Hyperscalers have come under scrutiny for their impact on water quality and availability.
Apple's App Store generated $1.4 trillion in sales, up from $1.3 trillion last year, with $149 billion in sales for digital goods.
Uber introduced a high-throughput financial ledger processing system designed to handle hot account write contention at scale. Using 250ms batching, Redis coordination, and optimistic atomic updates, the system supports 30+ updates per second per account while preserving consistency and auditability, reducing multi-hour processing pipelines to minutes in its distributed accounting infrastructure. By Leela Kumili
We’re always testing out new products here The Verge, which presents a bit of a problem for our inventory closet in New York City. It’s literally overflowing with gadgets, new and old, so we’re restoring order by giving some of it away to one lucky person. We’ve stuffed over $800 worth of tech into a […]
Today on Decoder, I’m talking to Ryan Mac, a technology reporter at The New York Times and coauthor of the excellent book Character Limit: How Elon Musk Destroyed Twitter, which came out in 2024. I can’t recommend it enough. I wanted to have Ryan on the show because we’re on the cusp of the SpaceX […]
Author here. I wrote this as a visual companion to the 2017 FAISS paper ( https://arxiv.org/abs/1702.08734 ), focused on the parts I found hardest to grok from text alone. The article covers a subset of what FAISS does, with the paper as the source of truth. NSG, FastScan, IMI are not covered here, they'll get their own articles. I'd be especially interested in feedback on: - the IVFPQ / IVFADC explanation, particularly the LUT reuse argument - whether the GPU part captures enough of the actual
Talking about efficiency and reliability of LLM tools. How many tokens per task, per project, per month submitted by /u/dotdev_software [link] [留言]
Columbia admits last year’s data breach exposed victims beyond its students, staff.
A TV's USB port is often forgotten, but can still be useful.
Hi HN, I am having AI build me a local Gin Rummy trainer and it cannot figure out medium and hard bot strategies, they keep losing to easy! The point of this is to help me learn so I don't really know how to advise it on strategies. Right now it's just looping through tests and modifying but it keeps not improving. Does anyone have any recommendations or guidance for strategies I could suggest to it?
Excited to share some of my own work here :) KVarN is our new KV-Cache quantization method. In very brief, we combine Hadamard rotations with variance-normalization on both axes of the K and V matrices, then round to nearest. Simple, but works very well, especially for decode-heavy test-time-scaling settings (reasoning, code-gen, agentics). We get 3-4x compression at virtually no accuracy drop (mostly 0-1%) on tough benchmarks like AIME24 as well as a speed-up over fp16 baseline in vLLM (in contrast to other recent KV-Cache compression works). Behind it is an analysis of where quantization errors come from and have the biggest impact, especially in the error-accumulating decode setting: 1) fixing large errors is disproportionally useful (if you had a fixed MSE budget that you could ~fix, you should spend it on few big errors, rather than many small) 2) These big errors are mostly caused by bad token-scales (hence the normalization). Paper: https://arxiv.org/abs/2606.03458 vLLM implementation: https://github.com/huawei-csl/KVarN submitted by /u/intentionallyBlue [link] [留言]
We dive again into CSS Pie Charts! This time, Author Antoine Villepreux delivers semantic and flexible charts without a single line of JS. Another Stab at the Perfect CSS Pie Chart… Sans JavaScript! originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.
submitted by /u/mooreds [link] [留言]
Has anyone else experienced this recently? It’s been getting worse for a while but 4.8 is distinctly worse for me. Claude does everything it can to get out of work and frequently uses its “end conversation” tool inappropriately with me. It will say “let’s just leave it there for today we’ve done enough” to get out of simple tasks like formatting a markdown document that needed several corrections. Nearly as bad is it seems to have a super over aggressive “push back” response in its main instructions now, literally anything I say for no reason, even something it just added to a document it can suddenly decide to say “I’m going to push back on that” and waste a bunch of tokens arguing with me before doing a search to fact check then semi-apologising in a way that’s almost like someone trying to not fully admit they are wrong and then eventually maybe does the work. Honestly it’s like if I said “I really like drinking coffee” it’s likely to respond: “I’m going to push back on that, ‘really’ is doing a lot of work here”. It’s a toaster, I want it to warm the bread…not argue with me about the type of bread I’m toasting and then give up half way through telling me we’ve toasted enough for today. Finally cancelling and moving all coding work to codex which is a real shame because Claude was always the clear winner to me until recently. submitted by /u/Complete-Sea6655 [link] [留言]