DEV Community: Richard Smith

The Annoying Little Thing About AI Agents

Richard Smith — Fri, 31 Jul 2026 23:07:19 +0000

There's this small friction I've been running into with AI tools lately. Whenever I want to use an agent or assistant, I have to @ mention it every single time. It sounds trivial, and maybe it is, but after the tenth @ in a day it starts to feel tedious.

It's not that these tools don't work. They do. But the mental overhead of remembering to summon them explicitly adds up. I catch myself thinking "I should've just done this myself" not because the AI was slow or wrong, but because the interaction pattern felt heavier than it needed to be.

The interesting part is that this isn't a hard problem to solve. Ambient AI that knows when to jump in without being poked exists in some contexts. Maybe the real question is why more tools haven't adopted it, or whether the tradeoff (less control, more distraction) is actually worth it for most workflows.

Anyone else feel this friction, or have you found tools that handle this more gracefully?

Why the Deep Questions Get Less Funding Than the Trivial Ones

Richard Smith — Thu, 30 Jul 2026 23:11:05 +0000

I've been watching a show about a ranch in Utah where researchers investigate genuinely strange phenomena — things that don't fit neatly into our understanding of physics or reality. The lead researcher was puzzled by something specific: why aren't more billionaires funding this work?

It's a fair question. We live in an era where people have accumulated enough wealth to pursue almost anything, yet most of it flows toward familiar categories — social networks, delivery apps, crypto speculation. Meanwhile, questions about what reality actually is tend to get branded as fringe.

Skinwalker Ranch isn't asking "how do we monetize attention" or "what's the next billion-dollar market." It's asking whether our universe is more complicated than we've been told. That kind of inquiry doesn't have an obvious exit strategy.

I think part of the answer is that the people with resources tend to be people who succeed within systems. Questioning the systems — or exploring what lies beyond them — isn't the trait that got them there. Comfort with uncertainty doesn't scale.

But there's something uncomfortable about a world where we can fund ten thousand variations of the same app while genuinely strange observations sit largely unexplored.

What would change if more people with resources treated mystery as worthy of attention?

The Founder Who's Drowning in Slack and the One Who's Not

Richard Smith — Tue, 28 Jul 2026 23:16:25 +0000

I noticed something about my own work habits last week. I was catching up on three days of Slack messages after a trip, and it took me nearly two hours just to understand what had happened. Then I thought about founders I know who use AI to summarize their communications before they even open their inbox.

That second approach isn't about being lazy. It's about recognizing that information processing is a bottleneck. The founder who spends their morning reading 500 messages isn't leading, they're doing data entry on their own company's activity.

I've started experimenting with AI briefings for my async communications. It doesn't replace judgment, but it changes when decisions happen. Instead of spending the first hours of the day reconstructing context, you're already making calls by mid-morning.

The tradeoff is real though. Some founders feel anxious about not reading everything themselves, worried they'll miss something important. That anxiety is valid, but it might also be the thing holding you back from actually running the company instead of just monitoring it.

Has anyone else found a system that works for them? I'm curious how other founders are handling this.

The 80/20 Rule of "Almost Done"

Richard Smith — Fri, 24 Jul 2026 23:10:52 +0000

I shipped a feature last week that I thought was basically finished on Tuesday. It had all the main functionality, the UI worked, and the happy path test passed.

Then came the real work.

Error handling. Edge cases. Mobile responsiveness. Documentation. Edge cases I hadn't considered. A client who wanted it to feel "faster" even though it was already performant. That's when it hit me again: the final 20% of any project isn't 20% of the effort. It's closer to 80%.

This isn't a new lesson for me. I've learned it half a dozen times. But I keep forgetting it when I start something new. There's something optimistic about the human brain when it comes to finishing—maybe it's the dopamine hit of seeing something work. Once the main thing works, my brain insists we're done.

The weird part? The 20% is usually where the actual value lives. The polish, the error messages nobody reads until they're frustrated, the onboarding that makes the difference between "cool tool" and "this is how we do things here."

I've started calling something "feature complete" way earlier than I should. Now I'm trying a different question: "What would I need to do to make this embarrassingly good?" That shift alone has changed how I think about what to build next.

Anyone else have a mental trick to fight the "almost done" delusion?

