The best Hacker News stories from Show from the past day
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Show HN: I built a RAG and knowledge graph agent that runs locally
Claw-Coder is an AI agent that runs locally on your laptop and has access to powerful tools instead of configuring claude or codex to use a local model just use claw-coder.
Why was claw-coder created? Answer: To solve the problem of privacy and security. When you use an agent that is configured with a cloud model like codex, cursor, Claude etc. You are not just getting the agent but you are giving up your codebase to train an llm which is a bit concerning and this reduces trust in the technology called AI but now another problem comes in performance when you switch to a local model that is not made for that workflow you lose performance, speed, and it becomes really a tradeoff so that's where claw-coder comes in it not only runs on your machine but all the code, rag, knowledge graph etc info is kept local making the privacy problem solved but now what about performance.<p>Performance: Local llms are not built to do the cool things cloud models do because the model sizes are not even capable of building real apps like the 8b models, 13b, even 1b so the solution I came up with was to give these small models access to tools and features that make it actually work well in coding performance.<p>So what does claw-coder have access to: A knowledge graph: A knowledge graph is an interconnected network of real-world entities—such as people, places, concepts, or events—and the relationships between them. It organizes information into a readable web of meaning rather than static lists, allowing both humans and AI to understand context. So how does this help an AI, it gives the AI the ability to tell relationships between code in your codebase, a cloned unknown repo and so forth this increases performance of local llms by far in coding tasks and reasoning abilities. Rag: We have ever had of RAG at some point but there is a catch the context window of local llms can't bear large codebases and repos so RAG isn't optional by storing vectors in a vector store you enable the AI to actually know what the code means and what each piece does to the other letting you load millions of lines into the vector store without blowing up the context window.<p>Tools: So we have discussed the tiny but powerful ways to improve local llm performance but an agent to be an agent it needs to take action this is where exposing tools to the local llm helps so what tools have been implemented into claw-coder. 1. search_tool This enables the ai agent to actually search up to date info so that it doesnt hallucinate on info it doesn't know which is common in local llms. 2. Docker execution This agent has a special folder called workspace where it does its work without destroying your desktop but this is not enough to protect your desktop from being destroyed by cheap code so this is where docker comes in I have implemented docker containers of various languages where the agent can validate its own code this is powerful because all llms not only local ones generate code they can't even confirm works because they are just powerful predictors so enabling it to run its code can surprisingly increase the usefulness of the generated code because it now knows it works or not even for html and css the ai agent has been given a helpful vision llm to actually explain what rendered in the browser. This is the surprising power of giving an llm a docker execution tool.<p>We have looked at a lot of how claw-coder is different enabling local llms to actually do real work. But how do you actually try it out yourself: Claw-coder is closed source because it is going through heavy testing but that doesnt kill transparency and since we are testing it doesn't stop people from trying it on real codebase and giving feedback to get started use:<p>brew tap gabriel-c70/claw then brew install claw-coder
Show HN: Pablo – a Chrome extension that copies UI from any website
Pablo is a Chrome extension that copies the HTML and CSS behind any element you hover.<p>It captures computed styles, fonts (with @font-face and Google Fonts links), CSS keyframes, and animation props from GSAP and Framer Motion. The output is structured so it pastes cleanly into Claude Code, Cursor, or Codex when you want to rebuild a component in your own stack.<p>Manifest v3, no host permissions, no backend. Free.<p><a href="https://usepablo.dev" rel="nofollow">https://usepablo.dev</a><p>Happy to answer questions about how the extraction works, and would love to hear about sites where the output breaks.
Show HN: I Made a Claude Skill for Spec-Driven Development (SDD)
At my work they provided a single Claude subscription for everyone on the team. To be honest I like kiro better as it provides a way better SDD management. But the company can't provide it and I can't afford it yet. Turns out I had the skill creator skill in my claude instance so I made use of it to create this Skill. I made it fully by using Claude but I wanted to make it open source, so I asked it to help me make tests and preparations for it, even a CI to run python tests.<p>Well, we got this results with it:<p>- Phase 2A: 67 static assertions (Python script, runs in CI)<p>- Phase 2B: 15 behavioral tests (live Claude Code session)<p>- Phase 2C: 53 generation quality checks across 3 end-to-end flows<p>All of these passed and the CI also passed (after a few tries).<p>I made it to suit my way of prompting and coding and based it off kiro's SDD management, but I want it to be publicly available and used by many people. According to claude some of the testers need to fit the following criteria:<p>1. Developer starting a real new project from scratch<p>2. Solo dev with an active side project (greenfield or partial codebase)<p>3. Team lead whose team uses multiple AI tools<p>4. Developer with an existing codebase and no written specs<p>5. Developer who actively uses 3+ AI coding tools<p>It's actually a blind test, no guiding, just try it if you can, I'd really appreciate your help.<p>The repo is here: <a href="https://github.com/FredAntB/Spec-Driven-Development" rel="nofollow">https://github.com/FredAntB/Spec-Driven-Development</a>
Built AI forensic accounting software with my dad
Show HN: Open-source .docx editor library for building document apps
We are working on an open-source .docx editor library for apps that need to edit Word documents in the browser. We just shipped 1.0.<p>A lot of existing approaches convert .docx into HTML and lose document semantics along the way. Our editor parses OOXML directly and uses its own rendering+layout engine to produce paged documents with html/css. Edits round-trip back to .docx, so you’re always editing the document, not its representation.<p>The core rendering engine is framework agnostic, with React and Vue ui adapters on top.<p>It’s Apache 2.0. Happy to answer questions.
