The best Hacker News stories from Show from the past day
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Show HN: Hacker News Client with Claude Code and Codex Integration
I often find interesting things in Hacker News posts and comments, but threads are often long and take a lot of time to go through.<p>I built Rundown for this. It's a cross-platform desktop app with Claude Code and Codex integration that gives me a rundown of the post and comments, with links back to the comments it pulled each bit from. I can also chat with the post or the entire thread.
Show HN: Laser Graffiti
Show HN: Conduct, open-source guardrails for LLM and MCP tool calls
Show HN: Watches user sessions, finds bugs that matter, and fixes them
Hey HN,<p>I’m Abhishek. I'm building Opslane, an open-source agent that identifies user-facing issues and investigates them. It only creates a PR if it can verify the fix.<p>Demo: <a href="https://youtu.be/ccuOTYQMeYg" rel="nofollow">https://youtu.be/ccuOTYQMeYg</a>
Docs: <a href="https://docs.opslane.com">https://docs.opslane.com</a><p>At my last job at Robinhood, we used to do a quarterly bug bash. We would go through our Sentry backlog and try to fix as many of them as possible. We only fixed bugs we knew were reported by customers. We had hundreds of bugs, and Sentry’s default priority levels made no sense. After the bug bash, we would declare bankruptcy - select all remaining bugs and mark them as resolved.<p>This problem has only gotten worse since coding agents have become more prevalent.<p>So I started thinking: what would Sentry look like if it were built in 2026?<p>To me, error trackers have two failure modes:<p>1. False positives: They show you thousands of errors, and you can’t tell the impact on the user<p>2. False negatives: Many user-facing issues don’t throw exceptions, so they go unnoticed.<p>Opslane combines error tracking and session recording. And there is an agent that acts on both. To get started, you install the Opslane SDK. It captures everything the user did: errors, console logs, network requests, and session recordings.<p>Opslane reduces false positives by ranking issues based on how many users are facing a particular issue. It also learns about your product by reading your code and watching your session recordings.<p>False negatives are harder. Opslane reviews session recordings to spot frustration. They look for rage clicks, dead clicks, and abandoned forms.<p>This recently caught a bug in an early customer’s onboarding flow: a dropdown that closed itself when clicked. No exception, no bug report. The recordings showed users clicking it, selecting nothing, and dropping out of onboarding. Opslane flagged it and the team fixed it.<p>Three guiding principles when building Opslane:<p>1. Open Source: Self-host with one Docker Compose file.<p>2.Agent-first: I never want to open an error dashboard again. Opslane ships an MCP server, so you can ask "what broke for users this week" from Claude Code. You get back issues that need your attention and you drive the resolution.<p>3. It knows about your product: Opslane is continuously learning about your product. Every investigation begins with what it knows about your product.<p>It’s early. Frontend apps work end to end today.I am currently focused on improving reliability and accuracy.<p>Here is a link to our repo: <a href="https://github.com/opslane/opslane" rel="nofollow">https://github.com/opslane/opslane</a><p>Would love to get feedback from folks on our approach to this problem!
Show HN: Typebase – A single-folder back end you write in TypeScript
Hey HN!<p>I built Typebase, a library that gives you Convex's DX with Supabase's openness.<p>After trying Supabase I liked how fast it is to spin up a DB and auth, but really didn't like using RLS and SQL for authorization. With Convex I loved how your server "lives" in your code, but disliked the DB model and the realtime-first defaults.<p>With Typebase you just write TS files inside a typebase/ folder in your existing repo. You can define your DB tables inside a schema.ts file and export server functions that your frontend calls like local functions, fully typed. Auth is built in.<p>Then one CLI command uploads your server to any of the available providers (Vercel, Cloudflare Workers or Deno Deploy for the servera and Neon for the DB), or generates the code so you can deploy it wherever you want.<p>Built on top of oRPC, Drizzle, and better-auth.<p>Happy to answer any questions or feedback!
Show HN: Galaxium, an experimental WebGPU space explorer
Show HN: Yet another minimal and lightweight terminal multiplexer written in Go.
