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Followers
191K
Posts
15.7K
Following
354
Account age
16 yrs
💰 Suggested sponsorship rate
$1.1K – $1.7K / sponsored post
Estimated from audience size & typical reach — every deal is negotiable and held in SocialDB escrow.
🎁 Free analysisfor Stanford NLP Group
Known for
214.3K views
A 2023 update of the CS224N Natural Language Processing with Deep Learning YouTube playlist is now available with new lectures on pretrained models, prompting, RLHF, natural language and code generation, linguistics, interpretability and more. #NLProc
https://t.co/6hb0EIaR5Z
74.3K views
An introductory talk by Christopher Manning @chrmanning on “Large Language Models in 2025 – How much understanding and intelligence?” at the Workshop on a Public AI Assistant to Worldwide Knowledge at @Stanford, covering 3 eras of LLMs, RAG, Agents, DeepSeek-R1, using LLMs, ….
56.3K views
“Look ma, no RL!”
@tatsu_hashimoto gave a great talk at the @StanfordAILab faculty lunch on Friday 2025-03-14 on his work exploring the simplest way to achieve o1/R1-like test-time scaling. s1 shows you can do it with just supervised fine-tuning (SFT)!
https://t.co/o9X4gK3KzS
📊 Post performance
Recent posts · views
Mar 25latest
201.9K
Avg views / post
74.3K–277K
Typical post (views)
1.1×
Reach vs followers
77/100
Very consistent reach
Feb 2010
On X since
Half of recent posts land between 74.3K and 277K views (median 214.3K) — a dependable floor a sponsored post can count on.
🔥 Top post: The final admission that the 2023 strategy of OpenAI, Anthropic, · 310.9K views
📊 Activity & format
Posting cadence
0.04 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly images
Recent: 0 video · 12 image · 6 text.
Follower / following
540×
Follows 354 back. A strong ratio — an audience that follows them, not a follow-for-follow network.
Verification
✓ X Premium
A paid X Premium (Blue) verified account.
Recent posts
View on X ↗
Want to learn the engineering details of building state-of-the-art Large Language Models (LLMs)? Not finding much info in @OpenAI’s non-technical reports?
@percyliang and @tatsu_hashimoto are here to help with CS336: Language Modeling fr…
“Look ma, no RL!”
@tatsu_hashimoto gave a great talk at the @StanfordAILab faculty lunch on Friday 2025-03-14 on his work exploring the simplest way to achieve o1/R1-like test-time scaling. s1 shows you can do it with just supervised fine…
An introductory talk by Christopher Manning @chrmanning on “Large Language Models in 2025 – How much understanding and intelligence?” at the Workshop on a Public AI Assistant to Worldwide Knowledge at @Stanford, covering 3 eras of LLMs, RA…
A 2023 update of the CS224N Natural Language Processing with Deep Learning YouTube playlist is now available with new lectures on pretrained models, prompting, RLHF, natural language and code generation, linguistics, interpretability and m…
We’re not quite convinced that this sort of on-demand learning actually works to build a foundation in a subject …
Announcing Stanza v1.0.0, the new packaging of our Python #NLProc library for many human languages (now including mainland Chinese), greatly improved and including NER. Documentation https://t.co/Lm2WdAddTz Github https://t.co/UyC24Qu5P3 P…
Stanford CS224N: Natural Language Processing with Deep Learning is back for 2020, starting Jan 7, with over 500 students enrolled: https://t.co/vDX5jnYlUa #cs224n
Yes, @GoogleAI (well, all of @AlphabetINC) produces a lot of awesome AI research, but @Stanford + @MIT together produce more (judging by @NeurIPSConf papers!), and @Stanford + @MIT + @UCBerkeley + @CarnegieMellon produces more than @Alphab…
“Google & DeepMind have hired 23 professors, Amazon 17, Microsoft 13, and Uber, Nvidia & Facebook 7 each. Tech companies disagree that they are plundering academia. A Google spokesman said the company was an enthusiastic supporter …
Our new-ish, neural, pure Python stanfordnlp package provides grammatical analyses of sentences in over 50 human languages! https://t.co/p6MhHyHLC2 Version 0.2.0 brought sensibly small model sizes and an improved lemmatizer. Try it out: pi…
CS224N Natural Language Processing with Deep Learning 2019 @Stanford course videos by @chrmanning, @abigail_e_see & guests are now mostly available (16 of 20). Big update from 2017. YouTube playlist: https://t.co/gFwwXJqYuQ – new CS224…
“Look ma, no RL!”
@tatsu_hashimoto gave a great talk at the @StanfordAILab faculty lunch on Friday 2025-03-14 on his work exploring the simplest way to achieve o1/R1-like test-time scaling. s1 shows you can do it with just supervised fine…
An introductory talk by Christopher Manning @chrmanning on “Large Language Models in 2025 – How much understanding and intelligence?” at the Workshop on a Public AI Assistant to Worldwide Knowledge at @Stanford, covering 3 eras of LLMs, RA…
A 2023 update of the CS224N Natural Language Processing with Deep Learning YouTube playlist is now available with new lectures on pretrained models, prompting, RLHF, natural language and code generation, linguistics, interpretability and m…
We’re not quite convinced that this sort of on-demand learning actually works to build a foundation in a subject …
Announcing Stanza v1.0.0, the new packaging of our Python #NLProc library for many human languages (now including mainland Chinese), greatly improved and including NER. Documentation https://t.co/Lm2WdAddTz Github https://t.co/UyC24Qu5P3 P…
Stanford CS224N: Natural Language Processing with Deep Learning is back for 2020, starting Jan 7, with over 500 students enrolled: https://t.co/vDX5jnYlUa #cs224n
Yes, @GoogleAI (well, all of @AlphabetINC) produces a lot of awesome AI research, but @Stanford + @MIT together produce more (judging by @NeurIPSConf papers!), and @Stanford + @MIT + @UCBerkeley + @CarnegieMellon produces more than @Alphab…
“Google & DeepMind have hired 23 professors, Amazon 17, Microsoft 13, and Uber, Nvidia & Facebook 7 each. Tech companies disagree that they are plundering academia. A Google spokesman said the company was an enthusiastic supporter …
Our new-ish, neural, pure Python stanfordnlp package provides grammatical analyses of sentences in over 50 human languages! https://t.co/p6MhHyHLC2 Version 0.2.0 brought sensibly small model sizes and an improved lemmatizer. Try it out: pi…
CS224N Natural Language Processing with Deep Learning 2019 @Stanford course videos by @chrmanning, @abigail_e_see & guests are now mostly available (16 of 20). Big update from 2017. YouTube playlist: https://t.co/gFwwXJqYuQ – new CS224…
💡 Facts
🗓️Joined X in 2010 — 16 years ago.
👁️Averages 201.9K views per post.
📤Posts about 0× per week.
💬0.65% engagement rate.
🏅Earned the 100K Followers for passing 100K followers.
🕵️ Fake follower check
Estimated69/100
Good
Credibility score
85%
Real
Real audience
Low
Fake-follower risk
High
Data confidence
- Est. 85% real, active audience · Low fake-follower risk.
- Engagement (~1.1× of followers engage each post) is around typical for X.
- Organic base — far more followers than accounts it follows.
- Verified account.
- Established account (16+ years old).
- 0.65% engagement — below the ~14.4% typical for this size.
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
Natural Language Processing/Machine Learning @chrmanning @jurafsky @percyliang @ChrisGPotts @tatsu_hashimoto @MonicaSLam @Diyi_Yang @YejinChoinka @StanfordAILab
📸 Gallery
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
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