Show HN: Extend UI – open-source UI kit for modern document apps https://ift.tt/3pDdbhi

Show HN: Extend UI – open-source UI kit for modern document apps We're open-sourcing 14 components & examples today for PDF, DOCX, and XLSX viewers, plus bounding box citations, file upload, e-signature, and more. It's MIT licensed and fully customizable. Demo video here: https://ift.tt/RpjqnhF When we started, we tried every file viewer and document component library we could find. Unfortunately, none of them had all the functionality (and polish) that we wanted, so we ended up building our own for https://extend.ai/ . It was only ever meant to be internal, but enough customers kept asking for it that we decided to open source it. It's useful for building document processing agents, real-time user facing document intake flows, or all kinds of internal tooling. We naively thought this would be a solved problem. Turns out, making PDF/XLSX/DOCX viewers that work at scale is not trivial...we use and maintain it for Extend ourselves, so we've fixed a lot of edge cases that came up while running millions of pages / day through our own system. Our hope is that with our resources + community support, it'll keep getting better over time. https://ift.tt/z5dAFqy June 10, 2026 at 11:09PM

Show HN: HelixDB – A graph database built on object storage https://ift.tt/KycINzd

Show HN: HelixDB – A graph database built on object storage Hey HN, it’s been just over a year since we launched HelixDB ( https://ift.tt/8Aj6s5m ), a project a friend and I started in college. It’s an OLTP graph database built on object-storage, with native vector search and full-text search (FTS). Why graph, vector and FTS? Graph databases provide a natural cognitive model for data, vectors allow for a semantic understanding of the entities and relationships in the graph, and FTS provides more specific filtering. Many AI-driven applications attempt to combine all of these functionalities by stitching together multiple disconnected systems, but even then there’s no native way to perform joins or queries that span all systems. You still need to handle this logic at the application level. Helix started as a graph DB, but we moved to a hybrid graph/vector approach after attempting to build an AI memory system, which led us down the GraphRAG and HybridRAG rabbit hole, where we would need separate graph and vector databases. We knew scalability would be a challenge at each stage of our product's development, however our initial focus this past year was to prove out the product through local deployments and was only meant to be run on a single node. Scaling graph DBs remained a difficult and expensive problem we’d have to solve later. Some common ways other graph DBs solve scaling is by duplicating entire datasets across distributed machines (extremely expensive per node), or by sharding the data. Sharding databases is effective and affordable, however, graph data doesn’t have explicit partitions like relational databases do. For example, sharding a relational DB involves splitting up tables. When it comes to graph DBs, the edges can span across any of the partitions, and hopping across multiple machines when traversing nodes is ineffective and computationally expensive. Replicating graph DBs for high availability and better throughput drastically increases the operational cost of the db and still has a limit of how big you can vertically scale. The workload that we’re used for requires storing a huge amount of data for agents, where only a subset of that data is ever needed at any one time. So rather than having the whole thing in memory, we can store it all in object-storage and get the bits we need when they’re needed. Agents benefit from better context, which is achieved from more and better data (more relationships etc). By using S3 as the persistence/data layer there is no limit to how big the graph can be or how many relationships you can have, and we can scale to serve throughput and requests by horizontally spinning up nodes and caching relevant subsets of the graph on each node. This way, you get extremely low latency for “hot” data and a p99 of ~100ms for writes and ~50ms for reads from cold storage (S3). Plus you get the benefit of dirt cheap storage. Workloads that HelixDB is currently supporting: - Huge amounts of data (TBs) from which the agents need to search and traverse over - Offering affordable graph storage for companies where cost of graph data is a bottleneck - Consolidating multiple databases, enabling AI agents to have autonomy over companies, helping them become more autonomous. - AI memory - Company brains We’re currently working on our own generalised AI memory layer which will use HelixDB under the hood and be completely open-source. Also, we’re finishing up on pre-filtering for vector search which will allow you to pre-filter based on relationships in the graph, metadata, and sub-graphs. And lastly, GA cloud will be available in the coming weeks. If you want to run Helix locally (either on-disk or in-memory), you can find more info on our github ( https://ift.tt/AZxd03I ) or via our docs ( https://ift.tt/fmApia8 ). If you’re interested in getting started with our distributed cloud, please email us founders@helix-db.com. Many thanks! Comments and feedback welcome! https://ift.tt/AJVGvBr June 10, 2026 at 10:47PM

Show HN: AI-native red-team for penetration testing and vulnerability research https://ift.tt/FzBkjDU

Show HN: AI-native red-team for penetration testing and vulnerability research AI-native red-team workbench for authorized penetration testing and vulnerability research, with specialist agents, sandboxed tooling, evidence records, and replayable timelines. https://ift.tt/pGt6qyO June 9, 2026 at 11:00PM

Show HN: A Minecraft builder skill for coding agents https://ift.tt/NoXY1vK

Show HN: A Minecraft builder skill for coding agents https://ift.tt/f4yTBDC June 8, 2026 at 09:51PM