Show HN: RealDiff – runtime behavior diffing for pull requests (six languages)
Show HN: Sesame - a local-first, open-source password manager
I have been working on Sesame, an open-source password manager that keeps your vault local by default. You don't need an account to create or use a vault, and the hosted service never receives the vault itself.
It's still early software and the independent security review isn't finished yet, so I am mainly interested in feedback, testing, and people looking through the code.<p>(Linux support is yet to be released on v0.1.2, but currently is in the works.)
Show HN: Sesame - a local-first, open-source password manager
I have been working on Sesame, an open-source password manager that keeps your vault local by default. You don't need an account to create or use a vault, and the hosted service never receives the vault itself.
It's still early software and the independent security review isn't finished yet, so I am mainly interested in feedback, testing, and people looking through the code.<p>(Linux support is yet to be released on v0.1.2, but currently is in the works.)
Show HN: SubSmith – Turn your own videos into language-learning material
I've been learning Japanese for a few years and kept running into a similar problem. I'd find a video I wanted to learn from, hear a useful sentence, and then realise that turning that sentence into something I could study later was both time consuming and draining at times.<p>I would end up jumping between a video player, subtitles/transcription, a dictionary, screenshots, audio clips and Anki. So I built SubSmith to bring that workflow together.<p>You can drop a video or audio file into it, generate a transcript locally and then use the transcript alongside the media to:<p>* look up words and sentences
* replay individual lines
* edit the transcript
* save useful sentences with their original context/audio
* export them as Anki cards<p>The important part for me is that it works with your own media. It isn't tied to a particular streaming service or library, so I can use the random anime episode, podcast, lecture, etc. that I'm actually interested in studying.<p>It's an offline-first desktop app, and transcription happens locally rather than sending the media to a transcription API.<p>I'm sharing it here because I'm now more interested in finding out where this workflow breaks down for other people rather than adding features randomly now that I have solid core/base.<p>For example:<p>* Would you actually save sentences from your own media?
* Which part of this process feels like too much work?
* Does having the audio/context attached make creating an Anki card more useful?
* Would you prefer this to work inside your existing video player/browser?
* Is installing a desktop app a significant barrier?
* And does requiring an account before starting the free trial make you give up?<p>The current version does require an account to start the trial, and I'm trying to work out whether that's meaningful friction for the people who would actually use this.<p>It's free to try, and I'd particularly appreciate feedback from people who already learn languages through their own videos, anime, films, podcasts or other media.<p>I'm the developer, so I'll be around in the comments to answer questions and discuss how it works.<p><a href="https://subsmith.app" rel="nofollow">https://subsmith.app</a>
Show HN: SubSmith – Turn your own videos into language-learning material
I've been learning Japanese for a few years and kept running into a similar problem. I'd find a video I wanted to learn from, hear a useful sentence, and then realise that turning that sentence into something I could study later was both time consuming and draining at times.<p>I would end up jumping between a video player, subtitles/transcription, a dictionary, screenshots, audio clips and Anki. So I built SubSmith to bring that workflow together.<p>You can drop a video or audio file into it, generate a transcript locally and then use the transcript alongside the media to:<p>* look up words and sentences
* replay individual lines
* edit the transcript
* save useful sentences with their original context/audio
* export them as Anki cards<p>The important part for me is that it works with your own media. It isn't tied to a particular streaming service or library, so I can use the random anime episode, podcast, lecture, etc. that I'm actually interested in studying.<p>It's an offline-first desktop app, and transcription happens locally rather than sending the media to a transcription API.<p>I'm sharing it here because I'm now more interested in finding out where this workflow breaks down for other people rather than adding features randomly now that I have solid core/base.<p>For example:<p>* Would you actually save sentences from your own media?
* Which part of this process feels like too much work?
* Does having the audio/context attached make creating an Anki card more useful?
* Would you prefer this to work inside your existing video player/browser?
* Is installing a desktop app a significant barrier?
