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Andrew Kang ✓

@Rewkang · joined 31 Jan 2013

@Robostrategy Not investment advice

423 024Followers
4 581Following
8 914Posts total
656.3KViews on collected posts

Latest posts

Andrew Kang ✓@Rewkang
Herbert has been a key media figure in the Tesla & Robotics community His channel is a go to for expert commentary and education for many Space, AI and Robotics investors. I first came across Herbert and @GoingBallistic5 through his channel years ago during my initial humanoid 24.3K views · 62 likes · 1 reposts · 12 replies Open on X →
Andrew Kang ✓@Rewkang
Super excited to join the RoboStrategy team!! I get to do what I love the most which is hosting and interviewing the smartest minds this time in the largest market in the world: robotics. I will continue doing my current channel (Brighter with Herbert) but now will also build 64.8K views · 984 likes · 45 reposts · 97 replies Open on X →
Andrew Kang ✓@Rewkang
AI Robotics research is showing that you can build increasingly powerful robot models by pretraining on the large preexisting corpus of web video data without collecting vast amounts of new data Visual dynamics understanding increases with more compute and that directly 30.5K views · 79 likes · 10 reposts · 20 replies Open on X →
Andrew Kang ✓@Rewkang
We led Maven Robotics' $100M Series A Apple previously had an incredible special projects group building autonomous systems that attracted some of the top talent relevant to building a great AI Robotics company. The core Maven Robotics team was assembled from that group after it 47.1K views · 273 likes · 20 reposts · 36 replies Open on X →
Andrew Kang ✓@Rewkang
Does scaling pre-training on general web video improve a complex manipulation task in real deployment? We scale model size and pre-training compute, and test on one industrial task. Yes. The better a pre-trained model predicts web video, the better its post-trained policy. 🧵 ht
Seven pre-trained video-model checkpoints on one chart. Task performance on a real robot (at-speed completion rate: every requirement met within 100 s, 0 to 100%) rises with pre-training quality (DINO FD on held-out web video, log scale, axis reversed so better is to the right). XS, S, M, L are model sizes. The four M points are one model at 0.08×, 0.18×, 0.37× and 1× of its pre-training compute.
85.6K views · 288 likes · 37 reposts · 33 replies Open on X →
Andrew Kang ✓@Rewkang
Over the last 2 years we went from “This AI thing is pretty cool but buggy” to “This is incredible we need to be tokenmaxxing or get left behind” We are entering that transition phase for robotics now 56.1K views · 214 likes · 17 reposts · 26 replies Open on X →
Andrew Kang ✓@Rewkang
Robots need to be purpose built for Physical AI They need to be able to sense, react and learn Standard Bots is leading this new generation of design and we believe they'll be one of the most important companies in building the future of the physical world 78.4K views · 268 likes · 12 reposts · 34 replies Open on X →
Andrew Kang ✓@Rewkang
https://t.co/XzLSqiKGYY 269.5K views · 430 likes · 54 reposts · 54 replies Open on X →

Against accounts of the same size

8 posts from the last 90 days, next to the 100K–1M follower range. shown widely, but few of those viewers react.

Median views60 463this account5 906median for 100K–1M
Reach, %14.29%this account1.59%median for 100K–1M
Engagement, %0.41%this account1.04%median for 100K–1M
MetricThis accountMedian for 100K–1MRatio
Median views per post60 4635 90610.2×
Reach (views ÷ followers)14.29%1.59%8.99×
Engagement rate0.41%1.04%0.39×

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

269.5K27 Jul
78.4K
56.1K27 Aug
85.6K10 Sep
47.1K
30.5K
64.8K14 Sep
24.3K

Last 8 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.20%27 Jul
0.40%
0.46%27 Aug
0.42%10 Sep
0.70%
0.36%
1.74%14 Sep
0.31%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 1.04%.

What the audience does

Likes69.8%2 598 in total
Reposts5.3%196 in total
Replies8.4%312 in total
Bookmarks16.6%617 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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