Martin Odersky

@odersky · joined 30 Nov 2008

lead designer of Scala

47 404Followers

243Following

1 308Posts total

69KViews on collected posts

Latest posts

Martin Odersky@odersky2 Sep

Liked this article from Galois: https://t.co/iYaoG3Qi9P. Great food for thought about the power and limits of formal specifications. 6.7K views · 62 likes · 10 reposts · 1 replies Open on X →

Martin Odersky@odersky27 Jul

New blog post that describes the ideas behind our award-winning paper on securing AI agents: https://t.co/Kj0kmZ1JBW 8.9K views · 97 likes · 17 reposts · 2 replies Open on X →

Martin Odersky@odersky29 May

Very happy that our work on securing agents with tracked capabilities got best paper award at #cais2026! https://t.co/JW4WkxKlDm 5.1K views · 71 likes · 24 reposts · 0 replies Open on X →

Martin Odersky@odersky20 May

For people who prefer listening to reading, there's a notebookLM that describes the paper which is actually pretty decent. https://t.co/vKRe6bvfyK 3.7K views · 9 likes · 1 reposts · 0 replies Open on X →

Martin Odersky@odersky20 May

How can we ensure critical safety properties of AI agents? Like, no API keys leaked, no prompt injection, no data loss? It's a hard question which will require fundamentally rethinking the way we build modular software. We have an answer in our paper "Tracking Capabilities for 28.6K views · 115 likes · 35 reposts · 1 replies Open on X →

Martin Odersky@odersky26 Mar

@odersky @scala_lang nice avatar 158 views · 1 likes · 0 reposts · 0 replies Open on X →

Martin Odersky@odersky26 Mar

@odersky Trust in agents is a function of authorization scope. The tighter the permission surface, the more predictable the failure mode. Most trust frameworks focus on model behavior. The real lever is what the agent is allowed to touch — not what it's likely to do. 241 views · 2 likes · 0 reposts · 0 replies Open on X →

Martin Odersky@odersky26 Mar

Just gave a my talk: "How Can We Trust Our Agents?" at Scalar conference. Slides: https://t.co/5OR0NJzrg2 10.7K views · 66 likes · 19 reposts · 2 replies Open on X →

Martin Odersky@odersky16 Mar

There's a new paper out from Martin Odersky and his team on applying Scala Capabilities to AI agents. I have to admit, it's a really fascinating use case! Listen for yourself 👇 (disclaimer: the podcast is generated by NotebookLM, but give it a shot - it's very well done!) https:

13:06

5K views · 31 likes · 3 reposts · 4 replies Open on X →

Against accounts of the same size

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

Median views7 818this account947median for 10K–100K

Reach, %16.49%this account3.58%median for 10K–100K

Engagement, %1.19%this account1.51%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	7 818	947	8.26×
Reach (views ÷ followers)	16.49%	3.58%	4.61×
Engagement rate	1.19%	1.51%	0.79×

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

5K16 Mar

10.7K26 Mar

241

158

28.6K20 May

3.7K

5.1K29 May

8.9K27 Jul

6.7K2 Sep

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

0.76%16 Mar

0.82%26 Mar

0.83%

0.63%

0.53%20 May

0.27%

1.88%29 May

1.30%27 Jul

1.09%2 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.51%.

What the audience does

Likes53.5%454 in total

Reposts12.8%109 in total

Replies1.2%10 in total

Bookmarks32.5%276 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

Against accounts of the same size

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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