Mike Knapp

Product Manager @ Goog. My views are entirely my own.

[Andrej Karpathy @karpathy]

This is the the quote I've been citing a lot recently.

[Sundar Pichai @sundarpichai]

Q1 earnings are in: 2026 is off to a terrific start.

Our AI investments and full stack approach are lighting up every part of the business: Search queries are at an all-time high with AI continuing to drive usage. Google Cloud revenue grew 63%, Gemini models have incredible momentum, and it was our strongest quarter ever for consumer AI subs, driven by @GeminiApp.

Thanks to our partners + employees around the world. Much more to share on our earnings call in 20 minutes… and at Google I/O in 20 days!

[Aaron Levie @levie]

Jevons paradox is happening in real time. Companies, especially outside of tech, are realizing that they can now afford to take on software projects that they wouldn’t have been able to tackle before because now AI lets them do so.

We’re going to start to use software for all new things in the economy because it’s incrementally cheaper to produce. Marketing teams at big companies will have engineers helping to automate workflows. Engineers in life sciences and healthcare will automate research. Small businesses will hire engineers for the first to build better digital experiences.

And as long as AI agents still require a human who understands what to prompt, how to review when an agent goes off the rails, how it guide back, how to maintain the system that was built, how to fix the ongoing bugs, and more, we will still have humans managing these agents.

This is why all the advice you get of not going into engineering is wrong. The world is going to increasingly be made up of software, and the people that understand it best will be in a strong economic position. This will happen in other roles as well where output goes up and demand increases.

[Mike Knapp @mikeee]

Super cool that this technique might allow you to backprop through the ENTIRE execution sequence - using GRPO - and build better intuition on how to write code...

[Christos Tzamos @ChristosTzamos]

1/4 LLMs solve research grade math problems but struggle with basic calculations. We bridge this gap by turning them to computers.

We built a computer INSIDE a transformer that can run programs for millions of steps in seconds solving even the hardest Sudokus with 100% accuracy

[Mike Knapp @mikeee]

@caviterginsoy @phyrooo @ChristosTzamos Isn't the main benefit here that you can backprop through the ENTIRE execution sequence and build better intuition on how to write the initial code?

You're literally teaching it how to be a classical computer.

Even more powerful when combined with something like GRPO.

[毛丹青 @maodanqing]

AIが復元した「清明上河図」に言葉を失う。確かに、AI特有の「滑らかすぎる質感」に違和感を覚える瞬間もある。だが、張択端が描こうとした千年前の喧騒が、圧倒的な解像度で迫ってくるのも事実だから、これは模写ではない。AIという異質なフィルターを通すことで、われわれは初めて「大宋」の熱気に触れる。賛否はあろうが、この没入感だけは否定できないかもしれない。

[Mike Knapp @mikeee]

@karpathy @archiexzzz The problem I’ve had with RL for language tasks is the model quickly learns to game the system. It’s super hard to write good scoring systems that allows the model to improve and also produce novel responses. The avg. score goes up, while quality goes down just as fast.

[Talor @Talor_A]

this is one of the most remarkable technical blog posts I’ve ever read

[ア @yuruyurau]

a=(x,y,d=mag(k=(4+sin(y*2-t)*3)*cos(x/29),e=y/8-13))=>point((q=3*sin(k*2)+.3/k+sin(y/25)*k*(9+4*sin(e*9-d*3+t*2)))+30*cos(c=d-t)+200,q*sin(c)+d*39-220)
t=0,draw=$=>{t||createCanvas(w=400,w);background(9).stroke(w,96);for(t+=PI/240,i=1e4;i--;)a(i,i/235)}//#つぶやきProcessing

[GREG ISENBERG @gregisenberg]

Email from Fiverr CEO to his team about AI ($1b company):

[𒐪 @SHL0MS]

my best one yet. load in 4k then zoom out

[Andrej Karpathy @karpathy]

Noticing myself adopting a certain rhythm in AI-assisted coding (i.e. code I actually and professionally care about, contrast to vibe code).

1. Stuff everything relevant into context (this can take a while in big projects. If the project is small enough just stuff everything e.g. `files-to-prompt . -e ts -e tsx -e css -e md --cxml --ignore node_modules -o prompt.xml`)
2. Describe the next single, concrete incremental change we're trying to implement. Don't ask for code, ask for a few high-level approaches, pros/cons. There's almost always a few ways to do thing and the LLM's judgement is not always great. Optionally make concrete.
3. Pick one approach, ask for first draft code.
4. Review / learning phase: (Manually...) pull up all the API docs in a side browser of functions I haven't called before or I am less familiar with, ask for explanations, clarifications, changes, wind back and try a different approach.
6. Test.
7. Git commit.
Ask for suggestions on what we could implement next. Repeat.

Something like this feels more along the lines of the inner loop of AI-assisted development. The emphasis is on keeping a very tight leash on this new over-eager junior intern savant with encyclopedic knowledge of software, but who also bullshits you all the time, has an over-abundance of courage and shows little to no taste for good code. And emphasis on being slow, defensive, careful, paranoid, and on always taking the inline learning opportunity, not delegating. Many of these stages are clunky and manual and aren't made explicit or super well supported yet in existing tools. We're still very early and so much can still be done on the UI/UX of AI assisted coding.

[Mike Knapp @mikeee]

@levelsio Does it have a “dry mode”? Try that

[Mike Knapp @mikeee]

@iannuttall

[兎 @fluopoika]

we're waiting for the end of the sentence

[Mike Knapp @mikeee]

@andrewpprice @bilawalsidhu @BackflipAI Great vid! Blackflip looks pretty cool, and agree with you, it’s only going to get better from here!

[Andrej Karpathy @karpathy]

More apps should natively offer this.

“Export for prompt” button

[Mike Knapp @mikeee]

How fast are we sliding into a world where all “jobs” are just reformatting ChatGPT output?