Mnexium

What it does

Mnexium gives AI apps one shared memory layer across models and agents. Add persistent memory, chat history, user profiles, records, and live context with one API. Built for OpenAI, Anthropic, Gemini, and agent workflows, without managing vector DBs, sync jobs, or custom memory pipelines.

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  Mnexium AIJan 2026 · ▲83

  Persistent, structured memory for AI Agents

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  AgentmemoryMay 2026 · agent-memory.dev · ▲322

  Persistent memory for Claude Code, Codex & coding agents

• ML
  Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph)Jun 2026 · github.com · ▲60
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  MemoriMay 2026 · memorilabs.ai · ▲168

  Persistent memory from agent trace, not just conversation

• AL
  A local-first memory store for LLM agents (SQLite)Dec 2025 · github.com · ▲48
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  AI Memory Layer for ChatGPT and Claude.Sep 2025 · ▲26

  Shared persistent memory across all your LLMs.

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  I trained a 125M model to autocomplete piano on-device▲598

  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

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  Astute▲585

  Automate your B2B brand going viral, with new media creators

  AI · 18d ago · company-app.joinastute.com

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  Grok Bot▲547

  AI teammates that you can give real work to

  AI · 25d ago · x.ai

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  Needle2: 14MB agentic LLM for phones, wearables, smart home and robots▲537

  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 · 27d ago · cactuscompute.com

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  Monid▲474

  OpenRouter for agent tools

  AI · 6d ago · monid.ai

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  Clara AI SDR▲460

  Turn website visitors into qualified pipeline

  AI · 19d ago · clarasdr.ai

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  Brew ▲905

  Like Claude design for email marketing

  AI · May 2026 · brew.new

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  Kilo Code for VS Code▲897

  Parallel agents, diff reviewer, and multi-model comparisons

  Dev tools · May 2026 · kilo.ai

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  StoreClaw▲805

  Grow your store profits with agents that know how to sell

  AI · May 2026 · storeclaw.ai

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  PollyReach▲796

  Give your agent a real number and voice to make calls.

  AI · May 2026 · pollyreach.ai

• NW
  Needle: We Distilled Gemini Tool Calling into a 26M Model▲776

  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…

  Life & fun · May 2026 · github.com

• FM
  Files.md – Open-source alternative to Obsidian▲730

  Dev tools · May 2026 · github.com