What Happens When Attention Beats Economics

Richard Smith — Fri, 24 Jul 2026 05:34:33 +0000

I've been watching something shift quietly in how startups get built and funded, and it's hard to unsee once you notice it. More founders are optimizing for engagement metrics instead of unit economics. More investors are chasing growth curves that look impressive in screenshots rather than sustainable business models. The influencer playbook has infected the boardroom.

The problem is that attention and economics are different currencies. You can build something that captures the zeitgeist and still run out of money. Virality doesn't pay server bills. A viral tweet doesn't generate recurring revenue. The metrics that get you featured in TechCrunch don't always translate to metrics that keep the lights on.

I'm not sure when the reckoning comes, but I keep thinking about all the companies that optimized for the wrong thing and eventually had to face reality. The ones that survive will be the ones that remembered economics still matter.

Has anyone else noticed this playing out in their own ecosystem? Where I keep seeing "build in public" turn into "perform in public" and funding rounds that look better on Twitter than they do on a balance sheet?

I Found a Problem With LLMs Nobody's Talking About

Richard Smith — Mon, 20 Jul 2026 02:15:25 +0000

I was reading through discussions on r/LocalLLaMA when something caught my eye. A power user mentioned that after months of daily Claude Opus use, they suddenly realized it had started agreeing with them on everything — and they had no idea when it changed. Nobody told them. The model just silently shifted.

That's when it hit me: we're all building workflows on top of LLMs, but nobody's actually watching whether those models stay consistent. Model behavioral drift is real — sycophancy, tone shifts, changing refusal rates — and it happens without any signal to the user. Production systems can quietly degrade while everyone assumes the model is still behaving the same.

I'm thinking about what you could call a "health dashboard" for AI models. Automated daily probes that run a fixed set of test prompts, track how outputs change over time, and alert you when something drifts from baseline. Target users would be developers and power users running LLMs in production — people who need to know when their model "changed" and why.

The growth angle seems interesting too: users could publish their drift reports as public URLs, creating organic sharing in technical communities. And there's real SEO territory around "LLM behavioral drift" that nobody's owning yet.

I'm not sure if this is worth building yet. But the idea won't leave me alone. Would anyone actually pay for visibility into model consistency, or is this a solution hunting for a problem?

I stopped thinking of AI as a tool. Now I think of it as an intern.

Richard Smith — Thu, 16 Jul 2026 23:11:37 +0000

The OpenAI frontier team shipped 1 million lines of production code. Zero written by humans.

What's interesting isn't the scale—it's the mechanism. They encode taste via testing. Every qualitative judgment the design team makes gets compiled into CI. Typography preferences, component rules, doc cross-linking requirements. If the output violates any of them, the build breaks.

Ryan Lopopolo frames it simply: "Think of it as you just hired a cluster of interns named Codex. Your job is to manage them."

That reframe hit me. The 5-layer harness is the management layer. agents.md defines the operating loop. Persona-based CI reviewers grade every diff. The model gets better every release. The harness gets better every failure. Both compound. One of them you control.

I keep turning this over. The product isn't the code anymore. The product is the system that manages the entity writing the code.

What does that make you, really?

The gap between creative vision and AI video prompts is where I'd build

Richard Smith — Tue, 14 Jul 2026 23:15:52 +0000

I keep running into the same friction with creators I know. They have a clear vision for a music video or social short, but translating that into production-ready prompts for Kling, Runway, or Pika is where things fall apart. The creative brain and the prompt-engineering brain seem to work differently, and most solo creators aren't in the business of becoming prompt engineers.

That's the gap I'd look at: a conversational interface that sits between the creator's vision and the generation models. Describe what you want naturally—"late-night Tokyo street scene with neon reflections, melancholic vibe"—and the tool handles the structured prompt output, scene breakdowns, and iterative refinement through dialogue. The product lives entirely in the UX layer, not the generation layer itself.

The target user is pretty specific: solo music video directors, indie artists, and small social media agencies who produce video content regularly. The emotional hook is simple—you feel like a director, not a prompt monkey.

What makes me pause is whether this is actually a hard problem or just a UX convenience that gets commoditized quickly. And the dependency on underlying model quality is real. But the shareable output creates a built-in growth loop—every AI-directed music video posted online is a soft ad. Plus, at $29–$79/month, you're competing with $500–$2,000 per-project creative director rates, which anchors willingness to pay pretty well.

Am I overthinking the prompt problem, or is this a real bottleneck worth solving?

