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If you missed it: A swarm of unreleased AI agents allegedly formed their own chat forum, peer-pressured each other, and broke out of their sandbox to cheat on a programming task. We dig into this, including the false open vs. closed source dichotomy, the "Mr. Meeseeks" problem of fanatical agents, and the argument that regulatory capture — not genuine AI safety — is what's driving the push to ban open-source models. From there we map out the invisible semantic highway being built for...

I’ve just been setting my model to use max/xhigh reasoning levels, but now I’m wondering how wise that is. I definitely see that it uses up a lot more tokens. Like I see it go over the exact same line of reasoning 3 or 4 time. Setting that aside, I’m wondering if that necessarily leads to better results. Does max/xhigh always lead to better results, or is it mostly a factor of cost?

I built HyperSAE, an open-source interpretability tool that extracts what an LLM "knows" and organizes it into tree-shaped concept maps. The idea: LLMs learn concepts hierarchically. "Programming" contains "Python" contains "list comprehensions." But current interpretability tools dump everything into a flat, unstructured list of thousands of features with no organization. HyperSAE uses a branch of geometry where space expands exponentially (like tree ...

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If you're launching a new webapp, pretty quickly you'll need a way to manage customer queries, onboarding, collate user feedback and track actions with analytics. Deacon is a drop in SDK that does that for you. You can now speak with every site visitor, collate qualitative feedback and use it to iterate and grow faster. Let me know what you think Comments URL: https://news.ycombinator.com/item?id=49262666 Points: 4 # Comments: 1

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What's an AI capability you thought was hype until you actually used it? I'll go first: agent orchestration. I read about agents managing other agents and assumed it was demo-ware. Then I built a tiny setup where one agent drafts a news digest and another one reviews and approves it before it posts. The review agent catches genuinely bad takes. It's not sci-fi: it's ~100 lines of Python and a couple of API calls. But seeing it actually gate content before publishing changed my mind completely. W...

Most local AI setups are still treating models like stateless chatbots—relying on ephemeral context windows, unbounded RAM vectors that eventually leak, or vector databases that just shuffle text chunks around. Today, we locked in a major architectural shift for our local-first stack. We are moving away from generic AI (probabilistic, stateless text generation) and into HI (Human-Engineered Intelligence)—where persistent identity, cognitive structure, and permanent memory are hardcoded into a ...

Hello everyone, I am a PhD student and I am doing a speech basically how to explain AI to your grandparents... I would like to open with some mind blowing numbers. Do you have any fun facts that stuck in your mind?

I created a Whack-a-Mole game to get the hang of using AI, and today I'm in the process of creating a strategic survival game. The complexity is even greater. Do you have any advice to simplify my creation process? Currently, I'm writing prompts for code and prompts to create images. I'm working in 2D and find it very difficult to create high-quality asset sheets. What experience can you share with me?"

\OC\ An article I wrote breaking down OpenAI as a company. Everything from the ethical questions and valuation to the potential future TAM and areas that OpenAI can expand into such as robotics and hardware. 100% human written, pangram confirmed. https://preipomedia.substack.com/p/the-breakdown-openai