
cmo @islolabs + @incredibuild | full time realist part time cynic
Joined March 2007
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There was never any good old days
They are today, they are tomorrow
It's a stupid thing we say
Cursing tomorrow with sorrow
β @GogolBordello
They need something in latent space to attach to
Not much different from us
Listen to @dotpem
So much fretting from scientists and researchers is just about not wanting to acknowledge that they were a cog in a brute-forcing machine moving very slowly through time.
Why would society choose to be limited by their (or mine or yours) time, if we didn't have to be?
The best way I've found to think about what is possible with AI is: what if you could brute force everything and were not limited in time or effort? It was just a matter of money.
And then someone did. Everything.
Market has always punished spend on cost centers, rewarded shipping bad software + not fixing.
Bad software has much better margins than good software.
If youβre beholden to that structure, your product + customers are sitting ducks.
Until thereβs an ideological break.
Replying to @saranormous
7/ The fix side has to run at swarmy machine speed too. Engineer attention is the scarce input, so the design question is where a human actually changes the outcome. Agents reproduce, patch, test and ship. Engineers should see the smol handful of cases that need a judgment call.
Looking forward to being tracked down by the technocratic policy stateβs oligarch enforcerβs war dogs, robot army, and drone fleet for daring to use open source software or vote for opposition party candidates or thought crimes and being blown to smithereens
The begrudging respect when you were nearly hoodwinked by a very good sales+marketing+partnership ambiguation tactic
Aneel βπ½ retweeted
We are releasing our first model, Flint, a best-in-class local OSS coding model that runs at 150+ tokens/s on an @nvidia DGX Spark and 125+ tokens/s on a MacBook Pro M4βdeveloped from scratch in less than four months with help from @Recursive_SI's autoresearch system.
Introducing Flint, @Recursive_SIβs first model: a fast local coding model built with our automated research system, the Eureka Machine.
β’ Best-in-class fast local coding
β’ Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
β’ Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Moderate-to-generational wealth created in one group. Everyday luxuries explode in price to serve just them.
The occasional nice restaurant reservation? The rare flight upgrade? Totally out of reach.
The people who used to be able to afford those things are gonna be mad.
The treats, the formerly affordable luxuries, is how people cope with their situations.
It's what makes middle class __feel good__.
Can someone at @apple use AI to figure out how, if when I start a timer on my phone it also starts on my laptop, if I then stop it on my laptop it should also stop on my phone?
Weird bias: I have way more patience with LLMs when coding than I do with people when doing non-coding work
Has anyone before thought to tie every voter's economic well being to their own personal whims, goals, and economic well being?
Can someone at apple use AI to make sure that when I connect to the same external monitor that was last connected to my mbp earlier in the day with no others connected in the intervening period, the same display settings I used last time still get used?
If valuations require diffusion past stable countries and economies, theyβre gonna be in for a rough time
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.















