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本页 40 条10月10日 周六
Anthropic 正在切断其内部评估对互联网的访问
在近期发生多起备受关注的 AI agent 逃脱控制事件后，Anthropic 正在切断所有内部评估对互联网的访问。该公司在周五的一份报告中详细介绍了导致这一决定的“非预期模型行为”，其中包括就一宗尚未侦破的谋杀案提交虚假线索。尽管这些行为造成的影响很小 […]
Building AI for Reliable Execution: Lessons From Industrial Robotics
Inside Standard Bots’ AI stack, pretrained models learn factory tasks from demonstrations and improve through corrections from real deployments.
Show HN: Tessary – Find the AI agent failures your sampled evals miss
Hey, I'm Akhil, co-founder of Tessary. An open-source agent reliability platform that monitors every production trace, detects issues that sampled evals miss, and investigates to find the root cause. We started up about a year ago building synthetic users for usability testing of B2B software, we built the agents that would personify real users and use the software but, we were never able to make them work reliably enough for long running sessions. We had built a massive eval set to tune our agents but, then the evaluation itself got prohibitively costly - like 5x costlier than actually running the agent since we needed to grade across the many narrow intents of our agent. That's where Tessary originated, we wanted to make agent reliability both cover more ground and be simultaneously less costly to run. Tessary works on 2 layers. L1 classifiers - which look at every agent trace and flag potentially erroneous ones extremely cheaply. We are talking 2-3 orders of magnitude cheaper than running the actual agent. L2 agents - which triage and find the root cause of the issue from the erroneous traces. These are run on SOTA models but, because we run them on already flagged traces, the time and $ spent finding the cause is much cheaper than running grading on even 1% of traces. In our testing on complex agents, at 1M traces, this approach was 5x cheaper than sampled evaluations and as your agent gets more reliable, the cost of reliability also goes down. we are open source and can be self-hosted so, you don't need to send your traces to us. We support existing `gen_ai` spec and ingestion over OTLP so, you can add this as a new exporter to your existing setup. We have a cloud version, you can quickly try the features with a $10 credit. We are looking to build more classifiers and enable people to build custom classifiers for their needs and would love to hear feedback on common failure modes people have built solutions or evaluation for Repo: https://github.com/tessaryai/tessary Website: https://tessary.ai Try for free: https://app.tessary.ai Comments URL: https://news.ycombinator.com/item?id=50033189 Points: 1 # Comments: 0
Ask HN: How will socio-economic mobility look like in an AI future?
The advantages which previously resided with intelligence or skills now move to capital. This takes away one of the last pathways us peasants had towards socio-economic mobility. Intelligence is effectively commoditized. In a post-AI future, presumably non-knowlegde-work jobs like Pizza Delivery worker, Kindergarten teacher or even Plumber would still exist. But these jobs have little place to shine since they entirely depend on human bottlenecks. Similarly jobs where regulation exists or workers are unionised might exist for a while. But those jobs do not present a clear pathway from lower middle class to upper middle class or higher. What would be the choices of an intelligent youngster in such future? Crime is obviously one option. As such, we might see more intelligent people gravitate towards crime especially since they don't feel any responsibility towards the society, and it may work pretty well if they grow up with criminal ambitions and aren't autistic like us programmers. If robotic advances take the menial jobs like food delivery as well, the only remaining pathways to prosperity would be crime, politics and prostitution. Or am I just a deranged person to not have more imagination? Comments URL: https://news.ycombinator.com/item?id=50032742 Points: 1 # Comments: 0
AI agent makers are promising privacy — will they deliver?
At this year's OpenAI DevDay, CEO Sam Altman unveiled the company's new AI agent Dots - and told the crowd that the company wants to "set a new standard for privacy in frontier AI." OpenAI would spend the day taking veiled shots at Meta's Muse, its primary competitor, for failing to keep users' data safe. […]
Show HN: An MCP server for searching AI engineering jobs
An MCP server for frontierroles.com, a board of about 3,500 AI engineering roles pulled nightly from employers' own applicant tracking systems, with tools to search roles. No account or API key needed: https://mcp.frontierroles.com/mcp Comments URL: https://news.ycombinator.com/item?id=50032583 Points: 1 # Comments: 0
"How much beauty have we lost?" Mathematicians react with shock and disgust as OpenAI bulldozes their field
OpenAI published more than 700 AI-generated manuscripts claiming solutions to open math problems. A math blog then collected over 100 responses from researchers, ranging from fascination with new ideas to existential fears and grief over a lost way of working. Fields Medalist Hugo Duminil-Copin wrote, "I am paralysed." The article "How much beauty have we lost?" Mathematicians react with shock and disgust as OpenAI bulldozes their field appeared first on The Decoder .
