gRPC over Nats is an excellent combination, very simple, but scales out massively, doesn’t require service mesh, makes micro services comms like a cakewalk. Running our services more than a year now using nats as our major backbone, thanks @nats_io
The biggest winner of the AI race will be distributed systems people. Everything is converging onto a distributed network of stuff and it is only accelerating in the last two years.
I often rant about how so much of cloud is a rip-off, but @Cloudflare is the exact opposite. I keep thinking we're getting away with robbery: So much utility, such small bills. @eastdakota has cracked some magic code! 🤘
Agency > Intelligence
I had this intuitively wrong for decades, I think due to a pervasive cultural veneration of intelligence, various entertainment/media, obsession with IQ etc. Agency is significantly more powerful and significantly more scarce. Are you hiring for agency? Are we educating for agency? Are you acting as if you had 10X agency?
Grok explanation is ~close:
“Agency, as a personality trait, refers to an individual's capacity to take initiative, make decisions, and exert control over their actions and environment. It’s about being proactive rather than reactive—someone with high agency doesn’t just let life happen to them; they shape it. Think of it as a blend of self-efficacy, determination, and a sense of ownership over one’s path.
People with strong agency tend to set goals and pursue them with confidence, even in the face of obstacles. They’re the type to say, “I’ll figure it out,” and then actually do it. On the flip side, someone low in agency might feel more like a passenger in their own life, waiting for external forces—like luck, other people, or circumstances—to dictate what happens next.
It’s not quite the same as assertiveness or ambition, though it can overlap. Agency is quieter, more internal—it’s the belief that you *can* act, paired with the will to follow through. Psychologists often tie it to concepts like locus of control: high-agency folks lean toward an internal locus, feeling they steer their fate, while low-agency folks might lean external, seeing life as something that happens *to* them.”
First recovery in the ByBit hack.
~$43m (15,000 cmETH) has been clawed back from the hacker.
I saw the recovery possibility soon after the hack and SEAL connected me with Mantle/mETH team who made it happen.
Huge shoutout to SEAL, Mantle, and mETH teams for their quick action.
New 3h31m video on YouTube:
"Deep Dive into LLMs like ChatGPT"
This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the models are developed, along with mental models of how to think about their "psychology", and how to get the best use them in practical applications.
We cover all the major stages:
1. pretraining: data, tokenization, Transformer neural network I/O and internals, inference, GPT-2 training example, Llama 3.1 base inference examples
2. supervised finetuning: conversations data, "LLM Psychology": hallucinations, tool use, knowledge/working memory, knowledge of self, models need tokens to think, spelling, jagged intelligence
3. reinforcement learning: practice makes perfect, DeepSeek-R1, AlphaGo, RLHF.
I designed this video for the "general audience" track of my videos, which I believe are accessible to most people, even without technical background. It should give you an intuitive understanding of the full training pipeline of LLMs like ChatGPT, with many examples along the way, and maybe some ways of thinking around current capabilities, where we are, and what's coming.
(Also, I have one "Intro to LLMs" video already from ~year ago, but that is just a re-recording of a random talk, so I wanted to loop around and do a lot more comprehensive version of this topic. They can still be combined, as the talk goes a lot deeper into other topics, e.g. LLM OS and LLM Security)
Hope it's fun & useful!
https://t.co/75mXcUBI8L
my father-in-law is an extremely talented electrical engineer. founded a super successful chip manufacturing biz. if you have an italian coffee-making machine, you likely have one of his chips in your home right now.
he only codes in assembly. learned the thing on his own building alarms. didn’t even go to college. a true hacker
when i talk to him about my job, and the high-level programming concepts we have today, he doesn’t get it. his mental model of programming machines is so low-level that it’s a completely different discipline from whatever i do, and we can’t really connect the two
that’s because, between us, there’s a compiler
looking at how our users are interacting with AI today, and the democratization of code that LLMs are enabling, i’m seeing what is happening to software after code
these users are not even looking at code anymore. they’re just copy-pasting it around, molding it with english, stitching it through the clipboard. they're programmers, but not coders. it’s a new type of compiler, in the form of probabilistic models instead of deterministic rules, that is empowering a new type of behavior
our current generation of developers struggle to relate (“you’re not a real developer,” “you can’t control it,” “clean code??”)
but the new generations of software builders don’t care about code anymore. they just focus on the high-level architecture of systems, and are much closer to users needs. just as i don’t have to care about ASM, i don’t have to, i can let the compiler handle the machine code for me while i can focus on product
this is the birth of a new discipline
Starting today, open source is leading the way. Introducing Llama 3.1: Our most capable models yet.
Today we’re releasing a collection of new Llama 3.1 models including our long awaited 405B. These models deliver improved reasoning capabilities, a larger 128K token context window and improved support for 8 languages among other improvements. Llama 3.1 405B rivals leading closed source models on state-of-the-art capabilities across a range of tasks in general knowledge, steerability, math, tool use and multilingual translation.
The models are available to download now directly from Meta or @huggingface. With today’s release the ecosystem is also ready to go with 25+ partners rolling out our latest models — including @awscloud, @nvidia, @databricks, @groqinc, @dell, @azure and @googlecloud ready on day one.
More details in the full announcement ➡️ https://t.co/hhJoLm5eLV
Download Llama 3.1 models ➡️ https://t.co/rRjvmxqCTC
With these releases we’re setting the stage for unprecedented new opportunities and we can’t wait to see the innovation our newest models will unlock across all levels of the AI community.