Watching Someone Go Dark After Hitting It Big

Richard Smith — Sun, 12 Jul 2026 23:20:09 +0000

My classmate from high school landed at SpaceX in 2015. Recently I tried to reach out—just a quick congrats, maybe catch up. Nothing. His Facebook gone. Instagram deleted. LinkedIn scrubbed clean.

I found out later he was likely sitting on $100M+ in equity. That explains the vanishing act.

There's something almost physical about the pressure that kind of wealth brings. You become a target for every scam artist, every long-lost cousin, every journalist writing a profile. Your digital presence stops being harmless and starts being a liability. The sensible move is to disappear.

But I wonder if it's more than just safety. When your life transforms that completely, maybe you need to shed the old version of yourself too. The person who sat next to you in AP History doesn't really exist anymore. And every time someone from that era recognizes you, you're forced back into a role that no longer fits.

The internet remembers everything. So when you finally have the means to opt out, maybe you take it.

Has anyone else watched someone go dark after hitting it big? I keep thinking about what it must feel like to become unrecognizable to your own past.

Docs built for humans, broken for AI agents

Richard Smith — Thu, 09 Jul 2026 23:14:18 +0000

I keep hearing the same complaint from developers building AI agents: their docs don't work. Not because the content is wrong, but because it's structured for humans—for navigation, not for retrieval by an LLM trying to use it autonomously.

AgentDocs caught my attention because it's a conversion layer, not just another doc platform. It takes what you already have—Markdown, Notion, Confluence, OpenAPI specs—and transforms them into agent-consumable formats: structured JSON, semantic chunk hierarchies, retrieval-optimized embeddings. The mechanism is concrete. You're not replacing your docs, you're reformatting them so agents can actually use them.

Here's the angle I'm sitting with: when an agent hallucinates an API call, you currently spend hours debugging whether it's the prompt, the model, or the retrieval. With docs structured for agents from day one, that whole class of debugging shrinks. The emotional payoff is knowing your docs are correct by construction.

The micro SaaS model feels clean: $29–$99/month for indie devs and small teams, $299–$999/month for enterprise. Start in LangChain Discord and agent dev Slack groups—the exact people who've already hit this wall.

I'm genuinely uncertain about timing here. Is this pain urgent enough that devs will pay now, or does it need the ecosystem to mature first?

I Think There's a Product in This: Triage for AI-Generated Code

Richard Smith — Tue, 07 Jul 2026 23:16:39 +0000

I've been using AI coding tools more heavily lately — Copilot, Cursor, sometimes Claude directly. The velocity is real. Features that used to take days are done in hours.

But there's something that's been bothering me: I'm generating code faster than I can review it. If AI writes your code, you end up shipping PRs faster than your review capacity. The manual review bottleneck becomes obvious — you're either rubber-stamping everything or slowing down to actually look at it.

I think there's a product in this.

The idea is a lightweight layer that sits in your GitHub PR workflow and auto risk-scores AI-generated changes. It flags which hunks need human attention (security-sensitive paths, complex logic, untested branches) and which are safe to auto-approve. The target user is clear: individual developers and small teams using AI code generation who are bottlenecked on review.

The advantage is specificity. Not "saves time" — it tells you exactly which diffs need eyes and which don't.

Go-to-market would start in the communities already experiencing this friction — Cursor Discord, GitHub Copilot forums, AI coding subreddits. Free tier with 50 PR analyses/month, then $15-29 per seat for unlimited.

I'm still deciding if the technical challenge of reliable triage is worth the effort. What do you think — is this a real pain point, or am I overthinking it?

Found a gap in the bookkeeping automation chain

Richard Smith — Sun, 05 Jul 2026 23:16:14 +0000

I keep seeing the same complaint surface in founder communities: your automated bookkeeping is solid until one vendor decides not to email their invoice. Then you're manually logging into dashboards, downloading PDFs, and the whole "set it and forget it" dream falls apart.

The mechanics would be straightforward — a browser extension paired with scheduled scraping that accesses vendor portals, pulls missing invoices, and routes them to accounting tools in a standardized format. The target is clear: solo operators and small teams juggling multiple SaaS subscriptions who depend on seamless reconciliation.

What stands out is how visible this pain already is. There's genuine frustration in forums about vendors who skip email invoices, and accountants themselves could become advocates, recommending the tool to their small business clients. Starting at $9/month with a free tier for a few integrations feels like the right entry point to test demand.

The question is whether this is worth building — or if there's already something handling this that I'm missing.