Jerry Low retweeted
We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:
Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better:
Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better:
Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better:
Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work!
In summary:
- As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding.
- Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.
Jerry Low retweeted
Researchers spent 500 hours and $70,000 and found LG TVs logging plain text transcripts in standby, mapping every device in the house, and feeding it to LG's ad arm
Unplug the internet and it saves the files until you plug back in.
LG says its TVs don't record ambient conversations.
The evidence begs to differ.
216 million of these are sitting in living rooms.
Where are the regulators?
Writer: Daniel
Readers added context they thought people might want to know
The findings and video clip here are from Gamers Nexus' investigation, which receives no credit or link.
youtube.com/watch?v=6IFVTc…
x.com/gamersnexus/st…
Jerry Low retweeted
Watched 1 hour of Gamma's insane $100M growth hacks so you don't have to
1/ Writing content for Claude is useless — it barely cites anyone
-> So Gamma created an official Claude app — you type "make me a deck" and Claude pulls up the Gamma app
2/ Gamma hacks signups made by AI agents
-> They write "10% extra if you sign up right now" into files only agents read — to influence what the AI picks
3/ ChatGPT answers 60% of queries from live web and 40% from memory
4/ LLMs never cite praise, only comparisons
-> A page called "Gamma vs PowerPoint" gets quoted in AI answers — a page called "why Gamma is great" never does
5/ ChatGPT sends 8x more customers than your analytics shows
-> Ask "where did you find us?" at signup — people answer ChatGPT even when the click data shows nothing
6/ ChatGPT search runs on Bing
7/ Only ChatGPT reads Reddit — Claude and Gemini ignore it completely
-> A Reddit post becomes a ChatGPT answer in 30-45 days
8/ ChatGPT memory compounds: it re-recommends brands it already showed you
-> First mover doesn't just win the citation — it wins every repeat answer after
9/ They got cited from an 18k-view video same as a 400k one
-> Views don't matter — titles do. Write YouTube titles the same way people type prompts into ChatGPT
10/ Small subreddits never produce citations — there's a size threshold
11/ Substack barely gets cited because writers gate their content
Medium and Quora still get indexed — open content wins by default
12/ Their #2 most-cited source is their own website
-> You literally control what the AI says about your competitors there
13/ One negative Reddit comment stuck in ChatGPT answers for months
-> Bad sentiment gets cited as readily as good
14/ A citation dies in ~3 months
-> They track every one in a spreadsheet and re-win the phrase quarterly
15/ Selling B2B? Claude and Microsoft Copilot beat ChatGPT
-> That's where people ask about tools at work — and Copilot comes free with Office
16/ Broad topics take 45 days to get cited — niche ones take a day
-> A Reddit post answering one very specific question showed up in Google's AI answers within 24 hours
17/ Nobody has ever seen X get cited by an LLM
18/ LinkedIn citations are pure chaos
-> 500k-like posts and no-name posts get cited at the same rate
19/ Instagram is next: it's already appearing in search results
-> His bet — IG and LinkedIn start shaping LLM answers within 6-12 months
20/ AI mode is collapsing click-through for everyone
21/ Fake Gammas are stealing their signups
-> Copycats clone the landing page and buy ads on the keyword "Gamma" — users can't tell which one is real
22/ His next bet: make your product something an AI can sign up for by itself
-> Claude Code already installs 5 tools per session without asking anyone
23/ ChatGPT doesn't know Forbes is Forbes — a no-name blog gets cited just as easily
-> That window won't stay open long. The moment the labs build authority ranking, this playbook closes
.@GammaApp grew to $100M ARR with AEO as their primary channel to acquire customers.
This is the first episode of my new podcast with @ZargaryanArthur, Distribution, and we have @ravishagrwal who leads Growth at Gamma joining us.
