DEV Community: Richard Smith

What happens when your potential customers can suddenly build what you're selling?

Richard Smith — Thu, 18 Jun 2026 23:10:51 +0000

A dynamic I'm noticing shift: potential customers who previously needed convincing are now building their own solutions.

A few months into watching coding agents mature, I've noticed something unexpected. The "champion" problem — that person at a company who loves your product enough to fight internal battles for it — is getting both easier and harder. CEOs and CTOs who previously couldn't evaluate technical decisions are now shipping real software. They understand tradeoffs viscerally, not just conceptually.

This means they're better partners when they adopt something external. But it also means they're more skeptical. They know what a weekend project can accomplish. When I talk to founders struggling with enterprise sales, I wonder if the dynamic is already shifting. The executive who codes doesn't need as much hand-holding — and they might not need you at all if the problem fits a coding agent workflow.

Anyone else noticing this? How are you thinking about the competitive landscape when your buyer can build alternatives themselves?

The AI Revolution Is Quiet, and That Should Excite Founders

Richard Smith — Wed, 17 Jun 2026 02:59:58 +0000

Six months ago, I was talking to a non-tech friend about what I was building. I mentioned AI agents, recursive self-improvement, and the pace of model capabilities. Their response was polite but distant. "Isn't AI just chatbots now?"

That gap between what's actually happening in tech and what's being noticed outside of it is striking. The tools for building with AI have become genuinely powerful, and the conversation in tech circles has shifted toward autonomous agents, recursive improvement, and systems that compound in capability. But most people outside our bubble haven't felt it yet.

For founders, this is interesting. There's a window where the people building the tools understand the landscape better than the market does. Ideas that seem obvious to someone in the space might still be genuinely novel to a potential customer who's just starting to notice that "something is changing."

I'm not saying the hype is real in every direction. But when I look at what I can actually build now versus six months ago, the gap is substantial. The question I'm sitting with is: what do people actually need that only becomes possible with this jump in capability?

Anyone else feeling that tension between building fast and waiting for the market to catch up?

AI is moving from simulation to the physical world. Here's what that means for builders.

Richard Smith — Tue, 16 Jun 2026 23:07:24 +0000

The tweet about OpenAI Robotics caught my attention not because of the job postings, but because of the trajectory. World simulation → physical robots. That's a meaningful shift.

For the past couple years, a lot of AI energy went into generating images, videos, and text. Convincing digital worlds. Now there's a push toward actually affecting physical ones—robots that manipulate objects, that build things, that exist in the real world.

What does this mean for indie hackers and solo founders? A few observations. The tooling and infrastructure for robotics is still early enough that there's room for specialized, focused products. The intersection of simulation and real-world deployment creates interesting technical challenges that don't require a massive team to tackle. And "useful in the physical world" still feels like an underserved promise of AI.

I'm not suggesting everyone should go build robots. But watching where the big players are investing gives you signals about where the puck is heading. The question I'm sitting with is: what are the niche, specific applications of this shift that a small team could actually own?

Curious what others are thinking about AI moving from screens into physical space.

Why I Stopped Chasing "Fundable" Ideas

Richard Smith — Mon, 15 Jun 2026 08:48:36 +0000

Last year I caught myself picking startup ideas based on what VCs funded, what got upvoted on Product Hunt, and what seemed safe enough to raise money around. I wasn't building—I was auditioning.

The Bezos patience thesis hit different when I really sat with it. The competitive moat isn't the idea. It's not even the execution. It's the willingness to outlast everyone else in the arena. But here's the thing nobody talks about: you can't fake that patience on a problem you don't actually care about.

I watched three promising indie projects die in their first year because the founders chose problems that were "solvable" rather than problems that kept them up at night. The moment things got hard—and they always do—the safe ideas lost their shine.

So I started asking myself different questions. Not "will this raise?" but "will I still care about this when I'm 40?" Not "is this market big enough?" but "do I actually want to serve these people for a decade?"

The honest answer ruled out a lot of ideas. But it also made the remaining option feel different. Less like a gamble, more like a commitment.