* And does requiring an account before starting the free trial make you give up?<p>The current version does require an account to start the trial, and I'm trying to work out whether that's meaningful friction for the people who would actually use this.<p>It's free to try, and I'd particularly appreciate feedback from people who already learn languages through their own videos, anime, films, podcasts or other media.<p>I'm the developer, so I'll be around in the comments to answer questions and discuss how it works.<p><a href="https://subsmith.app" rel="nofollow">https://subsmith.app</a>
Show HN: OpenTIE and OpenXWA, Modern Ports of Tie Fighter and X-Wing Alliance
Show HN: OpenTIE and OpenXWA, Modern Ports of Tie Fighter and X-Wing Alliance
Show HN: Restoredrill – proves your Postgres backups restore
Show HN: Restoredrill – proves your Postgres backups restore
Show HN: Build your own theme park
I built an agent that helps you build Rollercoaster Tycoon-influenced theme parks. You can prompt something like “Build me a cool theme park” and it’ll build a cohesive theme park with multiple worlds and connected with paths and rides.<p>The weird part is that I built it using the same ideas we use to make AI-generated websites follow a company’s design system.<p>I work as an engineer at Magic Patterns, focused on building our Design System Agent, aimed to use your existing brand, components, and conventions instead of producing something that looks like generic AI-generated UI.<p>By default, models tend to converge on similar-looking designs. This is part of that “vibe coded slop” feeling: the same typography treatments, shadows, cards, icons, animations, etc… My job is to figure out the right context and guardrails so that when you prompt “Build me a dashboard,” the result actually looks like your existing product.<p>At some point I realized those same ideas could be applied to RCT.<p>While in web, you might have rules about which typography, colors, spacing, and components should be used together.<p>In a theme park, you need rules like: rollercoasters need complete tracks, rides need entrances connected to paths, paths need to connect different areas of the park, and a pirate-themed world should actually use pirate-themed scenery.<p>It was interesting seeing how closely the problem of building a coherent theme park resembled the problem of building a coherent product UI. Similarly with web design, simply giving the model the right components wasn't enough.<p>I ended up building an eval loop where Magic Patterns would generate a park, another agent would grade it against a rubric, and then the agent would update its rules and skill files before trying again.<p>The rubric checked things like whether rollercoasters formed valid tracks with at least one drop, whether rides were accessible by paths, whether each world used the appropriate themed scenery, and whether the park worked as a whole.<p>After a lot of iterations, you can now prompt something as simple as “Build me a cool theme park.” and you should get back a fully functioning park with guests, rides, paths, rollercoasters, and themed worlds.<p>Best yet, you can also follow the guests, listen to their thoughts, and watch them ride the rides in RCT fashion.<p>Happy to answer any questions, but would love to see the parks you all create!
Show HN: Voronoi Go
I posted once before but wanted to share again because a lot has changed. There's now a fairly strong bot to play against (contributed by a community member) and also correspondence games. The combination of these things helps a bit in finding people to play against.
Show HN: Voronoi Go
I posted once before but wanted to share again because a lot has changed. There's now a fairly strong bot to play against (contributed by a community member) and also correspondence games. The combination of these things helps a bit in finding people to play against.
Show HN: We built open OpenRouter that turns usage into a better model
Hi HN, we built an open source model gateway. It's a single place to manage our own self hosted, frontier, and open source models in one place.<p>It’s is rust native, built for concurrency, and implements all the config quirks across models and providers (streaming formats, tool calls, model parameters, rate limits, and different error behavior).<p>The gateway adds under 1 ms for BYOK requests and under 2 ms when Experiential supplies the provider key. It has every major inference provider, and 1000+ models refreshed daily via a codex agent that opens a PR.<p>Compared to other similar projects we’re open source, take no markup, allow you to mix local models with a marketplace, and use your traffic to (opt in) train you a model. Simple routing doesn’t warrant a 10% token markup.<p>The way we do this is given standardized OTel traces, we mine representative real tasks, use text world models to simulate rollouts for various models, apply an LLM judge, and fit a nearest neighbor classifier on top of an embedding of a prompt to decide the optimal model for each request. Usually this can map out a better pareto curve on cost/quality than just calling single models but it’s not perfect.<p>Using these simulations we can also do things like suggesting cache hit optimizations, new model suggestions, and training models.<p>It’s open source, so you can deploy it on your own infrastructure, use our hosted version with 0 markup, or read how we design for maximum availability on our website.