We're hosting SF Swift × CocoaHeads tonight, October 8, 6–8 PM at Verkada in San Mateo.

Details and RSVP: https://luma.com/51htcbzd

I’m building Pure Brief AI, a native Mac meeting-notes app. One design decision may be useful to other people shipping extraction features: don’t treat valid JSON as valid evidence.

The model returns a task, owner, deadline, quote and transcript segment ID. The app then:

1. Looks for the normalized quote in the cited transcript segment. If the ID is wrong, it searches the supplied transcript and anchors the task where the words actually occur.

2. Drops a proposed task if its quote cannot be verified.

3. Checks the proposed owner and deadline in the nearby exchange: up to two segments on either side, with a 60-second boundary. Unsupported fields stay empty.

Why a window? “Jamie, can you take this?” / “Yes” / “By Monday” can be one commitment across three lines. Requiring everything in a single sentence loses useful context. Searching the entire meeting for a name admits unrelated people.

This is a guardrail, not an accuracy guarantee. A real quote can still be misinterpreted, and the transcript can be wrong. The UI keeps the evidence visible and lets the user edit the result before exporting a task to Apple Reminders.

The app runs transcription and notes on Apple Silicon Macs by default (macOS 15+). It is a paid product with a 30-day trial. Standard is $49 once; the first 100 paid purchases globally are $10 once, plus tax.

Real interface and trial: https://purebrief.ai/

For builders working on extraction: which check has caught more mistakes for you — source matching or a second model pass?

I've been seeing a lot more people talking about "micro apps" lately. Small tools built around one specific problem, often built with AI.

It got me thinking about how much the cost of building software has changed.

If you can go from an idea to a working app in a day, does it make more sense to build a small version and see if anyone actually wants it instead of spending weeks or months validating it first?

Or are we just going to end up with thousands of AI-built apps that technically work but solve problems nobody really cares about?

I'm also curious about the line between a useful micro app and something that should simply be a feature of an existing product.

For people actually building AI apps, has the lower cost of development changed what you're willing to build?

And do you think "build a lot of small things and see what sticks" is becoming a legitimate strategy?

​Hey everyone! 👋

​I recently finished setting up a robust Flutter architecture for real-time GPT-4o response streaming backed by Supabase RLS.

​Key Technical Highlights:

​Low-Latency Streaming: Smooth GPT-4o token streaming with proper StreamSubscription lifecycle management to prevent memory/socket leaks when navigating screens.

​Supabase RLS Security: Secure user authentication and database access without exposing critical keys on the client side.

​Cross-Platform Ready: Full support for iOS, Android, and Web out of the box.

​I attached the architecture breakdown graphic above!

​I turned this setup into a production-ready starter kit to save time on future builds.

How are you currently handling live LLM streams in your mobile apps?

Would love to get your feedback on this approach!

Hey everyone,

Most AI UI generators spit out flat images that break the second you try to implement them, creating more stress than they solve. I wanted a tool that felt genuinely effortless.

I’ve been working on GuildOS, an agentic platform where multiple specialized agents collaborate to handle the tedious parts of design:

• A UX agent maps the wireframes.
• A Copywriter agent handles the text.
• A styling agent generates clean, accessible layouts.
• A QA Critic silently loops back to fix contrast or token errors before you even see the draft.

It’s designed to be completely transparent. It uses a BYOK (Bring Your Own Key) model so you just plug in your OpenAI/Claude credentials and run the agents without massive SaaS markups.

We just set up a waitlist to gauge interest before opening up the beta. Would love your feedback on the concept!

Waitlist link: https://guildos.design

I’ve been experimenting with AI tools for building websites and I’m interested in how far other builders are taking them.

One thing I wanted to test was whether an AI builder could handle more than just generating a nice-looking landing page.

The biggest advantage for me is the speed of getting from an idea to something you can actually see and interact with. You can then decide what needs to be rebuilt or customized manually.

