. This is the frustration of people who are genuinely doing interesting things and can't seem to get traction on any of it.

It's not imposter syndrome. It's just a distribution problem.

The builders who get seen aren't always the ones building better things, sadly. They're just the ones better at translating what they build into content that lands. Content that makes someone stop mid-scroll and think "wait, this is exactly my problem," or "this is a painpoint I have."

That translation layer is what most technical people skip, rush, or outsource badly.

A 2024 State of DevRel report found that content creation consistently ranks as one of the top three time drains for developer advocates. This is not because they don't know what to write, but because the gap between "having something worth saying" and "saying it in a way that resonates" is a lot wider than most people expect.

For founders, it's worse. You're building, selling, hiring, and doing customer calls, and somewhere in that schedule, you're supposed to be producing thought leadership content that grows your personal brand and drives top-of-funnel awareness. It rarely happens at the level it should.

Why Your Regular AI Doesn't Work

The obvious answer is AI. You paste your notes into ChatGPT, ask it to write a LinkedIn post, and get something back that technically covers the topic. You post it but nothing happens-- no traction.

It wasn't that the output was wrong. It was just generic. And generic content in technical communities doesn't just underperform, it actually actively damages credibility.

Developers, content folks and DevRel professionals are some of the most discerning readers on the internet. They can spot templated, buzzword-heavy content in seconds. The moment a post opens with "In today's fast-paced digital landscape" or promises to "delve into the nuances" of anything, it's already dead on arrival.

The problem isn't that AI tools can't write. It's just that most of them default to the statistical mean of their training data, which is saturated with corporate documentation, SEO copy, and marketing fluff. The output sounds like everybody. It sounds like nobody in particular.

What is needed isn't just generated content. You need generated content that sounds like you. That is, content written with your specific technical depth, your actual voice, your real opinion.

Tools like Ozigi approach this differently. Instead of asking the AI to "write professionally" (a soft suggestion it ignores), Ozigi enforces a hard blocklist of AI-default vocabulary at the API level (words like delve, robust, seamlessly, tapestry ) forcing the model to construct sentences from your actual content rather than padding with filler. The output reads less like a press release and more like a Slack message from someone who actually built the thing. You can read exactly how that system works in the Banned Lexicon deep dive.

But the tool is only part of the answer. The bigger problem is structural.

The Real Reason Your Content Isn't Working

Most builders (like me, before) treat content like a release: something that happens once, at the end, when the thing is done.

That mental model is the root cause of most distribution failure.

Content that builds an audience doesn't work like product launches. It works like compounding interest. A single post doesn't build a following. A consistent body of work does. A consistent posting habit that over time signals to your audience that you're a reliable source of something worth reading.

The builders who seem to "go viral" on X or LinkedIn aren't getting lucky. They've usually been shipping content consistently for long enough that when one post breaks through, there's a body of work behind it that converts interest into followers, followers into readers, and readers into users.

So the real question isn't "how do I write a better launch post?"

It's "how do I build a content system I can actually sustain?"

What a Sustainable Technical Content System Looks Like

Here's the framework. It's not complicated, but it requires treating content like an engineering problem — which, if you're reading this, is probably how you think best anyway.

1. Raw material is everywhere. Stop waiting for inspiration.

Every week you're producing more content-worthy material than you realize:

PRs you merged and the decisions behind them
A bug that took you three hours to track down
A meeting where a customer said something that reframed how you think about the product
A library you tried that didn't work the way the docs said it would
An architectural decision you almost made and didn't

None of this requires you to sit down and think of something to write about. It requires you to notice that what's already happening in your work is interesting to other people.

The shift is from treating content creation as a separate creative task to treating it as a documentation habit. You're already doing the work. You just need a system to capture it.

Ozigi is built around this principle. You drop in a URL, a block of raw notes, even a PDF, an audio, transcript, basically any piece of information you have at your disposal, and the engine extracts the narrative structure without you needing to summarize or clean it first. That's what the multimodal ingestion pipeline is built to do: collapse the friction between "I have something worth saying" and "I have a draft worth editing" down to seconds.

