Google released Gemma 4 12B today. It’s a solid open-source model (Apache 2.0) that’s multimodal and runs really well on Macs with 16GB or more unified memory. Good at reasoning, coding, and agent stuff. Quick Mac-friendly info • 12B parameters, fits nicely on M2/M3/M4 Macs (especially with Q4/Q5 quant) • 256K context • Text + vision + audio support Easiest way to run it: Ollama 1. Download and install Ollama from ollama.com (the Mac app is super simple). Or use Homebrew if you prefer. 2. Open Terminal and pull the model: ollama pull gemma4:12b 3. Run it: ollama run gemma4:12b That’s it. You can start chatting right away. Mac tips: • Ollama uses Metal automatically so it runs pretty fast on Apple Silicon. • 16GB Macs handle the 12B model fine. 32GB feels even better. • Great for pairing with Continue.dev in VS Code if you code a lot. Other options if Ollama isn’t your thing: LM Studio (nice GUI), or llama.cpp for more control. Has anyone tried the image or audio features locally yet? How fast is it on your machine? Drop your specs and results if you test it.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ submitted by /u/nullvector88 [link] [留言]

Taiwan Semiconductor Manufacturing Co. - the world's biggest semiconductor-maker - is struggling to meet demands from American customers even with its factory buildout in the US, according to reports from Reuters and Bloomberg. "Customer demand is so high, and we can only support so much," TSMC CEO C.C. Wei said after a shareholder meeting on […]

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/allenaa3 [link] [留言]

Yesterday, I read a blog by Shubham on how Generative UI is changing entire frontend space. The frontend has always been something you build ahead of time and ship. The agent just works inside it. For 30 years that was the deal. What's actually shifting: the interfaces shipping in 2026 are drawn partly by the agent itself, in real time, from what the user actually asked for. He breaks down the different approaches (A2UI, MCP Apps, AG-UI) via CopilotKit and where each one actually falls apart depending on how deep in the app you go.. along with the token tax - how typical tool description with its JSON schema runs around 400 tokens. 25 components are 10,000 tokens on every turn, you pay that tax per request. worth a read especially the declarative generative UI pattern where you hand the agent a catalog and let it assemble layouts you didn't pre-build. blog link: https://x.com/Saboo_Shubham_/status/2062220865643982875 repo: https://github.com/CopilotKit/copilotkit do you think it's just marketing hype or actually the future of UI? submitted by /u/allenaa3 [link] [留言]

Hyperscalers have come under scrutiny for their impact on water quality and availability.

submitted by /u/legoman25 [link] [留言]

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.