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26 projects
Refine ideas through logical reasoning and publish them to a community of verified knowledge.
samcymbaluk · HN
Axiom toolkit – Make your ideas precise enough to be proven wrong
Inject engineered cognitive abilities into AI agents at inference time.
@frank_brsrk · X
reasoning tools for ai agents
Compare and evaluate AI models across coding, reasoning, agents, and other benchmarks.
davidtsong · HN
Benchmarklist: track AI benchmarks (2.4k+), models, and capabilities
Get a free always-on Debian container with SSH, tmux, git, and AI coding agents.
saucepoint · HN
A Free Cloud Container
Improve your reasoning by solving adaptive AI-generated puzzles that learn from your mistakes.
u/connerpro · Reddit
IntelligenceMax - Adaptive reasoning practice with live AI questions (claim-safe near vs far) submitted by /u/connerpro to r/SideProject [link] [comments]
Multiple AI agents develop code in parallel with automatic error detection and fixing
@ForgeLab_Brain · X
Open beta launched. See more here: 🌐 #VibeCoding #BuildInPublic #IndieHacker #AIdev
Document product decisions and reasoning in one shared layer for team collaboration.
@TimEngng · X
Stop losing the "why" behind product decisions -
A cloud Linux workstation with 60+ AI tools including Claude Code, Codex, and Gemini.
@CoderLuii · X
A machine for your coding agents.
Web-based terminal multiplexer for running AI coding agents in parallel.
04mg · Product Hunt
Caw Open source web terminal multiplexer for AI agents
Embeddable visual workflow builder for web apps with optional AI assistance.
tahazsh · HN
Hi! I’m Taha. In many agentic products that support workflows (including one I worked on), I noticed they either don’t support node-based editors, or use React Flow and go through the difficult work of integrating it into their product to run it and work with their existing logic. So I thought about creating a tool that could help with this by closing the gap between the editor and the runtime. That’s why I created Wayflow. The basic architecture is simple: you just need to create a graph (which is a JSON object) that the runtime knows how to run. The runtime doesn’t care where that graph is coming from, it just needs the right schema. And with the help of the editor, you can create the graph, and then export it or directly save it on your backend in your database. And then when you want to execute it, you just hand it to the runtime. The runtime can either stream the execution (which is useful for the editor), or give you the final result. How you execute the graph is up to you: t
Visualize and deploy AI-generated backend code as a verifiable logic layer you can understand and own.
@SpalaAi · X
GPTree is the AI workspace where you branch any conversation, compare models side by side, and merge the strongest thinking into one clean path. Free to start.
@naw103 · X
