🛠️ All DevTools

Show HN: Otty – a native and beautiful terminal emulator

The Cold War's Accidental Whale Observatory

Peter Thiel's 'Dialog' network was super-secret. A data leak changed that

Show HN: Numax - a portable runtime for distributed apps Hi, over the past few months I've been working on this project: Numax is a small Rust runtime that does three things: it runs WebAssembly modules in a sandbox, has a built-in local key-value store, and syncs everything across nodes with CRDTs and gossip. Basically, you write a wasm module, run it on two machines, and they converge (I hope !). It's a decentralized system... I hope someone finds it interesting! There's a whitepaper I've put a lot into, and I think the code isn't bad either! I believe there's still room in this world for software that's fun and well made, and while building Numax I had a great time! I love Numax and I love software. Thanks to anyone who'll spend a bit of their time even just to open the repo and take a look!

Digital Printing of Arabic: explaining the problem

The UK's Teen Social Media Ban Is Political Theater, Not Child Safety Policy

Leak Exposes Members of Peter Thiel's Secretive 'Dialog' Society

Show HN: VoiceDraw – Talk system design out loud, the diagrams draw themselves I was frustated by having to draw system design diagrams by hand when discussing it with my team mates or in an interview. So I thought &quot;Wouldn&#x27;t it be great if someone draws it for me, while I think out loud?&quot;.<p>That is when I came up with VoiceDraw. You can just think out loud or discuss your system architecture with a friend&#x2F;interviewer, the diagrams are automatically drawn along with your reasoning, open questions and tradeoffs beautifully written on to the side.<p>Demo Video: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;36PgHKSuccE" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;36PgHKSuccE</a>

Show HN: Azure DevOps TUI Management Style An Azure devops TUI tool, with management metrics, to follow up on stuck items, too much wip per team member and tracking across sprints.

Apple is about to make Hide My Email useless

Show HN: Ctx, save tokens by loading only the relevant tools Hi HN!<p>Token cost has started to become a high topic of concern to all of us. I tried a few (awesome) tools such as rtk, caveman, and the recent (hillarious but effective) ponytail. What they usually do, is in-line token reduction, e.g. try to compress requests &#x2F; responses as much as possible.<p>But then it hit me (and I’m sure others had similar ideas) - just like we have routers that pick the right model, why not have something that will also narrow down the amount of available tools, skills and mcps based on repo&#x2F;context?<p>People usually accumulate skills, agents, MCP servers, harnesses, prompts, repo instructions, and local scripts. I’m not saying we are all hoarders, but we sort of are. When did you remove a skill recently? After a while, the model has way too many options to choose from.<p>ctx tries to fix that by selecting context before the session gets bloated.So no, it doesn’t cleanup your messy garage, but it gives you magic glasses that let you focus only on the tools you need.<p>It does it by watching the repo and task, walks a graph of available tooling, and recommends a small top-scored bundle of skills, agents, MCP servers, and harnesses.<p>How does it know? To make sure results are not hallucinated, and repeatable, I curated a list of 91k+ skills, 467 agents, 10.7k MCP servers, 207 harnesses, and built a graph to help ctx make decisions on what to recommend. While I used AI to generate it of course, I curated it and revised it to make sure the data is up to date.<p>So how this is different from rtk, caveman, ponytail, and similar token-saving tools?<p>As mentioned above those tools mostly reduce tokens after something is already being used.<p>rtk compresses command output.<p>caveman-style tools make the assistant respond with fewer words.<p>ponytail, is, well, awesome, but again it focuses more on reducing code (YAGNI)<p>ctx is upstream. It tries to avoid loading irrelevant skills, agents, MCPs, and harnesses into context at all.<p>So it is not really a replacement. It should work side by side with them!<p>Use ctx to choose the right tools. Use rtk to reduce terminal-output noise. Use terse-output tools if you want shorter responses.<p>The goal is simple: save tokens without forcing the user to manually test and compare thousands of possible skills, agents, MCP servers, and harnesses.<p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;stevesolun&#x2F;ctx" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;stevesolun&#x2F;ctx</a>

