Show HN: We beat Cloudflare's bot detection (open-source stealth browser) https://ift.tt/rKc3uHO

Show HN: We beat Cloudflare's bot detection (open-source stealth browser) https://ift.tt/3mJuSU5 July 11, 2026 at 04:26AM

Show HN: SubjectiveZero, an open-source agentic node editor for creative coding https://ift.tt/x1HLIVZ

Show HN: SubjectiveZero, an open-source agentic node editor for creative coding Hey there, My name is Clem, I've been a solo indie dev for a couple years now, exploring frontier tech like XR and agentic workflows in the context of creative / interactive work. I've been building creation tools for a while and some common design challenge is to figure out the right level of abstraction for your tool. You can always make it super advanced and complex with low level concepts (shader composition, actual code etc.) but then you get something with a high complexity / learning curve. On the other hand, if you make your tool too high level, it might be easier to use at first, but people will most likely hit a wall eventually and start fighting with your tool to get their edge case done (you see that on mobile a lot actually). With this prototype (called SubjectiveZero), I'd like to imagine that we can kind of move the "slider" on the abstraction layer, meaning that you can actually start with prompts that describe the goal, and you can go as high level (stay with abstract prompts) or low level as you'd like (more specific prompts, or even edit the generated code directly)! The agent orchestration actually understand your context and work along side with you to figure out what could be the best node graph structure for your project (that and some fun little procedural UI done at the node level). If i had to pitch it in 30 seconds, I'd say "Think TouchDesigner and friends but with agent orchestration". When you use it, it will generate real native code (Swift/Metal for now) that you can actually hot reload and iterate on either manually or through agents. It's still an early prototype and macOS only for now, but I'd love to get genuine feedback that could help me drive where this project should go next (or not). Lastly, I'm absolutely open and upfront on the fact that I used agentic coding for this, but as people say: "kept on a short leash". The architecture and specs were relatively well thought out and I personally prefer to be in the loop and review all the code being written to make sure it's going in the right direction. Oh and it's open source :-) Hope you like it! https://ift.tt/dPt5Xlw https://ift.tt/dPt5Xlw July 10, 2026 at 07:23PM

Show HN: Wyrm – Solve algebra by touch, built on an open-source soundness engine https://ift.tt/noeCx1w

Show HN: Wyrm – Solve algebra by touch, built on an open-source soundness engine There is a mobile game called DragonBox. It sort of tricks you into learning algebra by starting with very abstract manipulations of a puzzle that must follow rules... gradually the game teaches you more and more rules and also strips out the more abstract elements until on the last levels you are finally solving real equations. I loved it, it taught my kids algebra.... and it was just fun. Over the years I often thought that there should be a calculator for Algebra that works this way... something where you can drag terms around and cancel & distribute with gestures, but most importantly enter your own problems. It should also do more kinds of problems than DragonBox allowed. So I finally decided to build it. https://dicroce.github.io/wyrm/home.html Here's a video showing it: https://www.youtube.com/watch?v=_STbS4zvIlU . If you'd rather just play with it: there's a limited in-browser demo (real engine, a few example equations, no download) on the landing page — https://dicroce.github.io/wyrm/home.html . The app can be found on iOS ( https://ift.tt/hXMEk4j ) and as of this week on Google Play ( https://ift.tt/2iywdZI... ). I also decided to open source the underlying math engine so others could build on it: https://ift.tt/ygtdXIL . My goal for the engine btw is to build it all the way up to Calculus. Monetization is deliberately boring: the engine is free (MIT), and the polished gesture app is $4.99 once. No subscriptions, ads, accounts, or analytics. I'd love feedback on the engine design — especially from anyone who's worked on CAS or proof-assistant-adjacent problems. And if you played DragonBox as a kid and wished it went further: this is for you! https://ift.tt/ygtdXIL July 9, 2026 at 03:16PM

Show HN: Pylon Sync, an agent-first full-stack realtime framework https://ift.tt/8NAPYi2

Show HN: Pylon Sync, an agent-first full-stack realtime framework I created Pylon to make it easier to move from hobby projects to full production apps. When I work on hobby projects, I usually use React or Next.js because they are quick to set up and easy to deploy on Vercel. For production apps, I separate the frontend and backend, then deploy the backend on AWS. But setting up a full backend on AWS can be complex and costly, especially for simple apps. Pylon is a full-stack, real-time framework that includes server-rendered React, TypeScript functions, entities, policies, real-time sync, built-in authentication, and support for background and scheduled jobs. By default, it uses SQLite, but you can switch to Postgres for production. The authentication system is heavily inspired by better-auth. The runtime is a Rust server that runs TypeScript functions and server-rendered React using Bun. Pylon itself is inspired by Rails and focuses on convention over configuration, so you have fewer decisions to make before deploying. This approach applies to modern React apps, real-time sync, TypeScript server functions, authentication, job management, and deployment. One of Pylon’s main goals is agent compatibility. It lets coding agents build and deploy apps with no setup, quick understanding, secure defaults, and easy deployment, all without requiring any third-party services. Pylon works for both quick projects and production apps where performance, observability, ownership, and self-hosting matter. While it’s easy to self-host Pylon apps, Pylon Cloud provides managed hosting with a developer experience similar to Vercel. You can deploy from git or the CLI, get an instant URL, add custom domains, and go live in seconds. Each app runs on its own server, which can scale to zero, with TLS and global caching enabled. If you have experience with Next.js, Vercel, Convex, Supabase, Firebase, better-auth, or Rails, I’d love to hear your feedback. Create your first app: npm create @pylonsync/pylon@latest Website: https://ift.tt/ASyXz4N Repo: https://ift.tt/wRbXkjs Docs: https://ift.tt/vU0KrC4 LLMS: https://ift.tt/xuzi65y Skill: npx skills add pylonsync/pylon Examples: https://ift.tt/IuLUKB1 https://ift.tt/ASyXz4N July 9, 2026 at 09:38PM