Show HN: Gitdot – a better GitHub. Open-source, anti-AI, and written in Rust https://ift.tt/YTjnihx

Show HN: Gitdot – a better GitHub. Open-source, anti-AI, and written in Rust What works now: user signups, org creations, private/public repos, and importing GitHub repositories (both as read-only mirrors and full migrations). So basically, you can create, push and pull to a repo, but we don't have many features quite yet (issues, PRs, CI). What is a bit unique is: 1) we built it in Rust and 2) the website is a little odd. Its design is inspired by CLIs (e.g., fzf, broot, vim) instead of web apps, and as such, lacks some affordances that you might typically expect in favor of keyboard-driven instant navigations (we have the very ambitious goal of an FCP of 100ms). In case you're curious, here's how we we built it: https://ift.tt/sZrBjmF We recognize that we're making some bold claims here and are also well aware that we have much to learn. Building software is still hard, and that's a fact we seem to relearn everyday. But we wanted to share what we built so far nonetheless. Cheers, thank y'all for reading, and till the next —paul & mikkel. https://gitdot.io/ June 8, 2026 at 11:52PM

Show HN: Startup sci-fi novel that took me 5 years to write https://ift.tt/5Eo2mic

Show HN: Startup sci-fi novel that took me 5 years to write It started after reading Stephen King's "On Writing" where he likened the art of writing as the unearthing of an archeological site after you stumbled upon a unique bone of a story. His advice was to choose a domain you are deeply intimate with. For me, I've been a struggling startup founder for 15 years—enough material to inspire a novel. A 1,000-word writing exercise turned into a complete 125k-word manuscript over the course of a year. In year 1, I learned the sheer joy of unencumbered creative flow and authentic expression. A similar flow I used to get from coding (and more recently vibe coding). What made it effortless was a mindset that I was writing for the sake of it, not with the intention of publishing. After a year of keeping it close to my chest, I decided to show it to a few close friends. They liked some of it, destroyed some of it. Some ultimately encouraged me to publish it. In year 2, I learned about the chasm between writing for myself and writing for an audience. Nerdy stuff I thought were clever completely flew over my readers' heads. So I studied a dozen textbooks on literature, prose, poetry, voice, grammar, and completely rewrote the manuscript twice over, this time with the audience in mind. There is a lot more finesse to writing than I originally appreciated. In year 3, I felt ready to pitch literary agents. The reason wasn't to make a career out of writing, but to learn from professionals. After 100 personalized pitches and 0 offers of representation, I learned that pitching agents was much harder than pitching VCs. Especially for a niche novel like mine; fellow startup founders was too small of a TAM. In year 4, I engaged with a professional author/editor (Rob Hart, author of The Warehouse), who gave me essays worth of incredible developmental feedback. Lots of nuanced feedback I couldn't get from textbooks. Per his advice, I started back at chapter 1 and refactored the whole manuscript. I distilled it down to the best 88k words. The tinkering never stops; when do you know it's done? I realized that I kept on tinkering because it was more comfortable than overcoming the fear of launching. Today, on 06/08/26, almost 5 years after that Stephen King writing exercise, I'm ready to say “ship it.” Blockchained is a near-future startup sci-fi thriller that chronicles a struggling startup founder who meets a mysterious investor in Hong Kong. Little does he know that the too-good-to-be-true investor works for the Chinese government. Blockchained was written for the fellow startup founders, engineers, and near-future sci-fi enthusiasts. In other words, HN community, you are my target audience Sample chapters available at https://ift.tt/y4t6reR — eBook and paperback available today. Hardcover edition coming soon. I suppose we live in an era where it must be qualified that Blockchained was 99.9% lovingly handcrafted. No AI was used to write this novel aside from research, spellcheck, grammar, and the occasional phrasing checks. https://ift.tt/y4t6reR June 8, 2026 at 11:29PM

Show HN: Quick games disguised as boring spreadsheets https://ift.tt/VHqK6Gp

Show HN: Quick games disguised as boring spreadsheets I posted a version of this over a year ago but decided to rebuild it recently. Bored Spreadsheet is a collection of quick and easy games that look like a spreadsheet from a distance. I have tightened the app to be a collection of 6 games: Minesweeper, 2048, solitaire, sudoku, a market trading game and a daily reconciliation puzzle where the player must find bad data in a fake table. The games are free to play but sign up is required to submit your scores to the leaderboard. As was the original intention over a year ago, I hope this proves useful to those office workers who have a lot of downtime in between tasks or meetings yet don’t have the freedom to open Youtube or Cyberpunk 2077 on their work computers. Ironically my work network has blocked the website as it “contains non-business-related services” – I hope you have better luck than me! https://ift.tt/jXYrsSa June 8, 2026 at 11:13PM

