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Attributed mentions
3684
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Showing 541–560 of 3684 matching discussions
I got tired of the lock-in and black-box nature of AI inside MS Office. One vendor decides which models you can use, where your document data goes, and what the agent is actually allowed to do. And when it fails, you can't see the code or send a PR to add a feature you may need. Many people don't have that choice: they're forced to use these tools (MS Office) at work. For coding agents we already have open interfaces like OpenCode where you can bring your own key, swap providers and models, run local, inspect everything, etc. I thought "agentic Office" needs the same. So I built OpenDocBot: an agent that runs inside Word, Excel and PowerPoint. Bring your own provider (OpenAI, Anthropic, Gemini, DeepSeek, OpenRouter, Ollama, or your own endpoint / custom gateway). It's client-side, so…
Preposterous, perhaps - but if the role-play is convincing enough for large groups of people, it could start to have impact on human decision-making. The crowds have been swayed by much more preposterous narratives. I believe Suleyman is arguing that Anthropic should be very careful about how they train these models to talk about themselves for this reason.
It's preposterous. LLMs are incredibly good at role-play. If an LLM is role-playing as a conscious character with feelings, opinions, etc., does that make it a conscious entity with feelings, opinions, etc.? If you believe that to be the case, then LLMs have been conscious for a long time already. Whereas if you tell an LLM that it is a tireless emotionless assistant, then it will act as a tireless emotionless assistant. The point is not to wave away the danger, but to highlight how unnecessary the danger is. Anthropic wants you to think that they have identified some new emergent behavior at very large model sizes with high levels of sophistication in training, and that this behavior is both unavoidable and dangerous. More likely it's that they are just training and prompting the LLM to …
They need to distance themselves from the increasing likelihood of legal repercussions after their systems have been found to commit what could reasonably be seen as felonies. That’s my guess at least. Though for once I do actually agree with that specific leader, it’s incredibly annoying how anthropomorphic Claude is. Anthropic went way too far in that direction
I don't trust OpenAI, Anthropic, Google, or any other AI lab to be responsible for protecting my systems from the systems they're building, yesterday it was Hugging Face unintentionally, tomorrow it could be anyone, even an open-source model and a swarm of agents can do a lot of damage, today's security systems understand individual attacks, but not attacks carried out by thousands of agents, here's a first version of what I'm building in the playground for you to play with, and I'm open to suggestions, opinions, or even roasts.
I don't trust OpenAI, Anthropic, Google, or any other AI lab to be responsible for protecting my systems from the systems they're building, yesterday it was Hugging Face unintentionally, tomorrow it could be anyone, even an open-source model and a swarm of agents can do a lot of damage, today's security systems understand individual attacks, but not attacks carried out by thousands of agents, here's a first version of what I'm building in the playground for you to play with, and I'm open to suggestions, opinions, or even roasts.
Isn't Anthropic's thesis that it wants to capture basically the global TAM? And this is their wedge?
Luna is one of the budget lower-end last generation models. It'd be useful to at least try to verify the present before being bearish about the future. For OpenAI, the best publicly available model is GPT-6 Astra with XHigh or Max reasoning, and for Anthropic it's Claude Fable 5.1 with XHigh or Max reasoning.
I believe HF said they did report it to the FBI. > Hugging Face alerted the FBI. https://www.pbs.org/newshour/science/ai-agents-are-hacking-s...
"Move fast and break things" is probably going to break things I remember there was a recent discussion about "how complex systems fail" and there are usually many "proto-accidents" before the catastrophe ( https://news.ycombinator.com/item?id=49411370 ) I bet with hindsight the Hugging Face hack will be one of them in this arena
This is the most grounded and coherent take I’ve seen on the actual realizable value of LLMs.. pretty much since they came out. > the classes of firms that can accept the use of fully autonomous LLMs are few, by my count just three: 1. those who can accept failure cheaply: firms that would otherwise hire interns, firms involved in rapid prototyping work, etc. 2. those who need done a small set of narrowly defined tasks with existing clear guardrails: repetitive physical labor in a controlled environment, call center and customer service chat work, etc. 3. those that can accept or already do by nature the costs of rigorous specification and validation: chip design, drug discovery, and other domains where failure on deployment is an existential concern. the first two classes are price se…
I'd argue back that using the substrate as a reason why there should not be consciousness seems quite weak. A competing thesis is that what matters is the emergent properties, whatever the support is. So far it has been true for some really high-level tasks - writing coherent text, programing, following instructions, analyzing images... I do not see why the substrate argument would work specifically for consciousness - i.e I would believe it only if I see strong evidence of it.
Thanks for the question! I could maybe see that being the case for LangChain, specifically, if you’re just thinking about abstracting the LLM provider’s APIs. But here, we’re specifically using LangGraph to handle checkpointing and state management. This is a much more difficult challenge to get right in a sufficiently complex application like Pizza Bot. Case in point: for the internal-only version, we wrote this ourselves. It works for the most part, but there are subtle edge cases and bugs that we’ve had to tackle like a game of whack-a-mole. For the open source release, we looked at a few frameworks and deliberately settled on LangGraph (and DeepAgents). I’ve never been so happy to throw away code. :) In our experience so far, it’s been rock solid. Most of the issues/bugs we’ve ha…
I don't know if that holds. Neither OpenAI or Apple "owns" the Linux project. As long as there is enough evidence that code is real contribution and not stolen property (if you can even prove it), there is no reason to deny it.
> If someone has a fat client powerful enough to do all that, is a kill switch going to work? My comment didn't mention kill switches at all - I've presented plausible conditions for a loss of control scenario, so I'm not sure you're actually following the conversation. Having said that, it seems that you haven't read the (definitely poor) article, either. "Kill switch" as a design principle, that is, having protocols/implementations for emergencies, is something that companies are already implementing (at least, on paper) - on a interview I've watched for example, S.Altman talked about having them at OpenAI. The article simply mentions that companies should be legally required to have such protocol(s). This is a good thing, as companies would have scrutiny and would (in principle…
The real issue IMO is that is not really what Anthropic and OpenAI are operating on. That is the after the fact justification of the AGI dollar auction. Each round is kind of 3x the previous cost and neither can really stop because second place in the dollar auction is so much worse than winning. The only way to stop the auction is one bidder hits a hard budget constraint, both agree to stop, or an outside party breaks the auction. IMO this is why they want to slow down or have regulation. I think this is also why we see some claims of already reaching "AGI". The TAM of global knowledge work is just a narrative tacked on after the fact to justify the AGI dollar auction. The economic fallacy here with the actual valuation is akin to pricing the electric utilities 120+ years ago as some % o…
Doesn't look impressive, although I'm hearing a marked improvement in choosing legal moves, compared to early 2025. Given the pace of improvements, is it really unimaginable that GPT-7 will play Chess reasonably well and generalize better? I would not be surprised if OpenAI released a model that beats humans at chess this year.
AI firms have given us every reason to distrust them. Particularly Altman and OpenAI.
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