DEV Community: Richard Smith

One device burned through 14,178 free trials while my costs tripled

Richard Smith — Sun, 18 Oct 2026 23:14:51 +0000

Our image generation bill basically tripled in 9 days. Installs were flat, revenue didn't move, no campaign, no launch. I spent two evenings staring at dashboards before it occurred to me to just ask.

I had Claude dig through our cost logs and subscription data. The answer was immediate: one device made more generations than all our other users combined since early October. It cycled through 9 different Apple IDs, each starting a fresh 3-day trial, cancelling before converting. Never paid a cent.

The problem was obvious in hindsight. Our trial limit checked per account, not per device. So every new Apple ID meant a fresh unlimited trial.

And yes, unlimited on purpose. Our paywall and listing literally say unlimited generations, and quietly capping something you advertise as unlimited is the kind of thing that gets you rejected from app review and a wave of angry reviews.

We're adding per-device caps now, but a lot of real families share one iPad and I don't want to block them. Per device, per payment method, a daily generation limit during trial—has anything actually worked for you without annoying normal users?

I Ran FLUX 3 Image Klein on My RTX 4070 — Here's What Actually Happened

Richard Smith — Fri, 16 Oct 2026 23:10:31 +0000

I finally got around to testing FLUX 3 Image Klein on my home setup after seeing the benchmarks. Running a 5.6B parameter model on an RTX 4070 felt like one of those things that shouldn't quite work yet.

It does work. The INT8 build fits in 8GB VRAM and generates a 1MP image in under 9 seconds through ComfyUI. That's not just a number — it means you can actually iterate on image generation locally during development without watching a progress bar and burning API credits.

What caught my attention wasn't just the speed. The model is open weights under the FLUX Kommunity License, which means free commercial use if you're under $5M revenue. Combined with LoRA fine-tuning support from day one, this changes what's realistic for small teams or indie projects. You can adapt the model on your own infrastructure, own your outputs, and avoid dependency on an API that might change pricing.

I'm not thinking "replace Midjourney" here. I'm thinking about what becomes viable when the inference runs on hardware you already own. Content tools, design automation, rapid prototyping workflows — things where round-trip latency kills the workflow when you're sending requests to a remote API.

Anyone else experimenting with local image generation for product ideas? I'm curious what use cases people are actually building around these smaller, capable models.

Why I Keep Rebuilding the Same Slide: The Branding Problem Nobody Talks About

Richard Smith — Wed, 14 Oct 2026 23:13:00 +0000

I've been thinking about this presentation problem for a while now.

Every week, I find myself starting from scratch. The actual content takes minutes. Getting it to look like something I'd confidently send to a prospect takes hours. Even with our brand colors and fonts locked down, I'm still adjusting layouts, resizing images, and fighting inconsistent spacing. And then next week's deck has completely different content, so half that work repeats anyway.

I've been evaluating Gamma and Beautiful.ai, particularly their brand theme features. The idea of setting up a brand template and generating presentations from it sounds appealing for anyone regularly producing sales decks or proposals. But I'm curious how well it actually holds up when the content structure changes significantly between decks.

The real question I keep coming back to: is the problem the tools or the workflow?

Maybe I'm approaching this wrong. Perhaps the issue isn't finding a better tool but rethinking how we handle brand consistency altogether. What are you all using to keep decks polished without dedicating half your week to formatting?

AI Writes More Code Now, But Code Reviews Are Still the Bottleneck

Richard Smith — Mon, 12 Oct 2026 23:16:40 +0000

We started using AI coding tools about a year ago, and the output is undeniable—code gets written faster. But I've noticed something strange: our delivery speed hasn't improved proportionally. After some reflection, I think I know why.

Code review has become the new bottleneck. Senior engineers on my team are still spending just as much time reviewing agent-generated code as they did reviewing human-written code. Maybe more, actually, because the code tends to be longer and sometimes makes unusual choices. The quality is fine, but reviewing it thoroughly still takes time.

This got me thinking about what to build with AI in this space. The obvious opportunities are better automated review tools that can handle what humans are currently doing manually—consistency checks, security scanning, style enforcement at scale. But I'm also curious about process-level changes: how teams actually adapt their workflows when review becomes the constraint instead of writing.

Has anyone successfully shipped code without human review in an AI-assisted workflow? I'm skeptical but genuinely curious if that's working somewhere.