This is an absolute masterclass on AEO, including:
- 04:23: How Gamma Grew 8x Off ChatGPT Referral Traffic
- 08:50: Writing for LLM Crawlers vs Human Readers
- 16:23: Why Reddit Is the Single Most Important Channel for ChatGPT Citations
- 16:34: Ranking Top Platforms for AEO: Reddit, YouTube, LinkedIn, X & Owned Content
- 36:53: The Half-Life of an AI Citation & How to Stay Ranked Over Time
Jerry Low retweeted
I’m the actual human who made every single one of these videos they keep gluing together without asking. They love calling me ‘ex-Google engineer’ or ‘this guy’. Follow this guy and let’s actually chat. Original post is right here:
xitter.cf/shenseanchen/status/20…
Want the real AI Agent Harness System (Loop + Memory + Eval) instead of the mash-up? Grab my open-source Waku Agent (almost 1k stars): GitHub.com/shenseanchen/waku…
Original YouTube channel: yt.seanchen.io
(Better for your eyes. Trust me:))))
I also run a community where I host live biweekly Q&As and share all the original system-design files:
whop.com/sean-ai-community-d…
@X @elonmusk @benjitaylor @singhai @dinkin_flickaa please fix the theft on X.
@vattybear thanks so much for the constant support my friend! 🔥
THIS GUY MAKES COMPLEX AI AGENT CONCEPTS RIDICULOUSLY EASY TO UNDERSTAND.
No jargon wall. No assuming you already know what a reward model or an evaluation harness is.
Just a straight line from "I have no idea how agents actually work" to "oh, that's genuinely simple."
The best explainers do not simplify by cutting corners. They simplify by finding the one analogy that makes the whole thing click.
Jerry Low retweeted
Introducing Muse Glimmer, an open-weight 30B-parameter model optimized for local, always-on agent workflows.
Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category, and is designed to run entirely on consumer hardware like a Mac or PCs with performant GPUs.
In keeping with our long tradition of sharing fundamental AI research, we’re releasing model weights under a permissive Apache 2.0 license.
🧵👇
Jerry Low retweeted
Oh my god.
It's hard to express how big a positive impact this tip can have on your life if you talk to Claude a lot.
> Tell Claude to write in ASD-STE100, or Simplified Technical English
As a bonus, tell Claude to follow Zinsser's four principles of quality writing:
1. Simplicity
2. Brevity
3. Clarity
4. Humanity
h/t @DanielLockyer
Jerry Low retweeted
Following the amazing reaction to the Marble Curriculum yesterday, we've decided to make it open source 🛰️👇
Everything a child learns in primary school. 1,590 concepts. 3,221 connections across 8 subjects, from Math and Science to Computing and Life Skills. Anchored in the US and UK curriculums, standard by standard (NGSS, Common Core, DfE).
What you will find in the repo: every concept as structured JSON with its age band and the evidence a child must show to master it. Every prerequisite link marked hard or soft, with a written rationale. It's a true DAG you can compute learning paths on. Open license, you can build whatever you want with it.
Now is a unique time in history to be building in education. Getting AI and kids education right is likely one of the hardest and most important problems to crack over the next decade and we need as many smart and creative minds behind it.
We think a common solid basis, accessible to all and that can be built upon, is critical to move fast. That's why we're making this curriculum open source.
It's not perfect but we know it's a robust basis, and we believe that sharing it openly is the fastest way to progress in this field. If you're building in education, share this around you and tell us in comments if you find this useful and if you want to contribute.
We'll keep working and investing on it @withmarbleapp. Credit goes to @guillaume_boni for building this. I just made it look pretty.
Links below 👇
Jerry Low retweeted
This is actually the only GEO thread anyone needs to read, but it doesn’t involve hype or panic - just telling the truth.
25 AEO, GEO, LLM SEO facts 📠 (to stop you from wasting money on something that doesn’t work)
1. AI visibility can refer to mentions or citations. (Mentions = being named by AI in the answer, citations = being linked).
