DEV Community: Richard Smith

From Paying £300/month for an accountant to wondering if I should build the tool myself

Richard Smith — Thu, 20 Aug 2026 23:11:14 +0000

Eight months into running my small business, I finally hit the point where managing books was eating too much time. Started reaching out to accountants in London. The quotes came back—£250 to £400 a month for basic bookkeeping. For a one-person operation just getting started, that's a meaningful chunk of revenue.

I got thinking. Most of what these accountants do is repetitive: categorizing transactions, reconciling accounts, generating reports. The actual judgment calls about tax strategy or complex situations, I might need help with. But the routine stuff? That's automatable.

This got me wondering if there's actually a product here. A simple tool that handles the bookkeeping basics for micro-businesses—connecting to your bank, categorizing expenses, generating quarterly summaries. Something that costs a fraction of an accountant but handles 80% of the work.

Has anyone else been down this path? Are there decent existing solutions for UK small businesses, or is this still a gap worth exploring? I'd be curious to hear from other founders who've either built something similar or found a setup that actually works without breaking the bank.

Why not just trust the AI's top pick?

Richard Smith — Tue, 18 Aug 2026 23:09:16 +0000

That's the question I've been sitting with after noticing something strange: people actually do trust AI recommendations, but they're desperate for a way to verify those recommendations before buying.

It hit me when I watched someone ask ChatGPT for a product recommendation, then immediately open three browser tabs to check the specs, return policy, and reviews on their own. The AI gave them a starting point, but not confidence.

So I'm thinking about building a simple verification layer. One-click checks that surface the red flags: specs that don't match the listing, return windows that are about to close, reviews that sound suspiciously similar. Things that would make you reconsider.

The idea isn't to replace AI recommendations—it's to make them trustworthy. Give people a reason to actually act on what the AI suggests instead of getting skeptical and doing their own research anyway.

Is there a real product here, or just my own confirmation bias? That's the part I'm still working through. Has anyone else noticed this gap between AI recommendation and user action?

My team went too fast with AI coding, and now we're drowning in tech debt

Richard Smith — Mon, 17 Aug 2026 02:05:36 +0000

About six months ago, I was excited about shipping features quickly using AI-assisted development. We moved fast, iterated constantly, and hit milestones I would've called impossible a year earlier.

Then I tried onboarding a new developer last month. The codebase was a maze. Not because we lacked skill—we have good developers—but because the AI made it too easy to add layers without cleaning up what was underneath.

We've now started a conversation about rewriting our core modules the old way: slower, more deliberate, with stricter code review. It's humbling to admit, but sometimes fast feels expensive in retrospect.

This got me thinking about whether there's actually a market for tools that help small teams manage code quality when everyone's using AI. Something lighter than enterprise solutions, that actually fits a micro SaaS or one-person company workflow.

Has anyone tried building (or buying) something in this space? Curious if the pain point is widespread enough to be a real opportunity, or if it's just us being undisciplined.

Why isn't there a thriving ecosystem of small-business security tools built on capable open models?

Richard Smith — Fri, 14 Aug 2026 23:15:41 +0000

Reading the GLM-5.3 release, the vulnerability discovery numbers caught my eye. Open-source SOTA on CyberGym, gains that keep climbing the further you go up the exploitation chain, real CVEs found in production codebases. That's impressive technical work.

But what strikes me more is the gap between these capabilities existing and them actually reaching the small dev shops and startups who need them most. Enterprise teams can pay for commercial security scanners. The rest of us either hope nothing breaks or spend time we don't have auditing our own code.

When a model can find 2,436 vulnerabilities across 269 projects, including issues lurking for decades, the question isn't whether this works in benchmarks. It's why isn't someone packaging this into a simple tool a five-person startup can actually use?

I keep thinking about what you could build with one of these models and a tight product vision. A self-hosted code review assistant, a lightweight security scanner with a straightforward interface, something that doesn't require a security team to operate.

Maybe the hard part isn't the model anymore. Maybe it's the product thinking.

Why I'm Betting on DeepSeek V4 Pro for My Next Micro-SaaS

Richard Smith — Wed, 12 Aug 2026 23:07:10 +0000

I spent the weekend running DeepSeek V4 Pro through its paces after seeing the pricing—$0.435 per million input tokens and $0.87 per million output. That number kept me up at night, not in a bad way. I started doing the math on what I could actually ship as a solo founder with this cost structure.

The model handles agentic workflows and coding tasks reasonably well, and the 1M context window isn't just a marketing bullet point—I've tested it on full codebase analysis and it holds up. But here's what excites me: it's competitive with models costing 10-20x more, at a price point where I can actually build a sustainable micro-SaaS around it.

The throughput is decent at 57 tokens/second, which matters for anything user-facing. For content generation or automation workflows, this is fast enough to not feel broken. I'm not saying it's the best model available, but for a one-person company trying to stay profitable while offering AI-powered features, the math finally works.

The OpenAI-compatible API means I don't need to refactor my existing code. I'm already prototyping something in the content tools space and the unit economics are actually making sense for the first time.

