sinab

The standard makes sense: models work much better when devices expose standardized, machine-readable interfaces.

But as far as I can tell, this technical "standard" isn't actually public yet (you have to apply for access to even see or implement it.) Anthropic says they plan to open source it later.

Quite a departure from the way foundational hardware standards like USB and CAN were developed. You shouldn't need permission to read a standard.

Why should universities prepare founders to start companies? The essay assumes that premise.

I didn't claim that I could determine whether AI wrote the prose, or even parts of it. Nor am I trying to discourage folks from writing (actually the opposite, I even thanked Ben for the demo!).

What I observed are overused, and recognizable, stylistic patterns that have become quite common in LLM generated prose. In this article alone there are three "here's the X" rhetorical signposts, well over a dozen neatly constructed reversals/antitheses ("not X but Y," "solved X and created Y"), repeated setup->complication->punchline paragraph structures, rhetorical questions used as transitions, and a high density of anthropomorphized technical metaphors.

None of these alone prove AI authorship. In fact their existence (and the authors own words!) are exactly the point I am making: his validateHypotheticalHooks linter flags "formulaic AI-tell openers," and he describes catching himself writing one because he'd "absorbed the cadence from reading too much generated text."

Thank you for the demo! I like the concept. However, I personally find that the writing style you've designed for reads very strongly as AI-generated. For example, opening a section with phrases like "Here's the irony: after automating everything up to this point," or "Turning emoji into images solved the missing-font problem and created a subtler one."

These are both strong tells of AI-generated prose, though I find it surprisingly difficult to articulate exactly why. It makes me wonder whether your style has evolved to sound more like AI-generated prose, or whether AI-generated prose has evolved to sound more like you!

Yes, and when the system is not auditable it makes it easy for biases to propagate!

This paper argues for machine-readable science and demonstrates its utility with a journal-scale comparison of AI and human peer review.

The app, "SnackPass", faces a similar issue. Coffee shops spend time fulfilling mobile orders, placed in advance to avoid waiting in line, resulting in a delay for in-person orders.

While I was unable to view the entire article (paywalled), I suspect that some kind of priority queue that weights an order's priority by the user's distance to the store may be useful to solve the waiting issue.

Thank you for sharing. This feels like a modern day version of https://letmegooglethat.com/.

I got an error when passing a prompt with about 20k tokens to the Llama 4 Scout model on groq (despite Llama 4 supporting up to 10M token context). groq responds with a POST https://api.groq.com/openai/v1/chat/completions 413 (Payload Too Large) error.

Is there some technical limitation on the context window size with LPUs or is this a temporary stop-gap measure to avoid overloading groq's resources? Or something else?

On the topic of low-noise bikes, why do some riders insist on having a loud freewheel? The clicking noise is unbearable in my opinion.

Source: https://bicycles.stackexchange.com/questions/4606/what-cause....

Where are health providers hosting these data? Could you give an example? Thank you.