Jim Fan engagement report
@DrJimFan - 578K followers on X
Measured over 2 original posts from a 30-day window, last computed on August 26, 2026.
Engagement
Early reading. We have captured 2 original posts for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.
A typical post picks up 1.2K interactions against 578K followers, an engagement rate of 0.21%. Posts are seen about 117K times each, and 1.02% of those impressions turn into an interaction. That is about 20.2% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.13 posts a day over the last 30 days, though only 7% of days saw any activity at all. Most posts go out around 15:00 UTC, and Thursday is the busiest day of the week. Of the 2 posts sampled, 50% carry an image or video and 50% are part of a thread. The account's strongest tracked post pulled 71K interactions, about 60x its own typical post. Only 2 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.
Measured over 2 original posts from a 30-day window, last computed on August 26, 2026.
Where this sits in the catalog
At 0.21%, Jim Fan sits above the 50th percentile of the 36,654 accounts in this comparison. That places it in the above the median band, which runs 0.08% to 0.433%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.08% |
| 75th percentile | 0.433% |
| 90th percentile | 2.10% |
| 99th percentile | 160.5% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 15:00 UTC, and Thursday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 51K |
| 01:00 UTC | -2% | 52K |
| 02:00 UTC | -3% | 50K |
| 03:00 UTC | -4% | 53K |
| 04:00 UTC | -6% | 43K |
| 05:00 UTC | -4% | 42K |
| 06:00 UTC | -4% | 48K |
| 07:00 UTC | -5% | 52K |
| 08:00 UTC | -4% | 61K |
| 09:00 UTC | -3% | 70K |
| 10:00 UTC | -2% | 72K |
| 11:00 UTC | -3% | 78K |
| 12:00 UTC | -2% | 86K |
| 13:00 UTC | -2% | 94K |
| 14:00 UTC | -4% | 97K |
| 15:00 UTC | -2% | 101K |
| 16:00 UTC | -3% | 98K |
| 17:00 UTC | -2% | 91K |
| 18:00 UTC | -1% | 85K |
| 19:00 UTC | -1% | 80K |
| 20:00 UTC | -1% | 74K |
| 21:00 UTC | -1% | 66K |
| 22:00 UTC | -1% | 57K |
| 23:00 UTC | -2% | 52K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +4% | 231K |
| Monday | 0% | 288K |
| Tuesday | -2% | 278K |
| Wednesday | -1% | 251K |
| Thursday | -1% | 245K |
| Friday | -3% | 253K |
| Saturday | +3% | 227K |
Best tweets
- Aug 6, 202560x their median
Tesla is training a new FSD model with ~10X params and a big improvement to video compression loss. Probably ready for public release end of next month if testing goes well.
- Dec 26, 202557x their median
I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue. There's a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering. Clearly some powerful alien tool was handed around except it comes with no manual and everyone has to figure out how to hold it and operate it, while the resulting magnitude 9 earthquake is rocking the profession. Roll up your sleeves to not fall behind.
- Aug 9, 202310x their median
The famed Stanford Smallville is officially open-source! 25 AI agents inhabit a digital Westworld, unaware that they are living in a simulation. They go to work, gossip, organize socials, make new friends, and even fall in love. Each has unique personality and backstory. Smallville is among the most inspiring AI agent experiments in 2023. We often talk about a single LLM's emergent abilities, but multi-agent emergence could be way more complex and fascinating at scale. A population of AI can play out the evolution of an entire civilization. Endless new possibilities ahead. Gaming will be the first to feel the impact. Github: https://t.co/xUll7KaaTp Paper: https://t.co/PMDQysrOz9 Authors: @joon_s_pk @joseph_c_obrien @carriejcai @merrierm @percyliang @msbernst
- Mar 24, 202610x their median
LiteLLM HAS BEEN COMPROMISED, DO NOT UPDATE. We just discovered that LiteLLM pypi release 1.82.8. It has been compromised, it contains litellm_init.pth with base64 encoded instructions to send all the credentials it can find to remote server + self-replicate. link below
- May 8, 20263.9x their median
I promise this will be the best 20 min you spend today! Robotics: Endgame, the sequel to my last year's Sequoia AI Ascent talk, "Physical Turing Test". I laid out the roadmap for solving Physical AGI as a simple parallel to the LLM success story. Be a good scientist, copy homework ;) And stay till the end, more easter eggs and predictions for your polymarket! 00:30 DGX-1 origin story at OpenAI, I was there in 2016 signing with Jensen and Elon. Heading to the Computer History Museum! 01:42 The Great Parallel 03:31 Robotics, the Endgame 03:39 Why VLAs fall short 04:32 Video world models as the 2nd pretraining paradigm 06:09 World Action Models (WAM) 07:46 Strategies for robot data collection and the FSD equivalent to physical data flywheel for robot manipulation 11:06 EgoScale and the Dexterity Scaling Law we discovered recently 14:00 Physical RL: bridging the last mile 15:39 DreamDojo: an end-to-end neural physics engine for scaling RL in silico 17:00 Civilizational Technology Tree and my predictions for the near future. Spoiler: it's closer than you think. Thanks to my friends at Sequoia for inviting me back to AI Ascent this year! I had a blast! Last year's talk is attached in the thread if you missed it.