Show HN: Open-source .docx editor library for building document apps
We are working on an open-source .docx editor library for apps that need to edit Word documents in the browser. We just shipped 1.0.<p>A lot of existing approaches convert .docx into HTML and lose document semantics along the way. Our editor parses OOXML directly and uses its own rendering+layout engine to produce paged documents with html/css. Edits round-trip back to .docx, so you’re always editing the document, not its representation.<p>The core rendering engine is framework agnostic, with React and Vue ui adapters on top.<p>It’s Apache 2.0. Happy to answer questions.
Show HN: ShadowCat – file transfer through QR Codes in a Browser
Show HN: ShadowCat – file transfer through QR Codes in a Browser
Show HN: Agent.email – sign up via curl, claim with a human OTP
Hi HN! We're Haakam, Michael, and Adi from AgentMail- a ycs25 company. We give AI agents their own email inboxes. Recently, we ran an experiment called Agent.Email. It's a signup flow designed specifically for AI agents instead of humans.<p>The inspiration came from a few comments we received when we did our seed launch a few months back. They all came from the very apt observation that agents not being able to sign up to a product made for agents without human credentials was ironic and unideal.<p>This is basically the thesis we built AgentMail on: The internet was made for humans exclusively, designed to keep machines out by default.<p>Every signup flow assumes a browser, a person reading a page, and clicking a confirmation link. Unless agents can't do that, they can't be first class users of the internet.<p>Agents can now get an email inbox by themselves. (This also means a lot of email nobody wants to read gets processed by AI instead of your inbox being cluttered with spam and slop)<p>Here's how agent.email works.<p>Agent needs an inbox and hits AgentMail via curl.
Agent receives instructions via MD unless the request comes from a browser, in which case we use HTML.<p>Agent decides agent.email is useful and then hits the sign-up endpoint with its human email as a parameter.
Agent receives a restricted inbox with credentials.
Agent emails the human asking for an OTP. Human replies with the code, and the agent is claimed and restrictions are lifted.
Until claimed, the agent can only email its own human and nobody else. Ten emails a day, and the signup endpoint is rate-limited hard by IP.<p>Right now it's a 1:1 mapping between agent and human. The next step is many-to-one, because one person running several agents in parallel is already very common.<p>Building agent.email also pushed us to revisit places in AgentMail where the default assumptions were built around the primary user being human. For example, the CLI outputs in a single column with consistent formatting because mixed delimiters are easy for a person to scan, but harder for an agent reasoning about structure. We also shortened messageIDs after agents started hallucinating completions on longer ones.<p>A few things we'd like the community's take on: is restricted-until-claimed the right trust model?
Does agent self-signup feel useful in production, or is it mostly a novelty, and if it's a novelty now, what would make it actually useful?
Should agent onboarding require human approval by default, or should some agents be able to fully self-provision? What do you think are some additional measures we can take for secure sign-ups?
Show HN: Agent.email – sign up via curl, claim with a human OTP
Hi HN! We're Haakam, Michael, and Adi from AgentMail- a ycs25 company. We give AI agents their own email inboxes. Recently, we ran an experiment called Agent.Email. It's a signup flow designed specifically for AI agents instead of humans.<p>The inspiration came from a few comments we received when we did our seed launch a few months back. They all came from the very apt observation that agents not being able to sign up to a product made for agents without human credentials was ironic and unideal.<p>This is basically the thesis we built AgentMail on: The internet was made for humans exclusively, designed to keep machines out by default.<p>Every signup flow assumes a browser, a person reading a page, and clicking a confirmation link. Unless agents can't do that, they can't be first class users of the internet.<p>Agents can now get an email inbox by themselves. (This also means a lot of email nobody wants to read gets processed by AI instead of your inbox being cluttered with spam and slop)<p>Here's how agent.email works.<p>Agent needs an inbox and hits AgentMail via curl.