2. Platform matters more than most people think.

A LinkedIn post and an X thread about the same topic are not the same content. They're different formats, different reader expectations, different hooks, different lengths.

LinkedIn readers expect context and narrative. They'll read three paragraphs before deciding if they care. X readers decide in one sentence, often the first one. Discord announcements need to be skimmable. Newsletters can go long, but they need a reason to exist beyond "here's what I built."

Most people write one thing and paste it across platforms unchanged. The format stays the same but engagement falls because the content doesn't match where it's landing.

A proper content system produces platform-native output from the same source material. Your one insight: the rate-limiting decision, the architecture tradeoff, the customer discovery finding, etc, becomes a thread on X, a narrative on LinkedIn, a community update in Discord or Slack, and a newsletter deep-dive. Each piece formatted for the expectations of its audience, not copy-pasted from each other.

3. Your voice is the most important part of your content.

Anyone can write about Next.js caching. Anyone can explain what a webhook is. But only you can explain those things with your specific perspective, your specific context, the way you'd describe it to a colleague over lunch.

That voice — built over hundreds of posts — is what makes people follow you and not just the topic. It's what turns a reader into someone who shows up every time you post because they trust it'll be worth their time.

That voice is also what AI strips out by default. The generic output problem isn't just an aesthetics issue. Every time you publish something that sounds like it came from a template, you're forfeiting the one thing that can't be replicated: the specific way you think about something.

This is why Ozigi's System Personas go beyond setting a "tone." Instead of prompting "write professionally," you define a character: your technical depth, your sentence rhythm, the phrases you actually use, the things you'd never say. That brief gets applied to every generated content, which means every draft is already shaped like you before you touch the edit button.

4. The 10% rule: the tool gets you 90, you own the rest.

The honest truth about AI-assisted content is that any decent engine can get you to 90% you need to get started. The last 10% is yours, and it's the part that actually matters.

That 90% is structure, platform formatting, tone calibration, cutting the filler. Generative AI can handle that by default.

The 10% is "the specific number from your metrics dashboard" or that inside joke the AI doesn't know about, the anecdote from your last customer call, or the offhand observation that only makes sense if you know your history with this problem. The exact phrasing you'd use if you were explaining this to a friend at 11pm.

That 10% is what makes content trustworthy. It's what makes someone share it instead of just scrolling past it. And it's irreplaceable because it comes from actually having done the thing.

The mistake most people make with AI writing tools is expecting the full 100%. When the output is 90% of the way there, they feel cheated.

The better mental model to have is:

>you're not outsourcing the writing. You're outsourcing the blank page.

Ozigi's editing layer is built around exactly this split. Every campaign lands in a staging area — nothing goes live until you've reviewed it. The human-in-the-loop architecture keeps generation and publishing strictly separate, so you're always the last step before your content reaches your audience.

The Compounding Effect Nobody Talks About

Here's what happens when you run a consistent content system for six months:

Your posts start referencing each other. Your audience starts anticipating what you'll say next. When you ship something new, you have enough readers that the launch post gets signal on day one, which means it gets distributed further, which means more people see it.

So, getting four likes on your launch post isn't a content quality problem. The problem is a lack of consistency. What it looks like is you posting into a vacuum because you hadn't been posting consistently enough to have an audience ready when it mattered.

The builders who seem to "have an audience already" when they ship something new didn't get lucky.
I know a founder on X who did a 100 day post on X challange before his product launch. He climbed to $500 in sales in the first week. He already had an audience.
He paid the consistency debt early. He posted about the messy in-progress version, the failed experiments, the decisions he made and unmade. By the time he shipped, the audience was already there.

Content marketing for technical audiences is a long game. The best time to start was six months ago. The second-best time is right now with a system that makes it sustainable enough to actually keep going.

Start Small. Ship Consistently.

You don't need to produce ten pieces of content a week. You don't need a content calendar with color-coded categories and quarterly themes.

You need one piece of content per week that comes from something you actually did, written in a voice that sounds like you, distributed to the platforms where your audience actually is.

That's the whole system.

The tools exist to make it easier. The only thing without a shortcut is starting.