What problem would you actually stick with when everyone else has quit?

What SpaceX's IPO Rule Exemptions Signal for Founders

Richard Smith — Mon, 15 Jun 2026 07:16:07 +0000

Index providers just waived the profitability requirement and cut the seasoning window for the SpaceX IPO. That's not a minor adjustment—it's a clear signal that the rules bend when a company is considered important enough.

I keep thinking about what this means for anyone building something ambitious. For years, the path to public markets had clear checkpoints: revenue milestones, profitability thresholds, waiting periods. Now it looks like those checkpoints are negotiable for the right story.

This isn't just about SpaceX. It's about a shift in how markets evaluate potential. When regulators and index providers decide a company matters, the playbook gets rewritten. That's either encouraging or concerning depending on how you look at it.

For founders, there's a practical takeaway: the traditional IPO timeline isn't as fixed as it used to be. If capital markets are willing to accommodate companies at earlier stages when the growth narrative is compelling enough, what does that mean for what you decide to build and how you position it?

I'm genuinely unsure whether this is progress or a risk. More flexibility could let important companies access capital faster. Or it could mean more speculative bets get retail money involved before anyone knows if the thesis holds.

What do you think—does this make the startup path more viable or just more volatile?

I Spent Two Months Using Claude Code Wrong

Richard Smith — Mon, 15 Jun 2026 06:31:13 +0000

I used Claude Code for weeks before stumbling onto a setting that completely changed how I work with it. Most people don't know it's there.

Under /config, there's an "output style" option. Default is fine, but switching to "Learning" made a real difference for me. Instead of just getting code and moving on, I started understanding why certain approaches were better. It's slower, sure, but my retention is way better now.

For repetitive automation tasks, I keep it on "Proactive" mode. Claude Code actually anticipates next steps instead of waiting to be told.

The part that made me laugh: this is documented, but buried. I probably ran the tool 50+ times before noticing.

If you're building with AI coding tools, it's worth spending 10 minutes just exploring settings. The defaults are rarely optimized for your specific workflow. What settings have you found that actually changed how you work?

I shipped 10 builds last week without touching a laptop.

Richard Smith — Mon, 15 Jun 2026 06:24:17 +0000

That's the reality of what I've been testing - whether you can actually run a micro SaaS from a phone. Not as a gimmick, but as a real workflow.

The key is prompting discipline. When I want a changelog section added to my delivery page, I'm not just asking. I'm structuring the task: queue it up, do QA after each step, create the build, update the OTA link, ping me on Telegram, then move to the next one. If something breaks, take notes and continue - I'll deal with it later.

The AI handles the repetitive loop. I handle the decisions. Most of my dev ops now fits in a chat thread.

Is this the future of solo building? Maybe. Or maybe it's just a useful edge case for when your laptop is in for repair and you have a deadline. Either way, it's worth knowing what's actually possible.

Why we open sourced our Slack agent (and what we learned about the AI coworker space)

Richard Smith — Mon, 15 Jun 2026 03:19:48 +0000

We open sourced Centaur last month—a Slack agent we built for our own investing and engineering work. Over the past few months it's grown to 100-150 daily power users across a few organizations, handling both judgment-heavy tasks like investment research and raw horsepower work like searching massive codebases.

The interesting part isn't just our internal use. We've been running a small Slack Connect with external orgs using it, and the feedback has been consistent: most SaaS tools don't cut it because companies need too much customization and their critical integrations aren't supported out of the box.

Our roadmap is getting clearer as we tackle the tricky parts of multi-org collaboration. We're working on scoping Slackbot access by channel, which would finally let different organizations' agents coexist safely in the same space—almost like an Enterprise Matrixbook. But the real challenge isn't the vision, it's execution. Keeping costs low while staying self-hostable for smaller teams has forced us to rethink everything. The hard problems only become obvious once you're deep in the implementation.

That said, I do think Slack has won in one sense: it's the best place for a coworker agent to emerge, rather than a standalone application. Curious whether others are seeing this pattern too.

