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[Other] Show HN: Luminal – Open-source, search-based GPU compiler Hi HN, I’m Joe. My friends Matthew, Jake and I are building Luminal (<a href="https:&#x2F;&#x2F;luminalai.com&#x2F;">https:&#x2F;&#x2F;luminalai.com&#x2F;</a>), a GPU compiler for automatically generating fast GPU kernels for AI models. It uses search-based compilation to achieve high performance.<p>We take high level model code, like you&#x27;d have in PyTorch, and generate very fast GPU code. We do that without using LLMs or AI - rather, we pose it as a search problem. Our compiler builds a search space, generates millions of possible kernels, and then searches through it to minimize runtime.<p>You can try out a demo in `demos&#x2F;matmul` on mac to see how Luminal takes a naive operation, represented in our IR of 12 simple operations, and compiles it to an optimized, tensor-core enabled Metal kernel. Here’s a video showing how: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;P2oNR8zxSAA" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;P2oNR8zxSAA</a><p>Our approach differs significantly from traditional ML libraries in that we ahead-of-time compile everything, generate a large search space of logically-equivalent kernels, and search through it to find the fastest kernels. This allows us to leverage the Bitter Lesson to discover complex optimizations like Flash Attention entirely automatically without needing manual heuristics. The best rule is no rule, the best heuristic is no heuristic, just search everything.<p>We’re working on bringing CUDA support up to parity with Metal, adding more flexibility to the search space, adding full-model examples (like Llama), and adding very exotic hardware backends.<p>We aim to radically simplify the ML ecosystem while improving performance and hardware utilization. Please check out our repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;luminal-ai&#x2F;luminal" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;luminal-ai&#x2F;luminal</a> and I’d love to hear your thoughts!

[Other] Improvements to OCaml code editing: the basics of a refactor engine

[CLI Tool] Show HN: Claude Code workflow: PRDs → GitHub Issues → parallel execution I built a lightweight project management workflow to keep AI-driven development organized.<p>The problem was that context kept disappearing between tasks. With multiple Claude agents running in parallel, I’d lose track of specs, dependencies, and history. External PM tools didn’t help because syncing them with repos always created friction.<p>The solution was to treat GitHub Issues as the database. The &quot;system&quot; is ~50 bash scripts and markdown configs that:<p>- Brainstorm with you to create a markdown PRD, spins up an epic, and decomposes it into tasks and syncs them with GitHub issues - Track progress across parallel streams - Keep everything traceable back to the original spec - Run fast from the CLI (commands finish in seconds)<p>We’ve been using it internally for a few months and it’s cut our shipping time roughly in half. Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;automazeio&#x2F;ccpm" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;automazeio&#x2F;ccpm</a><p>It’s still early and rough around the edges, but has worked well for us. I’d love feedback from others experimenting with GitHub-centric project management or AI-driven workflows.

[Other] Tidewave Web: in-browser coding agent for Rails and Phoenix

[Other] Show HN: Because I Kanban Just wanted to share my latest project Taskstax, it&#x27;s just a simple Kanban kind of trello clone, built mainly for the learns, but it works too so it&#x27;s online.<p>Simple Kanban boards, easy login that takes you straight to it.<p>It uses socket.io for data xfer after login which was fun to setup and also makes it work well.<p>Totally free, any feedback would be cool or if you wanted some info on the tech just ask.

[Other] Docker container for running Claude Code in "dangerously skip permissions" mode

