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Show HN: Learn while you wait for your agents to code Hi HN,<p>While waiting for Claude Code to finish running, It&#x27;s very tempting to start another task or browse the internet. This is what happened to me so I built Foyer to try to learn about what the agents are working on instead of losing focus.<p>Product is an early MVP and would love some feedback on this.

The state of building user interfaces in Rust

Show HN: A 150M model that extracts verbatim evidence spans for RAG, no LLM call

Show HN: Extend UI – open-source UI kit for modern document apps We&#x27;re open-sourcing 14 components &amp; examples today for PDF, DOCX, and XLSX viewers, plus bounding box citations, file upload, e-signature, and more. It&#x27;s MIT licensed and fully customizable.<p>Demo video here: <a href="https:&#x2F;&#x2F;share.extend.ai&#x2F;kRmSGKRF">https:&#x2F;&#x2F;share.extend.ai&#x2F;kRmSGKRF</a><p>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 <a href="https:&#x2F;&#x2F;extend.ai&#x2F;">https:&#x2F;&#x2F;extend.ai&#x2F;</a>. It was only ever meant to be internal, but enough customers kept asking for it that we decided to open source it.<p>It&#x27;s useful for building document processing agents, real-time user facing document intake flows, or all kinds of internal tooling.<p>We naively thought this would be a solved problem. Turns out, making PDF&#x2F;XLSX&#x2F;DOCX viewers that work at scale is not trivial...we use and maintain it for Extend ourselves, so we&#x27;ve fixed a lot of edge cases that came up while running millions of pages &#x2F; day through our own system. Our hope is that with our resources + community support, it&#x27;ll keep getting better over time.

Show HN: HelixDB – A graph database built on object storage Hey HN, it’s been just over a year since we launched HelixDB (<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=43975423">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=43975423</a>), 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).<p>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.<p>Helix started as a graph DB, but we moved to a hybrid graph&#x2F;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.<p>We knew scalability would be a challenge at each stage of our product&#x27;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.<p>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.<p>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.<p>Agents benefit from better context, which is achieved from more and better data (more relationships etc). By using S3 as the persistence&#x2F;data layer there is <i>no limit</i> 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.<p>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<p>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.<p>If you want to run Helix locally (either on-disk or in-memory), you can find more info on our github (<a href="https:&#x2F;&#x2F;github.com&#x2F;HelixDB&#x2F;helix-db" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;HelixDB&#x2F;helix-db</a>) or via our docs (<a href="https:&#x2F;&#x2F;docs.helix-db.com&#x2F;database&#x2F;local-development">https:&#x2F;&#x2F;docs.helix-db.com&#x2F;database&#x2F;local-development</a>). If you’re interested in getting started with our distributed cloud, please email us founders@helix-db.com.<p>Many thanks! Comments and feedback welcome!

GitHub Authentication issues related to API requests

The iPad was on Tailscale: a WebRTC debugging story

Show HN: Social network where inviting someone makes you accountable for them Chirpper is invite-only. When you vouch someone in, they join your TrustChain. Their behavior affects your TrustRank, and that propagates up the lineage. No moderators. The accountability is architectural, not policy-based. You can be pseudonymous, but you can&#x27;t be unaccountable. Happy to get into the mechanics in comments.

Apache Burr: Build reliable AI agents and applications

Spoiling Linux Kernel with "sanctioned" code

Show HN: Turn your name into a tree in an infinite procedural shanshui landscape Hi HN! I made this after collecting hundreds of &quot;name → tree&quot; submissions at ITP.<p>Live: <a href="https:&#x2F;&#x2F;landscape.bairui.dev&#x2F;" rel="nofollow">https:&#x2F;&#x2F;landscape.bairui.dev&#x2F;</a> Source: <a href="https:&#x2F;&#x2F;github.com&#x2F;pearmini&#x2F;infinite-landscape" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;pearmini&#x2F;infinite-landscape</a> Plant a tree: <a href="https:&#x2F;&#x2F;tree.bairui.dev&#x2F;" rel="nofollow">https:&#x2F;&#x2F;tree.bairui.dev&#x2F;</a><p>Pan and zoom an infinite procedural landscape. Each name is converted to ASCII codes, which grow into a unique tree (breadth-first branching; repeated digits become mathematical roses). Mountains use midpoint displacement + Perlin noise, with SVG radial gradients in the blue&#x2F;green&#x2F;gold palette from Wang Ximeng&#x27;s &quot;A Thousand Li of Rivers and Mountains.&quot;<p>Inspired by Lingdong Huang&#x27;s {Shan, Shui}* (<a href="https:&#x2F;&#x2F;github.com&#x2F;LingDong-&#x2F;shan-shui-inf" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;LingDong-&#x2F;shan-shui-inf</a>). Every tree is someone&#x27;s name, signed with an APack stamp (<a href="https:&#x2F;&#x2F;apack.bairui.dev&#x2F;" rel="nofollow">https:&#x2F;&#x2F;apack.bairui.dev&#x2F;</a>).<p>Try planting your name, then pan along the ridgeline to find it. &quot;My trees&quot; lets you jump back to ones you planted.<p>Happy to answer questions about the terrain algo, name→tree encoding, or the Riso print we tiled at ITP Winter Show!

TorchCodec 0.14: HDR Video Decoding for CPU and CUDA, and Fast Wav Decoder

PgDog is funded and coming to a database near you

Show HN: Lightweight Task queue on Erlang/OTP, SQLite-backed, no overengineering Setting up Kafka or such enterprise oriented software with their clusters or dedicated servers is heavy and bothering enough that most small teams or indie hackers skip it entirely and making compromise to use in-memory queues.<p>I wanted something in between: a persistent queue that is simple to run (one binary, which makes one sqlite db), gets real fault isolation and crash recovery due to Elixir, easy to inspect (open ezra.db in any SQLite browser and see every task), and requires no new client library - it speaks the Redis Streams wire protocol, so any Redis client in any language just works out of the box.<p>Very short demo video: [<a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MLYyD3DVWmE" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MLYyD3DVWmE</a>]

Building an HTML-first site doubled our users overnight

Show HN: macOS menu bar gauges for your Claude Code quota

Port React Compiler to Rust

Show HN: Artie – Real-time data replication to your warehouse, now self-serve Hey HN, cofounder of Artie here. We’ve built a real-time data replication tool that captures every row-level change in your source database and streams it to your warehouse in under 60 seconds.<p>The last time I posted here, people had to book a call with us in order to access Artie. Today, that’s no longer the case. You can now connect your source and destination and start streaming immediately.<p>I spent years of my career building large-scale data pipelines and experienced how difficult it was to get real-time data firsthand. I believed there must be a better way to stream data into our warehouse, which resulted in Artie being born. And now with AI agents, reducing data latency has become more and more crucial as agents need to make decisions off of fresh data.<p>When I first started building Artie, I quickly learned that the components meant to keep CDC running smoothly are very much bolted on with tons of edge cases. Unfortunately in practice, they were not built to work together. We ended up dealing with schema drift, backfill race conditions, Kafka offset commits, and TOAST columns. I’d love to know if others have hit these same issues while building in-house.<p>artie.com, would love feedback!

Vibe coding my way to a healthy family: Introducing Gamow Labs

Why a Computer Science Degree Still Opens Hidden Doors