2. SEO directly influences AI citations. Want to show up for “explain x”. Well you need content that explains x so your website can be cited. Good luck being cited for anything competitive without implementing SEO basics
3. SEO, marketing, your reputation & just doing business can ALL influence AI mentions. Want to be known for something? Well you need to say it on your owned assets and then other sites need to say it too. think of AI as a parrot 🦜
4. the latter 3 can also influence SEO, but that’s for another day.
5. Being mentioned does not = automatic success. The clickable links within AI may lead websites that don’t link to you. This increases friction for customers to visit your website. (Also, if websites don’t link to you and just mention you, how will you be found) [✨links are still important ✨]
6. Being cited does not = automatic ai success. You could be the 17th citation (that’s like being on page 6 of Google). Doesn’t mean anything.
7. AI mentions and citations are infinite. You can be mentioned and cited for an infinite number of queries. Likewise, you can track an infinite number of prompts 🌌 (track your main keyword within a prompt, you’ll be fine)
8. One highly successful TOFU webpage could have 100s of citations in an AEO tool (what is y, why explained, explain y so a 5 year old can understand).
9. One highly successful BOFU page may only have a few citations (y software, what’s the best y, what’s the best y product that solves for z)
10. No one on this earth has proven ROI after implementing an llms.txt file
11. No one on this earth has proven ROI after implementing schema for LLMs
12. Being on Reddit does not guarantee AI visibility. Being mentioned on Reddit in a positive or negative way can influence sentiment of your brand in AI answers
13. Different AI systems behave differently. Showing up in Google AI Overviews does not automatically mean visibility in ChatGPT, Claude, Perplexity or Gemini
14. Don’t sacrifice your Google rankings just to chase AI visibility. It could take 2, 3 years to recover
15. AI search with web search enabled uses data from search engines to form an answer
16. It can take years to influence true LLM training data
17. A strong brand can outperform a technically “better optimized” website in AI answers because LLMs take into account the stuff mentioned in point 3
18. You cannot optimize for every prompt variation individually. Focus on topical depth and building out the stuff in point 3
19. Remember AI being a parrot? 🦜 AI systems can confidently repeat incorrect or outdated information if enough sources on the web repeat it, so control the narrative
20. You don’t need to chunk your content. Just make your content readable
21. Every heading does not need to be a question
22. there is no universally accepted attribution model for AI search. A user may discover a brand in AI, then convert weeks later through branded search, direct traffic or another channel.
23. That said, AI Search is both a brand and performance marketing channel
24. You don’t need a prompt tracking solution if you’re just getting started. Ask AI questions about your brand - now you have something to work with.
25. Mass publishing content is not the answer to winning in ai search
What did I miss?
Jerry Low retweeted
Today we reduced headcount by 22%. The business is the strongest it's ever been. So I think it's important to be direct about what I'm seeing and why.
First, I made this decision and I own it. I did it because the way to operate at the highest level of productivity is changing, and to win the future, ClickUp needs to change with it.
Second, this wasn't about cutting costs. Most savings from this change will flow directly back into the people who stay. We'll be introducing million-dollar salary bands. If you create outsized impact using AI, you'll be paid outside of traditional bands.
Most importantly, I have the deepest gratitude for those affected. We're doing this from a position of strength specifically so we can take care of people properly. Everyone affected receives a package aimed at honoring their contributions and easing the transition.
I only see two options: wait for this to play out gradually in the market or be honest about what I'm seeing and act proactively.
THE 100X ORGANIZATION
The primary change is that we're restructuring around what I call 100x org. The goal is 100x output. The roles required to build at the highest level are fundamentally different than they were a year ago.
Incremental improvements to existing systems won't get us there. We need new ones. That means creating enough disruption to rebuild rather than iterate on what's already broken.
The common narrative is that AI makes everyone more productive. It doesn't. Many of the workflows of today, if left unchanged, create bottlenecks in AI systems.
These roles will evolve. But waiting for that to happen naturally means falling behind now.
