Tony Hoare has died
The article pays tribute to Tony Hoare, a pioneering computer scientist who made significant contributions to the field of programming languages, algorithms, and the theory of computation. It highlights Hoare's influential work, including the development of Quicksort and Communicating Sequential Processes, and his lasting impact on the computer science community.
Yann LeCun's AI startup raises $1B in Europe's largest ever seed round
https://archive.md/5eZWq
Redox OS has adopted a Certificate of Origin policy and a strict no-LLM policy
The article outlines the contribution guidelines for the Redox operating system project, covering topics such as reporting issues, submitting patches, and code of conduct expectations for contributors.
Intel Demos Chip to Compute with Encrypted Data
The article discusses Intel's work on Fully Homomorphic Encryption (FHE), a cryptographic technique that allows data to be processed while it remains encrypted. This could enable cloud-based computation on sensitive data without compromising security and privacy.
Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
Hi HN, we're Sanchit and Shubham (YC W26). We built a fast inference engine for Apple Silicon. LLMs, speech-to-text, text-to-speech – MetalRT beats llama.cpp, Apple's MLX, Ollama, and sherpa-onnx on every modality we tested. Custom Metal shaders, no framework overhead.
Also, we've open-sourced RCLI, the fastest end-to-end voice AI pipeline on Apple Silicon. Mic to spoken response, entirely on-device. No cloud, no API keys.
To get started:
brew tap RunanywhereAI/rcli https://github.com/RunanywhereAI/RCLI.git
brew install rcli
rcli setup # downloads ~1 GB of models
rcli # interactive mode with push-to-talk
Or: curl -fsSL https://raw.githubusercontent.com/RunanywhereAI/RCLI/main/install.sh | bash
The numbers (M4 Max, 64 GB, reproducible via `rcli bench`):LLM decode – 1.67x faster than llama.cpp, 1.19x faster than Apple MLX (same model files): - Qwen3-0.6B: 658 tok/s (vs mlx-lm 552, llama.cpp 295) - Qwen3-4B: 186 tok/s (vs mlx-lm 170, llama.cpp 87) - LFM2.5-1.2B: 570 tok/s (vs mlx-lm 509, llama.cpp 372) - Time-to-first-token: 6.6 ms
STT – 70 seconds of audio transcribed in *101 ms*. That's 714x real-time. 4.6x faster than mlx-whisper.
TTS – 178 ms synthesis. 2.8x faster than mlx-audio and sherpa-onnx.
We built this because demoing on-device AI is easy but shipping it is brutal. Voice is the hardest test: you're chaining STT, LLM, and TTS sequentially, and if any stage is slow, the user feels it. Most teams fall back to cloud APIs not because local models are bad, but because local inference infrastructure is.
The thing that's hard to solve is latency compounding. In a voice pipeline, you're stacking three models in sequence. If each adds 200ms, you're at 600ms before the user hears a word, and that feels broken. You can't optimize one stage and call it done. Every stage needs to be fast, on one device, with no network round-trip to hide behind.
We went straight to Metal. Custom GPU compute shaders, all memory pre-allocated at init (zero allocations during inference), and one unified engine for all three modalities instead of stitching separate runtimes together.
MetalRT is the first engine to handle all three modalities natively on Apple Silicon. Full methodology:
LLM benchmarks: https://www.runanywhere.ai/blog/metalrt-fastest-llm-decode-e...
Speech benchmarks: https://www.runanywhere.ai/blog/metalrt-speech-fastest-stt-t...
How: Most inference engines add layers between you and the GPU: graph schedulers, runtime dispatchers, memory managers. MetalRT skips all of it. Custom Metal compute shaders for quantized matmul, attention, and activation - compiled ahead of time, dispatched directly.
Voice Pipeline optimizations details: https://www.runanywhere.ai/blog/fastvoice-on-device-voice-ai... RAG optimizations: https://www.runanywhere.ai/blog/fastvoice-rag-on-device-retr...
RCLI is the open-source voice pipeline (MIT) built on MetalRT: three concurrent threads with lock-free ring buffers, double-buffered TTS, 38 macOS actions by voice, local RAG (~4 ms over 5K+ chunks), 20 hot-swappable models, and a full-screen TUI with per-op latency readouts. Falls back to llama.cpp when MetalRT isn't installed.
Source: https://github.com/RunanywhereAI/RCLI (MIT)
Demo: https://www.youtube.com/watch?v=eTYwkgNoaKg
What would you build if on-device AI were genuinely as fast as cloud?
