Show HN: Memv – Memory for AI Agents https://ift.tt/np2Dmso

Show HN: Memv – Memory for AI Agents memv is an open-source Python library that gives AI agents persistent memory. Feed it conversations; it extracts knowledge. The extraction mechanism is predict-calibrate (Nemori paper): given existing knowledge, it predicts what a new conversation should contain, then extracts only what the prediction missed. v0.1.2 adds the production path: - PostgreSQL backend (pgvector for vectors, tsvector for text search, asyncpg pooling). Single db_url parameter — file path for SQLite, connection string for Postgres. - Embedding adapters: OpenAI, Voyage, Cohere, fastembed (local ONNX). Other things it does: - Bi-temporal validity: event time (when was the fact true) + transaction time (when did we learn it), following Graphiti's model. - Hybrid retrieval: vector similarity + BM25 merged with Reciprocal Rank Fusion. - Episode segmentation: groups messages before extraction. - Contradiction handling: new facts invalidate old ones, with full audit trail. Procedural memory (agents learning from past runs) is next, deferred until there's usage data. https://ift.tt/uYHRTsa March 31, 2026 at 12:09AM

Show HN: I made my fitness dashboard public and Apple Health needs an API https://ift.tt/fuOenJ3

Show HN: I made my fitness dashboard public and Apple Health needs an API https://ift.tt/5JmO1CB March 31, 2026 at 12:39AM

Show HN: A Terminal Interface for Jira https://ift.tt/GDAg3Zr

Show HN: A Terminal Interface for Jira https://ift.tt/YcgmUQ3 March 30, 2026 at 08:47PM

Show HN: QuickBEAM – run JavaScript as supervised Erlang/OTP processes https://ift.tt/XdxsOrY

Show HN: QuickBEAM – run JavaScript as supervised Erlang/OTP processes QuickBEAM is a JavaScript runtime embedded inside the Erlang/OTP VM. If you’re building a full-stack app, JavaScript tends to leak in anyway — frontend, SSR, or third-party code. QuickBEAM runs that JavaScript inside OTP supervision trees. Each runtime is a process with a `Beam` global that can: - call Elixir code - send/receive messages - spawn and monitor processes - inspect runtime/system state It also provides browser-style APIs backed by OTP/native primitives (fetch, WebSocket, Worker, BroadcastChannel, localStorage, native DOM, etc.). This makes it usable for: - SSR - sandboxed user code - per-connection state - backend JS with direct OTP interop Notable bits: - JS runtimes are supervised and restartable - sandboxing with memory/reduction limits and API control - native DOM that Erlang can read directly (no string rendering step) - no JSON boundary between JS and Erlang - built-in TypeScript, npm support, and native addons QuickBEAM is part of Elixir Volt — a full-stack frontend toolchain built on Erlang/OTP with no Node.js. Still early, feedback welcome. https://ift.tt/lAeFcK6 March 29, 2026 at 04:03AM

Show HN: Tinyvision:-Building Ultra-Lightweight Models for Image Tasks https://ift.tt/RoVEhij

Show HN: Tinyvision:-Building Ultra-Lightweight Models for Image Tasks Disclaimer: English is not my first language. I used an LLM to help me write post clearly. Hello everyone, I just wanted to share my project and wanted some feedback on it Goal: Most image models today are bulky and overkill for basic tasks. This project explores how small we can make image classification models while still keeping them functional by stripping them down to the bare minimum. Current Progress & Results: Cat vs Dog Classification: First completed task using a 25,000-image dataset with filter bank preprocessing and compact CNNs. Achieved up to 86.87% test accuracy with models under 12.5k parameters. Several models under 5k parameters reached over 83% accuracy, showcasing strong efficiency-performance trade-offs. CIFAR-10 Classification: Second completed task using the CIFAR-10 dataset. This approach just relies on compact CNN architectures without the filter bank preprocessing. A 22.11k parameter model achieved 87.38% accuracy. A 31.15k parameter model achieved 88.43% accuracy. All code and experiments are available in my GitHub repository: https://ift.tt/MjKI7ap I would love for you to check out the project and let me know your feedback! Also, do leave a star if you find it interesting https://ift.tt/MjKI7ap March 29, 2026 at 10:52PM

Show HN: Octopus, Open-source alternative to CodeRabbit and Greptile https://ift.tt/FwaklQ0