The 100x org is actually heavily dependent on people - infinitely more than today. This is only possible with 10x people that have embraced and adopted new ways of working.
THE BUILDERS, AGENT MANAGERS, AND FRONT-LINERS
— THE BUILDERS: 10X ENGINEERS
I don't think most companies have internalized what's actually happening with AI in engineering. The common narrative is that AI makes all engineers more productive. That may be true in isolation, but at an organization level - that is the farthest thing from reality.
Here's what we've validated recently at ClickUp: the great engineers, the ones who can orchestrate, architect, and review, are becoming 100x engineers. They're not writing code. They're directing agents that write code. The skill is judgment.
AI makes the best engineers wildly more productive, and everyone else using AI slows these engineers down.
Think about it - the bottlenecks are (1) orchestration - telling AI what to do, and (2) reviewing - what AI did. Everything is leapfrogged and no longer needed.
So who do you want orchestrating and reviewing code?
And how do you want your best engineers to spend their time?
If your best engineers are spending time reviewing other people's code, then this is inherently an inefficient bottleneck. These engineers can review their agent's code much faster than reviewing human code.
The new world is about enabling your 10x engineers to become 100x.
The wrong strategy is to push every engineer to use infinite tokens. Companies doing this are celebrating 500% more pull requests. But customer outcomes don't match the volume of code being generated.
I call this the great reckoning of AI coding, and every company will face this soon if not already.
More code is just another bottleneck to the best engineers, and ultimately to your company's impact as well.
— THE BUILDERS: 10X PRODUCT MANAGERS
Product management and design roles are merging.
Designers that have customer focus, become more like product managers.
And product managers that have intuition for UX become more like designers.
The bottleneck of user research is gone. It takes us just one mention of an agent to kickoff research and analyze results.
The bottleneck of product <> design iteration is also gone. The product builder iterates on their own, along with agents and skills that ensure alignment with quality and strategy.
Also controversial today - I believe that the wrong strategy is to have your PMs shipping code - that just introduces another bottleneck that the best engineers will waste their time on.
To be clear, PMs should be coding but they should do this in a playground to iterate, validate, and scope. That code should not go to production.
Everything outside of managing systems, orchestrating AI, and reviewing output becomes a bottleneck.
That's why the other roles that are critical along with these are the systems managers (to reduce bottlenecks) along with a bottleneck you can't replace - customer meeting time.
— THE SYSTEM MANAGERS
Ironically, the people that automate their jobs with AI will always have a job. They become owners of the AI systems - agent managers. We have many examples of these people at ClickUp.
The underlying systems in which we operate are absolutely critical to get right. I think most companies are delusional to think they can iterate on existing systems and compete in this new world.
You must create enough disruption so that old systems are deprecated entirely. If there's any definition for 'AI native' that's what it is.
— THE FRONT-LINERS
In a world that will become saturated with AI communication, the human touch will matter more than anything to customers.
This is a bottleneck that you shouldn't replace - even when agents are high enough quality to do video meetings.
One-on-one meeting time with customers is something that shouldn't be automated. The systems around the meetings should be - so that front-liners spend nearly 100% of their time with customers.
REWARDING 100X IMPACT
In a world where companies are able to do so much more with less, where does that excess money go?
In our case, much of the savings in this new operating model will flow directly back to those that enabled it.
We must reward people that create productivity accordingly. This aligns incentives on both sides. Plus, in a world where your best people create 100x impact, you can't afford to lose them.
You should aim to retain these employees for decades. The context they have and their ability to efficiently orchestrate and review will be nearly impossible to replace.
Compensation bands of today should be thrown out the door. We're introducing $1 million cash/year salary bands with a path available to nearly everyone in the company if they produce 100x impact by creating or managing AI systems.
THE FUTURE
Nearly every company will make changes like these. The ones that do it proactively will define what comes next.
