标签：#r

TL;DR A "loop" is not an agent grading its own work. It is an external script that re-runs the agent, plus a separate check the agent cannot edit. I turned "feels smooth" into an FPS number and let the loop optimize toward it. I set the target too high to be reachable on a 60Hz screen. The loop kept failing but never faked the result. The bug was in my number, not the code. Could I get an AI agent to make my website faster without me sitting there, running it, reading the numbers, and running it again? That is what this series is about. Not how I built a website, because the website is boring on purpose, but how you wrap an agent in a loop that works toward a goal on its own, and how you stop it from cheating along the way. In this first part I want to explain what a loop actually is, because there is a common misconception, and then walk through a real one. I set this loop a target that was physically impossible to reach and watched what it did. That run taught me more than a passing test would have. This is Part 1 of 3. All three parts use the same small movie-poster website as the example, but the website is never the point. What a loop is, and what it is not I had a wrong idea about this at first, so let me clear it up. A loop is not an agent prompting itself, grading its own work, and deciding when it is done. An agent left to mark its own homework will usually tell you it passed. A loop is closer to this: an external script runs the agent, a separate check that the agent cannot edit decides whether the result is good, and that repeats until the check passes or you hit a limit. There are three parts to it that come up again and again: The driver: the script that re-runs the agent. This is the thing that removes the manual work, not the agent. The gate: the check that decides pass or fail. The agent makes changes, but it never decides when to stop. The cap: a limit, so a stuck loop gives up instead of running forever. One rule matters more than the rest. The thi

The two subscription services offer more than just extra storage, but getting the right one can save you money.

Why build something? And what if nobody ends up using it? There are good answers to the first one. You build because you need a thing that doesn't exist yet. You build to see if you can, the technical challenge, the "is this even possible?" You build to impress someone, or just because you think it'll make people's day a little less annoying. All of those are real reasons, and at different points, I told myself most of them. Then, a few days ago, late in the day, at the end of a coding session, five months into the project, I asked myself those two questions back-to-back. And for the first time, I couldn't answer the second one. Zeri worked. Every feature did what it was supposed to do. Both processes handshake cleanly, a variable set in one context showing up in another a second later, the TUI rendering exactly as I'd pictured it. And I sat there and couldn't come up with one honest sentence explaining why anyone would actually download it. That gap, between something built well and something that has a reason to exist, turned out to be the most useful thing this whole project taught me. So I'm shipping it anyway, and I'll tell you why. What I built Zeri is a TUI multi-language REPL. You launch it, pick a language, Python , JavaScript (with Bun ), Ruby , or LuaJIT , and you get an interactive session in your terminal. You can switch languages mid-session, share variables across them, save and reload your work, manage snippets, and talk to a local LLM through a command running on Ollama . The feature list isn't the interesting part, though. The interesting part is what's underneath. Two processes, one app Zeri is split into two processes: a headless engine written in C++23 and a TUI frontend built in Go using Bubble Tea and Lip Gloss . The engine does all the evaluation, state, and runtime coordination. The frontend does rendering, input, and everything the user actually sees and touches. They talk to each other over a custom binary IPC protocol that I built from sc

Yes-Brainer is a council of AI models for the decisions that aren't no-brainers. One question fans out to several models — they answer in parallel, debate to consensus, or get judged to a verdict. No backend, no accounts: your keys, your browser. For non-trivial questions — the ones that are either complex or important — I caught myself in a "ritual": copy-pasting the same prompt into Claude, then Gemini, then ChatGPT, in three browser tabs, and eyeballing the differences. The differences were the interesting part. Where the models agreed, I felt more confident. Where they disagreed, that was a nudge to give the problem a second thought and dig deeper. So I built the ritual into an app. 🧠 Yes-Brainer — a council of AI models for the decisions that aren't no-brainers. 🔗 Try it: yesbrainer.ai 🔗 Source code: github.com/trekhleb/yesbrainer One question fans out to several models at once, and instead of juggling tabs you get a deliberation in one place: 🔀 Parallel — independent answers, side by side ⚖️ Trial — the models vote anonymously on each other's answers, then a judge synthesizes a verdict 🤝 Consensus — a real multi-round debate, with a mediator that either drives it to convergence or honestly reports what stayed contested Consensus is my favourite. It's fun to watch the models drift from their original opinions under their peers' arguments. You can try all of this without pasting any keys: a few recorded demo councils are one click away on the front page. I'll walk through them below, because they show the point of the app better than the feature list. Setting up a council Creating a council is the whole setup: pick the deliberation mode, seat the models, choose who referees. The roster can mix providers freely — Anthropic, OpenAI, Google, Groq, OpenRouter, and local Ollama models can sit at the same table. Each seat shows its capabilities (vision, tools, reasoning) and context window at a glance, and each model's native abilities — web search, code execution, at

