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Jerry Liu

Jerry Liu✓ Add us as a preferred source on Google

X @jerryjliu0
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🔒 Paid proposals held safely in escrow — released only when the work's approved.

📣Sponsored postOne tweet with your link and creative. $667→ 🧵Sponsored threadA multi-tweet thread — X rewards long-form, threads out-reac… $1,668→ 🎬Sponsored video postA dedicated video tweet — X pushes video hard, so it gets pr… $934→ 📌Pinned postA sponsored post pinned to the top of my profile for the mon… $2,001/mo→ 📍Post + pin comboA sponsored post that also stays pinned for a set number of … $1,067→ 🔁Repost / quote-tweet packageI repost or quote-tweet your posts through the month — distr… $1,334/mo→ 🔗Bio link placementYour link in my bio for the month — X allows only one, so it… $1,001/mo→ 🎙️X Spaces appearance / hostI host or feature you in an X Space (live audio) — a branded… $1,334→ 🤝Cross-promo / mutual shoutoutWe shout each other out — a value swap between accounts. Swap→ 🎬UGC HireBranded content for your own channels & ads from $292→ 🎙️Podcast guest spotInvite Jerry Liu onto your podcast (~45 min, remote) $292→ 📞1:1 ConsultPaid video call — advice, coaching or Q&A $75/hr→

Every booking is a normal escrow-protected deal.

Followers
83.4K
Posts
7.6K
Following
1.6K
Account age
15 yrs
🎁 Free analysisfor Jerry Liu
🕵️Fake Followers CheckReal-audience % & bot risk→ 📊Engagement rateBenchmarked vs. peers→ 💰What they chargeFair sponsored-post price→

Known for

I’m excited to introduce RAGs - create a “ChatGPT over your data” purely with natural language 💬 Setup and use a full RAG pipeline over files/web urls in three simple steps: 1️⃣ RAG Builder: Describe your RAG pipeline in natural language (data, parameters like top-k, system 346.1K views I’m excited to introduce RAGs - create a “ChatGPT over your data” purely with natural language 💬 Setup and use a full RAG pipeline over files/web urls in three simple steps: 1️⃣ RAG Builder: Describe your RAG pipeline in natural language (data, parameters like top-k, system If you’re building “chat over your PDFs” with LLMs, you need to deal with the pesky issue of how to parse embedded tables/diagrams. Native text splitting + top-k on your tables == bad results! ⛔️🚫 A nuanced, hierarchical data representation over your PDF can help 👇 242.8K views If you’re building “chat over your PDFs” with LLMs, you need to deal with the pesky issue of how to parse embedded tables/diagrams. Native text splitting + top-k on your tables == bad results! ⛔️🚫 A nuanced, hierarchical data representation over your PDF can help 👇 Our gift to you this new year is an advanced RAG diagram you can print out and frame 🖼️ @_nerdai_ compiled 15+ advanced RAG techniques, categorized them, and added full @llama_index recipes + notebook links for each of these components. They’re categorized to help you jointly 218.4K views Our gift to you this new year is an advanced RAG diagram you can print out and frame 🖼️ @_nerdai_ compiled 15+ advanced RAG techniques, categorized them, and added full @llama_index recipes + notebook links for each of these components. They’re categorized to help you jointly Today I’m excited to announce the launch of LlamaIndex Chat 🦙💬 It is two things: 🔥 A production-ready, full-stack application where you can create and share LLM bots over your data 🔗🤖 🔥 A fully open-source (MIT license) JS/TS application template that you can easily 215K views Today I’m excited to announce the launch of LlamaIndex Chat 🦙💬 It is two things: 🔥 A production-ready, full-stack application where you can create and share LLM bots over your data 🔗🤖 🔥 A fully open-source (MIT license) JS/TS application template that you can easily

📊 Post performance

181.8K
Avg views / post
2.2×
Reach vs followers
Sep 2011
On X since

🔥 Top post: Google vs OpenAI is the beef we’re all here for Dumping update · 1.8M views

