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Hacker News Show HN

Show HN: Throng, a project first, terminal 2nd IDE for command line people

I've been beavering away at this since July, and its finally getting to a point where its stabilising.It started Windows only, but I recently converted over to Linux, so I've been ironing out the kinks in the last week. You can install it via apt or rpm, or .exe or .msi on Windows.Many more features to come, including native agent support and more preview features (images, PDF, document formats). Claude works pretty well in it, but I've not tried many other agents yet.I rarely leave throng now - 99% of my work happens inside it. I stopped using Visual Studio around 10 months ago (in favour of VSCode), and now I don't even use VSCode. Comments URL: https://news.ycombinator.com/item?id=50412226 Points: 1 # Comments: 0

vLLM · 官方技术博客

Migrating vLLM to the LibTorch Stable ABI

How vLLM migrated its CUDA build and compiled dependencies to the LibTorch Stable ABI, enabling compatible PyTorch upgrades without rebuilding vLLM.

Hacker News Show HN

Show HN: Overhead – the planes overhead, painted on an e-ink frame

Hi HN,I've been fascinated by aviation for some time now. Inspired by Fugleramme, I wanted a framed print of a cluster of aircraft over my house or a busy airport. So I built Overhead. (Photos in the README)The hero photo shows planes departing and landing in Changi, Singapore.How it worksFlight data for a location is gathered from adsb.lol and adsb.fi as a fallback. (you can also point it to your own ADS-B receiver).The photo of the exact aircraft is gathered from planespotters.net and the photographer is credited on the page.The photo is then handed to an AI image gen service via fal.ai using your own key.It's then cut out and packed into a cluster on the page with packers adapted from Fugleramme.It costs about US$0.04 a painting but images are painted once and kept for the next time that aircraft is seen.You can also choose to only show photos.This widget is built on my open source, self-hosted e-ink platform, Tesserae. It's a one click install from the widget catalog.I'm looking for feedback and contributors.https://github.com/arnegiacomo/fugleramme https://github.com/dmellok/tesserae Comments URL: https://news.ycombinator.com/item?id=50390605 Points: 1 # Comments: 0

Apple Machine Learning

Faster Retrieval for Better, More Efficient RAG

Maximum inner product search (MIPS) sits at the core of recommendation systems, search, and retrieval-augmented generation (RAG). Given a query vector, MIPS systems search within a database of millions of candidate key vectors for the one that best matches the query. The original data point associated with that best-matching key is then used to handle the query: in RAG, the document that is best suited to answer the question is typically prefixed to the query. As the number of keys grows to millions and vector dimensions reach thousands, the computational cost of running an exhaustive…

arXiv cs.CL

Supervised Fine-Tuning for Reasoning Large Language Models: A Survey

arXiv:2610.37681v1 Announce Type: new Abstract: Large reasoning models have recently demonstrated remarkable capabilities in solving complex tasks, where supervised fine-tuning (SFT) on long chain-of-thought data serves as a crucial foundation for eliciting and enhancing their reasoning abilities. Despite rapid progress in both improving and analyzing reasoning-oriented SFT, the field still lacks a systematic survey that consolidates its fast-growing literature. To fill this gap, we present a comprehensive review of recent advancements in reasoning SFT, examining the literature through the dual lenses of methodological design and analytical investigation. First, we review methodological improvements across the SFT pipeline and categorize them into data-centric approaches and algorithm-cent

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SemiEngineering

Blog Review: Oct. 28

STCO for heterogeneous design; physical AI interactions; signing off AI-generated designs; workload profile analysis. The post Blog Review: Oct. 28 appeared first on Semiconductor Engineering.

新闻源配图 · SemiEngineering
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arXiv cs.DC

Quantum Cadence: Post-Quantum Consensus with Multiple Concurrent Proposers

arXiv:2610.32803v1 Announce Type: new Abstract: The consensus protocols of most high-performance blockchains use quorum certificates, which bundle the signatures of a quorum of validators. Signature schemes such as BLS can aggregate the signatures of a whole quorum into a single short signature, which keeps these certificates small. A large-scale quantum computer would break these schemes, and no post-quantum scheme with comparable aggregate size and aggregation time is known. Without aggregation, a certificate holds one large post-quantum signature per member of the quorum, so it becomes infeasible for each validator to broadcast certificates, as modern consensus protocols often require. We present Quantum Cadence, a post-quantum consensus protocol with multiple concurrent proposers that

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Simon Willison

LaTeX renderer

Tool: LaTeX renderer Inspired by Paul Ford and Aboard Foundary's new web app Tiny Little Office, which provides a suite of web app content editors (a Word Processor, Outliner, Presentation Builder, Spreadsheet, Calculator, Drawing Tool, Diagram Maker, Animator, Calendar, and Trip Planner). Tiny Little Office's signature trick is to store all of the document data in the #fragment section of the URL, so every document created using it becomes a bookmarkable and shareable link. It also has an llms.txt file telling agents how to construct URLs that will render as documents. Claude used that file to generate this link to a deck that explains how the URL format works: Tiny Little Office inspired me to have Claude Code (Opus 5.5 Extra) build a similar tool that works with LaTeX. Here's the sequen

Individual
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Qwen

Extending the Context Length to 1M Tokens!

API Documentation (Chinese) HuggingFace Demo ModelScope Demo Introduction After the release of Qwen2.5, we heard the community’s demand for processing longer contexts. In recent months, we have made many optimizations for the model capabilities and inference performance of extremely long context. Today, we are proud to introduce the new Qwen2.5-Turbo version, which features: Longer Context Support: We have extended the model’s context length from 128k to 1M, which is approximately 1 million English words or 1.