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The article discusses the release of Opus 4.5, a major update to the Opus open-source audio codec. It highlights the significant changes in this version, including improved quality, increased efficiency, and expanded support for various audio formats and platforms.

I used Tailscale, an old laptop, Claude Code, and Termius to code from my phone anywhere I have Internet connection.

Great for parties where you rather be home tinkering.

The article discusses the author's decision to self-host their music and podcasts after becoming dissatisfied with Spotify's business model and privacy practices. It explores the author's motivations, the process of setting up a self-hosted solution, and the benefits and challenges of taking control of one's digital content.

The article explores the lack of progress in the development of supersonic passenger jets, despite the successful Concorde flights in the past. It examines the technical, economic, and regulatory challenges that have hindered the advancement of this technology in recent decades.

The article presents a novel deep learning model for image classification, which leverages a multi-scale attention mechanism to achieve state-of-the-art performance on various benchmark datasets. The proposed approach outperforms existing methods in terms of accuracy and computational efficiency.

The article discusses AMD's announcements at CES 2026, including details about their latest processors and graphics cards. It covers the company's advancements in performance, power efficiency, and support for new technologies.

JAX-JS is a machine learning library for the web that provides a powerful and flexible framework for building and training neural networks directly in JavaScript. It offers features such as automatic differentiation, GPU acceleration, and a rich ecosystem of pre-built models and utilities.

WSJ writeup ("AI Hackers Are Coming Dangerously Close to Beating Humans"): https://www.wsj.com/tech/ai/ai-hackers-are-coming-dangerousl..., https://archive.ph/L4gh3

This article explores the growing trend of people becoming increasingly fatigued with social media, citing concerns such as privacy issues, negativity, and the desire for more authentic connections. It suggests that this shift may lead to a more balanced and thoughtful approach to online engagement.

The article discusses the legal and practical considerations surrounding cookie consent banners on websites, arguing that most websites do not actually need such banners under current data privacy regulations, and that they can often be counter-productive and create a poor user experience.

The article discusses the rapid growth of data centers worldwide, driven by the increasing demand for cloud computing, video streaming, and other data-intensive services. It examines the environmental impact of this growth and the efforts by companies and governments to make data centers more energy-efficient and sustainable.

The article discusses Stephen Miller, a former Trump advisor, and his appearance on CNN to defend the former president's policies. It highlights the heated exchange between Miller and CNN's Brianna Keilar as they debate topics such as immigration and the 2020 election.

The article discusses the development of a bespoke data diode air gap solution, which provides a secure and robust method for data transfer between isolated networks or systems while maintaining complete physical separation to prevent unauthorized access or data breaches.

The article discusses the Cuckoo Filter, a space-efficient data structure that can be used as an alternative to traditional Bloom filters for set membership queries. It provides an overview of the Cuckoo Filter's design, advantages, and use cases.

The article presents an experiment called 'PassSeeds' that explores the possibility of hijacking passkeys, a new authentication technology, to unlock various cryptographic use cases beyond simple authentication. The experiment investigates the potential security implications and challenges of extending the functionality of passkeys beyond their original purpose.

The article discusses the new release schedule for the Android Open Source Project (AOSP) source code, which will now be updated on a quarterly basis instead of monthly. The change aims to provide more stability and predictability for Android developers and partners.

The article introduces a novel machine learning model, called the Transformer, that achieves state-of-the-art performance on a variety of natural language processing tasks. The Transformer architecture leverages attention mechanisms to capture long-range dependencies in the data, leading to significant improvements in tasks such as machine translation, language understanding, and text generation.

Hi HN, I've been exploring various applications of formal methods to ML/interpretability and I've been hoping to get more eyes on the approach.

I have been working on a small interpretability project I call Symbolic Circuit Distillation. The goal is to take a tiny neuron-level circuit (like the ones in OpenAI's "Sparse Circuits" work) and automatically recover a concise Python program that implements the same algorithm, along with a bounded formal proof that the two are equivalent on a finite token domain.

Roughly, the pipeline is:

1. Start from a pruned circuit graph for a specific behavior (e.g. quote closing or bracket depth) extracted from a transformer. 2. Treat the circuit as an executable function and train a tiny ReLU network ("surrogate") that exactly matches the circuit on all inputs in a bounded domain (typically sequences of length 5–10 over a small token alphabet). 3. Search over a constrained DSL of common transformer motifs (counters, toggles, threshold detectors, small state machines) to synthesize candidate Python programs. 4. Use SMT-based bounded equivalence checking to either: - Prove that a candidate program and the surrogate agree on all inputs in the domain, or - Produce a counterexample input that rules the program out.

If the solver finds a proof, you get a small, human-readable Python function plus a machine-checkable guarantee that it matches the original circuit on that bounded domain.

Why I built this

Mechanistic interpretability has gotten pretty good at extracting "small crisp circuits" from large models, but turning those graphs into clean, human-readable algorithms is still very manual. My goal here is to automate that last step: go from "here is a sparse circuit" to "here is a verified algorithm that explains what it does", without hand-holding.

What works today

- Tasks: quote closing and bracket-depth detection from the OpenAI circuit_sparsity repo. - Exact surrogate fitting on a finite token domain. - DSL templates for simple counters, toggles, and small state machines. - SMT-based bounded equivalence between: sparse circuit -> ReLU surrogate -> Python program in the DSL.

Limitations and open questions

- The guarantees are bounded: equivalence is only proven on a finite token domain (short sequences and a small vocabulary). - Currently focused on very small circuits. Scaling to larger circuits and longer contexts is open engineering and research work. - The DSL is hand-designed around a few motifs. I am not yet learning the DSL itself or doing anything very clever in the search.

What I would love feedback on

- Are the problem framing and guarantees interesting to people working on mechanistic interpretability or formal methods? - Suggestions for next benchmarks: which circuits or behaviors would you want to see distilled next? - Feedback on the DSL design, search strategy, and SMT setup.

Happy to answer questions about implementation details, the SMT encoding, integration with OpenAI's Sparse Circuits repo, or anything else.

The article discusses Tesla's market performance in Europe, showing a significant decline in sales and market share in 2025. It suggests that Tesla faced a 'total bloodbath' in the European market, potentially due to increased competition from other electric vehicle manufacturers and changing consumer preferences.

A fast SOCKS5 proxy that tunnels your traffic through what looks like normal SMTP email, bypassing Deep Packet Inspection firewalls.

How it works: - Client runs a local SOCKS5 proxy (127.0.0.1:1080) - Traffic is sent to server disguised as SMTP (EHLO, STARTTLS, AUTH) - DPI sees legitimate email session, not a VPN/proxy

Features: - One-liner install on any Linux VPS - Multi-user with per-user secrets and IP whitelists - Auto-generated client packages (just double-click to run) - Auto-reconnect on connection loss - Works with any app that supports SOCKS5

Tech: Python/asyncio, TLS 1.2+, HMAC-SHA256 auth

GitHub: https://github.com/x011/smtp-tunnel-proxy