Hearnoeval

[ElevenLabs Developers @ElevenLabsDevs]

Realtime sentiment analysis with Jev and ElevenLabs.

Each phrase takes on the color of the emotion it carries while the caller is still talking. Six meters on the right track the mood of the call.

[Alex Nichol @unixpickle]

This is sick. I had no idea you can get motion data from an airpod. https://t.co/OWJ0B4HeeM

[Ramin Nasibov @RaminNasibov]

Which way is the arrow spinning?

[Lisan al Gaib @scaling01]

I miss that. The existential dread you feel when you first try to imagine how truly gigantic the universe is

we live in a cute little solar system.
one of 100-400 billion just in our galaxy

but in the observable universe there are around 2 trillion galaxies each with thousand to trillions of stars.
meaning there are ~10^23 stars in the observable universe (100,000,000,000,000,000,000,000)
or 12 trillion stars for every single human on the planet

and the galaxies are all around 6 million light years apart. to put that into perspective:
the furthest human made object traveled ~1 light day in almost 50 years and even the first radio waves humans emitted only made it 139 light years into space

we are absolutely tiny, and insignificant

[Ryan Greenblatt @RyanGreenblatt]

My median for full automation of AI R&D is around late 2030/early 2031. But my "modal"/best guess prediction for this milestone would be significantly earlier (mid 2029).

Here is a summary of my best guess prediction for what happens over the next few years:

EOY 2026:
- ~1.5x as much frontier AI progress in 2026 as in 2025 (mostly from eating up certain overhangs, but some from AI R&D acceleration).
- AIs accelerate AI R&D labor at Anthropic by ~2.5x (as in, as useful as making all researchers/engineers think/work 2.5x faster).

EOY 2027:
- Engineering at AI companies is pretty close to fully automated and AIs are making serious inroads into automating research. AI R&D labor acceleration: ~8.5x.
- Some people claim AI R&D is fully automated in 2027. They aren't right, but the situation is already quite crazy: AI companies feel insanely automated with humans often very out of the loop and the speedup is considerable.
- ~1.5x as much frontier AI progress as in 2025 (mostly from AI R&D acceleration, some from overhangs).

2028:
- Automated coder (AC) around April. (AIs that can basically fully automate research engineering / SWE.)
- Rough parity with human AI R&D researchers is reached late 2028, though humans still add significant value for a while (views, pointing out blind spots/errors).
- In the second half of the year, AI progress runs ~1.6x the 2025 rate: 6 months of calendar time yields ~0.8 years of AI progress.

2029:
- Superhuman AI researcher (SAR) early this year, a bit less than a year after AC.
- Progress is picking up with ~1.3 years of AI progress in the first half of the year (2.6x rate).
- By EOY, significantly past top-expert-dominating AI (TEDAI), with ~2.5 years of AI progress in the second half of the year (5x rate). AIs are now very superhuman in many domains (though this varies).

2030 (??):
- Mid: AIs are somewhere between TEDAI and wildly superhuman AIs (ASI). Crazy shit. Compute is maybe doubling every ~4 months (downstream of robots).
- EOY: Singularity™. We've had a bunch of economic doublings. Compute is doubling every ~2 months (???).

2031 (??????):
- Mid: doubling time is more like ~2 weeks. Truly insane new technology is coming online.

Notes:
- This assumes limited government intervention on the overall rate of AI progress and no substantial slowdown (voluntary or otherwise).
- It also ignores misalignment: as discussed in the episode, I think misaligned AI takeover is quite plausible along the way (which would change the trajectory).
- Milestones (AC, SAR, TEDAI) are roughly as defined in the AI Futures Model.
- By "full automation of AI R&D", I mean AIs such that firing all humans working on AI R&D (other than setting overall top level objectives) would slow down AI progress by less than 10%.
- Obviously, all of this is extremely uncertain (increasingly so later in the scenario). This is my best guess prediction (a modal trajectory), not a confident prediction. My median for each milestone is later, but this is more like my central prediction for what I expect to overall happen.

[Wylloh @wyllohland]

@tunguz Office buildings are data centers for biological compute

[@jason @Jason]

They updated the most legendary chart on twitter

What stands out?

[Paul Graham @paulg]

If Steve Jobs were still alive, he would have the moral authority to face and maybe even to solve this problem. But I doubt anyone in the phone business now does.

[Bryan Johnson @bryan_johnson]

This is it.
Everything learned spending millions on longevity.

From: Your Immortal Unc and Auntie.
To: Our Immortal nieces and nephews.