[API/SDK] Show HN: Lemonade: Run LLMs Locally with GPU and NPU Acceleration Lemonade is an open-source SDK and local LLM server focused on making it easy to run and experiment with large language models (LLMs) on your own PC, with special acceleration paths for NPUs (Ryzen™ AI) and GPUs (Strix Halo and Radeon™).<p>Why?<p>There are three qualities needed in a local LLM serving stack, and none of the market leaders (Ollama, LM Studio, or using llama.cpp by itself) deliver all three: 1. Use the best backend for the user’s hardware, even if it means integrating multiple inference engines (llama.cpp, ONNXRuntime, etc.) or custom builds (e.g., llama.cpp with ROCm betas). 2. Zero friction for both users and developers from onboarding to apps integration to high performance. 3. Commitment to open source principles and collaborating in the community.<p>Lemonade Overview:<p>Simple LLM serving: Lemonade is a drop-in local server that presents an OpenAI-compatible API, so any app or tool that talks to OpenAI’s endpoints will “just work” with Lemonade’s local models. Performance focus: Powered by llama.cpp (Vulkan and ROCm for GPUs) and ONNXRuntime (Ryzen AI for NPUs and iGPUs), Lemonade squeezes the best out of your PC, no extra code or hacks needed. Cross-platform: One-click installer for Windows (with GUI), pip&#x2F;source install for Linux. Bring your own models: Supports GGUFs and ONNX. Use Gemma, Llama, Qwen, Phi and others out-of-the-box. Easily manage, pull, and swap models. Complete SDK: Python API for LLM generation, and CLI for benchmarking&#x2F;testing. Open source: Apache 2.0 (core server and SDK), no feature gating, no enterprise “gotchas.” All server&#x2F;API logic and performance code is fully open; some software the NPU depends on is proprietary, but we strive for as much openness as possible (see our GitHub for details). Active collabs with GGML, Hugging Face, and ROCm&#x2F;TheRock.<p>Get started:<p>Windows? Download the latest GUI installer from <a href="https:&#x2F;&#x2F;lemonade-server.ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;lemonade-server.ai&#x2F;</a><p>Linux? Install with pip or from source (<a href="https:&#x2F;&#x2F;lemonade-server.ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;lemonade-server.ai&#x2F;</a>)<p>Docs: <a href="https:&#x2F;&#x2F;lemonade-server.ai&#x2F;docs&#x2F;" rel="nofollow">https:&#x2F;&#x2F;lemonade-server.ai&#x2F;docs&#x2F;</a><p>Discord for banter&#x2F;support&#x2F;feedback: <a href="https:&#x2F;&#x2F;discord.gg&#x2F;5xXzkMu8Zk" rel="nofollow">https:&#x2F;&#x2F;discord.gg&#x2F;5xXzkMu8Zk</a><p>How do you use it?<p>Click on lemonade-server from the start menu Open http:&#x2F;&#x2F;localhost:8000 in your browser for a web ui with chat, settings, and model management. Point any OpenAI-compatible app (chatbots, coding assistants, GUIs, etc.) at http:&#x2F;&#x2F;localhost:8000&#x2F;api&#x2F;v1 Use the CLI to run&#x2F;load&#x2F;manage models, monitor usage, and tweak settings such as temperature, top-p and top-k. Integrate via the Python API for direct access in your own apps or research.<p>Who is it for?<p>Developers: Integrate LLMs into your apps with standardized APIs and zero device-specific code, using popular tools and frameworks. LLM Enthusiasts, plug-and-play with: Morphik AI (contextual RAG&#x2F;PDF Q&amp;A) Open WebUI (modern local chat interfaces) Continue.dev (VS Code AI coding copilot) …and many more integrations in progress! Privacy-focused users: No cloud calls, run everything locally, including advanced multi-modal models if your hardware supports it.<p>Why does this matter?<p>Every month, new on-device models (e.g., Qwen3 MOEs and Gemma 3) are getting closer to the capabilities of cloud LLMs. We predict a lot of LLM use will move local for cost reasons alone. Keeping your data and AI workflows on your own hardware is finally practical, fast, and private, no vendor lock-in, no ongoing API fees, and no sending your sensitive info to remote servers. Lemonade lowers friction for running these next-gen models, whether you want to experiment, build, or deploy at the edge. Would love your feedback! Are you running LLMs on AMD hardware? What’s missing, what’s broken, what would you like to see next? Any pain points from Ollama, LM Studio, or others you wish we solved? Share your stories, questions, or rant at us.<p>Links:<p>Download &amp; Docs: <a href="https:&#x2F;&#x2F;lemonade-server.ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;lemonade-server.ai&#x2F;</a><p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;lemonade-sdk&#x2F;lemonade" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;lemonade-sdk&#x2F;lemonade</a><p>Discord: <a href="https:&#x2F;&#x2F;discord.gg&#x2F;5xXzkMu8Zk" rel="nofollow">https:&#x2F;&#x2F;discord.gg&#x2F;5xXzkMu8Zk</a><p>Thanks HN!

