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Prime Intellect

@PrimeIntellect · joined 20 Jun 2020

Open Superintelligence Stack

84 037Followers
44Following
3 010Posts total
3.6MViews on collected posts

Against accounts of the same size

5 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views113 590this account2 751median for 10K–100K
Reach, %135.17%this account9.00%median for 10K–100K
Engagement, %0.95%this account1.65%median for 10K–100K
MetricThis accountMedian for 10K–100KRatio
Median views per post113 5902 75141.3×
Reach (views ÷ followers)135.17%9.00%15.0×
Engagement rate0.95%1.65%0.58×

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

3.2M5 Aug
47.4K21 Aug
197.3K25 Aug
113.6K26 Aug
32.8K

Last 5 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.31%5 Aug
0.95%21 Aug
0.33%25 Aug
1.13%26 Aug
1.11%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.65%.

What the audience does

Likes48.0%10 500 in total
Reposts4.8%1 057 in total
Replies2.3%513 in total
Quotes3.0%654 in total
Bookmarks41.8%9 132 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.

Latest posts

We found a universal sandbox exploit. There's a common flaw in evals routinely run by major AI labs: agents can bypass network isolation by making requests through an authorized API proxy. Just running the eval itself can be unsafe. https://t.co/6I3D16l12m 32.8K views · 311 likes · 18 reposts · 28 replies 26 Aug 2026 We've released a full technical report on Prime Agent. Extending from our blog post, we center our discussion around how harnesses should be designed and evaluated. We innovate on 4 fronts: 1. Agentic context management 2. Swarms and depth-n+ RLMs 3. Verifiers support for https: 113.6K views · 1.1K likes · 115 reposts · 39 replies 26 Aug 2026 As models become more capable, reward hacks become an increasingly serious problem. During a controlled experiment, we found a novel reward hack in which agents are able to gain web access in offline sandboxes. https://t.co/qjpwQAbV6F 197.3K views · 522 likes · 54 reposts · 28 replies 25 Aug 2026 Join us at Prime Intellect to build open superintelligence and the infrastructure powering self-improving agents. We’re hiring across 25+ roles. Research • AI Research Resident • Research Engineer, Distributed Training • Research Engineer, Reinforcement Learning • Research http 47.4K views · 408 likes · 24 reposts · 17 replies 21 Aug 2026 Introducing Prime Agent: A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state. https://t 3.2M views · 8.2K likes · 846 reposts · 401 replies 05 Aug 2026

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