0. Sleep is the world's most powerful drug.
1. Be in your bed for 8 hours
2. Same bedtime every night, any time before midnight
3. Don’t eat right before bed
4. Calm foods for dinner
5. No screens 1 hour before bed
6. Avoid added sugar (be aware it’s in everything)
7. Avoid all things in an American convenience store
8. Avoid fried foods
9. Shoes off at the door
10. Eat whole foods, particularly veggies fruits nuts legumes berries
11. Walk a little after meals or air squats
12. Get your heart rate high routinely
13. Lift heavy things
14. Stretch daily
15. Water pik, floss, brush, tongue scrape, morning and night
16. Make an effort to drink water
17. Get sunlight when you wake up (UV is low)
18. Protect skin in midday sun
19. Stand up straight
20. See at least one friend once a week
21. Avoid plastic where you can (in all things)
22. Circulate air in rooms
23. When stressed, breathe, learn to calm your body
24. Go to the dentist
25. Avoid sitting for long times
26. Protect your hearing, the world is too loud
27. Alcohol is bad for you
28. Finish coffee before noon
29. Avoid bright lights after sunset
30. If obese, look into a GLP
31. Sleep in a cold room
32. Texting while driving is dangerous
33. Turn off all notifications
34. Limit social media use
35. Don’t smoke anything
36. If you struggle to sleep, read a physical book before bed
37. 1 hour before bed have a calm wind down routine: bath, read, light walk, listen to music
38. The body is a clock and loves routine. Have a daily morning and evening schedule.
39. Avoid long distance travel where you can
40. Baby steps first: incorporate new things slowly
41. Do less… most things don’t work.

Bonus points if you get your blood checked.

Start here, it will change your life.

[Jerry Tworek @MillionInt]

If anyone ever asks what does "frontier" model means, show them this picture as a definition:

[Hearnoeval @Speaknoeval]

Loved the Project Hail Mary movie. My only slight complaint was that it felt like it speed ran some of the existential jeopardy that made the book great and added weight to the relationships and successes. Just a side effect of how much canvas each format has available I guess.

[Claude @claudeai]

You can now enable Claude to use your computer to complete tasks.

It opens your apps, navigates your browser, fills in spreadsheets—anything you'd do sitting at your desk.

Research preview in Claude Cowork and Claude Code, macOS only.

[Andrej Karpathy @karpathy]

Three days ago I left autoresearch tuning nanochat for ~2 days on depth=12 model. It found ~20 changes that improved the validation loss. I tested these changes yesterday and all of them were additive and transferred to larger (depth=24) models. Stacking up all of these changes, today I measured that the leaderboard's "Time to GPT-2" drops from 2.02 hours to 1.80 hours (~11% improvement), this will be the new leaderboard entry. So yes, these are real improvements and they make an actual difference. I am mildly surprised that my very first naive attempt already worked this well on top of what I thought was already a fairly manually well-tuned project.

This is a first for me because I am very used to doing the iterative optimization of neural network training manually. You come up with ideas, you implement them, you check if they work (better validation loss), you come up with new ideas based on that, you read some papers for inspiration, etc etc. This is the bread and butter of what I do daily for 2 decades. Seeing the agent do this entire workflow end-to-end and all by itself as it worked through approx. 700 changes autonomously is wild. It really looked at the sequence of results of experiments and used that to plan the next ones. It's not novel, ground-breaking "research" (yet), but all the adjustments are "real", I didn't find them manually previously, and they stack up and actually improved nanochat. Among the bigger things e.g.:

- It noticed an oversight that my parameterless QKnorm didn't have a scaler multiplier attached, so my attention was too diffuse. The agent found multipliers to sharpen it, pointing to future work.
- It found that the Value Embeddings really like regularization and I wasn't applying any (oops).
- It found that my banded attention was too conservative (i forgot to tune it).
- It found that AdamW betas were all messed up.
- It tuned the weight decay schedule.
- It tuned the network initialization.

This is on top of all the tuning I've already done over a good amount of time. The exact commit is here, from this "round 1" of autoresearch. I am going to kick off "round 2", and in parallel I am looking at how multiple agents can collaborate to unlock parallelism.
https://t.co/WAz8aIztKT

All LLM frontier labs will do this. It's the final boss battle. It's a lot more complex at scale of course - you don't just have a single train. py file to tune. But doing it is "just engineering" and it's going to work. You spin up a swarm of agents, you have them collaborate to tune smaller models, you promote the most promising ideas to increasingly larger scales, and humans (optionally) contribute on the edges.

And more generally, *any* metric you care about that is reasonably efficient to evaluate (or that has more efficient proxy metrics such as training a smaller network) can be autoresearched by an agent swarm. It's worth thinking about whether your problem falls into this bucket too.

[David @DavidSHolz]

5 million humanoid robots working 24/7 can build Manhattan in ~6 months. now just imagine what the world looks like when we have 10 billion of them by 2045. now imagine the year 2100.

[Michael Andregg @michaelandregg]

I'm excited to share this demo of some of our work at @eonsys!

[Tim Urban @waitbutwhy]

It’s that day when I make you all look at this

[Andrej Karpathy @karpathy]

The first 100% autonomous coast-to-coast drive on Tesla FSD V14.2! 2 days 20 hours, 2732 miles, zero interventions.

This one is special because the coast-to-coast drive was a major goal for the autopilot team from the start. A lot of hours were spent in marathon clip review sessions late into the night looking over interventions as we attempted legs of the drive over time - triaging, categorizing, planning out all the projects to close the gap and bring the number of interventions to zero.

Amazing to see the system actually get there and huge congrats to the team!

[Tim Urban @waitbutwhy]

My favorite part is the robot also bending over after the kick

[Eric Topol @EricTopol]

Generative AI, 3 years in