[API/SDK] Show HN: Twick - React SDK for Timeline-Based Video Editing

[Other] D2 (text to diagram tool) now supports ASCII renders

[Other] Show HN: Built a memory layer that stops AI agents from forgetting everything Tired of AI coding tools that forget everything between sessions? Every time I open a new chat with Claude or fire up Copilot, I&#x27;m back to square one explaining my codebase structure.<p>So I built something to fix this. It&#x27;s called In Memoria. Its an MCP server that gives AI tools persistent memory. Instead of starting fresh every conversation, the AI remembers your coding patterns, architectural decisions, and all the context you&#x27;ve built up.<p>The setup is dead simple: `npx in-memoria server` then connect your AI tool. No accounts, no data leaves your machine.<p>Under the hood it&#x27;s TypeScript + Rust with tree-sitter for parsing and vector storage for semantic search. Supports JavaScript&#x2F;TypeScript, Python, and Rust so far.<p>It originally started as a documentation tool but had a realization - AI doesn&#x27;t need better docs, it needs to remember stuff. Spent the last few months rebuilding it from scratch as this memory layer.<p>It&#x27;s working pretty well for me but curious what others think, especially about the pattern learning part. What languages would you want supported next?<p>Code: <a href="https:&#x2F;&#x2F;github.com&#x2F;pi22by7&#x2F;In-Memoria" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;pi22by7&#x2F;In-Memoria</a>

[Other] Show HN: GiralNet – A Privacy Network for Your Team (Not the World) Hello, for some time I&#x27;ve been developing this project now that I am happy that it finally can see the light. I love Tor, but I believe the biggest thing with Tor is that the nodes are strangers which in itself requires some sort of level in just that, complete strangers.<p>For this reason, I decided to build this private network inspired by the Onion router. Unlike other public networks, GiralNet is not for anonymous connections to strangers. It is built for small teams or groups who want privacy but also need a level of trust. It assumes that the people running the nodes in the network are known and verifiable. This provides a way for a group to create their own private and secure network, where the infrastructure is controlled and the people behind the nodes are accountable. The goal is to provide privacy without relying on a large, anonymous public network.<p>In terms of technical details, it is a SOCKS5 proxy that routes internet traffic through a series of other computers. It does this by wrapping your data in multiple layers of encryption, just like the onion router does it. Each computer in the path unwraps one layer to find the next destination, but never knows the full path. This makes it difficult for any single party to see both where the traffic came from and where it is going.<p>I will gladly answer any questions you might have, thank you.

[IDE/Editor] Positron, a New Data Science IDE

[Other] Show HN: Python file streaming 237MB/s on $8/M droplet in 507 lines of stdlib Quick Links:<p>- PyPI: <a href="https:&#x2F;&#x2F;pypi.org&#x2F;project&#x2F;axon-api&#x2F;" rel="nofollow">https:&#x2F;&#x2F;pypi.org&#x2F;project&#x2F;axon-api&#x2F;</a><p>- GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;b-is-for-build&#x2F;axon-api" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;b-is-for-build&#x2F;axon-api</a><p>- Deployment Script: <a href="https:&#x2F;&#x2F;github.com&#x2F;b-is-for-build&#x2F;axon-api&#x2F;blob&#x2F;master&#x2F;examples&#x2F;deployment_scripts&#x2F;deploy-axon.sh" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;b-is-for-build&#x2F;axon-api&#x2F;blob&#x2F;master&#x2F;examp...</a><p>Axon is a 507-line, pure Python WSGI framework that achieves up to 237MB&#x2F;s file streaming on $8&#x2F;month hardware. The key feature is the dynamic bundling of multiple files into a single multipart stream while maintaining bounded memory (&lt;225MB). The implementation saturates CPU before reaching I&#x2F;O limits.<p>Technical highlights:<p>- Pure Python stdlib implementation (no external dependencies)<p>- HTTP range support for partial content delivery<p>- Generator-based streaming with constant memory usage<p>- Request batching via query parameters<p>- Match statement-based routing (eliminates traversal and probing)<p>- Built-in sanitization and structured logging<p>The benchmarking methodology uses fresh Digital Ocean droplets with reproducible wrk tests across different file sizes. All code and deployment scripts are included.

