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eulgro

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loading a few thousand tokens of raw JSON into a context window, asking a language model to reformat data it did not need to reason about...

That trap catches almost everyone right now.

Has everyone really become so stupid now? I wouldn't even have thought of doing such a horrible thing as using AI to format JSON...

Needless to say I stopped reading there.

Unfortunately that's not how it works. Productivity gains have already increased tenfold in the past, yet still all work full time.

It used to be that 80+% of the population worked in agriculture. In developed countries that number is now around 1-2%. Some of the freed labour was funneled into improving living standards, some of it was funneled into new jobs created by the increasingly complex society (the "intermediate economy").

With AI, the same is true: labour is freed by the productivity gains (which I doubt are 10x sustainably but whatever), more labour is needed for power generation, mineral extraction, maintaining this new extra layer of complexity in the intermediate economy, etc. In the end we might see, say, a net 3% increase in global productivity per year over the next 10 years, which will be funneled into increasing living standards and increasing economic inequalities, but not in reducing working hours.

If you accept living below average standards, you could easily work a single day of the week for the rest of your life. But why would an employer hire 5 people working one day a week, instead of one working 5 days a week? They won't, hence we don't see a reduction in working hours.

The alternative is to work full time but retire earlier, much earlier, than you would otherwise, which in the end is the equivalent of having worked one day a week for your whole life.

I highly recommend reading Lean Logic by David Fleming, it explores several of these concepts in a very interesting way.

The report estimates that carbon emissions from models with the least efficient inference are over 10 times as high as those with the most efficient inference. DeepSeek’s V3 models were estimated to consume around 23 watts when responding to a “medium-length” prompt, while Claude 4 Opus was estimated to consume about 5 watts.

This makes absolutely no sense. I suppose they meant watt hours, and that's a weird way to explain carbon emissions...

That's 58 GWh, but considering each food calorie actually require 5-10 calories of input energy (oil mostly), let's say 290 GWh.

I couldn't find much on training AI models. Apparently GPT-3 used 1.3 GWh for training. So maybe ~10 GWh for newer models?

So... let's stop training humans I guess.

Tiny C Compiler 5 months ago

To be honest, these all seem like pretty basic features.

Goto is easier to implement than an if statement. Postincrement behaves no differently in a switch statement than elsewhere.

This makes no sense. With the li-on batteries you would need everything you listed, minus the extra batteries.

First, make me care 6 months ago

Thanks. Ironically, the article started off great with that but clearly it wasn't going to answer the question, so I only read the first paragraph.

From the stats we see that most bugs effectively come from the limitations of the language.

Impressive results on the model, I'm surprised they improved it with very simple heuristics. Hopefully this tool will be made available to the kernel developers and integrated to the workflow.

Some of these come intuitively when you know how to use vim. I expect to be able to search when pressing / in terminal programs, just like I expect Ctrl+F to work in GUIs.