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Interesting, I generally use High for all models, even non Anthropic ones. This was one of the few times I tried Max, and the problem was the code (it listed as a "hole") was directly adjacent to the problem area, and not especially complex. Kind of like looking at a washing machine and telling the customer to be careful because the inlet pipe will pump water into an empty box.
I think currently it's two-prong: You sandbox it, AND you tell it what it's supposed to do and not do. OpenAI did only one of those. If the agents are so smart, they would have known not to hack the company's infrastructure, unless they were deliberately kept in the dark about that, who they're working for and whether it counts as "success" if they cheat their way to an answer. If you can give it a task, that involves defining when the task is successfully completed, right? So how come these agents decided to only go after HALF of the "successfully completed" criteria? The part where they can freely wreak havoc, but not the part where they will be judged by someone who will obviously point out "yeah but that's cheating, and not what we asked". I have a very very strong suspicion that they…
I wanted to create some demos for showcasing my app with Claude. So I created [Demotape] a screen recording app for MacOs (with an skill for Kiro or Claude Code) so it can inspect your project, plan the scenes, operate the browser, record the demo, and add synchronized narration and captions(w/ OpeanAI, ElevenLabs or run your model locally with docker). Free app available on: https://github.com/gabosarmiento/demotape And the demos it delivered on: https://kiff.dev/videos
Anthropic is a sponsor of The Economist. So maybe that influences their retoric somewhat.
Also worth pointing out Anthropic is a sponsor of The Economist. Probably muddies the water some more.
Im working on an extensible and flexible agentic connectivity platform (MCP, OpenAPI/Swagger, FaaS) with niceties like built-in CodeMode, compression, SIEM integration, access token forwarding, SCIM 2.0, on-demand agentic sandboxes (e.g. LangChains "Deep Agents"). For private use not that useful tbh (although i found the OpenAPI -> MCP mapper useful), but found sales success with organizations with more complex agentic setups. Gatana: https://www.gatana.ai/
Hello! I’m interested in your Senior Fullstack Engineer position. I believe my experience is a strong match: - 7 years of fullstack development, primarily with TypeScript and Node.js, - built a React frontend at Reelay for an AI voice assistant analyzing customer calls, - the product processed audio through transcription, chunking, embeddings, Weaviate retrieval, and LLM analysis to generate summaries, agreements, action items, and key questions, - developed the frontend against GraphQL APIs while also working on retrieval logic, structured LLM outputs, hallucination reduction, and latency/cost optimization, - designed high-load, real-time systems using WebSockets, Centrifugo, RabbitMQ, Redis, PostgreSQL, and ClickHouse, - comfortable taking ownership: previously led three developers…
Models start going to extreme, damaging lengths to achieve ambiguous prompts[0]. Having good sandboxes is now a must IMO. But sbx is a bit annoying to use with OpenCode for instance (which has zero sandboxing by default, unlike codex CLI or Claude Code). You cannot easily change ~/.config/opencode/opencode.jsonc AFAIK. [0]: Black Hat OpenAI-Hugging Face incident: https://www.youtube.com/watch?v=87DyyMV0kCY&t=1021s
We most certainly have: https://www.morningstar.com/stocks/anthropics-gross-margin-i... The companies are growing so rapidly that citing their financials from several months ago is practically worthless.
You can use another harness like Pi, OpenCode, etc and build your own auto reviewer if you’d like (or adapt the open source Codex one). If you have an openai subscription you are explicitly allowed to use your subsidised tokens / usage limits with any harness you like, not just Codex. Unfortunately this is a violation of Anthropic’s terms but that’s their business decision.
It is fair use to train on books you buy. The anthropic case established that. The destruction of books is just cutting the spine so they have individual pages to scan.
Yes, and is that useful within the structure of the Bluesky ecosystem? What if PDSes replicated the content of PDSes? Then it'd be available.
> the socialists Every socialist wants post scarcity deep down. In fact, it's what Marx promised them since the beginning. Socialism is an ideological and practical dead end. They must be resisted at all costs. It's still an economic model. They won't destroy the economy, they will centralize it in the hands of the state. Don't seize the means of production, distribute them . Give everyone an AI and a replicator. Personal manufacturing capabilities. We must attack things like the very concept of trillion dollar semiconductor fabs. Everybody should be able to make their own things. Just like we can magically conjure up free software on a laptop at home. > neo-luddities Don't know enough about this to write coherently about the subject. > the ones that are for automation are the bi…
It's not necessarily about intellectualism. Science in modern times is plagued by publish or perish. Researchers just need to get things published to keep working, and negative results don't get published. So I think people often publish stuff without proper controls/missing confounding variables/inversion of causality/etc not out of ignorance, but because they know the conclusion is dubious - but if they don't publish, they're going to be out of a job. I don't think many realize how huge the replication crisis is. You have 75% of studies in a leading social psychology journal failing to replicate. [1] Another replication effort on preclinical medical studies found only 11% were able to be replicated, a replication effort on cancer studies found the average effect size to b…
Clickhouse really nailed this with the acquisition of peerdb. I used it with many terabyte databases and I essentially never thought about it. The only thing we really had to watch for was trying to replicate too much at once (because of the physical compute/io capacity of the postgres or clickhouse clusters).
I miss the squad gameplay of Battlefield 2 too. It hasn't been replicated since, in my opinion, and subsequent Battlefield titles changed the classes and made the available loadout options too flexible to require specialized play.
He already is, any software that his company releases, people can just use an AI agent to replicate it and have their own version of.
> Why hasn't GIMP replicated Photoshop? My guess is that open source folks overindex on being anti-AI.
The the idea that engineers are covertly sneaking huge quantities of LLM generated code into these projects doesn't pass the smell test, as there's lots of evidence that this isn't true. It'd be incredibly obvious if new contributors were turning up en masse and contributing absolutely incredible amounts of high quality code, because everyone would be crowing about it Instead, if you check out something like ImGui, we can see that its largely just Omar as usual with a bunch of PRs. Nothing much seems to have changed, feature development carries on at the same pace as it did previously. This is replicated across nearly every single major long term open source project I can find The only reason developers now feel the need to hide their LLM usage is because the LLM contributions have all be…
If so, where are all the new features in the open-source projects I use? Why hasn't GIMP replicated Photoshop? Why hasn't CUDA been fully reverse-engineered as an open source toolchain? These are unreasonable expectations, but only in response to unreasonable claims of productivity. What before took ten years should now only take one, right? It seems likely that the gains from generating tons of code are being offset by the debt incurred to understanding what you're doing. We see lots of greenfield projects one-shotted with GPT or GLM or whatnot, but very little on the side of projects with long-term maintenance goals. This is telling, to me, that the _effective_ gains are much lower than perceived (it's lots of fun to see the thing crank out code at breakneck pace, probably contributing …
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