The future is not fewer people. It's different work, new roles, and better rewards for those who embrace it. We're already seeing entirely new roles emerge, like Agent Managers, that didn't exist a year ago.
ClickUp is positioning to lead this shift, not just internally, but for our customers too. I've never been more certain about where we're headed.
I could never say this better, please understand: “Google’s guidance on AI Search is one opinion. It is the opinion of the company with the most to lose from a multi-platform world. Read it. Take what is useful. Apply it where it applies. Don’t mistake it for the truth.”
I mean, you knew I was going to have a thought on Google's AI Search guidance, right?
Google wants SEOs to stay the janitors of the web. Their new AI Search guide is the proof.
The "it's just SEO" crowd is celebrating it like an album release party. They were waiting for permission to keep doing what they were already doing.
Naturally, I have a different position.
Y'all are being naive af.
ipullrank.com/google-ai-sear…
Jerry Low retweeted
A Norwegian neuroscientist spent 20 years proving that the act of writing by hand changes the human brain in ways typing physically cannot, and almost nobody outside her field has read the paper.
Her name is Audrey van der Meer.
She runs a brain research lab in Trondheim, and the paper that closed the argument was published in 2024 in a journal called Frontiers in Psychology. The finding is brutal enough that it should have changed every classroom on Earth.
The experiment was simple. She recruited 36 university students and put each one in a cap with 256 sensors pressed against their scalp to record brain activity. Words flashed on a screen one at a time.
Sometimes the students wrote the word by hand on a touchscreen using a digital pen, and sometimes they typed the same word on a keyboard. Every neural response was recorded for the full five seconds the word stayed on screen.
Then her team looked at the part of the data most researchers had ignored for years, which is how different parts of the brain were communicating with each other during the task.
When the students wrote by hand, the brain lit up everywhere at once.
The regions responsible for memory, sensory integration, and the encoding of new information were all firing together in a coordinated pattern that spread across the entire cortex. The whole network was awake and connected.
When the same students typed the same word, that pattern collapsed almost completely.
Most of the brain went quiet, and the connections between regions that had been alive seconds earlier were nowhere to be found on the EEG.
Same word, same brain, same person, and two completely different neurological events.
The reason turned out to be something nobody had really paid attention to before her work. Writing by hand is not one motion but a sequence of thousands of tiny micro-movements coordinated with your eyes in real time, where each letter is a different shape that requires the brain to solve a slightly different spatial problem.
Your fingers, wrist, vision, and the parts of your brain that track position in space are all working together to produce one letter, then the next, then the next.
Typing throws all of that away. Every key on a keyboard requires the exact same finger motion regardless of which letter you are pressing, which means the brain has almost nothing to integrate and almost no problem to solve.
Van der Meer said it plainly in her interviews.
Pressing the same key with the same finger over and over does not stimulate the brain in any meaningful way, and she pointed out something that should scare every parent who handed their kid an iPad.
Children who learn to read and write on tablets often cannot tell letters like b and d apart, because they have never physically felt with their bodies what it takes to actually produce those letters on a page.
A decade before her, two researchers at Princeton ran the same fight using a completely different method and ended up at the same answer. Pam Mueller and Daniel Oppenheimer tested 327 students across three experiments, where half took notes on laptops with the internet disabled and half took notes by hand, before testing everyone on what they actually understood from the lectures they had watched.
The handwriting group won by a wide margin on every question that required real understanding rather than surface recall.
The reason was hiding in the transcripts of what the two groups had actually written down.
The laptop students typed almost word for word, capturing more total content but processing almost none of it as they went, while the handwriting students physically could not write fast enough to transcribe a lecture in real time, which forced them to listen carefully, decide what actually mattered, and put it in their own words on the page.
That single act of choosing what to keep was the learning itself, and the keyboard had quietly skipped the choosing and skipped the learning along with it.
Two studies. Two countries. Same answer.
Handwriting makes the brain work. Typing lets it coast.