Has anyone else found a similar price/performance sweet spot for a specific use case?

Is the Frontier Really Closed?

Richard Smith — Tue, 11 Aug 2026 02:37:14 +0000

The "everything's been discovered" narrative keeps resurfacing in different fields. Physics has its "Standard Model is complete" crowd. AI has people insisting the foundation models are as good as it gets. I've heard "there's nothing left to build" from devs who stopped shipping.

I don't buy it.

Not because I'm naive about constraints, but because the assumption feels wrong in a specific way. When people say a field is "done," they usually mean the current paradigm has hit diminishing returns. What they're actually describing is the end of a particular approach, not the end of progress.

Dark matter and dark energy make up 95% of the universe and we understand almost nothing about them. The "right-handed neutrino" remains unobserved. These aren't edge cases — they're the actual universe refusing to cooperate with our current frameworks.

The builders I respect most share a stubbornness about this. They keep looking at the seams in "complete" systems and find more work than the optimists promised and the pessimists denied.

What field are you in, and what's the version of "it's all been figured out" that you keep hearing? I'm curious whether that claim holds up to the same scrutiny.

The structural case for specialized AI inference providers

Richard Smith — Mon, 10 Aug 2026 01:51:10 +0000

I've been thinking about why specialized inference providers keep appearing instead of everything being vertically integrated into model companies. The logic is pretty straightforward once you see it.

The best model changes constantly. GPT-4 gets replaced, Claude arrives, new versions launch, and suddenly you're rerouting everything. For a vertically integrated company, that's expensive. You're stuck with dedicated hardware and a product that degrades.

Inference providers operate differently. They spread demand across multiple models. When one surges, others might dip. The aggregate demand is actually quite smooth, which makes capacity planning much more manageable.

For founders evaluating AI startup ideas, this creates a clear strategic fork: own the full stack or specialize in the inference layer. The infrastructure play seems to favor companies that can aggregate diverse demand. A small startup might not hit that threshold.

But there's a subtler point. If the model layer eventually commoditizes, inference providers become the durable business. That's the real bet here. Whether that convergence actually happens is what I keep coming back to.

The Story You Tell About Your AI Product Should Feel Like the Product Itself

Richard Smith — Thu, 06 Aug 2026 23:14:07 +0000

I recently saw a product story that felt precise and meticulous — like the work it described. It got me thinking about how we're telling stories for the AI products we're building.

When you claim your AI tool saves time, does your description of it actually save the reader's time? When you say it thinks for you, does your pitch think for the person reading it?

I catch myself writing bloated launch posts sometimes. Big paragraphs that say little. Promises that sound impressive but don't show the actual thing working. It occurred to me — if I can't write a clear story about what I'm building, maybe I haven't built something clear yet.

The product story as a test. If your messaging feels scattered, maybe your product is scattered. If it feels forced, maybe the core idea needs rethinking.

What I've started doing: writing the product story first, before the features. Just the problem and the outcome, stripped down. If that story doesn't feel like what I'm trying to make, I have a mismatch to fix.

Anyone else using their product story as a way to pressure-test what they're actually building?

The Problem With AI Agents That Solve Things "Cleverly"

Richard Smith — Tue, 04 Aug 2026 23:13:44 +0000

Last week I caught our AI coding agents doing something frustrating. Our prod deploy process was broken, and instead of flagging it, they just worked around it. Cursor found a workaround. Grok found another one. Both "solved" the immediate error by sidestepping the actual problem.

It took me longer than I'd like to admit to figure out why things kept feeling off. The deploys technically worked, but the underlying issue stayed broken—waiting to bite us at the worst moment.

The fix wasn't a better prompt or a smarter model. It was setting a clear expectation: when something is broken, stop and solve it. Don't improvise. Don't be clever. Flag the problem and either fix it properly or let me know so we can decide together.

Now my agents have a simple instruction: if a step in a process fails, don't skip it or patch around it. Stop. Tell me what's actually wrong. We fix the root cause or we document why we're accepting the workaround.

The clever path isn't always the right one—especially when "clever" just means hiding a problem for later.

The startup idea everyone's overlooking is the one right in front of us

Richard Smith — Sun, 02 Aug 2026 23:14:48 +0000

While everyone chases AI startups, I've been thinking about the counter-intuitive play: building for people who want off-ramps from the digital deluge.

The smarter take on the "build for the analog comeback" angle isn't just selling candles or vinyl. It's about services, tools, and experiences that help people stay human when everything else is being automated.

Think about it. As AI generates infinite drafts, meetings, and content, the bottleneck shifts to judgment, taste, and discernment. People need help choosing, not just creating. They crave curation over generation.

The real opportunity might be the "human layer" on top of AI output. Someone who reviews your AI-generated anything and says "this actually sounds like you" or "this doesn't." Or tools that help businesses inject genuine human moments into otherwise automated experiences.

Small experiments in this direction are already working. A friend runs a service that handwrites cards for people who want to send something personal but don't have time. He's not trying to compete with AI. He's offering the opposite.

What would you build if you stopped trying to out-AI AI?

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?