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  • Windows Codex app release open sources native agent sandbox
    Windows Codex app release open sources native agent sandbox

    By

    @reach_vb

    –

    The underrated part of the windows codex app release is that the native agent sandbox is fully open source Use it, fork it, build w/ itt! https://
    github.com/openai/codex/t
    ree/main/codex-rs/windows-sandbox-rs
    …

    → View original post on X — @reach_vb

  • Guitar amp and pedals simulator in NAM format by @jessyseonoob

    By

    @jessyseonoob

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    oki let's create an amp and pedals simulator for guitar using NAM format

    → View original post on X — @jessyseonoob

  • New JAX Course: Build and Train LLMs from Scratch
    New JAX Course: Build and Train LLMs from Scratch

    By

    @andrewyng

    –

    New course: Build and Train an LLM with JAX, built in partnership with @Google and taught by @chrisachard. JAX is the open-source library behind Google's Gemini, Veo, and other advanced models. This short course teaches you to build and train a 20-million parameter language model from scratch using JAX and its ecosystem of tools. You'll implement a complete MiniGPT-style architecture from scratch, train it, and chat with your finished model through a graphical interface. Skills you'll gain: – Learn JAX's core primitives: automatic differentiation, JIT compilation, and vectorized execution – Build a MiniGPT-style LLM using Flax/NNX, implementing embedding and transformer blocks – Load a pretrained MiniGPT model and run inference through a chat interface Come learn this important software layer for building LLMs! deeplearning.ai/short-course…

    → View original post on X — @andrewyng, 2026-03-04 18:41 UTC

  • Codex for Windows launches with native app and PowerShell sandbox
    Codex for Windows launches with native app and PowerShell sandbox

    By

    @reach_vb

    –

    Codex for Windows is here! > Native Codex app on Windows via the Microsoft Store
    > Local PowerShell execution with a native Windows sandbox
    > WSL support, but not required
    > Full app features: Skills, Automations, Worktrees, review, parallel threads
    > Windows-native setup for

    → View original post on X — @reach_vb

  • Models Self-Regulate Reasoning Length

    By

    @godofprompt

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    the deeper point here connects to something the field keeps rediscovering. we trained reasoning models to think longer. then we discovered longer doesn't mean better. now this paper shows the models themselves already know that. they're generating stop signals that our inference

    → View original post on X — @godofprompt

  • SAGE: Efficient Step-by-Step Reasoning

    By

    @godofprompt

    –

    their solution: SAGE (Self-Aware Guided Efficient Reasoning). instead of generating token by token, SAGE extends chains in whole reasoning steps. after each step, it checks: is the model confidently signaling it wants to stop? if yes, reasoning ends. no fine-tuning. no new

    → View original post on X — @godofprompt

  • Three Key Observations on Model Paths

    By

    @godofprompt

    –

    three observations back this up: > high-confidence paths (measured by cumulative probability) are shorter AND more accurate than standard samples > at those natural endpoints, the stop token is the model's top prediction. it knows it's finished > as you widen exploration (more

    → View original post on X — @godofprompt

  • Overthinking hurts accuracy in AI responses

    By

    @godofprompt

    –

    and it's not just wasted compute. overthinking actively hurts accuracy. DeepSeek-R1 produces responses 5x longer than Claude 3.7 Sonnet on AIME 2025 with comparable accuracy. QwQ-32B scores 2 percentage points HIGHER with its shortest answers using 31% fewer tokens. 72% of

    → View original post on X — @godofprompt

  • Overthinking in AI models is a sampling issue
    Overthinking in AI models is a sampling issue

    By

    @godofprompt

    –

    reasoning models already know when they've solved the problem. we just don't let them stop. new paper from Beihang University and ByteDance shows that the overthinking problem in models like DeepSeek-R1 and Qwen3 isn't a training failure. it's a sampling failure. the fix cuts

    → View original post on X — @godofprompt

  • Pocket Recording Device Privacy and Security Audit Reveals Major Concerns
    Pocket Recording Device Privacy and Security Audit Reveals Major Concerns

    By

    @abhi1thakur

    –

    Pocket (@Heypocket) sold 30,000 units in 5 months recording doctor visits, legal calls, and board meetings. So I did my due diligence like I always do. Their Google Play listing says "No data collected." Their own privacy policy lists audio recordings, transcripts, device IDs, ad identifiers, cookies, IP location, and behavioral inferences. One of those is wrong. They brand themselves "open source". Their GitHub org (github.com/heypocketai) contains exactly 4 repositories: – Raspberry Pi Zero prototype called icecream-project [from the CEO's prior Omi work], – FFmpeg Flutter fork, – docs repo, – org profile page. The actual Pocket device firmware, mobile app, backend infrastructure, and audio processing pipeline are NOT here. There is NO Pocket source code to audit. Recordings go to unnamed "cloud/AI vendors" with no disclosure of which LLMs process your audio, what jurisdiction they operate in, or how long they retain it. Users can't choose their provider or bring their own keys. If you cancel your $19.99/month subscription, your recordings get processed by whichever default model Pocket selects. The contact mic is marketed for recording phone calls without speakerphone. In 11+ US states including California (where Pocket is based) — that requires all party consent. The device has NO consent mechanism. Their terms cap liability at the amount you've paid or $100 (whichever is greater) – for data breaches involving your medical appointments, legal consultations, and proprietary meetings. A third‑party LobeHub skill has already reverse‑engineered Pocket’s web API and can pull recordings, transcripts, summaries, and action items using short‑lived Firebase bearer tokens extracted from the user’s browser session. Their privacy policy confirms that if Pocket is acquired — every recording, transcript, and summary transfers to the buyer. Y Combinator (@ycombinator) Pocket (@heypocket) is your notetaker for real world meetings. In the last 5 months, the team has delivered over 30k units with a $27M annualized run rate, growing 50% month over month. Congrats on the launch, @AkshayNarisetti and @gabrieldymowski! ycombinator.com/launches/PaX… — https://nitter.net/ycombinator/status/2028878066630709455#m

    → View original post on X — @abhi1thakur, 2026-03-03 20:26 UTC