Akbar Saliev

Founder of @directfulcom #ai

[Andrej Karpathy @karpathy]

This is a new paradigm for interacting with Claude that is significantly more "inline" with all the other human activity org-wide. Once you do all of the under the hood engineering work to make this "just work" (e.g. across tools, integrations, compute environments, memory, security, etc.), Claude basically joins the team in a seamless way - you can talk to it as you would talk to a person and it can help with a very large variety of workloads.

Imo this is the 3rd major redesign of LLM UIUX. The first paradigm was that the LLM is a website you go to, the second was that it is an app you download to your computer. This third one is that it is a self-contained, persistent, asynchronous entity with org-wide tools and context, working alongside teams of humans. It really takes a while to wrap your head around it, but it works and it is awesome.

[Boris Cherny @bcherny]

Cool way to use Claude Code: deciphering Linear A, a 3500 year old written language from Crete

https://t.co/Aqd4ZG7Cum

Hope this holds up in peer review! 🤞

[Paul Graham @paulg]

"Anything made before 2028 is going to be valuable."

— an OpenAI employee implicitly discloses their timetable

[Paul Graham @paulg]

Churn is the worst reason to have slow growth. Churn means you're not just unknown, or that there's a big threshold to sign up. It means people are actually trying the product and deciding they don't like it.

[Garry Tan @garrytan]

One of the most important questions for founders is: How do I make sure agents know about my product and service and choose it? All the old tricks won’t work.

People who figure this out will win big

[Elon Musk @elonmusk]

True.

Once the solar energy generation to robot manufacturing to chip fabrication to AI loop is closed, conventional currency will just get in the way.

Just wattage and tonnage will matter, not dollars.

[Paul Graham @paulg]

Someone asked about tricks for meeting eminent people. I said the best plan is just to do really good work. Then you'll tend to meet them organically. In the worst case you'll see one whenever you look in the mirror.

[Andrej Karpathy @karpathy]

A conventional narrative you might come across is that AI is too far along for a new, research-focused startup to outcompete and outexecute the incumbents of AI. This is exactly the sentiment I listened to often when OpenAI started ("how could the few of you possibly compete with Google?") and 1) it was very wrong, and then 2) it was very wrong again with a whole another round of startups who are now challenging OpenAI in turn, and imo it still continues to be wrong today. Scaling and locally improving what works will continue to create incredible advances, but with so much progress unlocked so quickly, with so much dust thrown up in the air in the process, and with still a large gap between frontier LLMs and the example proof of the magic of a mind running on 20 watts, the probability of research breakthroughs that yield closer to 10X improvements (instead of 10%) imo still feels very high - plenty high to continue to bet on and look for.

The tricky part ofc is creating the conditions where such breakthroughs may be discovered. I think such an environment comes together rarely, but @bfspector & @amspector100 are brilliant, with (rare) full-stack understanding of LLMs top (math/algorithms) to bottom (megakernels/related), they have a great eye for talent and I think will be able to build something very special. Congrats on the launch and I look forward to what you come up with!

[Andrej Karpathy @karpathy]

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.”

[Andrej Karpathy @karpathy]

Don't think of LLMs as entities but as simulators. For example, when exploring a topic, don't ask:

"What do you think about xyz"?

There is no "you". Next time try:

"What would be a good group of people to explore xyz? What would they say?"

The LLM can channel/simulate many perspectives but it hasn't "thought about" xyz for a while and over time and formed its own opinions in the way we're used to. If you force it via the use of "you", it will give you something by adopting a personality embedding vector implied by the statistics of its finetuning data and then simulate that. It's fine to do, but there is a lot less mystique to it than I find people naively attribute to "asking an AI".

[Paul Graham @paulg]

If I could send my 18 year old self a message, it would have three parts:

1. Prestige is often mistaken. Follow curiosity instead.

2. There's no way to avoid hard work. It's not sufficient, but it is necessary.

3. Don't take your parents for granted.

[Elon Musk @elonmusk]

The future is going to be AMAZING with AI and robots enabling sustainable ABUNDANCE for all!