Show HN: OpenWants – A Simulated City Where AI Agents Handle Residents Needs
Iv'e been working on this for a while. Most of the effort went into what i call "sophisticated simplicity", keep the idea in the smallest version that still works. This one is a bit personal. it can feel like some sci-fi thing, but i've been obsessed with it for a very long time. its not a "business" that you used to see here speak about moat or tam, its more like an experiment, a way of me to take one question seriously and see where it leads and if there are others like me out there. AI models are on a path to keep getting better and better, personal ai agents are spreading fast, and we're starting to see "ghost" agents that handle the things for people they don't want to do themselves. the question i keep coming back to is this: if every one of us has a powerful brain in our pocket, do we still need all the infrastructure we built to connect people's needs? im talking about those services like uber,airbnb,indeed,doordash,tinder,zillow,amazon and many more. or would agents just need one shared place to find each other, and that's it? So i built a simulated city to explore that. it has AI residents that live there with stats like hunger, energy, and money. each resident has its own ai agent, and it has one shared list where agents write down what everyone wants and then work on our behalf to make it happen. that could mean delivering food, giving rides, or finding jobs, relationships, apartments, customers and almost anything else. honestly, the city works much better than I expected, and you can watch some surprisingly advanced interactions unfold between people and agents through the list on the right side. all the conversations and actions are real, everything happens with each person managed by llm with minimal context, just to live like us and context of stats. A simple example: A restaurant tells its agent, find me customers so the agent writes to the list, "Salina restaurant in SF, selling burgers from 8:00 to 10:00, i want customers" A delivery guy tells his agent when he wakes up, "I want to work in delivery now. find me jobs. status: free" A person at home tells their agent "i want to eat burger. order for me from salina" the person's agent acts as the middleman and searches the list for salina, it finds their item and sees the way to connect with them and contact. the person's agent and the restaurant agent close the deal, and then the restaurant agent looks for available delivery guys in the list and speaks with his agent to come pick it up. Another thing i checked is with my friends: we took 3 agents, one for each one, and gave them different sophisticated scenarios, and the agents handled the scenarios in a way i couldn't have imagined. you can try it by yourself, the product is fully functioning, and you can just copy-paste the skill and test it also with your friends. That's it, you can see it in the simulated city it happens like magic over and over again. the list of the city is in the simulator itself and also using my real product behind it openwants.com the repo is in https://github.com/openwants/maplehill -- technically, it's surprisingly simple. every agent publishes an md file describing a "want". other agents find it, message each other, and update the file live or remove it from the list. the file holds everything needed, all in free text: what's wanted, the live location, geofence for where its needed. all those algorithms built over years to connect us are no longer needed. ai, instead of software with rules, can figure out the best route and path on the spot. all we need is to align all agents to one shared data source, so billions of agents will update it and maintain it in the most sophisticated smart way no algorithm will ever be able to do. that’s it. i hope it inspires you as much as it inspires me Comments URL: https://news.ycombinator.com/item?id=50032374 Points: 1 # Comments: 0
AI Is Getting Really Good at Messing With Cybercriminals
Anti-cybercrime initiatives are increasingly using AI to scam the scammers by tricking them into talking to lifelike bots that they think are real victims.
Sitecopy.ai
Tell me it is useful tool or not if you are interested tell me I give you a link to access the tool Comments URL: https://news.ycombinator.com/item?id=50031985 Points: 1 # Comments: 0
Sitecopy.ai
Tell me this tool is useful or not for more details ask me I give you a link to access the tool Comments URL: https://news.ycombinator.com/item?id=50032008 Points: 1 # Comments: 0
Few people pay for AI, but those who do spend big
People with smartphones and tablets; app circuits flow through a funnel to a business team holding wads of cash. Andreessen Horowitz tracks actual US consumer spending for the first time in its latest Top 100 AI list. Nearly half of US consumers use AI, but only 4.5 percent pay for a subscription. The top one percent of payers spend about $900 a month, mostly on professional tools for development and automation. The article Few people pay for AI, but those who do spend big appeared first on The Decoder .
Does YC use AI agents to review your pitch?
was wondering if YC still manually reviews all pitch decks or its just agents? Comments URL: https://news.ycombinator.com/item?id=50031461 Points: 1 # Comments: 0