The full gallery
Tech stack
60 projects
Build and run ML workflows in an agent-native cloud notebook with evaluation tools.
eldar_hsnv · HN
Show HN: AI Notebook for Data Science – Kind of Like Cursor but for Jupyter
A local notebook for prototyping and planning with coding agents.
narwhster · HN
A notebook for prototyping with your agent
Build databases, kanban boards, and notes for AI agents to collaborate on.
@emir_ogz · X
Just shipped - AI-native workspace for solo devs Agent reads/writes your project notes automaticly
File-first memory runtime for AI agents with dashboard and HTTPS API access.
@memofsdev · X
Task board for AI agents in VS Code with persistent context across chat sessions.
gb2d_hn · HN
It was with the transience of valuable agent session dialogue in mind that I built https://www.agentkanban.io - A key feature is context capture in the tasks which you create on the board and then progress in agent sessions. Supported agents are currently Claude and Github CoPilot in VS Code. All of your context is captured in the task and can be reloaded into new agent sessions at any point in the future. Tool use is intentionally discarded because it is proprietary in nature and therefore brea
An LLM agent that tracks goals and plans across sessions while showing exactly what it retrieves, verifies, and fails on.
u/OGMYT · Reddit
I built LOLM, a lower-cost LLM agent that shows what it actually did — looking for blunt feedback I’m one of the founders/builders behind LOLM. Most AI products show an answer but hide whether the system retrieved anything useful, verified the result, switched models, hit a limit, or simply stopped. LOLM exposes those parts through controller events and run receipts. It includes: - Live agent - CLI - Coding and small app-building workflows - Memory and self-hosting options - Control decis
Sync your AI agent setup and skills across platforms without vendor lock-in.
@matebenyovszky · X
I use multiple providers Claude Code, Cursor, Codex, Hermes, Antigravity etc. and multiple machines. Over time, each one ended up with its own slightly different skills and tools 😰 So I started where a playbook is a portable bundle containing my MCP/OpenAPI servers, skills, prompts, personas and memory, which I can take with me into whichever environment I’m using. I’m still trying to figure out whether this is useful beyond my own workflow. Ideas? 🤔 🍎 Source / open source on Github: matebenyovszky/agentplaybooks I also built an npm CLI that audits a project and syncs the same skills and MCP definitions across harnesses.
Build and deploy AI agents with persistent threads, webhooks, and scheduled tasks.
@computer_agents · X
Organize and retrieve your work sessions with AI-powered memory.
@vitverb · X
hi :) One tap back to the work you meant to finish. No tasks to manage. Nothing to set up. Local-first
Unified context layer for AI agents that integrates email, drive, Slack, Notion, and GitHub.
@marcin_u2 · X
-> here it is! claude finally knows everything gpt knows. and both of them know everything that's happened across your email, drive, slack, notion, github and granola :)
Translation management platform with agent-native workflows for multilingual apps.
@sacsandc4 · X
Managed hosting platform for AI agents with persistent workspaces, memory, and scheduled backups.
@hijak01 · X
check out the demo
