Kaival Shah 🤔

personalization @apple, ai agents @AsteraLabs | tactile sensing, robotics @HAND_ERC | math + cs at northwestern

[Nic Cruz Patane @niccruzpatane]

Very inspiring words from Elon Musk today:

"I always think about this. There are always problems on earth. There’s always things that we wish to be better, that we want to solve on Earth, and we should solve them. But there there also has to be things that get you excited about the future — that make you glad to wake up in the morning, because you can’t wait to see what happens next.

That’s the future @SpaceX wants to bring to you."

Let’s goooooo🚀

[Kaival Shah 🤔 @kaivalss]

Cool

[xin @bryanwangxin]

苹果的发布会视频中，但凡有说到 Siri 的地方，都会切掉音频中 3k、4k、5k、6kHz 频率部分，以防观看视频时附近的苹果设备激活 Siri。

[Tim Cook @tim_cook]

Always amazing to kick off #WWDC26 with our student developers! Congrats to our Swift Student Challenge Distinguished winners!

[Kaival Shah 🤔 @kaivalss]

well said

[Daniel Jeffries @Dan_Jeffries1]

This is the best and most balanced report I've read by Anthropic, free of many of the super sci-fi, everything-is-exponential language of some other reports I've read by this amazing team.

But one line is dead wrong. This one about recursive self-improvement:

"[If] AI systems themselves become capable of full recursive self-improvement, and begin building their successors...In this world, the pace of progress in AI development becomes determined entirely by the availability of compute (or the speed of discovering various efficiencies in algorithmic training or inference) for AI systems."

Compute is absolutely NOT the only limiting factor in recursive self-improvement and not even the most important one. They are two more:

1) Time
2) Multiplicity

Time is how long it takes to get an answer.

Multiplicity is when there is no right or wrong answers but only shades of gray with right(ish) answers and wrong(ish).

They even point to one of them (time) just a few paragraphs later:

"More intelligence can’t learn what a drug does over decades of use, can’t hold elections sooner than a constitution dictates, and can’t turn a stranger into an old friend in a weekend. For most people, the felt pace of this future will still be set by the bottlenecks, even if the laboratory upstream runs at the speed of compute. That collision, where recursive intelligence building itself ever faster meets the world of humans, relationships, and governance, is another part of this future we can’t predict."

But let me make it even more clear:

AI got good at code and games because they have great feedback loops and tight timelines. If the code works or does not, you know pretty quickly.

It good at driving for the same reasons. Don't die or drive off the road or hit someone are achievable (though difficult) goals with clear, fast feedback.

You cannot answer the question "is this a good article?" or "do I write well?" because that is multiplicity, shades of gray that are hard to judge.

Humans judge this by self-awareness and feedback from others. AI might be able to approximate the second but only if it develops more of the first (harder).

"Will my wife like this surprise present?" Hard to get good at that even if you're a master. Took me many years of trying and judging her responses. :)

Time is also a massive factor. The question of "did I make money in business?" can't be answered in a short time line. There is no way to know the answer faster, and short term success doesn't predict long term.

"Will this drug cause bad side effects twenty years from now?" That can only be answered in twenty years. No amount of compute changes that.

"Will this building fall down faster than this one if I build it a different way?" You can run basic physics and math rules to help you heuristically figure it out, but only time gives you the true answer.

These two constraints, time and multiplicity, are the death knell of any Doomer/Less Wrong fantasies about fast takeoff and instant super genius AI. You can have all the compute in the universe and you still can't compress twenty years of drug side effects into twenty minutes.

You can have a 500 trillion parameters and you still can't definitively answer "is this beautiful?" because beauty is not a optimization target with a clean gradient.

The recursive self-improvement loop doesn't hit a wall because of compute.

It hits a wall because of reality.

Reality is slow, messy, ambiguous, and full of questions that only time and lived experience can answer.

Compute is the bottleneck that engineers see because it's the one they can measure.

Time and multiplicity are the bottlenecks that the real world imposes and no amount of silicon can brute force past them.

That's why even nature only "solved" good/bad by brute force: evolution. Does this agent/human/creature survive and reproduce? That's good. Otherwise not good.

Companies follow the same rule. Did this survive and make money over time? Good. Otherwise bad.

Imperfect, lossy, dumb, blind, slow.

AI is changing the world already.

It will get better and better.

But the road to better is long and winding, not a vertical line to godhood.

And that should make you more hopeful, not less.

[Kaival Shah 🤔 @kaivalss]

@Ishaanbansal77 @joinCollegiate Very cool Ishaan!

[Sasha Rush @srush_nlp]

On-Policy Distillation is the most active new research direction being explored in RL for LLMs. Had the chance to discuss how it works with Dwarkesh and why it fits so nicely into large-scale pipelines.

[Polymarket @Polymarket]

JUST IN: Scientists say AI has decoded communication patterns in mice, dolphins, apes, birds, whales, & cuttlefish — could eventually lead to humans communicating directly with animals.

[Kaival Shah 🤔 @kaivalss]

great product!

[Jacob Fu @fujacobb]

if y'all don't wanna pay $80 for local ai autocomplete, we made a completely free, open-source version.

system-wide ghost text, tab-to-accept, inline :emoji: autocomplete, fully on-device.

check it out https://t.co/LOspmlEdk7

[Massachusetts Institute of Technology (MIT) @MIT]

Lisa Su ’90, SM ’91, PhD ’94 to the Class of 2026, “Run toward the hardest problems. Hard problems teach you what you're capable of.”

[Kaival Shah 🤔 @kaivalss]

@yang_yuzhe Very cool!

[Kaival Shah 🤔 @kaivalss]

Cool

[Robinhood @RobinhoodApp]

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[Yun-Ta Tsai @yunta_tsai]

The nature of Computer Science is not coding, but understanding the intrinsic value of computation that gains more meaningful information from the input—like nuclear fusion—so that such computation can self-sustain.

However, many parents and students believe computer science is only about software engineering. It is not.

[Kaival Shah 🤔 @kaivalss]

@NoahHWeiss @cursor_ai @elonmusk Sick

[Kaival Shah 🤔 @kaivalss]

@wzenus @YejinChoinka @jiajunwu_cs @ManlingLi_ @LINJIEFUN @chi_gui_1 @DeimosGN @qineng_wang @James_KKW @shiqi_chen17 @zhengyuan_yang Congratulations!

[Kaival Shah 🤔 @kaivalss]

@RobKnight__ Nice work

[Figure @Figure_robot]

Watch a team of humanoid robots running a full 8-hr shift at human performance levels. This is fully autonomous running Helix-02 https://t.co/bIgpYuaYCj

[Kaival Shah 🤔 @kaivalss]

/plugin marketplace add krzysztofdudek/ResearcherSkill
/plugin install researcher@researcher-marketplace

truly sensational

[Kaival Shah 🤔 @kaivalss]

@mihiranand True

[Kaival Shah 🤔 @kaivalss]

Great read 🤩

[Stephen Xie @stephenx_]

Longer chain-of-thought = slower inference, more context rot, and ballooning compute.

So what if the model could decide for itself when to go parallel?

Our new BAIR blog breaks down Adaptive Parallel Reasoning (APR) — the next paradigm in inference-time scaling. 🧵

[Kaival Shah 🤔 @kaivalss]

@cursor_ai brilliant