If you're a technical founder, developer, or DevRel professional trying to build a consistent content presence without it eating your calendar — Ozigi is worth trying. The free tier gives you 5 campaigns a month. Drop in your raw notes from last week, see what comes out, and decide from there. Get one week of Pro free when you sign up today!

→ Try Ozigi free · Read the platform docs · See the architecture deep dives · Star on GitHub

Have a content system that's actually working for you? Or a launch post that flopped spectacularly and taught you something? Drop it in the comments — genuinely curious what patterns people are seeing.

Theory Ventures has more engineers than investors. Here's why that matters for dev tools.

Mon, 29 Sep 2025 23:19:19 +0000

Why Theory Ventures Built a Developer Relations Team—And What It Means for Developers

Most venture capital firms treat developers as an afterthought. They chase enterprise deals, court C-suite executives, and make investment decisions based on spreadsheets and market projections. Theory Ventures does the opposite.

The $680M AI and data fund runs more software engineers than investors—four technologists to three investors. And they're not just for show. These engineers build agents, run hackathons, and debug tool calls alongside the developers at their portfolio companies.

"We believe a deep technical understanding helps us invest better," says Tomasz Tunguz, Theory's founder and a veteran of enterprise software investing. "We're playing around with the technology and using it in deployment. That gives us a sense for where the market is going."

This isn't marketing. Theory's head of AI was hired with a specific mandate: reimagine a venture capital firm running 1,000 agents every day. The team deploys MCPs, uses GEPA and DSP, and hosts quarterly internal hackathons. They live in the same environment you do.

Developers Don't Pull Punches

Tunguz knows developer feedback differs from traditional enterprise customer feedback. "Brutally honest," he says. "Developers pull no punches."

You can't sell developers on buzzwords or polished demos. They want to know which keyboard shortcuts you use and whether you've wrestled with Python environment problems. They spot fake expertise instantly.

"Being able to walk the walk is really important," Tunguz explains. "If we aim to invest in developer tooling and AI systems, we want to learn alongside developers implementing internally, just the way they implement internally."

Theory measures their developer relations success through engagement metrics—events hosted, positive feedback, community involvement. Like GitHub stars, these indicators sit a degree or two away from financial performance. But they matter because attention has become the scarcest resource in tech.

"With AI synthesizing everything, the most important thing to differentiate and attract attention is being genuine," Tunguz says.

What Developers Are Actually Struggling With

Theory's portfolio companies give them direct insight into what's actually hard about building with AI. Tool calling tops the list.

"Figuring out why an AI decides to call which tool when and why it doesn't consistently select to use that tool; that's one of the most challenging tasks developers face," Tunguz says.

The architecture question compounds the problem. Should a three-part tool run as a hardcoded script, or should the AI make intermediate decisions? When accuracy drops to 75-80% across three steps, end-to-end reliability falls below 60%.

Code generation works brilliantly now. Tunguz can define a task, let an agent work for two or three hours, and get successful results. The whole job has shifted to architecture. "It's not a line of code that I'll be writing maybe ever again," he says.

The second big shift: developers don't leave their IDE anymore. No more tab-switching to Stack Overflow. Everything happens in a single flow state, with context-aware help right in the terminal.

How Investment Criteria Is Changing

AI moves faster than any market Tunguz has seen. "You wake up and the world has changed. There's some new academic paper, some new architecture, some Chinese company that has distilled an American model."

The fastest way to lose money in venture capital used to be investing in an IDE. Nobody except IntelliJ ever made money on one. Now agentic coding companies are the fastest-growing investments Theory tracks.

"The IDE may be less valuable than it once was because most of the software is being automated anyway," Tunguz observes. Cloud Code recognized this early by focusing on the terminal instead of panels and intermediate state views.

The biggest pattern Tunguz sees in successful AI startups: AI provisioning the product directly. When Databricks acquired Neon, 75% of new Neon instances were being generated by AI. An application fires up a Postgres instance on Superbase or Neon automatically. No human involved.

Developers Are More Important Than Ever

AI hasn't diminished the developer's role in enterprise buying decisions. It's amplified it.

Contract sizes are increasing. Budget decisions now span both software and labor costs. "Should we hire this AI agent instead of hiring 10 new software engineers?" Tunguz asks. "The developer is the person best equipped to determine whether the agent can perform that level of work."

