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
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@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
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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
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If twitter or brevity isn't your thing, the un-split talk is on YouTube https://t.co/TePWYSoECw
6K views · 64 likes
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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
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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
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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
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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
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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
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Very fun demo https://t.co/0mW9o4uozc https://t.co/Wx25oFXotn
56.5K views · 383 likes
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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
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Against accounts of the same size
9 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.
Median views8 276this account924median for 10K–100K
Reach, %23.65%this account3.62%median for 10K–100K
Engagement, %0.69%this account1.52%median for 10K–100K
| Metric | This account | Median for 10K–100K | Ratio |
|---|---|---|---|
| Median views per post | 8 276 | 924 | 8.96× |
| Reach (views ÷ followers) | 23.65% | 3.62% | 6.53× |
| Engagement rate | 0.69% | 1.52% | 0.45× |
Others in this range → Compare with another account → How these benchmarks are built →
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
6K
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.
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