DEV Community: Richard Smith

What Fable 5.2's Speed Improvements Mean for Builders

Richard Smith — Wed, 11 Nov 2026 00:23:03 +0000

After reading through the benchmarks and user reports on Fable 5.2, one thing keeps hitting me: the speed and reliability improvements might matter more than raw capability for most of us building real products.

I've been using Opus 5.5 on a medium-sized codebase, and honestly, it's been plenty. But the reports on Fable 5.2 being noticeably faster with fewer token overflow issues make me wonder if I'm being too conservative with my tool choices.

The part that caught my attention was about image rendering. Someone mentioned preferring the images generated by Fable/Opus over dedicated image models. If an AI can handle both code and images reliably in one pipeline, that's a different kind of workflow.

This makes me think about what you could actually build with these improvements. Faster, more reliable models open up different use cases than just "better outputs." Real-time applications, automated pipelines, things that need consistent response times rather than occasional brilliance.

Anyone else thinking about AI startup ideas that depend on reliability over peak performance? I'm curious whether the speed gains change what kinds of products become viable.

Looking for Call Monitoring Tools for a Small Sales Team

Richard Smith — Mon, 09 Nov 2026 06:25:47 +0000

I run a small operation with 9 employees handling sales and support through company-provided SIM cards. The challenge is that I have no real visibility into what's happening on those calls—call activity, follow-ups, customer history, the whole picture.

I'm weighing whether to build something internal or find an existing tool that already solves this. The business need is clear: we need to track customer interactions without relying on people's memory or scattered notes.

Has anyone dealt with something similar? What tools or approaches have actually worked for a team this size? I'm curious whether call analytics + CRM combos exist that don't require enterprise budgets or months of setup.

Any recommendations would be appreciated.

How do you actually track profit on your online store?

Richard Smith — Mon, 02 Nov 2026 23:19:05 +0000

I'm researching how small online sellers keep an eye on profit, not just sales. Most advice says "track your numbers," but I rarely see what that looks like in practice.

If you sell online, I'd love to hear your experience:

Once a week, how do you actually check whether you're making money? Spreadsheet, Shopify report, your bank statement, or something else?

Has a product or ad campaign ever been quietly losing you money for weeks before you noticed? What finally tipped you off?

If you pay for reports or tools, what's one thing they still don't tell you?

Not selling anything myself right now—just trying to understand the actual workflows people use. Even a quick one-liner helps.

Looking for affordable image/video moderation API – pain points

Richard Smith — Sat, 31 Oct 2026 23:19:24 +0000

I've been evaluating content moderation APIs for a new project, and finding the right balance between cost, reliability, and data security is harder than expected.

Major cloud providers like AWS Rekognition and Google Cloud offer solid solutions, but their pricing can get steep fast, especially when processing video at scale. Smaller, specialized APIs like Sightengine and Clearview have more granular pricing, but I haven't used them extensively.

My priorities are straightforward: affordable per-image or per-video costs, reliable detection for adult content and violence, and clear data handling guarantees (no using my data for training). For indie projects, all three matter equally.

I'm leaning toward testing a couple niche providers that seem more indie-friendly, but I'd rather hear from anyone who's actually deployed these in production. What are you using, and have you run into hidden costs or reliability issues at higher volumes?

Happy to share findings once I've tested a few options.

What if your team included AI agents you could actually see working?

Richard Smith — Fri, 30 Oct 2026 23:14:05 +0000

I've been experimenting with something that feels genuinely different—a 3D office where AI agents are my coworkers. Not in an abstract "AI will change everything" way, but literally coworkers I can see.

The setup is straightforward: you create an agent, give it a job, connect it to the tools it needs, then review what it produces. Each agent runs on a cloud machine with browser, file, and terminal access. The 3D part isn't just cosmetic—it lets you observe what each agent is actually doing in real time.

For solo founders or micro-SaaS builders, this shifts the dynamic a bit. Instead of handing off work to a black box and hoping for the best, you get visibility into the process. You can spot when an agent goes down the wrong path before it wastes too much time.

I'm not sure if 3D visualization is the future of agent collaboration or just a gimmick, but there's something useful about making AI work more tangible. When you can see what your agents are doing, the feedback loop gets tighter.

Has anyone else tried this approach? Curious whether the visual element actually makes a difference in how you work with autonomous agents.

How do you handle ops automation when you can't build custom?

Richard Smith — Mon, 26 Oct 2026 23:17:16 +0000

Our team runs a lean SaaS operation. We've got about 30 small manual tasks that keep coming up — onboarding customers, provisioning accounts, sending invoices, updating the CRM, syncing data between tools. Individually each one is quick. But together they add up to roughly 15 hours a week spread across the team.

The real issue is our engineering team is heads down on product work. We can't justify pulling them off roadmap to automate processes that only save a few hours here and there. Hiring a dedicated ops person feels like overkill for where we are right now.

I'm curious how other small teams approach this. Are there no-code or low-code tools that actually handle this well? We've looked at Zapier and a few similar options but I'm not sure where the tradeoff is between flexibility and maintenance burden.

What does a reasonable ops automation setup look like for a team this size? Is there a point where it makes sense to just build the integrations into the product instead?

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.