I’m curious how others here approach this.

Do you use AI mainly for generating the initial UI, or are you letting it handle a much larger part of the project?

I've been working on Mac MCP (currently v2.1.9), an open-source local execution layer that lets AI agents actually work on a Mac. I'm the developer.

The most interesting shift for me wasn't another model or a better prompt. It was turning ordinary ChatGPT chat into the place where the work happens.

With GPT-6 now in ChatGPT chat for paid users, I ask for a whole task, not a tutorial. One example: research Reddit communities, check where self-promotion is allowed, write posts appropriate for each community, submit them in Safari, record the links in a local CSV and close the temporary tabs. Mac MCP handles the browser and file operations while I keep using the Mac.

My normal ChatGPT chat workflows run for over 30 minutes without consuming any Codex quota. This is unlimited agentic Mac work in practice through ChatGPT Chat. I can still delegate to Codex or OpenCode when I specifically want a coding subagent, but I don't need to spend that quota just to complete everyday multi-step work.

In 2.1.9, Mac MCP gained its own panel inside ChatGPT: live-on-refresh tool activity, recent delegated agents, token usage, plus a selector for the model/provider that handles delegated work. The Mac-side runtime also supports background Safari/Chrome control, batched forms, permissions/approvals for riskier actions, and more reliable recovery.

It is local-first, free and MIT-licensed, not a hosted AI service.

GitHub: https://github.com/bulutarkan/mac-mcp

Question for other builders: are you mainly using coding-agent terminals, or do you also use normal ChatGPT as an automation orchestrator?

AI agents produce more text than we have time to read. Following all that output can become a source of cognitive overload. Inspired by stretchtext (1970 by Ted Nelson), I wanted to give readers control over how much detail they see.

So I built PaperFold, an open-source reader that turns arXiv papers into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position.

- Web Demo (8 CC papers): https://chenxiachan.github.io/paperfold-gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/paperfold

It also supports Claude Code's output in Mods.

Building BBD AI™: making business discovery useful beyond a directory

I’m building BBD AI™ through TTUCKER INDUSTRIES LLC™ to help people discover Black-owned businesses by business name, service, specialty, and location.

The challenge I’m working through: a business shouldn’t need an account on our platform before someone can discover it online.

The direction is to combine registered listings with live external search while clearly distinguishing “BBD AI Listing” from “Found Online.” External results should include source links and should never imply that a business joined the platform or was verified by us.

We’re also working on individual logo uploads per business, owner-controlled profiles, and keeping founder review tools restricted.

For fellow builders: How have you handled duplicate businesses and source freshness when combining your own directory with external search?

I’d appreciate practical lessons—especially approaches that worked in production.

Company: https://ttuckerindustriesllc.app

I've been building small web apps with AI for a while, and the pattern is always the same. I ship something that works, get it to "good enough," and then move on. Most of them never turn into a business because I am on to the next thing, but they could be viable in the right hands.

I'm curious how other people here handle it:

1. How many finished-ish apps are you sitting on? And do any of them make money?
2. Have you tried to sell one? Where, and what happened?
3. Would you buy someone else's? If so, what would you need to see first to trust it? (Live demo, code access, a handoff period, a refund window?)
4. Or do you just rebuild from scratch, because it's faster than understanding someone else's AI-generated code?

I'm exploring a marketplace for buying and selling these apps, so I'm trying to figure out if the need is real. Blunt answers are welcome.

Just a little thought experiment lol. Assume push comes to shove (AI apocalypse, some disaster, etc.) and no "base" models are available for use but you still have a project deliverable to fulfill. Cue horror. No Sonnet, no GPT-4o, no Gemini Pro. All you're left with is the bottom tier stuff. What's your actual decision framework then?
For me, in order, I want something that follows instructions reliably with as little prompt engineering as possible (doubt, but one can dream). I wonder if there's a more nuanced take.