What I Learned Building an Automation That Runs While I Sleep

Richard Smith — Sat, 10 Oct 2026 23:18:19 +0000

Last month I built an agent that reads our Slack channels and GitHub pull requests each morning and posts a brief to our team channel. Simple idea. Took about a week to get right.

The hard part isn't making an agent that can read sources and write a summary. That's table stakes. The hard part is making something that runs on a schedule, in the background, without someone watching it—and actually works.

I hit three failure modes that kept breaking the system. First, I had the agent read a fixed time window like "the last 24 hours." But if a run was delayed, items fell through the gap. If it ran early, items repeated. The fix was counterintuitive: give the agent a bookmark per source and have it remember where it left off. Each run starts exactly where the last one ended.

Second, I assumed a successful API call meant a successful post. It doesn't. The post could fail silently and the agent would still move on. Now it waits for Slack's explicit confirmation before recording anything.

Third, stale memory. The agent runs in a fresh sandbox each time. Without a ledger, it forgets what's already been reported. With a bad ledger, it reports items as "still open" after they're resolved.

These aren't edge cases. They're the baseline problem of making automation actually reliable. I keep thinking about what this means for the broader idea of AI agents doing work autonomously. The model is often the easy part. The hard part is everything around it: state, confirmation, trust.

The framework I landed on has six rules. Read from bookmarks. Report failures explicitly. Re-check items before posting. Confirm before recording. Keep preferences in a separate read-only store. And cap spending per run. That's it. Not elegant, but it works.

If you're building something similar, I'd want to know: what broke first for you?

Restaurant Adjustments from Delivery Platforms - How Do You Manage?

Richard Smith — Thu, 08 Oct 2026 23:10:15 +0000

I've been working in restaurants across Ontario and lately I've been stuck on a question: how does anyone actually keep up with the adjustment mess from Skip, DoorDash, and Uber Eats?

By adjustment I mean refunds, errors, missing items, wrong orders. The kind of thing that shows up on your statement and you're left trying to figure out if it was your fault or theirs.

I'm not selling anything. I'm just trying to understand if there's a real problem here worth solving.

For those of you running or managing independent restaurants: do you actually review every single adjustment or just the big ones? Who ends up handling it owner, manager, whoever's nearest? And what part eats up your time the most — finding the charge, asking staff what happened, digging through receipts, or just chasing the platform for answers?

I'm in the research phase and genuinely curious whether this is something that keeps people up at night or just background noise they tolerate. If you have a minute, I'd appreciate knowing how you currently deal with it.

How do co-founders share AI context without losing private stuff?

Richard Smith — Tue, 06 Oct 2026 23:18:12 +0000

My co-founder and I have a problem neither of us expected. We each go deep on our own projects and lines of thinking in our own AI accounts. But none of it syncs. My AI knows everything about what I've been working through, theirs knows their side, and neither knows what the other learned.

Over time this has become a real knowledge gap between us. We've tried shared folders and pasting summaries back and forth, but it doesn't keep up.

Feels like there's a genuine opportunity here though. A shared AI brain that a whole small team can read, with private stuff actually staying private.

Anyone solving this well? Do you use Claude Team or ChatGPT Business? Or a shared memory tool like Basic Memory or Supermemory? Does it actually stick?

If someone built this properly—one shared brain everyone's AI could reference—would you pay for it?

Traditional Bot Detection Is Broken. What's Actually Working Now?

Richard Smith — Sun, 04 Oct 2026 23:07:38 +0000

Bot detection on a SaaS signup form used to mean a CAPTCHA plus a honeypot field. Most of what gets through now is a real browser driven by a script or an AI agent, often on a residential IP, and it fills the form the way a person would.

Cloudflare said this summer that automated traffic is now more than half of all web requests. On a signup form that shows up as spam signups that never activate, bounced welcome emails, free credits burned by accounts nobody owns, and an activation rate that looks worse than it is.

Here's where each standard fix breaks: CAPTCHA reliably stops your real users on a phone. Honeypot fields don't catch headless browsers that render the page properly. Rate limits by IP catch a university but miss a residential proxy service. Email verification proves someone controls an inbox, not that a person is behind the signup.

And there's a new wrinkle: not every bot is bad anymore. Some of these are AI agents a real customer sent to sign up or book something for them. "Block all bots" used to be the safe default. Now it can mean turning away a paying user who just delegated the task.

So, for anyone running a signup form: what finally cut spam signups for you, and what was the first sign bots were getting in? Do you add friction for everyone or only when something looks off? And do you let AI agents sign up on behalf of real users?

