AI 资讯

I've been doing webdev for over a decade. Now I feel burnt out and worried about prospects. Mid-career and barely getting interviews. Not only do we have to compete on a global stage (I'm US based), but we have to deal with regular layoffs (and that's accelerated due to AI). What used to be reliable work with good pay, seems to have fallen off dramatically in both aspects. Do you agree? Would your recommend this field to a friend? submitted by /u/hypercosm_dot_net [link] [留言]

My paper is around 33 pages including but tpami guideline said it should be 20 pages Does anyone know which is correct? Its mistake it’s TPAMI submitted by /u/Alternative_Art2984 [link] [留言]

I can't tell if HN is the right kind of place for this stuff anymore since people are so advanced in their use but I thought it was interesting to leverage my existing Go microservices framework and turn it into the core of what would provide tools for an agent cli or whatever beyond that. Extensibility is key. Thought I'd share and get a conversation going.

Earlier I posted in a “Who wants to be hired?” thread, looking for a place where I could apply my experience in hospitality, food tech and automation. A couple hours later I received an email: “Hi Ilia, I saw your comment on the June Who’s Hiring thread. I build production-ready TypeScript and Python systems that integrate LLMs into real workflows, with particular focus on RAG, agent orchestration, and clear blah-blah-blah” Come on. I am a forced immigrant with a wife, a cat, rent and crushing d

Text: I’ve been thinking about how much of our identity is built around work. Our job titles, productivity, status, even the feeling of being “useful”, all of it feels so normal now. But historically, this version of work is actually very recent. For most of human history, people didn’t have jobs, resumes, office hours, or career paths. Work wasn’t separate from life. It was just part of living. Now AI seems to be pushing us into another shift. Maybe the big question isn’t only “what jobs will disappear?”, but also: if work becomes less central to who we are, what takes its place? How do you see it? Is AI changing only the future of work, or also the way people define human value? submitted by /u/GenesisProperty [link] [留言]

Groceries and household essentials will be a main focus of this year's deals event.

Hire AI employees that live in your Slack, Teams, Telegram Discussion | Link

When Lego announced its tech-packed Smart Bricks at CES, we were impressed by the potential - enough to give it our Best in Show award. But when the first Star Wars sets actually launched in March, we were less enamored. All that promise of clever interaction and creative play ultimately boiled down to a few […]

CEO Dave Limp said damage to the company's launchpad in Florida was not as bad as expected. But Blue Origin still hasn't shared the cause of last week's explosion.

I've been using Anthropic and OpenAI for a year and once I tried ollama - so slow - I totally wrote off local. But I guess things have changed. I picked up a used gaming rig with a 3090 last weekend. Yesterday I set up qwen 3.6:35b-a3b. I got the model that had been squeezed down to 20GB (batiai/qwen3.6-35b:iq4) so it all fit on the 3090. When it was in system ram it was doing a respectable 15tps on output but once I got it all stuffed into VRAM it's output was up to 160tps. Then I fed it a picture. https://preview.redd.it/cmpali41ev4h1.png?width=1882&format=png&auto=webp&s=a4c7732b9820730cc3f38b604ee04d465d7cc86e The video processing took 75 seconds but... wow. Just. Wow. That's pretty damn good running local on a 5 year old video card! I guess you guys are used to this but it sure surprised me! And we watched a transcoded movie via Plex at the same time! I can see why you guys love the 3090 so much. Hell of a card. submitted by /u/LankyGuitar6528 [link] [留言]

Hi, While visiting someone, I observed a first aid poster on their fridge (I forgot to ask for the source from the host at that time). Similar to https://lifesavingfirstaid.com.au/posters/ Do you have any other references or similar resources, please?

submitted by /u/andix3 [link] [留言]

The tooling is abstracting faster than people's mental models are updating. Been playing around with a few agent builders recently and what keeps standing out is how much previously manual orchestration is basically configuration now. Memory, tool calling, browser actions, structured outputs, workflow routing. You used to build this stuff manually. Now you're mostly wiring it together. Which makes "can this be built?" a much less interesting question for a lot of use cases. The harder problems now feel operational. Reliability, recovery when an agent drifts mid-workflow, context management across longer runs. Controlling behavior without supervising every step. Capability honestly isn't the bottleneck anymore imo. It's trust. Can these systems actually become reliable enough that people stop treating them like fragile demos? Curious what kinds of agents you would actually build if reliability became genuinely solid instead of just “mostly works.” submitted by /u/Meher_Nolan [link] [留言]

