Show HN: GitHub Copilot Technical Writing Skill https://ift.tt/JjEtVa4

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/HUY8DSo March 29, 2026 at 12:03AM

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

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/oZK8wQ6... ). 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 08:19PM

Show HN: Cranki – Crosswords meet Anki flashcards https://ift.tt/u54ZOdz

Show HN: Cranki – Crosswords meet Anki flashcards Hi HN! I am sure most of you have heard of Anki flashcards? Using spaced-repetition is one of the best ways to learn more vocabulary when learning a language. However, I find flashcards super boring. I've been playing some crossword games in my target language, Spanish, but I wished that I could use my custom list of words that I've come across instead of random words. That gave me the idea to create this mini-app. It's super simple. Add your words and you get unlimited crosswords with spaced-repetition! If you get a word right you won't see it for the next few days. Works with most languages (I doubt it works with Arabic or Chinese for example). You can add words one by one or import a CSV (just make sure to follow the columns: word, answer) It's a PWA, so you should be able to install it via your browser and it should work offline! There's still some bugs and QoL things to add but let me know what you think! https://cranki.app March 27, 2026 at 10:16PM

Show HN: Foundry: a Markdown-first CMS written in Go https://ift.tt/GQoRf7L

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/SzRJdmo March 27, 2026 at 10:35PM

Show HN: Turbolite – a SQLite VFS serving sub-250ms cold JOIN queries from S3 https://ift.tt/Km7Jey1

Show HN: Turbolite – a SQLite VFS serving sub-250ms cold JOIN queries from S3 I built a SQLite VFS in Rust that serves cold queries directly from S3 with sub-second performance, and often much faster. It’s called turbolite. It is experimental, buggy, and may corrupt data. I would not trust it with anything important yet. I wanted to explore whether object storage has gotten fast enough to support embedded databases over cloud storage. Filesystems reward tiny random reads and in-place mutation. S3 rewards fewer requests, bigger transfers, immutable objects, and aggressively parallel operations where bandwidth is often the real constraint. This was explicitly inspired by turbopuffer’s ground-up S3-native design. https://ift.tt/fE4oVb8 The use case I had in mind is lots of mostly-cold SQLite databases (database-per-tenant, database-per-session, or database-per-user architectures) where keeping a separate attached volume for inactive database feels wasteful. turbolite assumes a single write source and is aimed much more at “many databases with bursty cold reads” than “one hot database.” Instead of doing naive page-at-a-time reads from a raw SQLite file, turbolite introspects SQLite B-trees, stores related pages together in compressed page groups, and keeps a manifest that is the source of truth for where every page lives. Cache misses use seekable zstd frames and S3 range GETs for search queries, so fetching one needed page does not require downloading an entire object. At query time, turbolite can also pass storage operations from the query plan down to the VFS to frontrun downloads for indexes and large scans in the order they will be accessed. You can tune how aggressively turbolite prefetches. For point queries and small joins, it can stay conservative and avoid prefetching whole tables. For scans, it can get much more aggressive. It also groups pages by page type in S3. Interior B-tree pages are bundled separately and loaded eagerly. Index pages prefetch aggressively. Data pages are stored by table. The goal is to make cold point queries and joins decent, while making scans less awful than naive remote paging would. On a 1M-row / 1.5GB benchmark on EC2 + S3 Express, I’m seeing results like sub-100ms cold point lookups, sub-200ms cold 5-join profile queries, and sub-600ms scans from an empty cache with a 1.5GB database. It’s somewhat slower on normal S3/Tigris. Current limitations are pretty straightforward: it’s single-writer only, and it is still very much a systems experiment rather than production infrastructure. I’d love feedback from people who’ve worked on SQLite-over-network, storage engines, VFSes, or object-storage-backed databases. I’m especially interested in whether the B-tree-aware grouping / manifest / seekable-range-GET direction feels like the right one to keep pushing. https://ift.tt/VmAbXWo March 27, 2026 at 12:28AM

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

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/VitOS7y March 26, 2026 at 10:37AM

Show HN: I built a voice AI that responds like a real woman https://ift.tt/KgTG2Aw

Show HN: I built a voice AI that responds like a real woman Most men rehearse hard conversations in their head. Asking someone out, navigating tension, recovering when things get awkward. The rehearsal never works because you're just talking to yourself. I built vibeCoach : a voice AI where you actually practice these conversations out loud, and the AI responds like a real woman would. She starts guarded. One-word answers, a little skeptical. If you escalate too fast or try something cheesy, she gets MORE guarded. If you're genuine and read the moment right, she opens up. Just like real life. Under the hood it's a multi-agent system : multiple AI agents per conversation that hand off to each other as her emotional state shifts. The transitions are seamless. You just hear her tone change. Voice AI roleplay is a proven B2B category : sales teams use it for call training. I took the same approach and pointed it at the conversation most men actually struggle with. There's a hard conversation scenario too : she's angry about something you did, she's not hearing logic, and you have to navigate her emotions before you can resolve anything. That one's humbling. Live at tryvibecoach.com. Built solo. Happy to answer questions. March 26, 2026 at 12:38AM