Agent receives instructions via MD unless the request comes from a browser, in which case we use HTML.<p>Agent decides agent.email is useful and then hits the sign-up endpoint with its human email as a parameter.
Agent receives a restricted inbox with credentials.
Agent emails the human asking for an OTP. Human replies with the code, and the agent is claimed and restrictions are lifted.
Until claimed, the agent can only email its own human and nobody else. Ten emails a day, and the signup endpoint is rate-limited hard by IP.<p>Right now it's a 1:1 mapping between agent and human. The next step is many-to-one, because one person running several agents in parallel is already very common.<p>Building agent.email also pushed us to revisit places in AgentMail where the default assumptions were built around the primary user being human. For example, the CLI outputs in a single column with consistent formatting because mixed delimiters are easy for a person to scan, but harder for an agent reasoning about structure. We also shortened messageIDs after agents started hallucinating completions on longer ones.<p>A few things we'd like the community's take on: is restricted-until-claimed the right trust model?
Does agent self-signup feel useful in production, or is it mostly a novelty, and if it's a novelty now, what would make it actually useful?
Should agent onboarding require human approval by default, or should some agents be able to fully self-provision? What do you think are some additional measures we can take for secure sign-ups?
Show HN: Agent.email – sign up via curl, claim with a human OTP
Hi HN! We're Haakam, Michael, and Adi from AgentMail- a ycs25 company. We give AI agents their own email inboxes. Recently, we ran an experiment called Agent.Email. It's a signup flow designed specifically for AI agents instead of humans.<p>The inspiration came from a few comments we received when we did our seed launch a few months back. They all came from the very apt observation that agents not being able to sign up to a product made for agents without human credentials was ironic and unideal.<p>This is basically the thesis we built AgentMail on: The internet was made for humans exclusively, designed to keep machines out by default.<p>Every signup flow assumes a browser, a person reading a page, and clicking a confirmation link. Unless agents can't do that, they can't be first class users of the internet.<p>Agents can now get an email inbox by themselves. (This also means a lot of email nobody wants to read gets processed by AI instead of your inbox being cluttered with spam and slop)<p>Here's how agent.email works.<p>Agent needs an inbox and hits AgentMail via curl.
Agent receives instructions via MD unless the request comes from a browser, in which case we use HTML.<p>Agent decides agent.email is useful and then hits the sign-up endpoint with its human email as a parameter.
Agent receives a restricted inbox with credentials.
Agent emails the human asking for an OTP. Human replies with the code, and the agent is claimed and restrictions are lifted.
Until claimed, the agent can only email its own human and nobody else. Ten emails a day, and the signup endpoint is rate-limited hard by IP.<p>Right now it's a 1:1 mapping between agent and human. The next step is many-to-one, because one person running several agents in parallel is already very common.<p>Building agent.email also pushed us to revisit places in AgentMail where the default assumptions were built around the primary user being human. For example, the CLI outputs in a single column with consistent formatting because mixed delimiters are easy for a person to scan, but harder for an agent reasoning about structure. We also shortened messageIDs after agents started hallucinating completions on longer ones.<p>A few things we'd like the community's take on: is restricted-until-claimed the right trust model?
Does agent self-signup feel useful in production, or is it mostly a novelty, and if it's a novelty now, what would make it actually useful?
Should agent onboarding require human approval by default, or should some agents be able to fully self-provision? What do you think are some additional measures we can take for secure sign-ups?
Show HN: CPU-only transcription for YouTube, TikTok, X, Instagram videos
Show HN: CPU-only transcription for YouTube, TikTok, X, Instagram videos
Show HN: I made a tactical map-based WWII submarine simulator (public beta)
I've seen quite a few simming discussions on HN, so thought some of you might like this - I've created a map-centered, tactical submarine simulator and it's been a blast to make.<p>I grew up playing Silent Service II on Atari ST with my dad, then got into Silent Hunter IV in the 2000s, and most recently have been loving the more recent UBoat. In each case, the part I always enjoy the most is the plotting and charting aspect - essentially beating uncertain estimates with geometry.<p>So I decided to see how far I could get making my own sim that focused nearly entirely on that aspect. You listen on the hydrophone, estimate course and speed, identify ships through the periscope to get the mast height, use a working stadimeter for range estimates, and then try to build a good enough firing solution before getting discovered and hunted by any escorts.<p>Things I'm particularly proud of are the working stadimeter, the dynamic music (Holst Mars stings when your torpedo is nearing a ship), and pretty intelligent destroyer logic. I've found great reference materials online and have modeled several of the gauges directly after actual submarine instruments.<p>Tech-wise it’s a Vite/TypeScript app which enables me to offer the whole free version of the app as a browser version.<p>The Steam page is here => <a href="https://store.steampowered.com/app/4705650" rel="nofollow">https://store.steampowered.com/app/4705650</a><p>The landing page is here => <a href="https://silentshark.app" rel="nofollow">https://silentshark.app</a><p>I plan on releasing a full version soonish, including a WWII campaign with progression, patrol zones, and much more on Steam (PC, Mac, Linux/Steam Deck), App Store (iPhone, iPad, Mac), and Play Store (Android).<p>Would highly appreciate any feedback anyone has!
