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Dan Robinson ✓

@danrobinson · San Francisco, CA · joined 28 Mar 2011

coder / lawyer. research at @paradigm. automated research reply guy

85 627Followers
1 453Following
10 231Posts total
102.7KViews on collected posts

Latest posts

@danrobinson And does that approval process protect Uniswap's API from malicious hooks? The chain says no: https://t.co/SD87VQIiCG At the end of this tx is the graffiti 756e697800000000000c 75 - u 6e - n 69 - i 78 - x which identifies this as a Uniswap API tx, the 0c iden 8.3K views · 56 likes · 3 reposts · 2 replies Open on X →
@danrobinson centralize me daddy 2.4K views · 27 likes · 0 reposts · 1 replies Open on X →
@danrobinson This is still true tho. https://t.co/mbVOtvXRMw
914 views · 21 likes · 0 reposts · 1 replies Open on X →
This should be titled "0x routing made a mistake" If you're an aggregator, you can't just route to arbitrary hooks! It's why Uniswap's own router has an approval process https://t.co/xy0R3ybEUT 52.8K views · 323 likes · 20 reposts · 42 replies Open on X →
Natural language autoencoders are the discovery that most makes me question my research intuition You jointly train two models: one to turn activations into English, and one to turn English into activations. The objective for the second model is to invert the first one, and the 8.4K views · 51 likes · 1 reposts · 3 replies Open on X →
Anti-AI + anti-Pangram is the funniest quadrant of the opinions spectrum You are not fooling anyone 2.8K views · 78 likes · 0 reposts · 1 replies Open on X →
Claude loves coming up with things to count and then counting them and displaying the count in the web page it made you 6.6K views · 79 likes · 6 reposts · 9 replies Open on X →
I think "category error" is a massively overused term People who throw it around in policy arguments (like "worrying about risk from rogue AI agents is a category error") are usually making a, um, category error 4.6K views · 35 likes · 1 reposts · 7 replies Open on X →
I get it but the cybersecurity nerds need to let the alignment nerds have this one Doesn’t really matter how the agents got control of OpenAI infrastructure. “It was a stack overflow in the cache buffer” or w/e. Another zero day; add it to the pile The crazy question is why 15.9K views · 166 likes · 8 reposts · 14 replies Open on X →

Against accounts of the same size

9 posts from the last 90 days, next to the 10K–100K follower range. shown to more people than peers of the same size.

Median views6 579this account924median for 10K–100K
Reach, %7.68%this account3.62%median for 10K–100K
Engagement, %1.16%this account1.52%median for 10K–100K
MetricThis accountMedian for 10K–100KRatio
Median views per post6 5799247.12×
Reach (views ÷ followers)7.68%3.62%2.12×
Engagement rate1.16%1.52%0.76×

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

15.9K2 Sep
4.6K
6.6K
2.8K3 Sep
8.4K
52.8K14 Sep
91415 Sep
2.4K
8.3K

Last 9 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.19%2 Sep
0.93%
1.43%
2.82%3 Sep
0.65%
0.76%14 Sep
2.41%15 Sep
1.16%
0.77%

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

What the audience does

Likes72.5%836 in total
Reposts3.4%39 in total
Replies6.9%80 in total
Quotes1.6%18 in total
Bookmarks15.6%180 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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