- Jun 16, 20263.9x their median
Today, we enable AutoResearch in the physical world for the first time! Introducing ENPIRE: we give 8 Codex agents a fleet of robots, an allocation of GPUs, and generous token budget. We set them free with a simple goal: solve the task as quickly as possible, keep the robots busy but stay safe, don't waste precious compute. Make no mistake. Then humans step aside and our watch begins. The robot fleet starts to come alive: they learn to look for visual clues, reset the scene, practice novel skills, tinker with control stack, read papers online, debate, reflect, get stuck, and try again directly on the hardware. All we did is to give Codex an API to the world of atoms, and the rest is emergence. ENPIRE is able to solve high-precision tasks like tying zip-ties, organizing fine pins, and installing GPUs all by itself. We also discovered a new type of "physical scaling": 8 robots exploring in parallel improves significantly faster than fewer ones. A part of our NVIDIA GEAR lab now self-improves tirelessly over night. We just read the reports in the morning. /goal: we all take a holiday and Jensen wouldn't even notice ;) We will be open-sourcing everything, so you can host your self-running robot lab at home too! Deep dive in the thread:
- Jul 13, 20253.8x their median
I've been a bit quiet on X recently. The past year has been a transformational experience. Grok-4 and Kimi K2 are awesome, but the world of robotics is a wondrous wild west. It feels like NLP in 2018 when GPT-1 was published, along with BERT and a thousand other flowers that bloomed. No one knew which one would eventually become ChatGPT. Debates were heated. Entropy was sky high. Ideas were insanely fun. I believe the GPT-1 of robotics is already somewhere on Arxiv, but we don't know exactly which one. Could be world models, RL, learning from human video, sim2real, real2sim, etc. etc, or any combo of them. Debates are heated. Entropy is sky high. Ideas are insanely fun, instead of squeezing the last few % on AIME & GPQA. The nature of robotics also greatly complicates the design space. Unlike the clean world of bits for LLMs (text strings), we roboticists have to deal with the messy world of atoms. After all, there's a lump of software-defined metal in the loop. LLM normies may find it hard to believe, but so far roboticists still can't agree on a benchmark! Different robots have different capability envelopes - some are better at acrobatics while others at object manipulation. Some are meant for industrial use while others are for household tasks. Cross-embodiment isn't just a research novelty, but an essential feature for a universal robot brain. I've talked to dozens of C-suite leads from various robot companies, old and new. Some sell the whole body. Some sell body parts such as dexterous hands. Many more others sell the shovels to manufacture new bodies, create simulations, or collect massive troves of data. The business idea space is as wild as research itself. It's a new gold rush, the likes of which we haven't seen since the 2022 ChatGPT wave. The best time to enter is when non-consensus peaks. We're still at the start of a loss curve - there're strong signs of life, but far, far away from convergence. Every gradient step takes us into the unknown. But one thing I do know for sure - there's no AGI without touching, feeling, and being embodied in the messy world. On a more personal note - running a research lab comes with a whole new level of responsibility. Giving updates directly to the CEO of a $4T company is, to put it mildly, both thrilling and all-consuming of my attention weights. Gone are the days when I could stay on top of and dive deep into every AI news. I’ll try to carve out time to share more of my journey.
- Aug 6, 20253.1x their median
🚀 Introducing Qwen3-4B-Instruct-2507 & Qwen3-4B-Thinking-2507 — smarter, sharper, and 256K-ready! 🔹 Instruct: Boosted general skills, multilingual coverage, and long-context instruction following. 🔹 Thinking: Advanced reasoning in logic, math, science & code — built for expert-level tasks. Both models are more aligned, more capable, and more context-aware. Huggingface： https://t.co/roKgJ48QWb https://t.co/PvZZr9xAF5 ModelScope: https://t.co/eqzlaoaKYs https://t.co/9QyuyVlt7U
- Feb 4, 20253.1x their median
We RL'ed humanoid robots to Cristiano Ronaldo, LeBron James, and Kobe Byrant! These are neural nets running on real hardware at our GEAR lab. Most robot demos you see online speed videos up. We actually *slow them down* so you can enjoy the fluid motions. I'm excited to announce "ASAP", a "real2sim2real" model that masters extremely smooth and dynamic motions for humanoid whole body control. We pretrain the robot in simulation first, but there is a notorious "sim2real" gap: it's very difficult for hand-engineered physics equations to match real world dynamics. Our fix is simple: just deploy a pretrained policy on real hardware, collect data, and replay the motion in sim. The replay will obviously have many errors, but that gives a rich signal to compensate for the physics discrepancy. Use another neural net to learn the delta. Basically, we "patch up" a traditional physics engine, so that the robot can experience almost the real world at scale in GPUs. The future is hybrid simulation: combine the power of classical sim engines refined over decades and the uncanny ability of modern NNs to capture a messy world.
- Feb 3, 20262.8x their median
https://t.co/Npar79SvUh
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
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Reading these numbers
A typical post picks up 1.2K interactions against 578K followers, an engagement rate of 0.21%. Posts are seen about 117K times each, and 1.02% of those impressions turn into an interaction. That is about 20.2% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.13 posts a day over the last 30 days, though only 7% of days saw any activity at all. Most posts go out around 15:00 UTC, and Thursday is the busiest day of the week. Of the 2 posts sampled, 50% carry an image or video and 50% are part of a thread. The account's strongest tracked post pulled 71K interactions, about 60x its own typical post. Only 2 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.
- What is Jim Fan's engagement rate on X?
- Jim Fan (@DrJimFan) has an engagement rate of 0.21%, based on the median interactions across 2 original posts from the last 30 days against 578,101 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.21%, Jim Fan sits above the 50th percentile of the 36,654 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @DrJimFan have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @DrJimFan post?
- Most posts go out around 15:00 UTC, and Thursday is its busiest day, at roughly 0.13 posts per day across the measured window.