denverllc
Complicit?
Claude: probably not (officially against the rules after the openclaw fiasco)
Which is to say that a normal organization would not be changing things so quickly based on the whims of a single individual.
Maybe I’d be ok to use omarchy just for fun, but could never use it seriously in any capacity.
Even if we do accept that models have some “g” and that it correlates with all domains, there’s no reason to think that the current trajectory of LLM intelligence will grow more quickly or even as quickly. In every other technology we have an early period of slow growth during experimentation, and then a rapid period of growth, and then stagnation.
For all we know, 2026 could be the year of stagnation. It’s possible that we will reach finishing returns. If thats true, then it won’t matter how much that task correlates with overall g, because g won’t be increasing that much.
I did. Originally I had a project that I had been wanting to do and thought to use a decision model for it. Jev, OpenAI, etc. are all within percentage points of each other.
Then I used traditional ML and found a small classifier (gemma 4) with traditional embeddings worked 2x as well.
Jev is the general purpose ML pipeline for when you want average results. Nearly every application has a "better" option available with a small amount of work.
https://www.youtube.com/watch?v=xNgQtzEl4lY
Jev is used as an example of a successful marketing launch where they worked with many X "creators" prior to its release, so that all the creators would repost to put it to the top of everyone's feed. Then, over the following days they'd repost so it maintained momentum.
See: doomers.ai, clickstrike, growth matrix, etc. They use coordinated engagement, paid influencer networks, customized messaging, etc.
Jev isn't a terrible product, but it's way overhyped.
FDA is just way too much red tape.
1. Companies are trying to decrease costs, not increase it, and are looking at alternatives
2. Competitors are catching up, and even if the frontier labs are "better" at some things (like writing plans or complicated analysis), the competitors can take a lot of the inference on routine tasks like implementing a well-defined plan
3. The frontier labs don't just need to have high margins right now. They have to pay back their massive liabilities.
I was very optimistic about it when it was announced and saw all the demos, but a week later I find it only marginally better (and in some cases worse) than before.
Just consider how even paid streaming services are bringing in ads, or how TVs were caught using ACR to spy on us.
That's not at all what the drama is.
I think it's the dishonesty, the threats of "destroying the career" of one of the mathematicians, and the request that one of the authors disavow *the other individual he was working with for the last 1-2 years* so he could claim the Clay prize as part of OpenAI.
It doesn't surprise me that OpenAI's team were surprised he'd turn it down; it shows that they just assume everyone else is as slimy as they are.
Two mathematicians, through insight and thought, wrote out the proof over 1-2 years.
It took OpenAI a cost of $15m and with 10,000 subagents; that's around 60-120 mathematician's salaries ($250k-125k salary) for 1 year.
And, given now the cloud that OpenAI may have just "interpolated" (aka stole) the result, it's even more of a bear case for AI.
That is absolutely *ridiculous* in academia to deny authorship because of affiliation of the author worked on a substantial portion. You’d be ostracized because nobody would ever want to work with you again.
The LLM presumably generates f(input, RNG) but we only can observe f(RNG).
> Funny story no one will believe, but it’s true. A good friend of mine joined a startup as CTO 10 years ago, high growth phase, maybe 200 devs… In his first week he discovered the company had a microservice for generating new UUIDs. One endpoint with its own dedicated team of 3 engineers …including a database guy (the plot thickens). Other teams were instructed to call this service every time they needed a new ‘safe’ UUID. My pal asked wtf. It turned out this service had its own DB to store every previously issued UUID. Requests were handled as follows: it would generate a UUID, then ‘validate’ it by checking its own database to ensure the newly generated UUID didn’t match any previously generated UUIDs, then insert it, then return it to the client. Peace of mind I guess. The team had its own kanban board and sprints.