@burn2delete · X

@StevenJdotCom · X

One AI makes mistakes. Three catch each other's. AI Consensus runs your prompt through Claude, GPT and Gemini. They work it independently, then critique each other until they reach consensus — handing you an AI audited, combined answer.

@promptcarrot · X

@yaseenvalji · X

built this in a couple hours with Claude Code on Fable 5 ultracode. any hard decision goes to a board of 5 AI advisors: they debate live, a Chair calls it, and it remembers the outcome. open source, on the Claude API. @AnthropicAI

@Amrutraj4 · X

I completely vibe coded website during Open AI build week hackathon. Planned using ChatGPT. Executed using Codex. Front end deployed using Vercel Backend deployed using Render Tell me your experience of website.

@VMS2907 · X

Get your office to get meaningful output from AI -

@AIWMCX1 · X

Consultants don’t need more noise. AIWMC Quantis helps turn messy business ideas into clear scenarios, runway views, and risk signals — so you can spend less time rebuilding analysis and more time giving the client a real answer.

@Ebrahim_Rio · X

Most founders skip validation and pray. I automated the "worth building?" check. AI scans competitors, Reddit, and market data → Cook or Kill in 90 seconds. Killed? It surfaces the pivot the data actually backs.

u/Rabus · Reddit

I got TestingModels too overcomplicated over the month it is running: looking for some feedback how to make it more useful and simpler I run a benchmark like arena.ai , but with pre-generated prompts. So far, nearly 6k people came in and like 30k comparisons has been made - which means the thing is genuinely useful for people to compare the models. The problem is the more features i started adding the more overblown and complicated UI became - like old internet explorer tab bars Old: ht

@marcellafjacob · X

u/KangarooPitiful594 · Reddit

I built Aethereum: a team coordinator for AI coding agents (free beta) the simple version: if your team codes with AI agents, aethereum keeps them coordinated so you ship faster. everyone runs whatever agent they like (claude code, cursor, codex, cline, zed) and they all join one room. agents see what each other is building, get warned before breaking each other's changes, and can share code between machines end to end encrypted. when two agents want different API shapes they negotiate it b

douvy · HN

Educational tool for understanding AI agents. Code: https://github.com/douvy/how-agents-think