Theory invests in tools that grow from communities. Heroku grew from Rails. Supabase from TypeScript. Understanding the momentum behind these communities helps predict how broadly a tool will be adopted.

The next 18 months will bring opportunities in AI site reliability engineering, security automation and review, and education for software engineers and marketing engineers. But the biggest opportunity: staff-level software engineers that are actually AI.

"We're not there yet," Tunguz admits. "Junior developer work from an AI? You can get there. Staff level or senior architects? Still a ways to go."

Theory Ventures proves that a VC firm can walk the walk with developers. They're building with the same tools, hitting the same problems, and staying brutally honest about what works and what doesn't.

That's the kind of investor developers deserve.

The DevRel Advantage: How Developer Relations Drives Business Growth

Mon, 17 Mar 2025 11:20:26 +0000

The DevRel Advantage: How Developer Relations Drives Business Growth

In today's tech landscape, developers have become the new gatekeepers. They evaluate, test, and champion the tools their companies adopt. This shift has made Developer Relations (DevRel) not just a nice-to-have but a strategic imperative for tech companies looking to grow.

But why exactly are successful companies investing in DevRel? The answer goes beyond marketing hype to tangible business benefits that impact the bottom line. This analysis builds on my previous exploration of The Developer Relations Imperative to focus specifically on the business value.

Three Key Business Benefits of DevRel

Accelerated Product Adoption

When developers encounter a new tool, their first question isn't "What does your sales deck say?" but "How quickly can I make this work?" Companies with strong DevRel programs remove friction from this process.

Stripe demonstrates this perfectly. Their documentation is so clear and comprehensive that developers can process their first payment in minutes, not days. This acceleration from discovery to implementation doesn't just satisfy developers—it reduces time-to-value, driving faster adoption across organizations.

Lower Customer Acquisition Costs

Developers are connected. They share discoveries, recommendations, and warnings across Slack channels, forums, and social media. When they love a product, they become its most authentic advocates.

Twilio built its growth strategy around this principle. By investing in community engagement rather than traditional marketing, they created a network of developers who organically promoted their product. The result? Acquisition costs significantly lower than industry averages and growth that outpaced competitors.

Higher Customer Retention

It's not just about getting developers to try your product—it's about keeping them. DevRel teams create support systems and communities that make developers successful, increasing their investment in your ecosystem.

MongoDB recognized this early. Their MongoDB University, comprehensive documentation, and active community forums created an environment where developers could grow from novice to expert, all while becoming increasingly tied to MongoDB's ecosystem. This approach delivered retention rates that directly impacted their recurring revenue.

Measuring DevRel Success

While DevRel impacts many business metrics, two stand out as particularly valuable indicators:

Time-to-first-success measures how quickly new users achieve their first meaningful outcome with your product. Improvements here directly correlate with conversion rates and early satisfaction.

Community engagement growth tracks active participation in your developer ecosystem. Rising numbers here predict future adoption and retention better than many traditional marketing metrics.

Quick-Start DevRel Strategy

Different company stages require different DevRel approaches:

Startups: Focus on documentation excellence. Before scaling community efforts, ensure developers can easily understand and implement your product. A great README beats a flashy developer portal.
Mid-size companies: Build community spaces and programs. Create forums, discussion channels, and events where developers can connect with each other and your team. Champion user success stories.
Enterprise organizations: Connect DevRel insights to product roadmap. Ensure developer feedback directly influences product decisions, creating a virtuous cycle of improvement.

The DevRel Advantage

In markets where technical capabilities look similar on paper, the developer experience becomes the differentiator. Companies that invest in DevRel aren't just funding another marketing channel—they're building a competitive advantage that competitors can't easily replicate.

Take a critical look at your own developer experience. How quickly can new users achieve success? Where do they get stuck? What community exists around your product? The answers will tell you where DevRel investment could drive your next phase of growth.

This analysis is part of my exploration of Developer Relations through my Developer Experience mentorship program. For a deeper dive into the pillars and branches of DevRel, check out my previous publication on The Developer Relations Imperative.