[Code Quality] Show HN: I've made an easy to extend and flexible JavaScript logger hi! I&#x27;ve made a logger for JS&#x2F;TS. It&#x27;s easily extendable, easy to use and configure.<p>Would like to hear a feedback from you!

[API/SDK] Show HN: I'm building a "work visa" API for AI agents Hey HN,<p>I’m Chris, a solo dev in Melbourne AU. For the past month I&#x27;ve been spending my after work hours building AgentVisa. I&#x27;m both excited (and admittedly nervous) to be sharing it with you all today.<p>I&#x27;ve been spending a lot of time thinking about the future of AI agents and the more I experimented, the more I realized I was building on a fragile foundation. How do we build trust into these systems? How do we know what our agents are doing, and who gave them permission?<p>My long-term vision is to give developers an &quot;Agent Atlas&quot; - a clear map of their agentic workforce, showing where they&#x27;re going and what they&#x27;re authorized to do. The MVP I&#x27;m launching today is that first step.<p>The core idea is simple: stop giving agents a permanent &quot;passport&quot; (a static API key) and start giving them a temporary &quot;work visa&quot; for each specific task. AgentVisa is a simple API that issues secure, short-lived credentials, linking an agent&#x27;s task back to a specific user and a set of permissions.<p>To make this more concrete, I&#x27;ve put together a demo you can run locally showing how an agentic customer service bot uses AgentVisa to access an internal API. You can see it here: <a href="https:&#x2F;&#x2F;github.com&#x2F;AgentVisa&#x2F;agentvisa-customer-support-demo" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;AgentVisa&#x2F;agentvisa-customer-support-demo</a><p>Under the hood it’s JWTs for now. But the product isn&#x27;t the token - it&#x27;s the simple, secure workflow for delegating authority. It&#x27;s a pattern I needed for my own projects and I&#x27;m hoping it&#x27;s useful to you too.<p>I know there&#x27;s a &quot;two-sided problem&quot; here - this is most useful when the server an agent connects to can also verify the agent&#x27;s authenticity. Right now it&#x27;s ideal for securing your own internal services, which is where I started. My hope is that over time this can be built into a standard that more services adopt.<p>I&#x27;m keen for feedback from fellow devs working with AI agents. Does this problem of agent identity and auditability resonate with you? Is the &quot;visa vs. passport&quot; concept clear? What would you want to see on that &quot;Agent Atlas&quot; I mentioned?<p>The Python SDK is open and on GitHub, and there&#x27;s a generous free tier so you can build with it right away. I&#x27;ll be here to answer as best I can any questions you have. Thanks for checking it out!<p>SDK: <a href="https:&#x2F;&#x2F;github.com&#x2F;AgentVisa&#x2F;agentvisa-python" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;AgentVisa&#x2F;agentvisa-python</a> Demo: <a href="https:&#x2F;&#x2F;github.com&#x2F;AgentVisa&#x2F;agentvisa-customer-support-demo" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;AgentVisa&#x2F;agentvisa-customer-support-demo</a><p>Note: for us down under it’s getting late! So if I miss your comment while asleep, I’ll reply first thing in the morning AEST.

[Database] Show HN: Chroma Cloud – serverless search database for AI Hey HN - I’m Jeff, co-founder of Chroma.<p>In December of 2022, I was scrolling Twitter in the wee-hours of the morning holding my then-newborn daughter. ChatGPT had launched, and we were all figuring out what this technology was and how to make it useful. Developers were using retrieval to bring their data to the models - and so I DM’d every person who had tweeted about “embeddings” in the entire month of December. (it was only 120 people!) I saw then how AI was going to need to search to all the world’s information to build useful and reliable applications.<p>Anton Troynikov and I started Chroma with the beliefs that:<p>1. AI-based systems were way too difficult to productionize<p>2. Latent space was incredibly important to improving AI-based systems (no one understood this at the time)<p>On Valentines Day 2023, we launched first version of Chroma and it immediately took off. Chroma made retrieval just work. Chroma is now a large open-source project with 21k+ stars and 5M monthly downloads, used at companies like Apple, Amazon, Salesforce, and Microsoft.<p>Today we’re excited to launch Chroma Cloud - our fully-managed offering backed by an Apache 2.0 serverless database called Chroma Distributed. Chroma Distributed is written in Rust and uses object-storage for extreme scalability and reliability. Chroma Cloud is fast and cheap. Leading AI companies such as Factory, Weights &amp; Biases, Propel, and Foam already use Chroma Cloud in production to power their agents. It brings the “it just works” developer experience developers have come to know Chroma for - to the Cloud.<p>Try it out and let me know what you think!<p>— Jeff

