SwtCyber

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

That's just the basics. To craft a prompt for a complex architectural task, you need to know the solution at least on an abstraction level. If you don't have the right system design in your head, no llm is gonna conjure it out of thin air

Funny how almost every wave of automation starts the same way: "we're gonna cut headcount," but ends with "we're just shifting roles"

AI is pretty good at scaling existing knowledge, but if the actual knowledge is just in the head of an engineer who can hear that a press is acting up, the model doesn't really have much to go on

I think this depends a lot on how the message is phrased and what kind of action we're talking about

The "ask for no" approach works best where the boundaries of ownership are clear. Without that, it becomes much riskier

There's a difference between keeping someone informed and making them reown the problem

The phrasing is not just a communication trick, it changes who owns the decision

Good code is absent code LLMs by nature work like autocomplete on steroids. They're always trying to write more than necessary to please the prompt. Seniority now is measured by the ability to break down a task so that the agent doesn't even think to drag in unnecessary abstractions

It's good to see the hype around "programmers are no longer needed" giving way to a more realistic view. Generating lines of code was never the hardest part of engineering. What's much harder is understanding exactly what we're building, how it integrates with legacy systems, and making sure it doesn't crash the database under load

Unlived dreams are not always abandoned because we choose something else. Sometimes they are taken by circumstance

I think this gets at an important distinction: some dreams are painful because they are impossible and some are painful because they are unresolved