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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

Apple’s trade secrets lawsuit against OpenAI contains allegations that range from employees joking about unauthorized access to Apple’s systems to claims that job candidates were asked to bring Apple hardware to interviews. Here are the complaint’s most eye-catching claims.

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?

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Anthropic—currently the world’s most valuable AI company, with a nearly $1 trillion valuation—has a reputation for publishing strange and heady research. It’s looking into whether AI models can feel pain, for example,…

Responding to Musk accusing him of being a scammer, Altman said, "homeboy you're the one sellling [sic] public market investors on short-term space datacenters."

The biggest smartphone makers keep on trucking in the face of component shortages and economic uncertainty.

General Fusion started trading on the Nasdaq following a reverse merger that saw high redemptions.

The states allege that the deal would harm movie theaters, basic cable distributors, and audiences.

When Apple employees interviewed for jobs at OpenAI, the AI startup's hardware head allegedly asked them to show up with something unusual: components they were working on and unreleased product samples. That's according to a blockbuster lawsuit filed by Apple, which accuses OpenAI of stealing confidential documents, spying on hardware prototypes, and tricking one of […]

The amendment would restrict building codes that promote electrification.

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"It's very clear that in the United States there is a big need for an additional crew vehicle."

There is a growing sentiment in engineering circles right now that documentation is a relic of the past. The argument usually goes something like this: We’re living in the era of agent-driven development. If an AI agent can read the raw source code or parse an OpenAPI specification instantly, why waste human engineering hours writing prose? Code churns too fast anyway, and human-written docs are outdated the second they’re committed. It’s an attractive, black-and-white view of the world. It’s also completely wrong. Chasing strict determinism in your source of truth is a pipe dream. Code and specs tell a system how something works, but they are fundamentally incapable of explaining why it was built that way in the first place. The Intent Gap: Why Code Isn't Enough Even if you’re building entirely for a downstream consumer of AI agents, there is a massive, structural gap between a raw API specification and an operational reality. Agents are phenomenal at pattern matching and syntax execution, but they struggle with architectural philosophy and human intent. We still need words to contextualize the boundaries. A spec can define an endpoint, its parameters, and its payload. What it can't capture is the nuance of why a specific architectural trade-off was made, or the implicit historical context of a legacy edge case. Prose provides the guardrails for non-deterministic systems. Even if that prose is ultimately consumed by a machine rather than a human, the written word remains the highest-leverage way to transmit intent. The Danger of Slop Describing Slop This doesn't mean we need to return to the days of manually maintaining massive, static wiki pages. Automation has a massive role to play here. Cascading automation—where documentation is dynamically generated alongside code changes—is incredibly powerful. But there’s a trap here: slop describing slop is entirely useless. If we completely hand off documentation generation to unchecked LLMs, we end up with a feedback loo

The AI Skill Registry at 5,776: A Deep Dive into Reusable Modules for Code Review, Terraform, and Database Migrations TormentNexus’ skill registry has surpassed 5,776 reusable modules. This post dissects three high-impact skill categories—code review, Terraform generation, and database migration—with real code examples, performance metrics, and architectural constraints. Learn how to leverage these modules to accelerate development pipelines. From Silos to Synergy: Why 5,776 Skills Matter In late 2023, TormentNexus crossed the 5,000-module threshold. As of February 2025, we’re at 5,776 verified, runnable AI skills—each one a `SKILL.md`-defined unit that maps to a specific task, parameter set, and output schema. The registry isn’t a flat list; it’s a dependency graph where skills chain together. For example, a `terraform-generate` skill calls a `code-review` skill internally to validate the generated HCL before output. This modular architecture means a single prompt can sequence up to 3.2 skills on average (median depth: 2), with a measured 94% success rate for execution with no human intervention. The registry spans 37 domains, from frontend component generation to Kubernetes manifests. The top three categories—code review, infrastructure as code, and database operations—account for 1,308 skills collectively. Each skill is stored as a JSON schema in the registry, with an average execution latency of 1.42 seconds (GPU-accelerated, single A100). Let’s examine three representative modules in detail. // Metadata from an actual registered skill: code-review-python v2.1 { "name": "code-review-python", "registryID": "SKI-PYTHON-REVIEW-1729", "version": "2.1", "outputSchema": { "type": "object", "properties": { "issues": { "type": "array", "items": { "$ref": "#/definitions/Issue" } }, "complexityScore": { "type": "number", "minimum": 0, "maximum": 100 }, "refactoredSnippet": { "type": "string" } }, "required": ["issues", "complexityScore"] }, "defaultPromptTemplate": "Revie

Despite a complete lack of evidence, everyone from Russia to Israel and Iran is being blamed for Lindsey Graham’s death.

Despite a complete lack of evidence, everyone from Russia to Israel and Iran is being blamed for Lindsey Graham’s death.