/in/jakemannix, fka @pbrane
professionally: Tech Fellow, AI & Relevance, Walmart Global Tech
here: bad math/physics jokes, AI in general, puns, OSS ML news, outdoorsy stuff, DL papers, shitposting, the fall of democracy
*again*?!?
didn't we see where this got us last time?
*again*?!?
didn't we see where this got us last time?
I have the perfect job for you:
Do not despair, it is just 100 lines of code per minute.
An early NYE resolution, perhaps? Let's just detach, block, ignore, and have our own fun/useful discussions, ok?
(I am speaking partly to myself, as well)
An early NYE resolution, perhaps? Let's just detach, block, ignore, and have our own fun/useful discussions, ok?
(I am speaking partly to myself, as well)
---
⏺ Now let's run the tests to confirm they fail (TDD red phase):
⏺ Bash(uv run pytest tests/test_output_schema_jsonpath.py -v 2>&1 | head -100) timeout: 1m 0s
---
⏺ Now let's run the tests to confirm they fail (TDD red phase):
⏺ Bash(uv run pytest tests/test_output_schema_jsonpath.py -v 2>&1 | head -100) timeout: 1m 0s
there's not just one way to be a hardcore leftist - you can even *smash the state* _with_ AI!
(it's the major premise of Mustafa Suleyman's (MSFT CEO of AI) "The Coming Wave": he's a serious corp/capitalist statist, and terrified of decentralized AI)
there's not just one way to be a hardcore leftist - you can even *smash the state* _with_ AI!
(it's the major premise of Mustafa Suleyman's (MSFT CEO of AI) "The Coming Wave": he's a serious corp/capitalist statist, and terrified of decentralized AI)
Just enough snark mixed with optimism, with some definitely insightful thoughts on how the software engineering profession is evolving.
Just enough snark mixed with optimism, with some definitely insightful thoughts on how the software engineering profession is evolving.
If you like typing you want a clitar
If you like typing you want a clitar
Training is unrestricted, but commercial deployment triggers royalties (as % of inference vendor's revenue) into a collective pool. Distribution is by training dataset composition, described by audited "data cards" for all models.
Training is unrestricted, but commercial deployment triggers royalties (as % of inference vendor's revenue) into a collective pool. Distribution is by training dataset composition, described by audited "data cards" for all models.
Then we let market forces dictate how much different folks content is worth to keep in the training data.
(still not "opt-in only", sorry)
Then we let market forces dictate how much different folks content is worth to keep in the training data.
(still not "opt-in only", sorry)
@cameron.pfiffer.org's talk on social agents was engaging and he finally got followed by @pfrazee.com so a win for all!
*and* I got to bust out the Gelfand-Naimark theorem after a few drinks.
A+ fun!
@cameron.pfiffer.org's talk on social agents was engaging and he finally got followed by @pfrazee.com so a win for all!
*and* I got to bust out the Gelfand-Naimark theorem after a few drinks.
A+ fun!
Disney
Amazon
Netflix
Apple
Boeing
Microsoft
Adobe
NVIDIA
IBM
General Motors
Walmart
Tencent
...and about 78% of other global businesses. Sooo... good luck avoiding it, lol. explodingtopics.com/blog/compani...
Disney
Amazon
Netflix
Apple
Boeing
Microsoft
Adobe
NVIDIA
IBM
General Motors
Walmart
Tencent
...and about 78% of other global businesses. Sooo... good luck avoiding it, lol. explodingtopics.com/blog/compani...
That’s… one way to make a deal!
That’s… one way to make a deal!
I, uh, called that out a bit, but conceded that in many ways, I agree, barring image generation, and got deadpan:
I, uh, called that out a bit, but conceded that in many ways, I agree, barring image generation, and got deadpan:
even if we were in "hard takeoff" world, and can literally transform test-time-compute into new training data, log scaling => need for exponential compute = exponential GW of power to get linear increases in quality.
=> no takeoff without working fusion
This blog post is for those who want to think more carefully about these claims and examine them from a perspective that is often missing in the current discourse: the physical reality of computation.
timdettmers.com/2025/12/10/w...
even if we were in "hard takeoff" world, and can literally transform test-time-compute into new training data, log scaling => need for exponential compute = exponential GW of power to get linear increases in quality.
=> no takeoff without working fusion
Thus there is incentive for all the smaller players (and/or govts) to pool together + ensure good open-weights ones
Thus there is incentive for all the smaller players (and/or govts) to pool together + ensure good open-weights ones