Martian ✓

@withmartian

Understanding Intelligence. Measurement. Explanation. Application. That's how we're tackling AI interpretability: the greatest scientific problem of our age.

3 743Followers

14Following

433Posts total

280.3KViews on collected posts

Against accounts of the same size

8 posts from the last 90 days, next to the under 10K follower range. reaches fewer people than peers, but engages them much harder.

Median views863this account7 156median for under 10K

Reach, %23.06%this account505.37%median for under 10K

Engagement, %2.63%this account1.06%median for under 10K

Metric	This account	Median for under 10K	Ratio
Median views per post	863	7 156	0.12×
Reach (views ÷ followers)	23.06%	5.1× audience	0.05×
Engagement rate	2.63%	1.06%	2.47×

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

272.1K3 Sep

1.7K

693

492

586

564

3.1K

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

0.12%3 Sep

1.98%

2.23%

3.03%

4.47%

3.75%

3.55%

0.42%

Reactions — likes, reposts, replies and quotes — divided by views. Median for under 10K accounts is 1.06%.

What the audience does

Likes57.1%331 in total

Reposts3.4%20 in total

Replies10.9%63 in total

Quotes9.5%55 in total

Bookmarks19.1%111 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

@withmartian Thats fantastic!! Gj 3.1K views · 13 likes · 0 reposts · 0 replies 03 Sep 2026 We all knows the behaviour of LLMs can be a little random sometimes, so we introduce the concept of LLM Reliability. A measuring indicating how consistent an LLM is in successfully solving a problem, enabling a more reliable user experience. 564 views · 19 likes · 0 reposts · 1 replies 03 Sep 2026 LLM providers charge you for input, thinking, and output tokens yet you only control one of them. Some LLMs may have higher quoted token costs but are more efficient, answering the prompt with less thinking and fewer output tokens, resulting in a lower real cost. See real LLM htt 586 views · 21 likes · 0 reposts · 1 replies 03 Sep 2026 This research has was selected to for oral presentation at The International Conference of Learning Representations - https://t.co/txd97GHt0r 492 views · 20 likes · 1 reposts · 1 replies 03 Sep 2026 Analyse the behaviour of any LLM with our `Single Model Insights` view, seeing examples where the LLM is uniquely good and bad compared to it's peers. https://t.co/neGzj8kmnK 693 views · 20 likes · 0 reposts · 1 replies 03 Sep 2026 We call this analysis the Capability Frontier: A Pareto frontier of Quality vs Cost you can achieve by routing between models instead of locking into one. https://t.co/9mrRUU2OQE 1K views · 22 likes · 0 reposts · 1 replies 03 Sep 2026 Our insights: ✦ The best LLM system is often not the best LLM. ✦ Advertised token costs are bad predictors of real LLM cost. ✦ LLMs have variable reliability in consistently solving a problem. ✦ Single-model benchmarks are systematically understating current LLM capabilities. 1.7K views · 29 likes · 0 reposts · 5 replies 03 Sep 2026 We got 46% fewer errors than the single best LLM across the 16 most used benchmarks (TerminalBench, LiveCodeBench, etc). Here's how that's possible and what each model can achieve when used optimally (every benchmarks misses the majority of model capabilities) 👇 Interactive htt 272.1K views · 187 likes · 19 reposts · 53 replies 03 Sep 2026