/slot-machine development (CC vs. Codex; CE vs. superpowers)
I built an opensource skill that runs N implementations in parallel, has each one reviewed blind by a separate agent, then a judge picks the winner or synthesizes the best parts of each. Each slot can use a different skill (CE:work in one vs superpowers:test-driven-development) and harness (CC vs. Codex). Or put different emphasis on each slot (functional vs. robustness). Also works for non-coding tasks (writing) and you can create custom slot-machines. The main insight is simple enough: AI agents are probabilistic. The same spec produces different code every time; different designs,…
What it does
In the maker’s words, at launch
I built an opensource skill that runs N implementations in parallel, has each one reviewed blind by a separate agent, then a judge picks the winner or synthesizes the best parts of each. Each slot can use a different skill (CE:work in one vs superpowers:test-driven-development) and harness (CC vs. Codex). Or put different emphasis on each slot (functional vs. robustness). Also works for non-coding tasks (writing) and you can create custom slot-machines. The main insight is simple enough: AI agents are probabilistic. The same spec produces different code every time; different designs, different bugs, different quality.So running parallel implementations can increase quality if we can judge effectively. There is a real tradeoff here of increased time and tokens for quality but for production code this is often worth it; particularly on long running overnight loops on subscription plans. Also a simple way to learn which skills and harnesses actually work best for your codebase. Appreciate any feedback / PRs. This is early!
Does the same job
all alternatives →
OpenCode SuperappJul 2026 · opencodesuper.app · ▲128The power of Codex with local, self-hosted models and voice
- OSOpen-Source GTM Skills for Claude Code, Codex, and CursorMar 2026 · github.com · ▲8
We built an open-source library of 125 GTM (go-to-market) skills that plug into AI coding agents like Claude Code, Codex, and Cursor. With these skills an AI agent can automatically: - Find ICP leads from conference speakers, LinkedIn activity, or job boards - Generate personalized cold email sequences - Monitor competitor blogs, pricing pages, and hiring signals - Generate programmatic SEO pages from keyword lists - Track where your brand appears in ChatGPT, Perplexity, and Claude answers --- How skills work Each skill is a structured markdown file containing instructions, scripts, and tool…
- IBI built a firewall for agents because prompt engineering isn't securityJan 2026 · github.com · ▲7
Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
- SRShikigami, run AI coding agents in parallel, each in a Git worktreeJul 2026 · shikigami.dev · ▲7
Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
- JAJDS – a Copilot skill suite for structuring AI coding behaviorMay 2026 · github.com · ▲9
A few months ago I stumbled on obra's superpowers repository https://github.com/obra/superpowers. I really liked the approach and idea that you enforce discipline for your agent through a skill-based workflow. Even though coding agents (copilot included) have become a lot better at natively handling complex tasks, they still wander off and lose track of things. I really liked how superpowers fixed this and how it enabled long-running sessions without the agent losing its "focus". So I decided to build a Copilot tailored skill suite around the core idea of superpowers. I…
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, March 2026
the whole month →
Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
