Manex
Preserve useful answers, corrections, and context as memory
Manex launched on May 4, 2026 with 124 votes, #223 of 20,538 launches that month and more than 95% of that year's launches.
What it does
Manex is a private AI memory for documents and team knowledge. Upload files, ask grounded questions, and preserve useful answers, corrections, and context as memory. It runs locally where supported, keeps data private by default, and lets teams create a shared brain without per-seat pricing.
Capture field issues while they are fresh, import existing spreadsheet and PDF records, preserve their block context, and prepare useful reports without rebuilding the story later.
Acrelore helps growers capture issues while they are fresh, keep the context together, follow what happens next, and prepare useful reports without rebuilding the story later. “Five trees at the eastern end still look weak. Check again next Tuesday.” Follow the complete Acrelore workflow: capture what matters in the moment, keep it connected to the right place, and return to the story of a block without rebuilding it from scattered notes. A grower notices something important in seconds. Preserving enough information for another person, another visit, or a later report can take much longer. A photo captures what was visible, but often not why it mattered, where it was, what changed, or who…from acrelore.com
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Personal AI MemoryMar 2026 · ai-memory-beta.vercel.app · ▲106Captures and stores your chat from various AI platforms


Core – open source memory graph for LLMs – shareable, user owned2025 · github.com · ▲112I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…
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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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