ContextVault never lose AI conversations
portable memory layer for ChatGPT, Claude, Gemini & more
What it does
ContextVault is a local-first Chrome extension that automatically records your AI conversations across ChatGPT, Claude, Gemini, and other LLMs. It turns them into structured Markdown or ZIP archives — fully owned by you, fully local, with zero backend. Key Features 🧠 Real-time AI conversation capture 🔄 Works across multiple LLMs (ChatGPT, Claude, Gemini, etc.) 💾 Local-first storage (no backend, no tracking) 📦 Export to Markdown / ZIP 🏷️ Organize by tags & projects
Does the same job
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- CSContextVault – Shared memory layer for your AI and your teamJul 2026 · contextvault.dev · ▲12
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…
PromptVaultMay 2026 · mypromptsvault.com · ▲4Save & reuse AI prompts in 1 click — ChatGPT, Claude & more
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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…
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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