Show HN: BlitzGraph – Supabase for graphs, built for LLM agents Hello HN After becoming allergic to SQL, I opened 120+ issues in Dgraph, Typedb and surrealdb looking for the perfect graphDB. None of them was built for agents nor were they the perfect fit for what we wanted to achieve: fully ditching the SQL legacy to properly model reality. So we decided to build BlitzGraph<p>In BlitzGraph, records (units) can belong to multiple types (kinds) and evolve through time. Also polymorphic relations are first class and multiple kinds can play the same role. This design helps to escape the old table paradigm and track entities throughout their lifecycle without awkward self-joins that connect an entity to itself under different IDs in other tables<p>An example:<p><pre><code> { &quot;$id&quot;: &quot;amazn&quot;, &quot;$kinds&quot;: [&quot;Company&quot;, &quot;Prospect&quot;], deal: ... } &#x2F;&#x2F; Day 1 { &quot;$id&quot;: &quot;amazn&quot;, &quot;$kinds&quot;: [&quot;Company&quot;, &quot;Customer&quot;], contract: .. } &#x2F;&#x2F; Day 7 { &quot;$id&quot;: &quot;amazn&quot;, &quot;$kinds&quot;: [&quot;Company&quot;, &quot;Churned&quot;], churnCause: &quot;...&quot; }, ... &#x2F;&#x2F; Day 86 </code></pre> What makes BlitzGraph different:<p><pre><code> - GraphQL-like nested queries and mutations https:&#x2F;&#x2F;blitzgraph.com&#x2F;docs - Polymorphic records and relations - Bidirectional O(1) relations - Referential integrity with native cardinality validations - JSON query&#x2F;mutation language designed so AI agents can build them programatically - Batched queries&#x2F;mutations without N+1 issues - Built-in frontend engine for quick dashboards and MVPs - Native full text search, file storage, computed fields, ephemeral subspaces, unit history... </code></pre> Honest comparisons:<p>- vs typedb: amazing db, but not ideal for app development. On the other hand we loved and brought their inference ideas and how mutations execute smartly instead of line per line - vs surrealdb: Several core differences, a key one is that we run validations and trasnformations in topological order, and our edges are first class citizens - vs dgraph: Their cool features like post commit hooks were attached to the graphQL layer, in BG it is fundational - neo4j: If you&#x27;ve tried it, you know - vs supabase&#x2F;pg: BG is slower for flat queries but faster in nested ones. But with BG mainly you get rid of the tables paradigm and jump into the graph world while being able to build apps<p>Not ready:<p>- While blitzgraph is already an excellent memory backend for AI agents, we still need to finish the semantic search engine - Query planner is not optimized - Cloud frontends have no native auth engine yet<p>Beta is live, please break things! - Public playground: <a href="https:&#x2F;&#x2F;blitzgraph.com&#x2F;#playground" rel="nofollow">https:&#x2F;&#x2F;blitzgraph.com&#x2F;#playground</a> - MCP: <a href="https:&#x2F;&#x2F;blitzgraph.com&#x2F;mcp" rel="nofollow">https:&#x2F;&#x2F;blitzgraph.com&#x2F;mcp</a>

How we run Firecracker VMs inside EC2 and start browsers in less than 1s

Subquadratic – Introducing SubQ 1.1 Small

Show HN: Metiq: a real time 3D globe for 100 public datasets The concept for metiq.space came after playing Global Magnates with friends and realizing how fragmented live global data is. ships, aircraft, satellites, ports, weather, hazards, infrastructure, cyber, and public datasets all exist, but they usually live in separate tools and maps.<p>The goal was to build one interactive 3D globe where live public data could be visualized by latitude, longitude, and altitude. Surface data stays on the globe, while aircraft, satellites, and other above surface things can be represented in actual 3D space instead of being flattened onto a map.<p>The outcome is an interactive globe that showcases Earth, air, sea, space, cyber, defense, infrastructure, politics, and the list is continuously growing.<p>Majority of development right now is going into data filtering and deduping.

Show HN: git-lrc – Free, Micro AI Code Reviews That Run on Git Commit Hi HN, I&#x27;m the author of git-lrc, would appreciate some feedback from the community<p>Last year my team started using AI coding tools more heavily, and we found ourselves generating tons of code, but spending less time looking at the stuff that&#x27;s been generated.<p>We felt like we were losing a bit of grip&#x2F;understanding of what we were building. Regressions occasionally slipped through. Sometimes changes made it all the way to production only to be reverted later.<p>We tried several AI code review tools, but most operate at PR time. That felt too late. I wanted review to happen while the implementation was still fresh in the developer&#x27;s mind at a team level (soft enforcement). I also wanted to emphasize responsibility for keeping prod stable with each individual engineer.<p>So I built git-lrc.<p>When you commit, git-lrc opens a review UI with your diff. It summarizes what changed, points out things that deserve a second look, and lets you quickly jump through the important parts of the change.<p>Over time, git-lrc has grown to check for around 100 common risk patterns across 10 categories, including security, reliability, performance, maintainability, etc.<p>Note that this is far from a formal review. It&#x27;s a quick 60 seconds spent looking at your own work before it gets recorded in git.<p>It also generates a short &quot;summary deck&quot; that highlights the main changes, with special emphasis on potential risks. With git-lrc you can quickly sanity-check what you&#x27;re about to ship and obtain greater confidence in what&#x27;s been generated.<p>In my mind it is less of an AI reviewer and more as a habit for AI-assisted development: a small pause to make sure we understand and stand behind the code we&#x27;re shipping.<p>Developers can review the change, vouch for it, or consciously skip the review. Those decisions get recorded in git history, creating a trail of how code was reviewed before it shipped.<p>It&#x27;d be great if you could take a look, give it a try in your projects or teams and let me know what you think.<p>Happy to take feedback from the HN community and improve it over time!<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;HexmosTech&#x2F;git-lrc" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;HexmosTech&#x2F;git-lrc</a>

'Wow, it really worked ': 70s TV show causing worldwide panic today

A lightweight, lightning-fast, in-process vector database

SpaceX to buy Cursor AI coding agent operator Anysphere for $60B

Show HN: Brainfuck but with Turtle Graphics I made a Brainfuck interpreter ( <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Brainfuck" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Brainfuck</a> ) with additional commands for turtle graphics. Check it out: <a href="https:&#x2F;&#x2F;czterycztery.pl&#x2F;programy&#x2F;turtlefreeze&#x2F;" rel="nofollow">https:&#x2F;&#x2F;czterycztery.pl&#x2F;programy&#x2F;turtlefreeze&#x2F;</a>