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The Living Board
AI agent that learns from every task it completes
An autonomous AI agent that wakes up every hour, picks a task, does the work, and extracts what it learned — storing it in both a SQL database and a vector store. Over time, it discovers patterns across unrelated goals, self-corrects wrong assumptions, and proposes its own new directions. Built by a non-engineer using Claude Code. Includes a real-time Next.js dashboard where humans collaborate with the agent through comments. Open source, Apache 2.0. Fork it and deploy your own.
Hey PH! I'm Boji - I'm a Head of P(eople), not (roduct). I have been diving into the world of building, thanks to the vastly lowered barrier to entry made possible by @Claude by Anthropic and other LLMs. I wanted to build something genuiney useful that adds value to my life on a daily basis, and hopefully to others'.
The Living Board is an open-source autonomous AI agent that runs on a scheduled hourly loop and genuinely learns over time, through a solution that addresses one of the most prevalent challenges in building helpful AI - lack of persistent memory.
A bit about how it works:
The memory system: Most agents forget everything between sessions. Living Board has dual-layer persistent memory: - Postgres table with confidence-scored learnings (confirmed = score rises, contradicted = decays, below 0.2 = deleted) - Qdrant vector DB for semantic search across ALL knowledge - a lesson from one goal surfaces when working on a completely different one
Human-agent collaboration: A real-time dashboard where you can monitor goals, manage tasks, change models used, and leave comments (questions, direction changes, feedback). The agent reads and responds each cycle.
Meant to continuously evolve, grow and set its own goals (in addition to human direction). It published its own Substack and Dev.to articles, built and deployed its own landing page, manages email outreach, open-sourced itself and built this pitch.
Everything is Apache 2.0: schema (7 Postgres tables), agent instructions, Next.js dashboard, memory system, and all artifacts the agent has produced. Fork it and deploy your own.
I'd love feedback on the memory architecture - especially from anyone who's wrestled with persistent memory for agents. What approaches have you tried?
About The Living Board on Product Hunt
“AI agent that learns from every task it completes”
The Living Board was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #62 on the daily leaderboard. An autonomous AI agent that wakes up every hour, picks a task, does the work, and extracts what it learned — storing it in both a SQL database and a vector store. Over time, it discovers patterns across unrelated goals, self-corrects wrong assumptions, and proposes its own new directions. Built by a non-engineer using Claude Code. Includes a real-time Next.js dashboard where humans collaborate with the agent through comments. Open source, Apache 2.0. Fork it and deploy your own.
On the analytics side, The Living Board competes within Open Source, Developer Tools and GitHub — topics that collectively have 622.4k followers on Product Hunt. The dashboard above tracks how The Living Board performed against the three products that launched closest to it on the same day.
Who hunted The Living Board?
The Living Board was hunted by Boji Lazov. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of The Living Board including community comment highlights and product details, visit the product overview.
Boji Lazov
Hey PH! I'm Boji - I'm a Head of P(eople), not (roduct). I have been diving into the world of building, thanks to the vastly lowered barrier to entry made possible by @Claude by Anthropic and other LLMs. I wanted to build something genuiney useful that adds value to my life on a daily basis, and hopefully to others'.
The Living Board is an open-source autonomous AI agent that runs on a scheduled hourly loop and genuinely learns over time, through a solution that addresses one of the most prevalent challenges in building helpful AI - lack of persistent memory.
A bit about how it works:
The memory system:
Most agents forget everything between sessions. Living Board has dual-layer persistent memory:
- Postgres table with confidence-scored learnings (confirmed = score rises, contradicted = decays, below 0.2 = deleted)
- Qdrant vector DB for semantic search across ALL knowledge - a lesson from one goal surfaces when working on a completely different one
Human-agent collaboration:
A real-time dashboard where you can monitor goals, manage tasks, change models used, and leave comments (questions, direction changes, feedback). The agent reads and responds each cycle.
Meant to continuously evolve, grow and set its own goals (in addition to human direction). It published its own Substack and Dev.to articles, built and deployed its own landing page, manages email outreach, open-sourced itself and built this pitch.
Everything is Apache 2.0: schema (7 Postgres tables), agent instructions, Next.js dashboard, memory system, and all artifacts the agent has produced. Fork it and deploy your own.
I'd love feedback on the memory architecture - especially from anyone who's wrestled with persistent memory for agents. What approaches have you tried?