[API/SDK] Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection Hi HN! This is Ben from Reality Defender (<a href="https:&#x2F;&#x2F;www.realitydefender.com">https:&#x2F;&#x2F;www.realitydefender.com</a>). We build real-time multimodal and multi-model deepfake detection for Fortune 100s and governments all over the world. (We even won the RSAC Innovation Showcase award for our work: <a href="https:&#x2F;&#x2F;www.prnewswire.com&#x2F;news-releases&#x2F;reality-defender-wins-most-innovative-startup-at-rsa-conference-2024-innovation-sandbox-302137326.html" rel="nofollow">https:&#x2F;&#x2F;www.prnewswire.com&#x2F;news-releases&#x2F;reality-defender-wi...</a>)<p>Today, we’re excited to share our public API and SDK, allowing anyone to access our platform with 2 lines of code: <a href="https:&#x2F;&#x2F;www.realitydefender.com&#x2F;api">https:&#x2F;&#x2F;www.realitydefender.com&#x2F;api</a><p>Back in W22, we launched our product to detect AI-generated media across audio, video, and images: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30766050">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30766050</a><p>That post kicked off conversations with devs, security teams, researchers, and governments. The most common question: &quot;Can we get API&#x2F;SDK access to build deepfake detection into our product?&quot;<p>We’ve heard that from solo devs building moderation tools, fintechs adding ID verification, founders running marketplaces, and infrastructure companies protecting video calls and onboarding flows. They weren’t asking us to build anything new; they simply wanted access to what we already had so they could plug it in and move forward.<p>After running pilots and engagements with customers, we’re finally ready to share our public API and SDK. Now anyone can embed deepfake detection with just two lines of code, starting at the low price of free.<p><a href="https:&#x2F;&#x2F;www.realitydefender.com&#x2F;api">https:&#x2F;&#x2F;www.realitydefender.com&#x2F;api</a><p>Our new developer tools support detection across images, voice, video, and text — with the former two available as part of the free tier. If your product touches KYC, UGC, support workflows, communications, marketplaces, or identity layers, you can now embed real-time detection directly in your stack. It runs in the cloud, and longstanding clients using our platform have also deployed on-prem, at the edge, or on fully airgapped systems.<p>SDKs are currently available in Python, Java, Rust, TypeScript, and Go. The first 50 scans per month are free, with usage-based pricing beyond that. If you’re working on something that requires other features or streaming access (like real-time voice or video), email us directly at yc@realitydefender.com<p>Much has changed since 2022. The threats we imagined back then are now showing up in everyday support tickets and incident reports. We’ve witnessed voice deepfakes targeting bank call centers to commit real-time fraud; fabricated documents and AI-generated selfies slip through KYC and IDV onboarding systems; fake dating profiles, AI-generated marketplace sellers, and “verified” influencers impersonating real people. Political disinformation videos and synthetic media leaks have triggered real-world legal and PR crises. Even reviews, support transcripts, and impersonation scripts are increasingly being generated by AI. Detection remains harder than we first expected since we began in 2021. New generation methods emerge every few weeks that invalidate prior assumptions. This is why we are committed to building every layer of this ourselves. We don’t license or white-label detection models; everything we deploy is built in-house by our team.<p>Since our original launch, we’ve worked with tier-one banks, global governments, and media companies to deploy detection inside their highest-risk workflows. However, we always believed the need wasn’t limited to large institutions, but everywhere. It showed up in YC office hours, in early bug reports, and in group chats after our last HN post.<p>We’ve taken our time to make sure this was built well, flexible enough for startups, and battle-tested enough to trust in production. The API you can use today is the same one powering many of our enterprise deployments.<p>Our goal is to make Reality Defender feel like Stripe, Twilio, or Plaid — an invisible, trusted layer that you can drop into your system to protect what matters. We feel deepfake detection is a key component of critical infrastructure, and like any good infrastructure, it should be modular, reliable, and boring (in the best possible way).<p>Reality Defender is already in the Zoom marketplace and will be on the Teams marketplace soon. We will also power deepfake detection for identity workflows, support platforms, and internal trust and safety pipelines.<p>If you&#x27;re building something where trust, identity, or content integrity matter, or if you’ve run into weird edge cases you can’t explain, we’d love to hear from you.<p>You can get started here: <a href="https:&#x2F;&#x2F;realitydefender.com&#x2F;api">https:&#x2F;&#x2F;realitydefender.com&#x2F;api</a><p>Or you can try us for free two different ways:<p>1) 1-click add to Zoom &#x2F; Teams to try in your own calls immediately.<p>2) Email us up to 50 files at yc@realitydefender.com and we’ll scan them for you — no setup required.<p>Thanks again to the HN community for helping launch us three years ago. It’s been a wild ride, and we’re excited to share something new. We live on HN ourselves and will be here for all your feedback. Let us know what you think!

