The California startup released the fourth-generation of its home assistance robot, Stretch.
Only 4,999 units of the HP Scuderia Ferrari AI PC will be available.
The advisory warns that Chinese spies are using public job search platforms to recruit people with access to non-public information.
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]
i'm having a hard time deciding which approach i should implement. i'm developing a Laravel api which is consumed by Vue & Nuxt and i didn't noticed that i actually implemented two approaches of the returned response: [1] return ArticleResource::collection($articles); this returns a JSON like this: { "id": 1, "title": "My Article" } [2] return response()->json([ 'data' => new ArticleResource($article), 'success' => true, 'message' => 'OK', ]); JSON: { "data": { // output of ArticleResource transformed $article }, "success": true, "message": "OK" } considering that the API and frontend are private repositories. does wrapping all of the response inside 'data' makes sense or should i just stick on [1] for less nesting? what do you guys think what do you usually do with your years of experience? submitted by /u/Totoro-Caelum [link] [留言]
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 […]
[ 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.