Here is a technique I use (in a recent livestream) to help your ai agent give better UI layouts for your apps. Prompt: Here’s my phone, an iPhone Pro Max. My thumb only reaches the bottom comfortably, left or right hand. Put every control the user taps in that bottom thumb zone. Use the top 65 percent as a passive zone with no controls, just the info that builds trust, like the detected input and the transcript. Make every interaction take about a second and a half, and make it so a baby could figure it out. And take out anything that isn’t pulling its weight.

I just published WAVES, the method I have been using to run AI agents in parallel without letting them run wild. An agent will always tell you the job is done, and WAVES treats that as a claim and checks the evidence behind it before it counts. You fan the work out to a team of agents, each one owns a slice, and the verifier is what decides when the work is done. It runs in Cursor, Codex, Claude, and Droid, and ships as a skill you can install today. The full breakdown is on the blog, with diagrams, the handoff format, and where it fits next to loops and parallel orchestration: https://www.rayfernando.ai/waves-workers-aggregate-verify-extend Skill: https://github.com/RayFernando1337/rayfernando-skills/tree/main#waves--fan-out-to-parallel-agents Give it a run on something real and tell me how it goes.

Starter Template: https://github.com/lzell/OpenAIRealtimeSample Repo: https://github.com/lzell/AIProxySwift AI Proxy: https://www.aiproxy.com/

I would love to try it out myself, but as a Startup CTO right now not haveing thaaaat much time to keep up with everything around ai. But ( and for sure I am not sponsored 😅 ), this “video call with any agent” feature of Pikka labs sounded interesting and haven’t found any good external review. Maybe something like video call with an agent and give him a task list for a broader time frame and discuss with it about I’d imagine can be cool.

I’m not 100% done with the feature I’m currently working on, but I’m far enough along that I wanted to share how I’m using AI right now to help ship production features in a Rails / Turbo / Hotwire / Postgres web app. _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ AI subs: chat Pro 100$ plan, Claude Max 100$ plan so both 5x dev tools: vscode, codex app, chat 5.5 pro, claude code CLI in warp ( do I have to try out ghosty? One thing I really like about Claude in Warp is that I can paste images right away ) codebase agent accessibility helpers: a small Claude.md, a conventions.md and a rules folder with categorized conventions. _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ The feature I’m currently building Right now I’m working on a multi-tenant tracking integration for communities on our platform., so they can add meta pixel and CAPI as well as gtag for google. Normally I start by brainstorming and talking with my colleagues about what creators actually need from the feature. After that, I look at examples from other companies and form my own thoughts around the product and technical direction. Until a few days ago my next step was a chat with codex or claude code ( switched mostly to codex for a while ) and than from our discussion it generates a spec.md ( depends on the complexity of the feature I sometimes also went with an investigation phase and .md in the first place and than a plan.md afterwards. If the feature includes new database tables or columns, a lot of the early discussion usually goes into data modeling and schema design. I kind of agree with Thariq that large .md files can be hard to consume. To help with that, I often had Claude create Mermaid diagrams inside those markdown files, because diagrams are usually easier to consume than long text.. But for the current feature I tried using an HTML-based spec instead of markdown ( will attach a reference image ). I like the idea of replacing markdown for this use case, but I’m not 100% happy with the result yet.