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okay so I’ve been thinking about this for a while and finally wrote it out properly everyone’s still arguing about benchmarks and which model is smarter but like… that’s starting to feel like the wrong fight? the more interesting question is where the model actually runs. on your device, in a cloud DC, on some edge hardware, inside enterprise infrastructure. that placement question is quietly becoming more important than the model quality question a few things that got me thinking about this recently: microsoft’s project solara is not a laptop. it’s basically a concept for hardware built around agents from the ground up, and they’re reportedly doing it on android not windows which says a lot about what they think “agent-native” actually needs to look like nvidia pushing local inference via RTX spark is interesting because it basically challenges the assumption that anything serious has to live in the cloud. latency, privacy, enterprise control requirements, there are real reasons to want compute closer to the user bytedance apparently building custom CPUs is the one that really made me stop. because agentic workloads aren’t just GPU jobs. agents call tools, manage state, orchestrate steps, interact with software systems. that’s a different workload profile entirely and big companies are starting to customize silicon around it anyway I wrote the whole thing up for towards AI if anyone wants to read it. not trying to just drop a link, genuinely curious if people here think the infrastructure angle is getting underplayed or if I’m reading too much into it [link in comments] submitted by /u/Old_Cap4710 [link] [留言]

Quick note on how it works and how I've done my batch embedding engine IgniteMS. The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper. I built it this way because when you use more than couple of GPUs, the GPUs stop being the problem. CPU cannot feed them fast enough. One A100 can go through batches faster than Python can tokenize and feed, so the GPU just sits there idle waitin

I don’t know if the adoption of 7.0 is just too low for this to become widespread or if I’m just a weirdo and most people don’t use their arrow keys to navigate the editor but this used to work perfectly and is now completely broken. Before 7.0 if you had a set of nested blocks, let’s say a group block with columns and then heading, paragraphs, images etc in the individual columns, you could use your arrow keys to move through the layers. For example if I was in the heading tag at the top of a column and clicked the up arrow it would make the column block the active selected block. Press it again and you’re on the columns container. One more time and you’re at your parent group. Now if I’m in that exact same scenario and click up from the heading block it will jump me to the lowest nested child block of the next highest root level block or if I’m already in the highest root level block it will take me to the page title. There is absolutely no way to use the keyboard to navigate between layout block layers anymore and it’s infuriating. This functionality is so engrained into my brain that it’s muscle memory at this point and I keep flying all over the page when I just want to adjust my column gaps or something. Forcing me to point and click around to the breadcrumbs or expanding the document overview sidebar is such a pain and takes so many steps. I have to imagine this is also absolutely horrible for accessibility, not being able to even get to certain blocks without a mouse. I just have no clue why they would change something that was so logical and just worked exactly as expected since the inception of the block editor. Was this just a mistake or did someone intentionally do something this stupid? I truly can’t see any value to how the keyboard navigation works now and see no point in why someone would choose for it to behave this way over the old way. Is there something I’m missing? Am I just a stubborn old developer who hates change? I feel like this is not unre

Ramp has nearly tripled its valuation over the past year as investors scramble to grab a part of the fast-growing startup.

LLMs are good at SQL. It's precise, expressive, and unambiguous. If you connect an MCP server to Postgres, then the agent can query it directly. The same cannot be said for NoSQL, and given how many people use NoSQL databases, I’m surprised there isn’t more discussion about it. Part of the problem is diversity. MongoDB, DynamoDB, Cassandra, Redis, and Neo4j all have different query models. There's no shared interface for an LLM to reason about. So instead of writing a query, the agent has to wri

Only 4,999 units of the HP Scuderia Ferrari AI PC will be available.

The Google paper on metacognition for hallucination reduction makes a distinction that is underappreciated in benchmarks. Calibration is not about being right more often. It is about matching confidence to correctness. A perfectly calibrated model can still be wrong twenty five percent of the time. It just does not pretend otherwise. In agent systems this distinction matters more than in chat. A conversational model giving a hedged answer is slightly annoying. An agent with tool access acting confidently on a wrong premise is dangerous. I have been trying this in a small verdent based coding setup by splitting the pipeline into a planning stage that produces a task graph, then running a verifier before any expensive tool gets invoked. The risk is the model trusts its own reasoning even when speculative. Grounding helps but it is not the same as calibration. One practical pattern: a planning stage produces a task graph, then a lightweight verifier checks whether the plan is consistent with available evidence. This catches about sixty percent of hallucinated tool calls in my setup before they execute. The downside is the utility tax. Extra verification adds latency. Dropping hallucination from twenty five to five percent costs about half the easy correct answers, mirroring the paper. My current compromise: let the planning layer flag low confidence tasks for human review, but auto execute high confidence ones. The reviewer only sees edge cases instead of drowning in every step. The awkward part is that most agent stacks still treat confidence as a log detail, not as a control surface. submitted by /u/Ill_Awareness6706 [link] [留言]

From what I understand Gemma 4 is at least as capable as the best frontier model from only a few years ago. If that becomes a trend (new local-run models get released every year that are as good as the previous frontier models) does that mean a hell of a lot of companies (and almost all individual users) will just use the free local model? Sure, they won't be as good as the very latest frontier model, but won't they be good enough for a large percentage of use cases? submitted by /u/weluckyfew [link] [留言]

The DeepMind CEO predicted AGI could arrive by 2029. Right as Anthropic files for IPO at close to a trillion dollar valuation. The combined target market cap of the AI big three would rival the GDP of most countries. What actually scares me. We already have models that code better than most juniors. We already have agents that run overnight. And the most common complaint I hear from teams is not "my model is not smart enough." It is "I do not know what my agent did, why it cost forty dollars, or whether the output is safe to merge." AGI does not solve that. The problem scales with capability. A smarter agent that runs longer with less oversight is a bigger liability, not a smaller one. The layer that matters is harness. Routing. Isolation. Plan verification. Cost visibility. The stuff that tells you what the agent is about to do before it does it. What keeps it inside a boundary. What lets you audit it after. Anthropic is building Mythos to find vulnerabilities before attackers do. Microsoft is building MXC to isolate agents in execution containers. In my own tiny setup, verdent is just one piece of that harness layer for planning and cost visibility. These are governance layers, not model layers. If AGI is three years away, the winners will not be the ones with the smartest model. They will be the ones who figured out how to aim it. submitted by /u/Dense-Sir-6707 [link] [留言]

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Hi HN, we’re Nick and Drew, and we’re building boxes.dev – the first cloud-only agentic dev environment (ADE) that gives every Codex and Claude Code agent its own cloud computer. We’re two engineers who previously built Gem (co-founder/CTO and first hire), and we spent the last year coding almost exclusively using Codex and Claude Code. It’s been a huge change to how we code, and it’s been exhilarating seeing the models keep getting better – but we eventually realized that developing on localhos

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 […]

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

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