Show HN: I made a tactical map-based WWII submarine simulator (public beta)
I've seen quite a few simming discussions on HN, so thought some of you might like this - I've created a map-centered, tactical submarine simulator and it's been a blast to make.<p>I grew up playing Silent Service II on Atari ST with my dad, then got into Silent Hunter IV in the 2000s, and most recently have been loving the more recent UBoat. In each case, the part I always enjoy the most is the plotting and charting aspect - essentially beating uncertain estimates with geometry.<p>So I decided to see how far I could get making my own sim that focused nearly entirely on that aspect. You listen on the hydrophone, estimate course and speed, identify ships through the periscope to get the mast height, use a working stadimeter for range estimates, and then try to build a good enough firing solution before getting discovered and hunted by any escorts.<p>Things I'm particularly proud of are the working stadimeter, the dynamic music (Holst Mars stings when your torpedo is nearing a ship), and pretty intelligent destroyer logic. I've found great reference materials online and have modeled several of the gauges directly after actual submarine instruments.<p>Tech-wise it’s a Vite/TypeScript app which enables me to offer the whole free version of the app as a browser version.<p>The Steam page is here => <a href="https://store.steampowered.com/app/4705650" rel="nofollow">https://store.steampowered.com/app/4705650</a><p>The landing page is here => <a href="https://silentshark.app" rel="nofollow">https://silentshark.app</a><p>I plan on releasing a full version soonish, including a WWII campaign with progression, patrol zones, and much more on Steam (PC, Mac, Linux/Steam Deck), App Store (iPhone, iPad, Mac), and Play Store (Android).<p>Would highly appreciate any feedback anyone has!
Show HN: Rmux – A programmable terminal multiplexer with a Playwright-style SDK
Author here. RMUX started from a frustration: I've used tmux for years and got tired of scraping output with grep and sleeps to automate anything. So I rebuilt the multiplexer from scratch in Rust, with a programmable layer on top.<p>Two surfaces: a tmux-compatible CLI (~90 commands, your keybindings just work), and a typed async Rust SDK on the same daemon — stable pane IDs, structured snapshots, locator-style waits. The idea is Playwright-style automation, but for terminals.<p>Native on Linux, macOS, Windows (real ConPTY, no WSL).<p>Demos and docs at rmux.io. Happy to answer questions about the daemon protocol, ConPTY, or the SDK design.
Show HN: Rmux – A programmable terminal multiplexer with a Playwright-style SDK
Author here. RMUX started from a frustration: I've used tmux for years and got tired of scraping output with grep and sleeps to automate anything. So I rebuilt the multiplexer from scratch in Rust, with a programmable layer on top.<p>Two surfaces: a tmux-compatible CLI (~90 commands, your keybindings just work), and a typed async Rust SDK on the same daemon — stable pane IDs, structured snapshots, locator-style waits. The idea is Playwright-style automation, but for terminals.<p>Native on Linux, macOS, Windows (real ConPTY, no WSL).<p>Demos and docs at rmux.io. Happy to answer questions about the daemon protocol, ConPTY, or the SDK design.
Show HN: Rmux – A programmable terminal multiplexer with a Playwright-style SDK
Author here. RMUX started from a frustration: I've used tmux for years and got tired of scraping output with grep and sleeps to automate anything. So I rebuilt the multiplexer from scratch in Rust, with a programmable layer on top.<p>Two surfaces: a tmux-compatible CLI (~90 commands, your keybindings just work), and a typed async Rust SDK on the same daemon — stable pane IDs, structured snapshots, locator-style waits. The idea is Playwright-style automation, but for terminals.<p>Native on Linux, macOS, Windows (real ConPTY, no WSL).<p>Demos and docs at rmux.io. Happy to answer questions about the daemon protocol, ConPTY, or the SDK design.
