SwtCyber

Journalists just took whichever numbers fit the Apple narrative better

Vercel data as proof of market dominance is hilarious. Obviously next.js pet projects are mostly generated by Claude, it's just unmatched for frontend right now. But enterprise with their rag pipelines is sitting on Azure with OpenAI, and there are completely different budgets spinning there that simply don't make it into any Vercel dashboards

Maybe, although I think cement would trade one set of manufacturing problems for another

The passive cooling is almost as clever as the filtration

There is something very satisfying about sawdust being a feature rather than an impurity

Disney killing the FiveThirtyEight archives is just basic s3 cost optimization, not `ai devouring humanity's memory`. A dead site just doesn't run ads

The "100% accurate and free of hallucinations" claim should probably be replaced with something much weaker

I think there's an underrated difference between information generation and pedagogy here. LLMs are very good at producing more explanation, yet "more explanation" is often exactly what you don't need when learning something difficult

I think the key difference here is that you're using the LLM to create exercises, not to replace the learning material

The test I'd like to see: take a problem set or task you couldn't solve beforehand, learn the topic this way, then try to solve it without the LLM in the loop

One concern though: "100% accurate and free of hallucinations" is doing a lot of work. A second LLM pass can catch some mistakes, but it can also confidently agree with the first one

That’s a direct result of how they’re fine tuned. RLHF and other mechanisms reward quick, locally correct answers that solve the user’s immediate problem. A reward for a solution like "take a two-day pause, rip out half the modules, and rewrite the core" just straight-up doesn't exist in training datasets

I think the best part about this benchmark is that they finally stopped pretending you can evaluate a complex engineering task with $ 5 worth of inference. If a task takes a human weeks, you need to give the agent a comparable search space to find solutions

People still cook in houses with microwaves and wash dishes despite owning dishwashers

The future arrived quietly

Bradbury wrote rockets the way other writers wrote sunsets

That is an amazing sleep indicator: once the rabbit starts discussing thermodynamics, dad has left the building

The reading task can stay largely automatic until both streams try to use the same speech-production machinery at once

It really does feel like reading and counting can occupy separate lanes, while writing and counting are both trying to use the same internal narrator

This is a nice example of using interference as a window into representation

This seems potentially useful for attention-steered hearing aids. A system that waits for complete disengagement from the old speaker may react too slowly

The thing is this isn't a schema generation or Typescript bug at all. This is just how openai's function calling works under the hood. Their weights were fine-tuned for tool use to output the most complete data structures possible. If the model sees a parameter name in the system prompt context it will try to fill it with a value, even if it is not in the required array

I would rather read an article with actual production experience migrating an agent, even if it is written in this style, than a perfectly crafted long read from another evangelist that has nothing but high level fluff and general phrases about the bright future of ai

Its ironic that under an article with a ton of deep infrastructure insights half the comments are crying about the "forced writing style". What does it matter if claude helped the author clean up the text when inside is a ready-to-use blueprint on how to save 30% of the api budget and fix empty file reads?

Its funny to see how researchers bypass Githubs praised guardrails with a simple word like "Additionally". It just proves that any attempt to build hard security boundaries inside an llm context window is bound to fail. The model is naturally built to follow instructions, so if you mix system rules and user input together, the newer or more persistent instruction will always win

The interesting bit is that they didn't solve division by rebuilding the whole cytoskeleton, they sort of sidestepped it

"Not alive, but doing a suspicious number of alive-looking things" is a pretty good summary of why this is cool

I think they arent even trying to build an AI detector. This is more of a social signal like "dont send us an automatically generated flood of changes"

And it just keeps looping like that until the context window bursts. In practice the model is great at writing new code, but when you feed it its own six month old spaghetti code with a floating bug in the state machine it just starts hallucinating and silently breaking neighboring features

AI accidentally found one of the most expensive resources in the industry: the free time of people who maintain open source in the evenings after their day job