📊 Activity & format

Posting cadence
0.11 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly images
Recent: 5 video · 10 image · 3 text.
Follower / following
53×
Follows 1.6K 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 Google vs OpenAI is the beef we’re all here for Dumping updates from I/O in this 🧵 1.8M❤ 2.4K🔁 217
If you are looking to parse large volumes of PDFs (and other document formats) while maximizing accuracy and cost, we're offering $1k in credits if you signup to the Pro plan! We're guaranteeing high quality and low cost. If you run into… 9.9K❤ 14🔁 1 ▶ Introducing NotebookLlama - an open-source version of NotebookLM! 📓🦙 NotebookLlama is a full implementation of NotebookLM that includes all the capabilities that makes it so great for researchers+business users: ✅ Create a knowledge repos… 137.9K❤ 1.3K🔁 233 I am a $200 / month ChatGPT Pro power user (and Claude and now Gemini), and I really really want a "fork" feature I have so much preloaded context I don't want to 1) deal with different tasks on the same convo thread, or 2) repaste that c… 490K❤ 2.1K🔁 278 The real value of agents is not just basic search/RAG (we can agree with that), but to automate domain-specific knowledge work and processes. I made a simple example of this for auto-insurance claim processing, in the latest edition of my… 122.2K❤ 801🔁 138 This weekend learn how to build a legal document agent from scratch 👨‍⚖️📑 I made a tutorial showing you how to build a contract review agentic workflow - given a vendor agreement, parse it into a set of key clauses, match it with relevant… 179K❤ 1K🔁 180 ▶ Introducing LlamaCloud Chat Get a full-blown ChatGPT UI over any production data source in minutes. Connect to data sources like S3, Sharepoint, Notion, Slack, parse and index complex document types (PDFs, powerpoints, spreadsheets), and… 47.7K❤ 293🔁 55 Inspired by @AndrewYNg’s threads on agents in the past few weeks, I’m excited to share my talk from the @weights_biases conference that outlines how to build a general context-augmented research assistant 🧑‍🔬 Naive RAG is mostly good for … 86.2K❤ 822🔁 164 I’ve talked about how a big step towards better RAG, no matter what downstream model you’re using (GPT-4, Mistral, Gemini 1.5 Pro, etc.) is to just have a really nice PDF parser. It’s so important because a good parser unlocks way more in… 194.3K❤ 826🔁 158 There was a lot of cool RAG research in the past year or two, and luckily for you, all of these efforts are tracked under one place! “Retrieval-Augmented Generation for Large Language Models: A Survey” by Gao et al. does an admirable job … 141.3K❤ 991🔁 192 Our gift to you this new year is an advanced RAG diagram you can print out and frame 🖼️ @_nerdai_ compiled 15+ advanced RAG techniques, categorized them, and added full @llama_index recipes + notebook links for each of these components. … 218.4K❤ 877🔁 187 New RAG technique alert 🚨 We’ve come up with an advanced RAG technique in @llama_index that lets you ask structured questions over many documents ✨: 1. Model each document as a metadata dictionary - store more attributes beyond a simple t… 186.7K❤ 944🔁 162

💡 Facts

🗓️Joined X in 2011 — 15 years ago.
👁️Averages 181.8K views per post.
📤Posts about 0.1× per week.
💬1.41% engagement rate.
🏅Earned the 10K Followers for passing 10K followers.

🕵️ Fake follower check

Estimated
83/100
Excellent Credibility score
93%
Real Real audience
Low Fake-follower risk
High Data confidence
  • Est. 93% real, active audience · Low fake-follower risk.
  • Strong engagement (~2.2× of followers engage each post) — an active, real audience.
  • Organic base — far more followers than accounts it follows.
  • Verified account.
  • Established account (15+ years old).
  • 1.41% engagement — below the ~18% typical for this size.

Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

Parsing the world's hardest PDFs @llama_index. cofounder/CEO Careers: https://www.llamaindex.ai/careers Enterprise: https://www.llamaindex.ai/contact

📸 Gallery

If you are looking to parse large volumes of PDFs (and other document formats) while maximizing accuracy and cost, we're offering $1k in credits if you signup to the Pro plan! We're guaranteeing high quality and low cost. If you run into issues on the Pro plan we're always Introducing NotebookLlama - an open-source version of NotebookLM! 📓🦙 NotebookLlama is a full implementation of NotebookLM that includes all the capabilities that makes it so great for researchers+business users: ✅ Create a knowledge repository of documents. Has likely higher The real value of agents is not just basic search/RAG (we can agree with that), but to automate domain-specific knowledge work and processes. I made a simple example of this for auto-insurance claim processing, in the latest edition of my series on agentic document workflows. This weekend learn how to build a legal document agent from scratch 👨‍⚖️📑 I made a tutorial showing you how to build a contract review agentic workflow - given a vendor agreement, parse it into a set of key clauses, match it with relevant clauses from a set of guidelines (GDPR), Introducing LlamaCloud Chat Get a full-blown ChatGPT UI over any production data source in minutes. Connect to data sources like S3, Sharepoint, Notion, Slack, parse and index complex document types (PDFs, powerpoints, spreadsheets), and get back an advanced retrieval endpoint. Inspired by @AndrewYNg’s threads on agents in the past few weeks, I’m excited to share my talk from the @weights_biases conference that outlines how to build a general context-augmented research assistant 🧑‍🔬 Naive RAG is mostly good for simple questions in a single-shot setting. I’ve talked about how a big step towards better RAG, no matter what downstream model you’re using (GPT-4, Mistral, Gemini 1.5 Pro, etc.) is to just have a really nice PDF parser. It’s so important because a good parser unlocks way more interesting indexing/retrieval strategies There was a lot of cool RAG research in the past year or two, and luckily for you, all of these efforts are tracked under one place! “Retrieval-Augmented Generation for Large Language Models: A Survey” by Gao et al. does an admirable job categorizing all RAG research into three

🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

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Jerry Liu has 83,351 followers on X.

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