Show HN: Policy enforcement for Claude Code, Cursor, and Codex https://ift.tt/GyOT9sF

Show HN: Policy enforcement for Claude Code, Cursor, and Codex Show HN: Runtime authorization for Claude Code, Cursor, and Codex Hi HN, Fernando and I built Kastra. Kastra intercepts AI agent tool calls and evaluates them against deterministic policies before they execute. This is aimed at developers using coding agents like Claude Code, Codex, Cursor, and OpenClaw. We built Kastra after one of our Cursor agents almost executed DELETE FROM customers WHERE status='test' against a production database. We caught it before it ran, but it made us realize that nothing in our stack actually decided what the agent was allowed to do. What mattered for us wasn't the mistake; it was realizing nothing in our setup would have stopped it if we weren't actively on top of it. LLMs are probabilistic, and prompts influence behavior, but they don't deterministically decide what an agent is allowed to do. Without a deterministic policy system, nothing could have decided what it was allowed to do. Kastra pushes an allow, hold, and deny decision before the action runs. You can build these policies in plain English from the web app. The interception engine evaluates the tools, targets, and parameters of every action. We also shipped many policy packs covering common high-risk scenarios, and every decision is recorded in an immutable audit trail. The desktop app, CLI, dashboard, and Recon scan are free to use for developers. If you often use Claude, Codex, Openclaw, and Cursor, Kastra can run a scan command on which risky actions your agents have already taken and automatically build rules to avoid them from happening again. Recon is a feature of Kastra that scans your local agent history. In order to run this scan, execute the commands below in your coding agent. brew install kastra-labs/tap/kastra-edge kastra-edge scan The scan reads your local agent session history, and it shows all the risky actions your agent has already taken before, the secrets written to tracked files, production databases touched, force pushes, curl-to-shell, and more. This runs on your machine, and secrets never leave. In our own use cases, we kept finding things we'd forgotten or didnt know agents had done. Each finding can be converted into a runtime policy, letting you delegate more work to AI without trusting the model itself. Kastra intercepts all workloads at runtime and makes sure these policy evaluations typically complete in under a millisecond. Instead of trusting the model, you trust the deterministic rules that govern its actions. One problem we are still working on to improve the stack is how to manage teams of agents with conflicting policies. We would love feedback from anyone building multi-agent systems. Fernando and I will be reviewing the comments. We are super curious what your first scan finds. Please post results below so we can see what the most common patterns are and adjust policy packs for our users based on your feedback. Documentation: https://kastra.ai/docs Download for MacOS Kastra Edge: https://ift.tt/6fq8Fge Check Kastra in action today: https://www.youtube.com/watch?v=6TUETu5lb3Q&feature=youtu.be https://kastra.ai/ July 9, 2026 at 07:26PM

Show HN: Getting GLM 5.2 running on my slow computer https://ift.tt/QtcO9f7

Show HN: Getting GLM 5.2 running on my slow computer A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context. How it responds in int4 and whether the quality is maintained or not. Until I got to the point, on my computer with 32GB of RAM, I was able to communicate with GLM 5.2 with times that, of course, aren't high in cold start, but even then, we're talking about 0.1 tok/s, but that wasn't important to me. The important thing was the journey to reach this goal. I just wanted it to work at all costs, even slowly. So I created Colibrì, which was born from a very simple idea, to be honest, but tested in every way, where a 744B Mixture-of-Experts model activates only ~40B parameters per token—and only ~11 GB of those change from token to token (the routed experts). So: The dense part (attention, shared experts, embeddings—~17B params) stays resident in RAM at int4 (~9.9 GB); The 21,504 routed experts (75 MoE layers × 256 experts + the MTP head, ~19 MB each at int4) live on disk (~370 GB) and are streamed on demand, with a per-layer LRU cache, an optional pinned hot-store, and the OS page cache as a free L2. The engine is a single C file (c/glm.c, ~1,300 lines) plus small headers. No BLAS, no Python at runtime, no GPU.No GPU or serious hardware because I don't have that hardware so I can't test it on hardware that is more powerful than my computer.Colibrì is a one-person project, written and tested entirely on a 12-core laptop with 25 GB of RAM — the numbers above are the ceiling of what I can measure at home. Any feedback is welcome! (and if anyone wanted to participate in the project I would be delighted) Repo: https://ift.tt/PMSBqRx https://ift.tt/PMSBqRx July 9, 2026 at 12:05PM

Show HN: EVconomics – EV vs. gas cost-of-ownership calculator with live prices https://ift.tt/C7NVZs6

Show HN: EVconomics – EV vs. gas cost-of-ownership calculator with live prices https://ift.tt/EGkHATl July 9, 2026 at 11:29PM