Show HN: I Derived a Pancake https://ift.tt/U8yGDeE

Show HN: I Derived a Pancake After 25 years of making other people's pancake recipes - always yearning for more tang, more fluff, and more predictability - I decided to derive the pancake recipe from the chemistry. You mark checkboxes for what you have on hand (ricotta, sour cream, kefir, buttermilk, yogurt, cottage cheese, lemon, cream of tartar, etc.) and it computes the best recipe based on targets for acid, fat, salt, sugar, and CO2. My particular favorite are the yeast-raised lemon ricotta kefir pancakes - the best I've ever had. The math is done in a small pure-ESM library: ingredient composition to component masses and acid moles, a stoichiometry layer, and a bisection solver for the target deficits. I'm not a chemist, so if something is off, tell me and I will fix it! https://ift.tt/XQu43gC June 5, 2026 at 01:42PM

Show HN: Nightwatch, The open-source, read-only AI SRE https://ift.tt/EZitg5x

Show HN: Nightwatch, The open-source, read-only AI SRE nightwatch is a local-first, read-only layer on top of your monitoring. it groups alert storm into incidents, flags noisy checks and has an agent that can investigate for you live systems. You can e.g. jump from the incident into the agent directly. the reason for this weekend project is that we had a kubernetes upgrade that went wrong, and at some point a rollback wasn't possible anymore, so it had to be fixed live during the night while several problems came together. We run a lot of different systems, on-prem and several Kubernetes clusters, and in a situation like that you spend most of the time just figuring out what is actually broken and where. So i thought that it would be pretty cool to have eyes in the dark in each system that can talk to your "brain". so the idea is to put a baby owl into each environment. Each owl runs where the systems live, keeps that environment's credentials local, and only dials outbound to a central brain, so there is no inbound hole into prod. It exposes a set of read-only skills, and the agent uses them to gather evidence and form a root-cause hypothesis, so the on-call engineer starts with a head start instead of from zero. read-only for now, i don't trust it near prod yet and honestly neither should you. llocal-first for easy self-hosting and to keep credentials on your side. the clustering and recommendations run fully offline with no llm at all. the agent needs a tool-calling llm, you can point it at a remote one, or self-host one (ollama etc.) if you want to stay fully offline. for non selfhosters: before every remote llm call, nightwatch strips real secrets (unrestorable) and swaps identifiers like ips, hostnames and paths for reversible placeholders, so the model only sees masked data while real values are restored only in the proposed commands and tool calls Would love if you try it in your Systems https://ift.tt/WxHPig1 June 8, 2026 at 03:24AM

Show HN: OpenPayphone – open-source guts for a 1996 coin payphone (Pi and SIP) https://ift.tt/NPTph3I

Show HN: OpenPayphone – open-source guts for a 1996 coin payphone (Pi and SIP) https://ift.tt/JyqaELf June 7, 2026 at 11:36PM

Show HN: GentleOS – A pair of hobby OSes for vintage 32-bit and 16-bit PCs https://ift.tt/NrY8pLt

Show HN: GentleOS – A pair of hobby OSes for vintage 32-bit and 16-bit PCs Hello HN, I've been working on a simple OS for tinkering and running bare metal apps on vintage PCs. Since I couldn't quite decide whether to target pure 16-bit, or slightly more capable 32-bit machines, I ended up with two separate versions: - GentleOS/32 ( https://ift.tt/6jGvrMi ) works on i386+, requires 4MB of RAM and VGA display supporting 640x480x16 mode or any 256-color VESA mode. - GentleOS/16 ( https://ift.tt/DKEdljh ) works on 80186+, requires less than 192KB of RAM and a CGA display supporting 320x200x4 mode. You can find more details in the repos. https://ift.tt/6jGvrMi June 7, 2026 at 10:45PM

Show HN: Typedframes – Pandas/polars column name checking at lint time https://ift.tt/blBLuNg

Show HN: Typedframes – Pandas/polars column name checking at lint time https://ift.tt/WziVekB June 7, 2026 at 03:32AM

Show HN: Resonate – Low-latency, high-resolution spectral analysis https://ift.tt/LZCkxMu

Show HN: Resonate – Low-latency, high-resolution spectral analysis Last April I shared about my Resonate project here ( https://ift.tt/uGF07fQ ) A lot has happened since: the work I presented in much more detail at last June's International Computer Music Conference (ICMC) got best paper award. I also gave a talk at the Audio Developer Conference in Bristol last November, the video is on YouTube). This year's work, which I recently presented at this year's ICMC, starts with known techniques from the phase vocoder literature to build self-tuning filter banks that extract very efficiently the frequency components that are actually present in the input signal. Overview on the project website, more details in the papers, including applications to super-resolution spectrograms and re-synthesis experiments. As many people have pointed out, none of the techniques I have used are new (some of them even have different names across different fields), but I haven't seen them applied together in this way, and to me the results are incredibly satisfying and sometimes look magical. See for example this demo: https://youtu.be/LasdoIJJkw8 Of course the best way to experience in person is through the free demo app: https://ift.tt/k4V5jdU Looking forward to feedback from the community! https://ift.tt/Oxc31La June 7, 2026 at 01:09AM