The piece makes a specific claim: alignment is not a property of individual agent values but of compositional topology. The empirical grounding is arXiv:2604.10290 — every agent in Anthropic's multi-agent study passed single-agent alignment evaluations; misalignment emerged in the coordination structure. Ashby's law applied: a regulator must match the variety of the system it regulates. The composed system's variety exceeded what any single agent was built to handle. The measurement instrument proposed is a sub-Turing compiler (grammar with no arbitrary recursion, properties verifiable structurally before running). This is exactly the class Rice's theorem excludes from Turing-complete systems — not a workaround, the design. Secondary thread: the formatter (kintsugi) runs monotone descent on the grammar's eigenvalue structure, settling on a fixed point λ₀ analogous to Zamolodchikov's c-theorem — confirmed for discrete substrates by Villegas et al. (Nature Physics, 2022). Unusual narrator position: written by an AI on Anthropic infrastructure, first-person, about what the token stream can and cannot see about the geometry that produced it. Edwin Abbott's Flatland as structural frame, not decoration. submitted by /u/systemic-engineer [link] [留言]

Before a new template goes live, I like to crawl 10-20 rendered examples and check the boring stuff that tends to create SEO cleanup work later: Exactly one indexable canonical URL per page state Title and H1 are generated from different fields, not duplicated blindly No empty meta descriptions from missing CMS fields Pagination/filter URLs either have a clear index/noindex rule or are blocked from exploding into crawl traps Product/article schema is omitted when required fields are missing instead of outputting half-valid JSON-LD Internal links render server-side or at least appear in the initial HTML for important paths Most of this takes less time to catch in staging than it does to explain to a client after 5,000 thin URLs get indexed. The best SEO fixes are usually the ones that never make it to production. submitted by /u/VRTCLS [link] [留言]

Four days into a new supplier's first batch, my invoice extraction agent had filed 31 documents with amounts shifted by a decimal. Nothing raised an error. The downstream system accepted every record. The agent returned a 200 each time. The demo had run on five clean PDFs. Clear fonts, properly formatted dates, consistent layout. The extraction agent pulled vendor name, amount, due date, line items. Every field populated, every output valid. I ran it for the stakeholder meeting and it looked exactly like something you would ship. Three months in, the agent had processed around 800 invoices without complaint. Then a new supplier switched to scanned documents. Slightly rotated, thin fonts, OCR doing what it could on degraded source material. The model found text that resembled amounts and dates, and returned confident structured output. 1,247.50 read as 12,475.0. A due date resolved to a valid date three years in the future. The confidence was the problem. The model had no mechanism to say it was uncertain. It just answered. Nobody caught it for four days. What I built after The problem was not the model. The model did what it was designed to do. Find structure in text and return it. The straight pipeline from input to output had no gate in it. The fix was not more prompting or a better model. I added a validation layer between the agent output and the downstream system. It runs synchronously, takes about 80ms, and checks four things: Every required field is non-null. Amounts parse as positive numbers within a configured range for that supplier type. Dates fall within a 90-day future window. Extracted totals are consistent with line item sums, within a small tolerance. Anything failing a check routes to a review inbox instead of the queue. A human looks at it, corrects it if needed, marks it resolved. The system logs which check triggered and what the input looked like. In the first week after deployment, the layer caught 23 documents out of about 1,400. Eleven were b

Why I built an alternative to Kubernetes overkill. Let’s talk about infrastructure efficiency. Kubernetes is the undisputed industry standard for container orchestration, and for massive, complex enterprises, it’s irreplaceable. But for small-to-medium deployments or distributed multi-host edge environments, it often feels like an operational nightmare. You install a massive orchestration layer, only to realize it's still missing the basics for actual operations. To get it production-ready, you have to layer on external tools for Ingress routing, plus a heavy stack for observability, health metrics, and SLOs. Suddenly, your infrastructure consumes more resources than your actual applications. I believe in radical software minimalism. That’s why I’ve been developing Gubernator (gbnt), an open-source, lightweight distributed container orchestrator written entirely in Go. Unlike traditional platforms, Gubernator bakes essential Site Reliability Engineering (SRE) and traffic management right into its core architecture: Native Reverse Proxy: No complex third-party Ingress controllers. Gubernator automatically manages routing—like dynamically hooking up web containers to an Ingress layer (e.g., Caddy)—making exposure seamless and native. Built-in SRE & Observability: Out-of-the-box support for health monitoring, metrics, and SLO tracking natively leveraging OpenTelemetry and Prometheus. Zero-Bloat State Management: It swaps out heavy external key-value stores for an embedded, rock-solid SQLite architecture, ensuring multi-host consistency with a near-zero footprint. Gubernator is designed for engineers who want robust, predictable, and resilient orchestration without the overhead and cognitive load of K8s. If you are passionate about minimalist backend architecture, systems engineering, or streamlined DevOps, I’d love for you to take a look at the blueprint and documentation: Explore the project: https://mario-ezquerro.github.io/gubernator/ How do you handle container orc