Show HN: I Dedicated 4 Years to Mastering Offline Password Cracking
Hi everyone,<p>I am Bojta Lepenye, and first of all, I want to thank the core developers of Hashcat. In my experience, it is quite literally the most capable tool available for offline password cracking across a wide range of use cases.<p>I have spent the last 4 years (from age 14 to 18) extensively working with Hashcat and the tools surrounding it, and I have documented what I have learned throughout that time (since January 18, 2022) in my first book. During that period, I also had to continuously update and rewrite major sections as the field evolved. One example was the introduction of GPU support for Argon2 and other memory-hard password hashing algorithms, which significantly changed some cracking workflows.<p>My passion for this book, or its “quick starter,” if you will, came from an ethically conducted penetration test I performed with full authorization at my school. This is something I am both hesitant and quite proud to acknowledge.<p>At the beginning, I simply wrote down everything I had learned from YouTube videos and online blogs. However, not long after starting my project, I realized I practically knew nothing about password security, and that small 10 to 15 pages I had written would never be enough if someone was looking for a professional guide to cracking passwords.<p>The other main driving force behind the book was the fact that while researching online, browsing forums, reading academic papers and white papers, watching videos, exploring blogs, inspecting presentations, and examining infographics, I did not find a single source that comprehensively covers and explains everything one needs to understand about offline password cracking. Literally. Not one.<p>Therefore, I continued my research and learned about password hashing algorithms, the security properties of hash functions, advanced hash cracking techniques, password analysis, attack optimization, and much, much more.<p>From the very beginning, I wanted to share this knowledge with the community because having access to a resource like this would have helped me tremendously when I first started learning password cracking.<p>I sincerely hope this work will be useful to both beginners and experienced professionals alike, and I look forward to hearing your thoughts and feedback.<p>I have also put together a little video to give you a little sneak peek into it. It is on Google Drive. It is the official domain, and you do not need to download anything. Here it is: <a href="https://drive.google.com/file/d/13LeysSZO8Mx-LGKt8UQjUGBKOYH7MqiS/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/13LeysSZO8Mx-LGKt8UQjUGBKOYH...</a><p>If you are interested, the book is now publicly available on Amazon, and can be read for free with a Kindle Unlimited subscription: <a href="https://www.amazon.com/dp/B0GX36XRCD" rel="nofollow">https://www.amazon.com/dp/B0GX36XRCD</a>
Show HN: I Dedicated 4 Years to Mastering Offline Password Cracking
Hi everyone,<p>I am Bojta Lepenye, and first of all, I want to thank the core developers of Hashcat. In my experience, it is quite literally the most capable tool available for offline password cracking across a wide range of use cases.<p>I have spent the last 4 years (from age 14 to 18) extensively working with Hashcat and the tools surrounding it, and I have documented what I have learned throughout that time (since January 18, 2022) in my first book. During that period, I also had to continuously update and rewrite major sections as the field evolved. One example was the introduction of GPU support for Argon2 and other memory-hard password hashing algorithms, which significantly changed some cracking workflows.<p>My passion for this book, or its “quick starter,” if you will, came from an ethically conducted penetration test I performed with full authorization at my school. This is something I am both hesitant and quite proud to acknowledge.<p>At the beginning, I simply wrote down everything I had learned from YouTube videos and online blogs. However, not long after starting my project, I realized I practically knew nothing about password security, and that small 10 to 15 pages I had written would never be enough if someone was looking for a professional guide to cracking passwords.<p>The other main driving force behind the book was the fact that while researching online, browsing forums, reading academic papers and white papers, watching videos, exploring blogs, inspecting presentations, and examining infographics, I did not find a single source that comprehensively covers and explains everything one needs to understand about offline password cracking. Literally. Not one.<p>Therefore, I continued my research and learned about password hashing algorithms, the security properties of hash functions, advanced hash cracking techniques, password analysis, attack optimization, and much, much more.<p>From the very beginning, I wanted to share this knowledge with the community because having access to a resource like this would have helped me tremendously when I first started learning password cracking.<p>I sincerely hope this work will be useful to both beginners and experienced professionals alike, and I look forward to hearing your thoughts and feedback.<p>I have also put together a little video to give you a little sneak peek into it. It is on Google Drive. It is the official domain, and you do not need to download anything. Here it is: <a href="https://drive.google.com/file/d/13LeysSZO8Mx-LGKt8UQjUGBKOYH7MqiS/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/13LeysSZO8Mx-LGKt8UQjUGBKOYH...</a><p>If you are interested, the book is now publicly available on Amazon, and can be read for free with a Kindle Unlimited subscription: <a href="https://www.amazon.com/dp/B0GX36XRCD" rel="nofollow">https://www.amazon.com/dp/B0GX36XRCD</a>