ContextVault – Shared memory layer for your AI and your team
Hi HN, I'm Kevin. I built ContextVault because I kept running into the same problem with AI tools. Every project accumulated prompts, coding conventions, architectural decisions, examples, and other pieces of context that made the models significantly more useful. The problem was that this information quickly became fragmented. Some lived in ChatGPT Projects, some in Claude, some in Markdown files, some in internal documentation, and some only existed in previous conversations. Late last year, I realized several people on our team were solving the same problems independently because previous…
In plain words
ContextVault is a shared memory layer that centralizes project context for AI tools and teams. It stores prompts, coding conventions, architectural decisions, examples, and other information that typically becomes scattered across ChatGPT Projects, Claude, documentation files, and previous conversations. The platform enables team members to search and access previous work, reducing duplicated effort and making AI models more useful by providing consistent project context across the organization.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, I'm Kevin. I built ContextVault because I kept running into the same problem with AI tools. Every project accumulated prompts, coding conventions, architectural decisions, examples, and other pieces of context that made the models significantly more useful. The problem was that this information quickly became fragmented. Some lived in ChatGPT Projects, some in Claude, some in Markdown files, some in internal documentation, and some only existed in previous conversations. Late last year, I realized several people on our team were solving the same problems independently because previous work was difficult to discover. I assumed this problem existed in other large organizations, so I started experimenting with a shared context store. I started with a local proof of concept and a rough MCP server. If I asked questions like "have we done this before?", the AI could search the database and find the most relevant item to review. If a conversation produced something worth remembering, I could say "save what we learned to the vault." After using that workflow for a few months, I found myself relying on it every day. I decided to make it available to others. I've never built a product before, and I thought it would be a valuable learning exercise to do. ContextVault is a a product for storing and organizing reusable context that can be shared across people, projects, and AI tools. Instead of copying the same instructions into every conversation, you can store them once and retrieve them through our MCP server. It is not limited to any one AI client. Your team can use ChatGPT, Codex, Claude, and Gemini and save/read from the vault all the same. It currently supports: - OAuth support for GitHub, Google, Microsoft, and GitLab - Structured context records with metadata - Multi-user organizations with role-based access - MCP server for all AI clients that support MCP - Organization-scoped storage keeps tenant data separated - Group visibility rules decide which memories each member can search - Authenticated MCP access ties every request back to a real user and workspace - Feedback signals can be captured now and used to improve ranking later - Supports desktop versions of AI clients, not just their CLI versions (mobile app support should also work) The backend is built with PostgreSQL, pgvector, Node.js, and TypeScript. The frontend uses Next.js, React, Tailwind CSS, and shadcn/ui (frontend is not my strong suit, please be kind). Authentication is handled with Clerk and billing with Stripe. I started building this for my own workflow, but after relying on it for several months I decided to make it available to others. We soft launched a few weeks ago, and I find it useful as a daily tool. Essentially, ContextVault offers a way to track memories and context, distribute them instantly to your team, and help reduce duplicated work. I'd be interested in feedback on a few things: - How are you managing reusable AI context today? - Are you relying on similar tools, or do you keep everything in Git or Markdown? - If you've built something similar, what did you learn that you would do differently? You can see the product here: https://www.contextvault.dev
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, July 2026
the whole month →- IR
I might be the only SRE on Earth with his own bowling center. It's a more in-depth gig than you'd think. My family and I bought an abandoned 8-lane bowling center in the rural mid-west. In our small town there weren't many recreation options for families. You've heard of a food desert? This is an R&R desert. It had been abandoned for a good reason. The roof leaks, the electrical system was constantly surging, and my 70-year-old bowling equipment (still) doesn't work perfectly. The system that keeps your score is particularly interesting to me. It's the thing you watch during your game, but…
Life & fun · Jul 2026
- EElevators▲1,680
Life & fun · Jul 2026 · john.fun
- 1W18 Words▲1,160
Life & fun · Jul 2026 · 18words.com
- BA
Over the past few months, our team has been building more and more slidedecks using web frontend technologies with coding harnesses like Claude Code, but a common complaint is to make even small edits we need to edit the code either manually or via the harness. To avoid this loop, I ended up creating Bento, a single HTML file with everything you need in a slide tool including animations and shared editing. There's no install or cloud login, everything works offline. The default deck is around 560 KB and it doesn't need to fetch anything once you got it. Open it in a browser and then you can…
Dev tools · Jul 2026 · bento.page
- GG
A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context.…
AI · Jul 2026 · github.com