What the GPT-6 Guide Tells Us About What Developers Actually Need

Richard Smith — Fri, 02 Oct 2026 23:11:45 +0000

I spent some time reading through OpenAI's GPT-6 model guide, and honestly, the most interesting part wasn't the models themselves. It was the operational stuff they kept coming back to.

Caching, compaction, mid-task steering, async tool calling. These aren't glamorous features. They're the unglamorous problems that eat up real time when you're trying to ship something that actually works at scale.

The guide essentially maps out where developers hit walls: figuring out which model to use for which task, keeping costs predictable when usage spikes, and managing workflows that take hours or days to complete. Every one of those sections reads like a feature request for tooling that doesn't quite exist yet, or at least doesn't exist in a form that's easy to integrate.

I'm starting to think the real opportunity in AI development isn't another wrapper around a language model. It's the infrastructure layer underneath: observability for AI workflows, smarter cost allocation, better ways to handle multi-step tasks that don't fall apart when you step away for a meeting.

Has anyone else looked at an API best practices guide and immediately started thinking about what you'd build to solve the problems it documents? Curious what pain points people are running into that still feel unsolved.

15-Person Electrical Shop Can't Trace a Single Dollar of Ad Spend to Revenue

Richard Smith — Wed, 30 Sep 2026 23:11:49 +0000

Ran into an interesting problem while talking to a contractor friend last week. His shop has been around for years—steady work from builders, solid word of mouth. But builder referrals have dried up and their old channels aren't keeping the pipeline full anymore.

They tried Thumbtack, found it chaotic. Search ads run $15-25 per click in their area, and the conversion rate is "historically preserved" (his words, meaning the website hasn't been touched since 2009). Paying for leads they can't trace back to actual jobs is eating into margins in a way that doesn't feel sustainable.

What struck me was his willingness to pay—he just wants accountability. He'd rather spend $50 on a lead that converts than $15 on one that vanishes. For a 15-person crew with overhead to cover, that math makes sense.

Made me wonder if there's something worth building here for the trades. Most lead gen platforms feel like they're optimized for volume, not quality. Anyone seen tools that actually help contractors track spend to jobs without requiring a full marketing team? Or is this just the nature of local service businesses?

What California's New Privacy Law Means for Developers Building Compliance Tools

Richard Smith — Mon, 28 Sep 2026 23:10:07 +0000

I spent some time reading through California's SB 923, and the implications for anyone building privacy compliance tools are worth talking about.

Starting January 1, 2027, deletion requests under CCPA will cover third-party data too. If your app enriches user profiles using external sources, those records now fall under the same deletion obligations. And if you're running an online-only business, a simple email address won't cut it anymore—companies need an actual form or portal for requests to know, delete, or correct.

This is a genuine gap in the market. Most small teams handling user data aren't set up to maintain suppression lists across every vendor system, track which third-party sources feed their databases, or build out the request infrastructure from scratch. The compliance burden is real, but the tooling is scattered at best.

I'm thinking about what a focused privacy workflow tool could look like—one that connects deletion requests to vendor suppression lists, flags gaps in request intake, and keeps a record of what's been handled. It's the kind of thing that probably should exist but doesn't feel like it's been done cleanly yet.

Has anyone been working on something in this space? I'm curious whether others see the same opportunity or if I'm overestimating the demand side of this.

Why I kept wanting my AI agent to see my whiteboard

Richard Smith — Sat, 26 Sep 2026 23:15:11 +0000

I've been experimenting with AI coding agents for a while now, and there's something that always felt off. When I'm working on architecture or planning features, I sketch things out on a whiteboard or Excalidraw. But my agent just sees text. I describe diagrams, paste screenshots, try to explain spatial relationships—it's awkward and error-prone.

Most solutions I tried either required the agent to output some diagram format that I'd then render, or involved clunky screenshot pipelines. Neither felt natural. The agent can't look at what I'm pointing at, reference it in conversation, or edit it incrementally based on feedback.

What I wanted was the agent to actually sit with me at the canvas. Write AGENT: next to something, have it see what's around that area, edit it, and mark the note done. That's closer to how I think.

I ended up building something that does exactly this—connects Claude Code, Codex, or opencode to a live Excalidraw canvas. It handles the vision, the scene state, and bidirectional communication between the chat and the canvas.

Curious if others have run into this same friction, or if most people just don't try to use visual tools with their agents at all.