Yann LeCun raises $1B to build AI that understands the physical world
https://web.archive.org/web/20260310153721/https://www.wired...
https://www.ft.com/content/e5245ec3-1a58-4eff-ab58-480b6259a... (https://archive.md/5eZWq)
RFC 454545 – Human Em Dash Standard
The article discusses the rise of remote work during the COVID-19 pandemic and explores the challenges and benefits of this shift, including increased flexibility, work-life balance, and the need for effective communication and collaboration tools.
Hisense TVs add unskippable startup ads before live TV
Hisense's Vidaa TVs are reportedly adding unskippable startup ads before users can access live TV, drawing criticism from consumers over the intrusive advertising practice.
Show HN: DD Photos – open-source photo album site generator (Go and SvelteKit)
I was frustrated with photo sharing sites. Apple's iCloud shared albums take 20+ seconds to load, and everything else comes with ads, cumbersome UIs, or social media distractions. I just want to share photos with friends and family: fast, mobile-friendly, distraction-free.
So I built DD Photos. You export photos from whatever you already use (Lightroom, Apple Photos, etc.) into folders, run `photogen` (a Go CLI) to resize them to WebP and generate JSON indexes, then deploy the SvelteKit static site anywhere that serves files. Apache, S3, whatever. No server-side code, no database.
Built over several weeks with heavy use of Claude Code, which I found genuinely useful for this kind of full-stack project spanning Go, SvelteKit/TypeScript, Apache config, Docker, and Playwright tests. Happy to discuss that experience too.
Live example: https://photos.donohoe.info Repo: https://github.com/dougdonohoe/ddphotos
Throwing away 18 months of code and starting over
The article discusses the decision to rewrite a codebase from scratch after over a year of development, highlighting the benefits of starting anew despite the significant time and effort invested in the previous iteration.
We are building data breach machines and nobody cares
The article discusses the increasing prevalence of data breaches and the lack of public concern, arguing that we are creating 'data breach machines' through the extensive collection and storage of personal data by companies and organizations. It highlights the need for more robust security measures and greater accountability to protect individuals' privacy and data.
EVi, a Hard-Fork of Vim
Evi is an open-source, privacy-focused virtual assistant that aims to provide an alternative to Alexa, Siri, and other commercial AI assistants. It focuses on user privacy, local processing, and customizability, allowing users to train it on their own data and deploy it on their own infrastructure.
Levels of Agentic Engineering
The article discusses the levels of agentic engineering, outlining four distinct approaches: self-directed, collaborative, participatory, and emancipatory. It explores how these levels differ in terms of the degree of user autonomy and control over the design process.
The Enterprise Context Layer
The article discusses the concept of the Enterprise Context Layer, which aims to bridge the gap between business strategy and IT implementation by providing a common understanding and holistic view of an organization's operations, goals, and constraints. It emphasizes the importance of this layer in aligning technology solutions with business needs and enabling more effective decision-making.
$3 ChromeOS Flex stick will revive old and outdated computers
A new ChromeOS stick allows users to transform old or outdated computers into modern, cloud-connected devices, providing a cost-effective solution for extending the life of legacy hardware.
Google to Discontinue Widevine Cloud License Service in April 2027
Google is retiring the Widevine cloud license service, which allows content providers to manage and deliver DRM-protected content. This change will impact content providers who rely on the Widevine cloud service, requiring them to migrate to alternative solutions or self-host the Widevine license service.
Flock Flocked up: How a license plate camera misread unraveled one man's life
Flock Safety's automated license plate reading (ALPR) cameras have been found to misread license plates, leading to false alerts and potential privacy concerns. The article examines the accuracy and privacy implications of this technology used by law enforcement and private communities.
Hugging Face Storage Buckets: Mutable, non-versioned object storage at $12/TB
The article discusses the importance of storage buckets in managing and organizing data, particularly in the context of machine learning and AI projects. It explores the key features and benefits of using storage buckets, including scalability, cost-effectiveness, and data security.
Pete Hegseth Blew Billions on Fruit Basket Stands, Chairs, and Crab
The article examines the business practices of Pete Hegseth, a co-CEO of Scribe Media, and his company's involvement in the controversies surrounding the Trump administration and right-wing politics. It delves into Hegseth's personal wealth and his company's role in conservative media and political initiatives.
OpenClaw Did Not Just Go Viral in China, It Solved a Structural Problem
The article discusses OpenClaw, a comprehensive AI stack developed in China that aims to provide a full-stack solution for AI development and deployment. It explores the key features and components of the OpenClaw platform, which includes tools for data management, model training, and deployment.