Divija

I like to build things👩‍💻

[Andrej Karpathy @karpathy]

I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue. There's a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering. Clearly some powerful alien tool was handed around except it comes with no manual and everyone has to figure out how to hold it and operate it, while the resulting magnitude 9 earthquake is rocking the profession. Roll up your sleeves to not fall behind.

[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.”

[Naval @naval]

Continuous learning is the only defensible moat.

[Nikhil Kamath @nikhilkamathcio]

Out now @elonmusk

[Divija @Divicodes]

thinking about Thinking

[Google DeepMind @GoogleDeepMind]

To celebrate five years of #AlphaFold, we’re making The Thinking Game available on YouTube. 🧬

Get a candid look at the triumphs, the challenges and the pivotal moments that led to a breakthrough on a 50-year-old grand challenge in biology.

Stream for free on @YouTube → https://t.co/sZv7r2VpQh

[Dwarkesh Patel @dwarkesh_sp]

The @ilyasut episode

0:00:00 – Explaining model jaggedness
0:09:39 - Emotions and value functions
0:18:49 – What are we scaling?
0:25:13 – Why humans generalize better than models
0:35:45 – Straight-shotting superintelligence
0:46:47 – SSI’s model will learn from deployment
0:55:07 – Alignment
1:18:13 – “We are squarely an age of research company”
1:29:23 – Self-play and multi-agent
1:32:42 – Research taste

Look up Dwarkesh Podcast on YouTube, Apple Podcasts, or Spotify. Enjoy!

[Star Sports @StarSportsIndia]

INDIA ARE THE WORLD CHAMPIONS!!!!!! 🇮🇳

Congratulations to @BCCIWomen on their maiden ICC Women’s Cricket World Cup title! 🏆

#CWC25 #INDvSA

[Andrej Karpathy @karpathy]

My pleasure to come on Dwarkesh last week, I thought the questions and conversation were really good.

I re-watched the pod just now too. First of all, yes I know, and I'm sorry that I speak so fast :). It's to my detriment because sometimes my speaking thread out-executes my thinking thread, so I think I botched a few explanations due to that, and sometimes I was also nervous that I'm going too much on a tangent or too deep into something relatively spurious. Anyway, a few notes/pointers:

AGI timelines. My comments on AGI timelines looks to be the most trending part of the early response. This is the "decade of agents" is a reference to this earlier tweet https://t.co/NiSn6jftqq Basically my AI timelines are about 5-10X pessimistic w.r.t. what you'll find in your neighborhood SF AI house party or on your twitter timeline, but still quite optimistic w.r.t. a rising tide of AI deniers and skeptics. The apparent conflict is not: imo we simultaneously 1) saw a huge amount of progress in recent years with LLMs while 2) there is still a lot of work remaining (grunt work, integration work, sensors and actuators to the physical world, societal work, safety and security work (jailbreaks, poisoning, etc.)) and also research to get done before we have an entity that you'd prefer to hire over a person for an arbitrary job in the world. I think that overall, 10 years should otherwise be a very bullish timeline for AGI, it's only in contrast to present hype that it doesn't feel that way.

Animals vs Ghosts. My earlier writeup on Sutton's podcast https://t.co/rSp1noyGBr . I am suspicious that there is a single simple algorithm you can let loose on the world and it learns everything from scratch. If someone builds such a thing, I will be wrong and it will be the most incredible breakthrough in AI. In my mind, animals are not an example of this at all - they are prepackaged with a ton of intelligence by evolution and the learning they do is quite minimal overall (example: Zebra at birth). Putting our engineering hats on, we're not going to redo evolution. But with LLMs we have stumbled by an alternative approach to "prepackage" a ton of intelligence in a neural network - not by evolution, but by predicting the next token over the internet. This approach leads to a different kind of entity in the intelligence space. Distinct from animals, more like ghosts or spirits. But we can (and should) make them more animal like over time and in some ways that's what a lot of frontier work is about.