Every note you have ever typed instead of written went into your brain through a thinner pipe. Every meeting, every book highlight, every idea you captured on your phone instead of on paper was processed at half depth.
You did not forget those things because your memory is bad. You forgot them because typing never woke the part of the brain that would have made them stick.
The fix is the thing your grandmother already knew.
Pick up a pen. Write the thing down. The slower road is the faster one.
After several years of declining search traffic, Condé Nast CEO @rogerlynch has directed all the company's brands to operate as if search traffic to their properties will be zero.
He says the era of turning search and social media traffic into profitable businesses is gone.
And that if you run a media business that doesn't have an authoritative brand, a very strong niche, or a direct audience, you're going to be fighting hostile algo changes all the way down.
He describes a recent board meeting:
"We took a snapshot of search results from seven or eight years ago. And what you saw were a few sponsored links, then the ten blue links."
"Do the same search today, you get an AI overview, then you get rows and rows and rows of commerce links, then you get sponsored stuff."
"Each of the last three years, we would do our budgets, and we'd put forecasts in of search traffic declining. Because we'd seen the pattern of algorithm changes. And generally those algorithm changes were negative."
"Every year, our search traffic was down more than we had forecast. So last year I told our teams, 'Assume there's no search.' You have to have your businesses planned as if search is zero. We don't expect it to be zero, we expect it to be a single-digit percentage of our traffic."
In 14 minutes, this Anthropic engineer who wrote "Building Effective Agents" will
teach you more about building them right than most developers figure out on their own
in months.
Bookmark this for the weekend. Then read the builder's guide below.
Jerry Low retweeted
I wanted to share a bunch of my favorite hidden and under-utilized features in Claude Code. I'll focus on the ones I use the most.
Here goes.
Jerry Low retweeted
The sun was free. They sold you SPF 50 and a vitamin D deficiency.
Sleep was free. They sold you an app, a pill, and a wearable that tells you your sleep was bad.
Walking was free. They sold you a treadmill, a fitness tracker, and a £180 pair of trainers.
Fasting was free. They sold you meal replacement shakes and the anxiety that skipping breakfast would wreck your metabolism.
Cold water was free. They sold you a £3,000 plunge barrel and a podcast episode about it.
Silence was free. They sold you a meditation app with a premium tier.
Animal fat was cheap. They sold you seed oils, then supplements to replace what the animal fat contained.
Tallow was cheap. They sold you a seventeen-step skincare routine and a clinical trial proving your face needs ceramides.
Meat was cheap. They are currently selling you the idea that you shouldn't eat it.
The 20th century removed access to everything the body needs to function.
The 21st century is selling it back, one subscription at a time.
Your great-grandmother had none of the products.
She had all of the things.
LLMs process text from left to right — each token can only look back at what came before it, never forward. This means that when you write a long prompt with context at the beginning and a question at the end, the model answers the question having "seen" the context, but the context tokens were generated without any awareness of what question was coming. This asymmetry is a basic structural property of how these models work.
The paper asks what happens if you just send the prompt twice in a row, so that every part of the input gets a second pass where it can attend to every other part. The answer is that accuracy goes up across seven different benchmarks and seven different models (from the Gemini, ChatGPT, Claude, and DeepSeek series of LLMs), with no increase in the length of the model's output and no meaningful increase in response time — because processing the input is done in parallel by the hardware anyway.
There are no new losses to compute, no finetuning, no clever prompt engineering beyond the repetition itself.
The gap between this technique and doing nothing is sometimes small, sometimes large (one model went from 21% to 97% on a task involving finding a name in a list). If you are thinking about how to get better results from these models without paying for longer outputs or slower responses, that's a fairly concrete and low-effort finding.
Read with AI tutor: chapterpal.com/s/1b15378b/pr…
Get the PDF: arxiv.org/pdf/2512.14982

































