Percy Liang ✓

@percyliang · Stanford, CA · joined 31 Oct 2009

professor of computer science @Stanford @stanfordnlp, co-founder of @togethercompute, https://t.co/7R5THVnJ6u, @simile_ai, pianist

121 567Followers

424Following

1 337Posts total

620.7KViews on collected posts

Postingan terbaru

Percy Liang ✓@percyliang2 Sep

Marin 535B-A23B is 13% through training. This hero run would not be possible without the generous support of the Jen-Hsun and Lori Huang Foundation, which provided the funding for the compute (Coreweave). Thanks @JensenHuang for supporting open models! https://t.co/1qBdgV79S8 45.9K views · 749 likes · 34 reposts · 15 replies Open on X →

Percy Liang ✓@percyliang2 Sep

The "What-If Machine" is a vivid articulation of what Simile is building. It's not just about forecasting the future passively. It's about understanding how active interventions on the world change its course. It's the classic difference between causation and correlation. 16.2K views · 85 likes · 5 reposts · 2 replies Open on X →

Percy Liang ✓@percyliang28 Aug

Marin 535B-A23B is ~7% done training, and so far things look on track. Next Tuesday (Sept 1 @ 10 PT), we will have a Zoom panel/discussion where the Marin team will talk about the design decisions that went into this run, the tradeoffs made, and our learnings. If you're https:// 82.1K views · 1.3K likes · 80 reposts · 23 replies Open on X →

Percy Liang ✓@percyliang25 Aug

The fact that Simile’s first technical blog post is about confidence is notable. Confidence is paramount to simulation. If a coding agent messes up, you can often tell and repair. If a simulation is wrong, you might never know and make a bad consequential decision based on it. 52.4K views · 391 likes · 23 reposts · 8 replies Open on X →

Percy Liang ✓@percyliang21 Aug

@percyliang congrats team! Excited that there's compute out there for non profits :) and will look forward to working with the model 12.7K views · 119 likes · 0 reposts · 1 replies Open on X →

Percy Liang ✓@percyliang21 Aug

Look at our data composition: https://t.co/ihNKQh93rf Watch the run live on wandb: https://t.co/Bc3CcV4FOm See all the gory details on GitHub: https://t.co/swss50Hkbb Assembling this hero run really required data, architecture, infra, kernels to all come together and was a huge 90.7K views · 648 likes · 51 reposts · 6 replies Open on X →

Percy Liang ✓@percyliang21 Aug

In addition to forecasting our final loss, we can also forecast the loss of intermediate checkpoints, so we can see if we’re on track over the voyage: https://t.co/CivuJfdPtT 43.9K views · 357 likes · 10 reposts · 4 replies Open on X →

Percy Liang ✓@percyliang19 May 2025

@percyliang looks really great! one question - is anyone fulltime on Marin (aka, it's their fulltime job), or is it made up of people volunteering time? 2.6K views · 8 likes · 0 reposts · 2 replies Open on X →

Percy Liang ✓@percyliang19 May 2025

Thanks to the Google TPU Research Cloud program for providing the much needed compute for this project. Thanks to the open-source community (including @AiEleuther @allen_ai @huggingface @llm360 @togethercompute @nvidia) for releasing tools and datasets. We are excited to give 4.6K views · 51 likes · 0 reposts · 1 replies Open on X →

Percy Liang ✓@percyliang19 May 2025

To learn more about Marin, visit: - Website: https://t.co/Kn8ynNMz7u - GitHub: https://t.co/wPCG2KrIDl - Discord: https://t.co/Lw7rHUmlVE - Documentation: https://t.co/tCwe0WeBHN - Announcement: https://t.co/qhXM9bmzSb 5K views · 41 likes · 3 reposts · 1 replies Open on X →

Percy Liang ✓@percyliang19 May 2025

Have a specific use case? Come to our Datashop to curate data and train models. Here’s how we curated more math data: https://t.co/PH4Yv4q9Lt Check out the data: https://t.co/XHq7NC10OI https://t.co/zub34K8yjC 3.3K views · 33 likes · 3 reposts · 1 replies Open on X →

Percy Liang ✓@percyliang19 May 2025

Have a new algorithm for training? Choose your compute budget and get on the speedrun leaderboard: how fast can you drive down validation loss? https://t.co/1oGTrcHAv5 https://t.co/zTleS6rLRs 3.7K views · 40 likes · 3 reposts · 1 replies Open on X →

Percy Liang ✓@percyliang19 May 2025

We’re also training Marin-32B-Base: it’s training - watch it live: https://t.co/cMFhErqBo2! All models are Apache 2.0 and available at https://t.co/qu74DYj33v 4.2K views · 41 likes · 3 reposts · 1 replies Open on X →

Percy Liang ✓@percyliang19 May 2025

We have trained some respectable models from scratch! - Marin-8B-Base: beats Llama 3.1 8B on 14/19 benchmarks - Marin-8B-Instruct: try it out on Together (https://t.co/OVy7Gr4gd7) 4.8K views · 45 likes · 4 reposts · 1 replies Open on X →

Percy Liang ✓@percyliang19 May 2025

What would truly open-source AI look like? Not just open weights, open code/data, but *open development*, where the entire research and development process is public *and* anyone can contribute. We built Marin, an open lab, to fulfill this vision: https://t.co/racsvmhyA3 241.8K views · 1.4K likes · 249 reposts · 72 replies Open on X →

Percy Liang ✓@percyliang19 May 2025

Marin (https://t.co/Kn8ynNMz7u) repurposes GitHub, which has been successful for open-source *software*, for AI: 1. Preregister an experiment as a GitHub issue 2. Submit a PR, which implements the experiment in code 3. PR is reviewed by experts in the community 4. Watch the https 6.8K views · 86 likes · 7 reposts · 2 replies Open on X →

Dibandingkan akun berukuran sama

7 postingan dari 90 hari terakhir, dibandingkan dengan rentang 100K–1M pengikut. tampil ke lebih banyak orang dibanding akun seukurannya.

Median tayangan45 851akun ini5 983median untuk 100K–1M

Jangkauan, %37.72%akun ini1.49%median untuk 100K–1M

Interaksi, %0.85%akun ini0.93%median untuk 100K–1M

Metrik	Akun ini	Median untuk 100K–1M	Rasio
Median tayangan per postingan	45 851	5 983	7.66×
Jangkauan (tayangan ÷ pengikut)	37.72%	1.49%	25.3×
Tingkat interaksi	0.85%	0.93%	0.91×

Akun lain pada rentang ini → Bandingkan dengan akun lain → Bagaimana tolok ukur ini disusun →

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

4.8K19 May

4.2K

3.7K

3.3K

4.6K

2.6K

43.9K21 Aug

90.7K

12.7K

52.4K25 Aug

82.1K28 Aug

16.2K2 Sep

45.9K

Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.04%19 May

1.07%

1.17%

1.11%

0.89%

1.13%

0.43%

0.85%21 Aug

0.79%

0.95%

0.81%25 Aug

1.75%28 Aug

0.58%2 Sep

1.76%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.

What the audience does

Likes66.0%5 398 in total

Reposts5.8%475 in total

Replies1.7%141 in total

Quotes1.0%80 in total

Bookmarks25.5%2 088 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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Dibandingkan akun berukuran sama

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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