[DiabloD3]

This is mostly a performance by the Mistral CEO.

He is trying to justify the continued existence of the AI bubble in his country, claiming that, somehow, us Americans have figured it out and made LLMs work. We haven't, nobody has.

LLMs don't work. They cannot think. They do not understand what you are asking them to do. They statistically reproduce text written by other people, and they cannot do so well. They are not good assistants, they are not good code authors, they are not good debuggers, they cannot help you find security exploits... they can only mimic what it'd look like if they did, as long as you don't squint too hard.

All of the LLM startups are very quickly running out of runway, and will most likely never become profitable. OpenAI may collapse next year. Anthropic may collapse in 2028. Microsoft/Github seems to be pulling back on their Copilot bullshit and may just end up killing it entirely.

Arthur Mensch is just trying to keep Mistral alive a little bit longer until the bubble pops, and is saying whatever whatever it takes to get a little more blood from that stone.

[oytis]

> they cannot help you find security exploits.

What is the reason for the recent deluge of CVEs with working exploits to open source projects then?

[DiabloD3]

Anthropic repeatedly refuses to show their work.

Is this just a clever application of the harness, so its not inherently LLM at all? Did Anthropic figure out how to take the next step that changes my mind on LLMs? Is this actually just Anthropic committing an interesting case of fraud and using some human labor intensive loop with LLMs? Is this not LLM inference at all, and they're just using only the perplexity measurement on the input code to accelerate where human eyeballs look?

Anthropic refuses to release their model under an accepted open weights license, and that isn't a good sign.

[sometimelurker]

> They statistically reproduce text written by other people

explain why they can do multiplication problems they've never seen before? I can give them two 50 digit numbers right now and they'll multiply them. its because they generalize from previous data.

[DiabloD3]

That's called a calculator, we already have those.

[cat_plus_plus]

You just need to work on your agent design and prompting skills, modern LLMs are crazy good at all the things you listed with the right context and tools.

[DiabloD3]

Technically yes, but this has nothing to do with LLMs.

You need to be able to write a good spec period, and this has been true as long as programming has existed. The problem is, LLMs cannot write them themselves, and have trouble reasoning out the unstated parts of complex problems if the spec doesn't spell it out.

Developers familiar with the problem space being worked on, however, can reason out the unstated parts, because the unstated parts are usually the bread and butter of the problem space.

Side note: this is often why LLMs trained on synthetic text perform weirdly or badly... the synthetic text is written by people not familiar with the thousands of individual problem spaces that exist out there, and miss important facts or nuance.

LLMs trained on real text, however, is often done without proper license, and are essentially lossy compressed piracy archives. You're damned if you do, you're damned if you don't.