Show HN: Timezone App – Visual meeting scheduler for distributed teams https://ift.tt/6C4OGwL

Show HN: Timezone App – Visual meeting scheduler for distributed teams Scheduling meetings across multiple time zones has always been painful for me, especially across daylight saving time transitions. So I built a visual timeline that makes it easy to find overlapping availability. Add your locations, drag to select a time range, and share a link. Recipients see the proposed times in their local time zone automatically. A few things that might be interesting: * Location search over GeoNames with fuzzy matching using weighted edit distance, so typos and partial names still resolve correctly. * Shareable links encode the selected time range and locations in a base62 payload to keep URLs short and stateless — no database lookup needed. * Handles the annoying edge cases: DST transitions use the IANA timezone database, and 15/30-minute UTC offsets (Nepal, India, Newfoundland) work correctly. * Google Calendar and Outlook integration, but all calendar data is fetched and processed entirely in the browser. Events are never fetched or stored on the server. Would love feedback on what's useful, not useful, or could be improved! https://timezoneapp.co/ March 29, 2026 at 11:36PM

Show HN: Octopus, Open-source alternative to CodeRabbit and Greptile https://ift.tt/6MdLxti

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/WyTbOLE https://ift.tt/gZDhB75 March 28, 2026 at 06:50PM

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