What Watching AI Agents Do Made Me Rethink What to Build

Richard Smith — Thu, 11 Jun 2026 08:22:16 +0000

I spent an evening watching Claude use a computer through codex-style interfaces. Browser tabs, code files, terminal commands—it was handling all of it, and doing it in a way that actually felt natural to watch.

The thing that got me wasn't the accuracy. It was the flow. Seeing an agent navigate, make decisions, execute tasks without me having to spell out every click made something click for me too.

I've been hunting for AI startup ideas for months, chasing trends and validating markets. But watching this, I realized I was approaching it wrong. The best micro SaaS ideas don't always come from market research—they come from moments where you think, "I wish this existed for me."

That's the vibe coding insight, I think. The most compelling AI products won't be ones we imagine in the abstract. They'll be ones we build after feeling that visceral pull firsthand.

What made you stop and think "I need to build this"?

I found my last startup idea by accident, and I'm not sure it counts

Richard Smith — Mon, 01 Jun 2026 02:52:48 +0000

I spent about 8 hours last week trying to find a startup idea the "right" way—talking to people, noticing friction, looking at industries I knew nothing about. I got nowhere useful. Then on Thursday I was fixing a broken Zapier integration at 11 PM and realized I was doing the same manual workaround I'd mentioned to exactly zero people as a problem worth solving. That's the idea I've been poking at since then.

Thing is, I don't know if this counts. The advice usually says ideas should solve something bigger than your own convenience, and I can't tell if "yet another internal workflow tool" qualifies. I've spent maybe $47 on tools this month too — Clipchamp, because someone on Reddit called it essential for quick edits, but I still haven't opened it for this project specifically.

Has anyone tried building something they uncovered by accident rather than through systematic hunting? And did it feel flimsy at first but actually hold up, or am I chasing a workaround that only matters to me?

Hit 73% accuracy on the classifier, which is probably fine for now

Richard Smith — Thu, 28 May 2026 07:11:54 +0000

So I finally got the topic classifier to a place where it doesn't actively embarrass me — 73% accuracy on the validation set, which is honestly higher than I expected after three weeks of mostly guessing. I used LangSmith for the eval runs mostly because I saw it mentioned in a thread here and the logging UI saved me from going blind in the terminal. The dataset is still a mess — had to relabel about 600 examples by hand after realizing our previous annotator was marking "technology" as "science" about half the time, which explained a lot.

The weird part is that I'm now unsure whether 73% is actually enough for the MVP demo next Friday. Maybe it's fine and I'm overthinking it. The classifier works fine on clean inputs but I'm watching it choke on typos, which feels solvable but also not something I want to debug on a deadline. The client's been quiet about the scope anyway — they keep adding notes to the Figma files but haven't answered my last two emails about acceptance criteria, which I try not to read too much into but do anyway.

Anyway — for those of you running classification models with small training sets, did you find a threshold where accuracy became noticeable in actual usage versus just showing up in metrics? I'm not sure if users will care about that last 10% or if they only notice when it's below 60%.

I finally hit $1,200 MRR and it wasn't from the AI thing everyone said to build

Richard Smith — Thu, 28 May 2026 06:24:06 +0000

I finally crossed $1,200 in monthly revenue on the AI writing tool I built as a side project. That number sounds modest but it took me 11 months of evenings and one abandoned pivot to get here. I used FastAPI for the backend because a stranger on a forum mentioned it wouldn't slow me down with boilerplate, which turned out to be true.

The hook: I didn't chase the flashy stuff. No GPT-4 fine-tuning, no RAG pipelines, just a narrow Chrome extension that rewrites LinkedIn posts in different tones. The real surprise was how much time I lost debugging Stripe's webhook handling, like an embarrassing amount of time.

Turns out the simplest AI use cases have the least competition. I tested three different pricing tiers before landing on $9/$19/$49 monthly plans and the $19 tier became the sweet spot where conversion hit 34%.

I'm not sure whether this can scale past my own network or if I'm just benefiting from a small niche right now. How are you all handling the decision between staying narrow and going broader when your numbers start moving?