On RL. I've critiqued RL a few times already, e.g. https://t.co/mYrMFVdVDW . First, you're "sucking supervision through a straw", so I think the signal/flop is very bad. RL is also very noisy because a completion might have lots of errors that might get encourages (if you happen to stumble to the right answer), and conversely brilliant insight tokens that might get discouraged (if you happen to screw up later). Process supervision and LLM judges have issues too. I think we'll see alternative learning paradigms. I am long "agentic interaction" but short "reinforcement learning" https://t.co/2L7FiaoKsw. I've seen a number of papers pop up recently that are imo barking up the right tree along the lines of what I called "system prompt learning" https://t.co/df5mJDdN3C , but I think there is also a gap between ideas on arxiv and actual, at scale implementation at an LLM frontier lab that works in a general way. I am overall quite optimistic that we'll see good progress on this dimension of remaining work quite soon, and e.g. I'd even say ChatGPT memory and so on are primordial deployed examples of new learning paradigms.

Cognitive core. My earlier post on "cognitive core": https://t.co/q2s1ihGy0T , the idea of stripping down LLMs, of making it harder for them to memorize, or actively stripping away their memory, to make them better at generalization. Otherwise they lean too hard on what they've memorized. Humans can't memorize so easily, which now looks more like a feature than a bug by contrast. Maybe the inability to memorize is a kind of regularization. Also my post from a while back on how the trend in model size is "backwards" and why "the models have to first get larger before they can get smaller" https://t.co/6k0FZRGXsb

Time travel to Yann LeCun 1989. This is the post that I did a very hasty/bad job of describing on the pod: https://t.co/fQgqaXPyp6 . Basically - how much could you improve Yann LeCun's results with the knowledge of 33 years of algorithmic progress? How constrained were the results by each of algorithms, data, and compute? Case study there of.

nanochat. My end-to-end implementation of the ChatGPT training/inference pipeline (the bare essentials) https://t.co/SIetgyoKWN

On LLM agents. My critique of the industry is more in overshooting the tooling w.r.t. present capability. I live in what I view as an intermediate world where I want to collaborate with LLMs and where our pros/cons are matched up. The industry lives in a future where fully autonomous entities collaborate in parallel to write all the code and humans are useless. For example, I don't want an Agent that goes off for 20 minutes and comes back with 1,000 lines of code. I certainly don't feel ready to supervise a team of 10 of them. I'd like to go in chunks that I can keep in my head, where an LLM explains the code that it is writing. I'd like it to prove to me that what it did is correct, I want it to pull the API docs and show me that it used things correctly. I want it to make fewer assumptions and ask/collaborate with me when not sure about something. I want to learn along the way and become better as a programmer, not just get served mountains of code that I'm told works. I just think the tools should be more realistic w.r.t. their capability and how they fit into the industry today, and I fear that if this isn't done well we might end up with mountains of slop accumulating across software, and an increase in vulnerabilities, security breaches and etc. https://t.co/8556ESSpyY

Job automation. How the radiologists are doing great https://t.co/FVUI872dkD and what jobs are more susceptible to automation and why.

Physics. Children should learn physics in early education not because they go on to do physics, but because it is the subject that best boots up a brain. Physicists are the intellectual embryonic stem cell https://t.co/p72Elk8lPV I have a longer post that has been half-written in my drafts for ~year, which I hope to finish soon.

Thanks again Dwarkesh for having me over!

[Beanie👾 @CoCoKruszynski]

Elon Musk @elonmusk shows how his companies work for the benefit of humanity:

"SpaceX, Tesla, Neuralink, and The Boring Company are philanthropy. If you say philanthropy is love of humanity, they are philanthropy.

Tesla is accelerating sustainable energy; this is philanthropy.

SpaceX is trying to ensure the long-term survival of humanity as a multiplanet species; this is the love of humanity.

Neuralink is here to help solve brain injuries and existential risk with AI–love of humanity.

The Boring Company is trying to solve traffic, which is hell for most people, that also is a love of humanity.

If you care about the reality of goodness instead of the perception of it, philanthropy is extremely difficult.

