Repeater22746 · HN

ContextVault – Shared memory layer for your AI and your team

@SouravDaaa · X

- End to end personalised memory retrieval in 10ms. Would love some feedback here

@burn2delete · X

Cloud9Trader · HN

1Presence – AI with memory at scale, layered recall and a vault you own

@Prajwalr04_ · X

Check out what I just built with Lovable!

@hijak01 · X

check out the demo

@sunvic567 · X

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

u/clausyn · Reddit

I built Pastepile because I was tired of re-explaining the same context to Claude and Cursor I originally built Pastepile as a fast way to share code, logs, configs, and text without creating an account. While using AI coding tools, I kept running into another problem: every new session meant explaining the same architecture, decisions, debugging history, and project context all over again. So I added an MCP server that lets Claude, Cursor, Windsurf, VS Code, and other MCP clients save us

coleaalkire · HN

Pupul, portable and revocable memory you carry into any AI

@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.

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