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Followers
124.6K
Posts
17.3K
Following
578
Account age
12 yrs
💰 Suggested sponsorship rate
$698 – $1.1K / sponsored post
Estimated from audience size & typical reach — every deal is negotiable and held in SocialDB escrow.
🎁 Free analysisfor Harrison Chase
Known for
1M views
Baby-AGI by @yoheinakajima is taking the world by storm
Here's an implementation within the @langchain framework, allowing you to easily substitute in other vectorstores and other LLMs
Docs: https://t.co/LK7juyVLYg
824.9K views
🤖Generative Agents🤖
Last week, Park et all released “Generative Agents”, a paper simulating interactions between tens of agents
We gave it a close read, and implemented one of the novel components it introduced: a long-term, reflection-based memory system
🧵
450.2K views
OpenAI recently released a guide on building agents which contains some misguided takes
There's a lot of FUD, confusion, hype, and noise around agents
I wrote a blog on how to think about agent frameworks. Includes:
Background Info
- What is an agent?
- What is hard about
412.3K views
LangChain 🤝 AIPlugins
A first open source attempt at using AIPlugins (the same ones ChatGPT is using)
s/o @vaibhavk97 for this. Excited to see what other techniques the @langchain community comes up with - it's only the beginning
Docs (Python and JS) in 🧵
📊 Post performance
245.1K
Avg views / post
2×
Reach vs followers
Jul 2014
On X since
🔥 Top post: Baby-AGI by @yoheinakajima is taking the world by storm Here's · 1M views
📊 Activity & format
Posting cadence
0.1 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly images
Recent: 0 video · 11 image · 7 text.
Follower / following
216×
Follows 578 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 ↗
🔥 Top post
Baby-AGI by @yoheinakajima is taking the world by storm
Here's an implementation within the @langchain framework, allowing you to easily substitute in other vectorstores and other LLMs
Docs: https://t.co/LK7juyVLYg
Post
OpenAI recently released a guide on building agents which contains some misguided takes
There's a lot of FUD, confusion, hype, and noise around agents
I wrote a blog on how to think about agent frameworks. Includes:
Background Info
- Wh…
My TED talk is out!
I talk about what it takes to build "context-aware reasoning applications", including:
4 different ways of providing context to LLMs
6 different types of cognitive architectures
🦜🔗Code Interpreter API
ChatGPT's code interpreter is the hottest thing in the streets. A new project by @Shroominic takes a stab at recreating that functionality locally using OpenAI's apis
Uses CodeBox - a sandboxed env with simple file…
The new @OpenAI functions are good for other things besides agents
Another killer use case is extracting structured information from unstructured docs
We've adding support for extraction AND tagging in @langchain - thanks to @fpingham fo…
🚨Emergency OpenAI Functions Release🚨
`pip install langchain==0.0.199`
✅ Support for functions in chat model wrapper
✅ Convert @langchain tools to functions
Docs: https://t.co/JjrKLU92L7
Next up... I think its time for a new type of age…
🌟privateGPT🌟 - this is sick!!!
I've always had people asking me if it was feasible to use @langchain with open source models, and my answer was "at the moment, not really..."
but @ivanmartit did it!!!!! this is a huge step forward 👏👏👏
Retrieval for QA systems is hard
Vector search is good for capturing semantically similar texts, but often queries specify desired attributes like time, authorship, or other "metadata" fields, which vector search is not great at
Enter...…
🤖Autonomous Agents & Agent Simulations🤖
Four agent-related projects (AutoGPT, BabyAGI, CAMEL, and Generative Agents) have exploded recently
We wrote a blog on they differ from previous @langchain agents and how we've incorporated som…
🤖Generative Agents🤖
Last week, Park et all released “Generative Agents”, a paper simulating interactions between tens of agents
We gave it a close read, and implemented one of the novel components it introduced: a long-term, reflection-b…
Yesterday @karpathy tweeted about using SVMs instead of KNN for retrieval (pros: better results, flexibility; cons: takes longer)
Today @RLanceMartin implemented it in @langchain 🚀🚀
Play around with it here!
https://t.co/fCbOisp3Gw
OpenAI recently released a guide on building agents which contains some misguided takes
There's a lot of FUD, confusion, hype, and noise around agents
I wrote a blog on how to think about agent frameworks. Includes:
Background Info
- Wh…
My TED talk is out!
I talk about what it takes to build "context-aware reasoning applications", including:
4 different ways of providing context to LLMs
6 different types of cognitive architectures
🦜🔗Code Interpreter API
ChatGPT's code interpreter is the hottest thing in the streets. A new project by @Shroominic takes a stab at recreating that functionality locally using OpenAI's apis
Uses CodeBox - a sandboxed env with simple file…
The new @OpenAI functions are good for other things besides agents
Another killer use case is extracting structured information from unstructured docs
We've adding support for extraction AND tagging in @langchain - thanks to @fpingham fo…
🚨Emergency OpenAI Functions Release🚨
`pip install langchain==0.0.199`
✅ Support for functions in chat model wrapper
✅ Convert @langchain tools to functions
Docs: https://t.co/JjrKLU92L7
Next up... I think its time for a new type of age…
🌟privateGPT🌟 - this is sick!!!
I've always had people asking me if it was feasible to use @langchain with open source models, and my answer was "at the moment, not really..."
but @ivanmartit did it!!!!! this is a huge step forward 👏👏👏
Retrieval for QA systems is hard
Vector search is good for capturing semantically similar texts, but often queries specify desired attributes like time, authorship, or other "metadata" fields, which vector search is not great at
Enter...…
🤖Autonomous Agents & Agent Simulations🤖
Four agent-related projects (AutoGPT, BabyAGI, CAMEL, and Generative Agents) have exploded recently
We wrote a blog on they differ from previous @langchain agents and how we've incorporated som…
🤖Generative Agents🤖
Last week, Park et all released “Generative Agents”, a paper simulating interactions between tens of agents
We gave it a close read, and implemented one of the novel components it introduced: a long-term, reflection-b…
Yesterday @karpathy tweeted about using SVMs instead of KNN for retrieval (pros: better results, flexibility; cons: takes longer)
Today @RLanceMartin implemented it in @langchain 🚀🚀
Play around with it here!
https://t.co/fCbOisp3Gw
💡 Facts
🗓️Joined X in 2014 — 12 years ago.
👁️Averages 245.1K views per post.
📤Posts about 0.1× per week.
💬1.11% 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 (~2× of followers engage each post) is around typical for X.
- Organic base — far more followers than accounts it follows.
- Verified account.
- Established account (12+ years old).
- 1.11% 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
@LangChain
Always hiring: https://www.langchain.com/careers
📸 Gallery
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
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17%
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14%
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13%
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12%
Alveda C. King, Ph.D.
11%
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10%
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