[Other] Show HN: Memori – Open-Source Memory Engine for AI Agents Hey HN! I&#x27;m Arindam, part of the team behind Memori (<a href="https:&#x2F;&#x2F;memori.gibsonai.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;memori.gibsonai.com&#x2F;</a>).<p>Memori adds a stateful memory engine to AI agents, enabling them to stay consistent, recall past work, and improve over time. With Memori, agents don’t lose track of multi-step workflows, repeat tool calls, or forget user preferences. Instead, they build up human-like memory that makes them more reliable and efficient across sessions.<p>We’ve also put together demo apps (a personal diary assistant, a research agent, and a travel planner) so you can see memory in action.<p>Current LLMs are stateless — they forget everything between sessions. This leads to repetitive interactions, wasted tokens, and inconsistent results. When building AI agents, this problem gets even worse: without memory, they can’t recover from failures, coordinate across steps, or apply simple rules like “always write tests.”<p>We realized that for AI agents to work in production, they need memory. That’s why we built Memori.<p>Memori uses a multi-agent architecture to capture conversations, analyze them, and decide which memories to keep active. It supports three modes:<p>- Conscious Mode: short-term memory for recent, essential context. - Auto Mode: dynamic search across long-term memory. - Combined Mode: blends both for fast recall and deep retrieval.<p>Under the hood, Memori is SQL-first. You can use SQLite, PostgreSQL, or MySQL to store memory with built-in full-text search, versioning, and optimization. This makes it simple to deploy, production-ready, and extensible.<p>Memori is backed by GibsonAI’s database infrastructure, which supports:<p>- Instant provisioning - Autoscaling on demand - Database branching &amp; versioning - Query optimization - Point of recovery<p>This means memory isn’t just stored, it’s reliable, efficient, and scales with real-world workloads.<p>We’ve open-sourced Memori under the Apache 2.0 license so anyone can build with it. You can check out the GitHub repo here: <a href="https:&#x2F;&#x2F;github.com&#x2F;GibsonAI&#x2F;memori" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;GibsonAI&#x2F;memori</a>, explore the docs, and join our community on Discord.<p>We’d love to hear your thoughts. Please dive into the code, try out the demos, and share feedback, your input will help shape where we take Memori from here.

[Other] Show HN: Typed-arrow – compile‑time Arrow schemas for Rust Hi community, we just released <a href="https:&#x2F;&#x2F;github.com&#x2F;tonbo-io&#x2F;typed-arrow" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;tonbo-io&#x2F;typed-arrow</a>.<p>When working with arrow-rs, we noticed that schemas are declared at runtime. This often leads to runtime errors and makes development less safe.<p>typed-arrow takes a different approach:<p>- Schemas are declared at compile time with Rust’s type system.<p>- This eliminates runtime schema errors.<p>- And introduces no runtime overhead — everything is checked and generated by the compiler.<p>If you’ve run into Arrow runtime schema issues, and your schema is stable (not defined or switched at runtime), this project might be useful.

[Database] EloqKV, a distributed database with Redis compatible API (GPLv2 and AGPLv3)