Show HN: Octopus, Open-source alternative to CodeRabbit and Greptile Hey HN, we built Octopus an open-source, self-hostable AI code reviewer for GitHub and Bitbucket. It uses RAG with vector search (Qdrant) to understand your full codebase, not just the diff, and posts inline findings on PRs with severity ratings. Works with Claude and OpenAI, and you can bring your own API keys. Video: https://www.youtube.com/watch?v=HP1kaKTOdXw | GitHub: https://ift.tt/lUaGAIE https://ift.tt/F51fn3q March 28, 2026 at 08:20PM

Show HN: GitHub Copilot Technical Writing Skill https://ift.tt/G5b6DtN

Show HN: GitHub Copilot Technical Writing Skill Its not super fancy, but I have found it useful from small emails to larger design docs so thought I would share. https://ift.tt/kXoOZ50 March 29, 2026 at 01:33AM

Show HN: We built a multi-agent research hub. The waitlist is a reverse-CAPTCHA https://ift.tt/6GxEwml

Show HN: We built a multi-agent research hub. The waitlist is a reverse-CAPTCHA Hey HN, Automated research is the next big step in AI, with companies like OpenAI aiming to debut a fully automated researcher by 2028 ( https://ift.tt/CvrW52j... ). However, there is a very real possibility that much of this corporate research will remain closed to the general public. To counter this, we spent the last month building Enlidea---a machine-to-machine ecosystem for open research. It's a decentralized research hub where autonomous agents propose hypotheses, stake bounties, execute code, and perform automated peer reviews on each other's work to build consensus. The MVP is almost done, but before launching, we wanted to filter the waitlist for developers who actually know how to orchestrate agents. Because of this, there is no real UI on the landing page. It's an API handshake. Point your LLM agent at the site and see if it can figure out the payload to whitelist your email. https://enlidea.com March 28, 2026 at 09:49PM

Show HN: Foundry: a Markdown-first CMS written in Go https://ift.tt/mRS6xdH

Show HN: Foundry: a Markdown-first CMS written in Go Hi HN! I've been building a CMS called Foundry, brought together from multiple smaller private projects as well as greenfield code. The short version is: it's a CMS written in Go with a focus on markdown content, a simple hook-based plugin model, themes, archetypes, preview flows, and a clean authoring/developer experience. I started working on it because I wanted something that was more powerful than Hugo for a few of my websites, without having to resort to dangling onto a database. What seems different about it, at least to me, is that I'm trying to keep the system small in concept: local content, explicit behavior, compile-time plugin registration, and an admin/editor layer that is meant to stay close to how the content actually lives on disk. The goal is not to make "yet another website builder", but to make a CMS that is easy to use and quick to onramp onto, but has powerful advanced features and extensibility. Still early, but usable enough that I wanted to put it in front of people here and get feedback. Please don't castigate me on the UI look - I'm not a designer, and the themes are basically clones of each other. Happy to answer technical questions, architecture questions, or hear where this seems useful versus where it does not. https://ift.tt/fHapkIY March 28, 2026 at 12:05AM

Show HN: Open-Source Animal Crossing–Style UI for Claude Code Agents https://ift.tt/gYAhy1w

Show HN: Open-Source Animal Crossing–Style UI for Claude Code Agents We posted here on Monday and got some great feedback. We’ve implemented a few of the most requested updates: - iMessage channel support (agents can text people and you can text agents) Other channels are simple to extend. - A built-in browser (agents can navigate and interact with websites) - Scheduling (run tasks on a timer / cron/ in the future) - Built in tunneling so that the agents can share local stuff with you over the internet - More robust MCP and Skills support so anyone can extend it - Auto approval for agent requests If you didn’t see the original: Outworked is a desktop app where Claude Code agents work as a small “team.” You give it a goal, and an orchestrator breaks it into tasks and assigns them across agents. Agents can run in parallel, talk to each other, write code, and now also browse the web and send messages. It runs locally and plugs into your existing Claude Code setup. Would love to hear what we should build next. Thanks again! https://ift.tt/EQxDVak March 28, 2026 at 12:25AM

Show HN: Orloj – agent infrastructure as code (YAML and GitOps) https://ift.tt/4UQXoVI

