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Graham Neubig ✓

@gneubig · Pittsburgh, PA · joined 02 Sep 2010

Associate professor @LTIatCMU. Co-founder/chief scientist @OpenHandsDev. I mostly work on modeling language.

46 776Followers
794Following
4 889Posts total
547.8KViews on collected posts

Latest posts

Today at CMU, we're having a talk by Hector Liu (@waterluffy), director at @IFM_AI, which has trained the strongest fully open language model, K2 Horizon! If you're at or around CMU please feel free to join! https://t.co/mPVsL0kVgj 5.3K views · 95 likes · 8 reposts · 3 replies Open on X →
This is an impressively thorough look into what seems to be a covert, coordinated, and highly successful operation to advocate for strict AI regulation. 8.6K views · 63 likes · 3 reposts · 3 replies Open on X →
Connecting the dots, does this mean that after Navier Stokes, OpenAI is training GPT-7 to play pickleball next? https://t.co/oJJKN2Y7Cz
3.3K views · 14 likes · 0 reposts · 0 replies Open on X →
Videos are being posted here! - Thread: https://t.co/lMW8WHfh93 - YouTube Playlist: https://t.co/HAJgKXve0x 1.6K views · 4 likes · 2 reposts · 0 replies Open on X →
We started to post videos for CMU 11-768, AI Agents! All of the videos will be posted to this playlist, so please bookmark/follow it if you want to be notified of new ones! I'll also try to post them on this thread too. https://t.co/HAJgKXve0x 141.9K views · 1.1K likes · 160 reposts · 26 replies Open on X →
@gneubig This is super! Will the lectures be posted online? And looking forward to seeing cool demos from students! 4.1K views · 8 likes · 0 reposts · 1 replies Open on X →
Slides and videos will be posted online on the main site, so please follow along if you're interested: https://t.co/D5ICCaYstN We're also looking for compute sponsors, so if you can help provide infra support (sandboxes, GPUs, etc) please reach out! 8.6K views · 170 likes · 18 reposts · 9 replies Open on X →
The instructors are me (CMU prof and developer of @OpenHandsDev) and @dan_fried (CMU prof and formerly on Meta's agent team). We have an all star team of TAs too! @yueqi_song @Jiarui_Liu_ @uilydna @apurvasgandhi @saujasv @sunweiwei12 @Aditya_Soni_8 @demisama_ 11.3K views · 65 likes · 7 reposts · 1 replies Open on X →
The schedule (https://t.co/eMq3PQkT43) covers: - Capabilities: tool use, context management, skills, memory, planning - Domains: coding, gui, deep research - Training: SFT, RL, and systems - Safety: sandboxing, adversarial defense - Interaction, frameworks, and search 11K views · 87 likes · 7 reposts · 3 replies Open on X →
Assignments (https://t.co/GqKeJUAho9) will include: - Building an agentic harness - Creating agentic evaluations - Training with RL - A final research project 8.8K views · 57 likes · 4 reposts · 1 replies Open on X →
This Fall at CMU we're teaching a new course on AI Agents! The goal is that you learn how to create a scaffold, build evals, and train an agentic LLM using RL. We'll try to balance theory and practice, and introduce modern frameworks and best practices. https://t.co/iPr8FMD5wp
343.4K views · 2.3K likes · 281 reposts · 51 replies Open on X →

Against accounts of the same size

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

Median views8 602this account924median for 10K–100K
Reach, %18.39%this account3.62%median for 10K–100K
Engagement, %0.78%this account1.52%median for 10K–100K
MetricThis accountMedian for 10K–100KRatio
Median views per post8 6029249.31×
Reach (views ÷ followers)18.39%3.62%5.08×
Engagement rate0.78%1.52%0.51×

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

343.4K2 Jul
8.8K
11K
11.3K
8.6K
4.1K
141.9K8 Sep
1.6K
3.3K
8.6K10 Sep
5.3K11 Sep

Last 11 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.78%2 Jul
0.70%
0.88%
0.65%
2.30%
0.22%
0.89%8 Sep
0.38%
0.42%
0.80%10 Sep
2.01%11 Sep

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

What the audience does

Likes45.5%3 977 in total
Reposts5.6%490 in total
Replies1.1%98 in total
Bookmarks47.8%4 174 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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