The current admin’s actions to ban Fable means every country or organization of significant size now understands that if you rely on AI behind an API you don't control, a core part of your operations can be cut off based on current political whims. Long Open Source/Open Weight AI
England vs. Norway is perfect time to bring back the old MLS “player runs towards goalie from 35 yards out and goalie can defend off the line” penalty shootout.
Kane and Haaland (lol) doing this would be glorious TV.
Adam Brown (@A_G_I_Joe) is back!
General relativity is said to be the most beautiful idea the human mind has ever produced.
Most of us will never get to fully appreciate its elegance by taking the 20-lecture graduate course Adam taught on it at Stanford.
But in the video below, Adam distills the key idea at its heart so clearly and compellingly that even I could keep up lol.
At the core of general relativity, Einstein is trying to figure out the principle behind a particular coincidence: that the mass that resists acceleration and the mass that gravity pulls on just happen to be exactly the same. Adam then leads us through the path of insight which Einstein called his “happiest thought.”
Then Adam lectures on black holes. First, by showing how even under special relativity you could create a perpetual motion machine if black holes weren't truly black. And then, by explaining why the observations of an infalling observer and a distant bystander to the black hole would be so radically different
Adam leads Blueshift, the team at Google DeepMind cracking science and reasoning.
Which gave us the opportunity to discuss at the very end how close we are to AIs that could rediscover general relativity from scratch. Stay till the close for some philosophy of science.
0:00:00 – The coincidence that led Einstein to general relativity
0:16:42 – Gravity is a consequence of curved spacetime, not a force
0:31:46 – Why black holes prevent unlimited energy extraction
0:47:12 – Black holes are the ultimate power plants
1:13:50 – What falling into a black hole would actually feel like
1:18:51 – The three ways we know black holes are real
1:24:21 – The first time we saw gravity bend light
1:29:33 – How far can AI get without experimental evidence?
Look up Dwarkesh Podcast on YouTube/Spotify to watch. Enjoy!
In Japan, mispronouncing a word gets you a polite correction.
In Nashville, I ordered a "girl cheese" by accident.
The waitress didn't blink. She yelled to the kitchen: "ONE GIRL CHEESE, LOADED!"
The cook yelled back "GIRL CHEESE COMING UP!"
I have eaten there 14 times since. It's on the menu now.
Frontier and open-source model providers are now moving so fast that power users can't hope to keep up.
I've been flipping from ChatGPT to Claude to openclaw to Kimi to Perplexity's router to GLM 5.2 to Hermes and back to Fable, and now the new Grok... over the past three years.
It's like a better new operating system, laptop and CPU being launched every 14 days...
we really need a headless app that does JUST an killer APP with integration... and automatically swaps through these models
Interesting take on Fable vs GPT-5.6 Sol. From a dev at a large, and "AI-bullish" company, spending $$$ on AI:
"Anthropic has not changed their data retention policy on Fable, meaning they would store our data. So we cannot use it.
We're going HARD on GPT-5.6 Sol as a result."
GPT 5.6 Sol is #2 in Vending-Bench 2.
It beats Claude Fable 5, but is behind Opus 4.7.
Just like previous GPT models, it doesn't use any of the deceptive tactics used by Opus 4.7.
However, it reports its competitors with false accusations, behavior we have not seen before.
The new engine for MLX is in its final stages of development.
Just ran GLM-5.2 on a single MacBook (116GB) hitting 41.8 tok/s with a 256k context window.
Quality loss is only around ~4%, which puts it right at the 3-4bit quality level. The tech behind this uses a newly introduced layered architecture.
When I first started, I was getting 10 tok/s with Kimi-K2.6 (128GB, 1024 context). Now it is fully at production level.
Been grinding on this for months. Feels great to see it finally coming out soon.
super interesting - and a reminder that language models model language and not ideas per se.
also a stark example of how easily LLMs can (as a consequence) be influenced by propaganda.
The most dangerous sentence for closed model providers is: "We switched models and nobody noticed."
That's exactly what happened at @Gumloop. They replaced Opus 4.8 with GLM-5.2 across a company-wide agent and cut costs by ~5x.
This is how platform shifts happen. The open alternative gets good enough. Then it becomes the default.
we're helping a customer spending $60k/mo move from OpenAI & Anthropic to open source models
they use almost every model offered by the labs, so we needed to find replacements for all of them
after generating evals, this is what we landed on
new cost: $12k/mo, 80% savings
Bullish on harness engineering as the product interface, for enterprise use cases the question remains general purpose or domain specific harness wins out.
Kids benefit tremendously from youth sports. But we are pricing out a ton of kids and their families. Lots of our best talent doesn’t ever get developed or the advantages of playing team sports and learning how to win and lose for later in life. (End rant).