[Cerebral Valley @cerebral_valley]

🗽 We just wrapped up the GPT-5 x Codex Startup Hackathon in NYC with @OpenAIDevs

Startups had 24 hours to build with Codex, judged by some of the leading minds in VC: @GraceIsford @vhankes @Sigalow @RebeccaKaden

NYC set the bar… which city should we land in next? 🌎

👇 Here's who took home the win and over $85,000 in credits

[Sridhar Vembu @svembu]

We have faced a 100x increase in Arattai traffic in 3 days (new sign-ups went vertical from 3K/day to 350K/day). We are adding infrastructure on an emergency basis for another potential 100x peak surge. That is how exponentials work.

As we add a lot more infrastructure, we are also fine tuning and updating the code to fix issues as they arise. We have all-hands-on-deck working flat out.

As a matter of fact, we had planned on a big release by November, with a lot of the features you expect, a huge capacity addition and a marketing push. And then it suddenly went vertical!

We have a lot more planned for Arattai, please give us some time. Thank you for your patience and support! Jai Hind 🙏

[Sridhar Vembu @svembu]

We understand the push for Zoho to go public. But let me state the reality: Arattai would very likely not have been built by a public company that faces quarter to quarter financial pressure. It was a "hopelessly foolish" project, and even our employees had expressed scepticism that Arattai would ever gain any traction.

We built it because we felt we need that kind of engineering capability in Bharat. We need a lot lot more of such capabilities in Bharat and we are on it.

We have some very ambitious, long range R&D projects going on in Zoho, including compilers, databases, OS, security, hardware, chip design, robotics (not to mention AI) and on and on. In addition, we have invested in many R&D intensive companies that we know won't make money soon.

Zoho is a kind of an industrial research lab that also makes money to fund itself. We essentially ignore short term profits, as long as we don't lose money. And we have a culture of founders and senior executives living frugally, like how good scientists and engineers in ISRO would live. To us that is the essence of Bharat. Japan operated that way when it was developing.

Imagine saying all that to Wall Street or Dalal Street!

[Paul Graham @paulg]

If you make your app 10% easier to use you'll get twice as many users.

[Divija @Divicodes]

@Aiswarya_Sankar @EntelligenceAI 🔥

[jack @jack]

my weekend project to learn about bluetooth mesh networks, relays and store and forward models, message encryption models, and a few other things.

bitchat: bluetooth mesh chat...IRC vibes.

TestFlight: https://t.co/P5zRRX0TB3
GitHub: https://t.co/Yphb3Izm0P

[Andrej Karpathy @karpathy]

Nice - my AI startup school talk is now up! Chapters:

0:00 Imo fair to say that software is changing quite fundamentally again. LLMs are a new kind of computer, and you program them *in English*. Hence I think they are well deserving of a major version upgrade in terms of software.
6:06 LLMs have properties of utilities, of fabs, and of operating systems => New LLM OS, fabbed by labs, and distributed like utilities (for now). Many historical analogies apply - imo we are computing circa ~1960s.
14:39 LLM psychology: LLMs = "people spirits", stochastic simulations of people, where the simulator is an autoregressive Transformer. Since they are trained on human data, they have a kind of emergent psychology, and are simultaneously superhuman in some ways, but also fallible in many others. Given this, how do we productively work with them hand in hand?
Switching gears to opportunities...
18:16 LLMs are "people spirits" => can build partially autonomous products.
29:05 LLMs are programmed in English => make software highly accessible! (yes, vibe coding)
33:36 LLMs are new primary consumer/manipulator of digital information (adding to GUIs/humans and APIs/programs) => Build for agents!

Thank you again for the invite @ycombinator and congrats again on an awesome events! I'll post some links/references in the reply.

[Entelligence AI @EntelligenceAI]

Introducing EntelligenceAI extension for VS Code, Cursor & Windsurf! 🚀

Review your code directly in your IDE before you even merge & get instant feedback as you vibe with your code!

⚡️ Check it out in the link below

[Divija @Divicodes]

Llama 4 Hackathon NYC

@MetaforDevs @cerebral_valley

[Paul Graham @paulg]

If kids have mostly stopped reading now, you can gain an advantage over your peers by being one of the few who reads a lot.

[Divija @Divicodes]

#GoogleIO #Day2

@DarrenAronofsky @demishassabis