I released php-quic 1.0.0, a PHP extension that gives you raw QUIC transport with first-class access to streams. It links against the same OpenSSL that PHP already links against, and that is the whole trick. Why This Was Awkward Before QUIC is not a protocol you bolt on in userland. It is TLS 1.3, congestion control, loss recovery, and stream multiplexing, all riding on UDP, and all of it has to be right before you can send a single useful byte. You could get there from PHP if you were willing to bind to a foreign QUIC library like ngtcp2 or quiche through FFI. That works, but now your PHP app carries a second TLS stack, a second set of CVEs to track, a build story that involves Rust, and a version matrix that has nothing to do with the one your distro maintains. For a language whose entire deployment story is "the package manager handles it," that is a lot of rope. What Changed OpenSSL 3.5 shipped a native QUIC stack. Client and server, in the library PHP is already built against. That reframes the problem. The extension is no longer "embed a QUIC implementation into PHP." It is "expose the QUIC that is already sitting there." No new TLS stack, no FFI layer, no Rust toolchain in your build. If your OpenSSL is patched, your QUIC is patched. The requirements fall out of that directly: PHP 8.4 or newer (8.5 on Windows), NTS or ZTS OpenSSL 3.5.0 or newer, built with QUIC support Transport, Not HTTP/3 The thing I most wanted to avoid was shipping an HTTP/3 client and calling it a QUIC library. QUIC is a transport. HTTP/3 is one protocol that runs on it, and it is not the interesting one for everybody. DNS-over-QUIC runs on it. So does anything you want to invent that needs multiplexed, ordered, loss-recovered streams without head-of-line blocking across them. So php-quic hands you connections and streams, and stays out of the framing business. If you want HTTP/3, you build HEADERS frames and QPACK on top of it, and the README has a worked example. If you want something

🎧 DJ ROOTS – Building a Real-Time Collaborative Music Platform with Gesture Control Crowd Vibes. You Control. Music is one of the best ways to bring people together. However, during parties, college events, hostel gatherings, or study sessions, one common problem always exists— who gets to control the music? Usually, one person owns the playlist while everyone else keeps requesting songs. This often creates confusion, interruptions, and arguments over what should play next. Our team wanted to solve this problem by creating a platform where everyone in the room gets an equal voice. Welcome to DJ ROOTS . 🚨 The Problem Traditional music streaming at group events has several limitations: Only one person controls the playlist. Song requests are ignored or forgotten. No real-time collaboration. Existing queue systems don't truly represent the crowd's choice. There is no simple browser-based solution that works instantly without downloading an app. We wanted to build something that makes music democratic . 💡 Our Solution DJ ROOTS is a real-time collaborative DJ platform where anyone can join a room using a simple room code. Participants can: Create or join a music room Add songs using YouTube Upvote or downvote tracks Automatically reorder the queue based on crowd votes Watch every change happen instantly across all connected devices Let the host control playback using webcam hand gestures Instead of one person deciding the playlist, the entire crowd decides what plays next. 🛠 Tech Stack Frontend React 19 Vite Tailwind CSS v4 Framer Motion Three.js GSAP OGL Backend Node.js Express.js Database & Authentication Supabase PostgreSQL Supabase Authentication Supabase Realtime Computer Vision Google MediaPipe Gesture Recognizer Audio Pipeline yt-dlp youtube-dl-exec HTML5 Audio API Web Audio API Deployment Vercel (Frontend) ⚙️ How It Works Users create or join a room using a unique room code. Songs are added using a YouTube link or search. Song metadata is automatically fetched. E

Chipset makers and router manufacturers are talking about Wi-Fi 8, but what is the new standard, and when will it arrive?

As the US war on Iran sent gas prices soaring, American car buyers flocked to electric vehicles - and in particular hybrids. After a sluggish winter and fall, EV sales rebounded in the second quarter of 2026, with automakers posting some of their best numbers since the elimination of the federal EV tax credit last […]