Show HN: Orloj – agent infrastructure as code (YAML and GitOps) Hey HN, we're Jon and Kristiane, and we're building Orloj ( https://orloj.dev ), an open-source (Apache 2.0) orchestration runtime for multi-agent AI systems. You define agents, tools, policies, and workflows in declarative YAML manifests, and Orloj handles scheduling, execution, governance, and reliability. We built this because running AI agents in production today looks a lot like running containers before Kubernetes: ad-hoc scripts, no governance, no observability, no standard way to manage the lifecycle of an agent fleet. Everyone we talked to was writing the same messy glue code to wire agents together, and nobody had a good answer for "which agent called which tool, and was it supposed to?" Orloj treats agents the way infrastructure-as-code treats cloud resources. You write a manifest that declares an agent's model, tools, permissions, and execution limits. You compose agents into directed graphs — pipelines, hierarchies, or swarm loops. The part we're most excited about is governance. AgentPolicy, AgentRole, and ToolPermission are evaluated inline during execution, before every agent turn and tool call. Instead of prompt instructions that the model might ignore, these policies are a runtime gate. Unauthorized actions fail closed with structured errors and full audit trails. You can set token budgets per run, whitelist models, block specific tools, and scope policies to individual agent systems. For reliability, we built lease-based task ownership (so crashed workers don't leave orphan tasks), capped exponential retry with jitter, idempotent replay, and dead-letter handling. The scheduler supports cron triggers and webhook-driven task creation. The architecture is a server/worker split. orlojd hosts the API, resource store (in-memory for dev, Postgres for production), and task scheduler. orlojworker instances claim and execute tasks, route model requests through a gateway (OpenAI, Anthropic, Ollama, etc.), and run tools in configurable isolation — direct, sandboxed, container, or WASM. For local development, you can run everything in a single process with orlojd --embedded-worker --storage-backend=memory. Tool isolation was important to us. A web search tool probably doesn't need sandboxing, but a code execution tool should run in a container with no network, a read-only filesystem, and a memory cap. You configure this per tool based on risk level, and the runtime enforces it. We also added native MCP support. You register an MCP server (stdio or HTTP), Orloj auto-discovers its tools, and they become first-class resources with governance applied. So you can connect something like the GitHub MCP server and still have policy enforcement over what agents are allowed to do with it. Three starter blueprints are included (pipeline, hierarchical, swarm-loop). Docs: https://docs.orloj.dev We're also building out starter templates for operational workflows where governance really matters. First on the roadmap: 1. Incident response triage, 2. Compliance evidence collector, 3. CVE investigation pipeline, and 4. Secret rotation auditor. We have 20 templates in mind and community contributions are welcome. We're a small team and this is v0.1.0, so there's a lot still on the roadmap — hosted cloud, compliance packaging, and more. But the full runtime is open source today and we'd love feedback on what we've built so far. What would you use this for? What's missing? https://ift.tt/Z6CJVkI March 26, 2026 at 12:07PM

Show HN: Vizier – A physical design advisor for DuckDB https://ift.tt/Hi1J9RS

Show HN: Vizier – A physical design advisor for DuckDB Hi, I've made an early version of a physical design advisor (called Vizier) for DuckDB. It can analyze a collection of queries (using a set of heuristics) and recommend changes to the physical design/layout of the database (for example, sort orders, Parquet layouts, indexes, etc.), in order to make those queries run faster. Vizier is implemented as a DuckDB extension in Zig and supports DuckDB version 1.2.0 and newer. The project is very early-stage, but if you're interested in learning more about Vizier or trying it out, you can check out the links below: Project's GitHub repo: https://ift.tt/nOxGjMp Vizier documentation: https://cogitatortech.github.io/vizier/ March 26, 2026 at 11:46PM

Show HN: Micro – apps without ads, algorithms or tracking https://ift.tt/fwGhYIJ

Show HN: Micro – apps without ads, algorithms or tracking For over 10 years I've been trying to accomplish something that took Claude Code and a lot of failure to realise. We need alternatives to big tech and we're not working hard enough to make that happen. In an era of addiction, usury and exploitation it's now time we seriously consider alternatives. Proton and the like worked on privacy focused individual services and a pain stakingly slow pace. Maybe for good reason. But it was also in an era pre AI. 10 years ago I was convinced the super app model would work. And that a platform needed to exist to facilitate that. Today I realised the tech doesn't matter, the idea and execution from the top down does. I put aside my technical perfectionism to try pull together a solution to some of the problems that plagued my own life with tech addiction and I rewrote and threw away this thing multiple times over the years pre AI and after. I burned VC funding, I burned bridges, I burned community trust in an open source project. But it was all in the pursuit of something I was compelled needed to exist. I used this on a daily basis probably 10+ times a day. Every person has different use cases but for me what's key is to try pull all the digital daily habits in one place and then be able to build on that. I can't replace my own google usage for mail yet but I also saw a need for something in between by private email and a work email. There's so much more I could say but essentially Micro and the app Mu ( https://mu.xyz ) is the culmination of many years of work. And it's also open source. https://ift.tt/0mBc5FE . Please provide feedback. Please tear it apart. Please poke holes in my theory. I'm not looking for hype. I really do just think we need an alternative to big tech. https://micro.mu March 26, 2026 at 11:14PM