Jon Barron ✓

@jon_barron · SF Bay Area · joined 11 May 2010

Principal research scientist at Google DeepMind. Synthesized views are my own.

34 998Followers

1 435Following

3 122Posts total

205.3KViews on collected posts

Derniers posts

Jon Barron ✓@jon_barron1 Sep

Very cool result 13.8K views · 106 likes · 2 reposts · 3 replies Open on X →

Jon Barron ✓@jon_barron31 Aug

@jon_barron Great presentation! One thing - it feels like it covers mostly the inference part and unintentionally pulls the other parts with it Like for robotics, 3d is useful for synthetic data, teleop, evals, etc., while for inference, the observations to actions pipeline mak 5.4K views · 12 likes · 1 reposts · 0 replies Open on X →

Jon Barron ✓@jon_barron31 Aug

Part 4: 3D models have questionable value for passive media (movies) but have tremendous value for interactive media (games) because they re-use compute. The Veo 2 speed and energy numbers are somewhat stale, if someone could crunch them for a newer model I'd appreciate it. http 3.1K views · 19 likes · 0 reposts · 1 replies Open on X →

Jon Barron ✓@jon_barron31 Aug

If twitter or brevity isn't your thing, the un-split talk is on YouTube https://t.co/TePWYSoECw 6K views · 64 likes · 4 reposts · 3 replies Open on X →

Jon Barron ✓@jon_barron31 Aug

Part 5: 3D has tremendous value when making things in the physical world (engineering, manufacturing, construction, etc), because here the 3D model is the final deliverable, not an intermediate representation. 3D vision's role here is in allowing us to leverage abundant 2D data. 3.2K views · 33 likes · 3 reposts · 2 replies Open on X →

Jon Barron ✓@jon_barron31 Aug

Part 2: my current sense of which research areas probably should and maybe shouldn't care about using explicitly 3D representations. The rest of the talk is a deeper dive into each research area. https://t.co/osyO52dZW6 8.3K views · 45 likes · 1 reposts · 1 replies Open on X →

Jon Barron ✓@jon_barron31 Aug

Part 3: In the limit, robotics probably shouldn't use explicitly 3D representations and should instead focus on the easier task of predicting actions. https://t.co/lk68s9aGBQ 17.2K views · 44 likes · 5 reposts · 1 replies Open on X →

Jon Barron ✓@jon_barron31 Aug

Here's my talk from the CVPR 2026 "Bitter Lessons" workshop earlier this summer. I've split it up into parts for the sake of discussion. Part 1: in which I wax poetic about my youth and force the audience to look at my dissertation results. https://t.co/tfwb8E3tiJ 66.2K views · 781 likes · 109 reposts · 17 replies Open on X →

Jon Barron ✓@jon_barron28 Jul

Someone please make this journal: - submission has a large fee - a LLM reads the paper and accepts/rejects instantly - the LLM tunes on all accepts ~daily, and measures the info gain from each - fees are paid back to authors proportionally to info gain, minus training costs 17.3K views · 105 likes · 2 reposts · 10 replies Open on X →

Jon Barron ✓@jon_barron9 Jul

Very fun demo https://t.co/0mW9o4uozc https://t.co/Wx25oFXotn 56.5K views · 383 likes · 20 reposts · 5 replies Open on X →

Jon Barron ✓@jon_barron7 Jul

One issue with the autoregressive video model approach to games: gamedevs tell you to make the first level *last*, because only after the game is done do you know how to best onboard new players. The world model is forced to make the game in exactly the opposite order it should. 8.2K views · 47 likes · 5 reposts · 4 replies Open on X →

Face aux comptes de taille comparable

9 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. diffusé largement, mais peu de ces spectateurs réagissent.

Vues médianes8 276ce compte924médiane pour 10K–100K

Portée, %23.65%ce compte3.62%médiane pour 10K–100K

Engagement, %0.69%ce compte1.52%médiane pour 10K–100K

Indicateur	Ce compte	Médiane pour 10K–100K	Rapport
Vues médianes par post	8 276	924	8.96×
Portée (vues ÷ abonnés)	23.65%	3.62%	6.53×
Taux d'engagement	0.69%	1.52%	0.45×

Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

8.2K7 Jul

56.5K9 Jul

17.3K28 Jul

66.2K31 Aug

17.2K

8.3K

3.2K

3.1K

5.4K

13.8K1 Sep

Last 11 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.69%7 Jul

0.73%9 Jul

0.69%28 Jul

1.38%31 Aug

0.31%

0.58%

1.19%

1.18%

0.64%

0.26%

0.80%1 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes54.9%1 639 in total

Reposts5.1%152 in total

Replies1.6%47 in total

Quotes0.6%17 in total

Bookmarks37.9%1 133 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Derniers posts

Face aux comptes de taille comparable

Growth & engagement

Views per post

Engagement rate per post

What the audience does

Comptes similaires