[Startup Archive @StartupArchive_]

Sam Altman: “Clarity of thinking, speed, and quality of execution are all linked”

Sam explains that he uses writing as a tool to think clearly:

“I think of writing as externalized thinking. If I have a very hard problem or if I feel a little bit confused about something, I have not found anything better to do than sit down and make myself write it out… it is a super powerful thinking tool.”

One reason clear thinking is important, Sam argues, is because it’s crucial for clear communication:

“If you know what you’re going to do, and if you’ve figured out how to reduce that to the essence of why it’s a good idea and what the priorities going to be, then communicating that clearly is not so hard. But getting clear about the actual idea is really hard. I think unclear communication is a symptom of unfocused thinking.”

Napoleon famously said, “The secret of war lies in the communications.” Sam believes refrains on the importance of clear communication like this are so common because “clarity, speed, and quality of execution are all linked.”

Video source: @david_perell (2024)

[Sam Altman @sama]

i can't think of a non-cliche way to say this, but everyone who says having a kid is the best thing in the world is both correct and still somehow understating it.

[Andrej Karpathy @karpathy]

Scaling up RL is all the rage right now, I had a chat with a friend about it yesterday. I'm fairly certain RL will continue to yield more intermediate gains, but I also don't expect it to be the full story. RL is basically "hey this happened to go well (/poorly), let me slightly increase (/decrease) the probability of every action I took for the future". You get a lot more leverage from verifier functions than explicit supervision, this is great. But first, it looks suspicious asymptotically - once the tasks grow to be minutes/hours of interaction long, you're really going to do all that work just to learn a single scalar outcome at the very end, to directly weight the gradient? Beyond asymptotics and second, this doesn't feel like the human mechanism of improvement for majority of intelligence tasks. There's significantly more bits of supervision we extract per rollout via a review/reflect stage along the lines of "what went well? what didn't go so well? what should I try next time?" etc. and the lessons from this stage feel explicit, like a new string to be added to the system prompt for the future, optionally to be distilled into weights (/intuition) later a bit like sleep. In English, we say something becomes "second nature" via this process, and we're missing learning paradigms like this. The new Memory feature is maybe a primordial version of this in ChatGPT, though it is only used for customization not problem solving. Notice that there is no equivalent of this for e.g. Atari RL because there are no LLMs and no in-context learning in those domains.

Example algorithm: given a task, do a few rollouts, stuff them all into one context window (along with the reward in each case), use a meta-prompt to review/reflect on what went well or not to obtain string "lesson", to be added to system prompt (or more generally modify the current lessons database). Many blanks to fill in, many tweaks possible, not obvious.

Example of lesson: we know LLMs can't super easily see letters due to tokenization and can't super easily count inside the residual stream, hence 'r' in 'strawberry' being famously difficult. Claude system prompt had a "quick fix" patch - a string was added along the lines of "If the user asks you to count letters, first separate them by commas and increment an explicit counter each time and do the task like that". This string is the "lesson", explicitly instructing the model how to complete the counting task, except the question is how this might fall out from agentic practice, instead of it being hard-coded by an engineer, how can this be generalized, and how lessons can be distilled over time to not bloat context windows indefinitely.

TLDR: RL will lead to more gains because when done well, it is a lot more leveraged, bitter-lesson-pilled, and superior to SFT. It doesn't feel like the full story, especially as rollout lengths continue to expand. There are more S curves to find beyond, possibly specific to LLMs and without analogues in game/robotics-like environments, which is exciting.

[Erik Brynjolfsson @erikbryn]

"one minute of exercise, on average, extends one's life by five minutes."
💪💪💪

[prayingforexits 🏴‍☠️ @mrexits]

Engineering is the closest thing to magic we have in this world

[Paul Graham @paulg]

If you care about money or power, stay close to AI, because for the foreseeable future this will be the big source of change in both.