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Seeing people do 100+ replies a day to build an audience on X felt impossible.
Then I remembered 2023, when I challenged myself to help 100 people on @StackOverflow in the #elasticsearch tag, one of my areas of expertise.
It felt the same. I ended up doing it. I learned a lot, met great people, and the score compounded for years, until AI killed SO.
So here's the new challenge. Same game, new platform.
30 days. 30 replies a day, going up every week.
Goal: 1,000 followers.
Day 1 starts now 👇
OpenAI should acquire both Theo and Leo
I have a strong feeling that OpenAI will have another code red very soon
I have a couple of reasons for this but the main reason for this is that we are almost in the same position as last time
What happened last time was that OpenAI started focusing on lots of different things like ChatGPT Atlas and Sora. Those were cool experiments but they never brought any revenue and they never actually went anywhere.
Here are a couple other reasons why I think this code red will happen soon:
1. Opus 5.5 is an objectively better model by both benchmarks: how well it codes and, in general, how it feels to work with it/its taste
2. The second issue OpenAI faces at the moment is that the limits, compared to Claude Code, are so much worse. On Claude Code you get around 2 to 3 times more on the $200 plan.
3. Third of all OpenAI is kind of falling behind with its Codex app. Now this might be a hot take but in my opinion the Codex app is a lot worse than something like the Claude desktop app, and ChatGPT dots were just a complete flop. While Claude Duets will probably be better than it in less than a week.
4. My fourth reason is that most enterprises don't really choose OpenAI. The way that OpenAI actually grows further is mainly by earning money off enterprise revenue and currently I don't think this is happening.
I don't know how OpenAI is going to fix this. If I were Sam Altman I'd personally go ahead and acquire the T3 team. They'd get awesome developers. They'd also get literally the best orchestration plus the best performance app and finally they'd also improve their website a lot more (cause t3 chat is awesome).
Again this is my opinion but I do strongly feel like OpenAI is losing heavily.
Two important changes to timeline recommendations:
- New AI/SI ranking model that predicts probable time spent much better
- Retrieval model trained to surface posts people read all the way through
User active seconds is growing exponentially and hitting all-time highs almost every day:
Asking agents to summarize -> video explain on your style is quite and upgrade.
But don't address the core problem: How do we consume all this volume of information we can produce now?
I was the first advocate of explainer videos but it is not enough
I've been thinking about something as more and more AI agents start generating videos.
A lot of people are asking: if agents can create hundreds of videos, how are we supposed to review all of them?
I actually think that's the wrong way to look at it.
Today, most agents communicate with us through text. They do research, analyze information, and give us a long report to read.
But why does everything have to come back as text?
I think it depends on what the agent is trying to do.
If an agent needs me to make a decision, confirm something, or give feedback, text is probably the best format. It's quick, precise, and easy to interact with.
But if an agent is trying to explain something to me, that's a different story.
For example, recently I wanted to understand how JEV works, what it does, and the different use cases. Instead of spending an hour going through documents and links, I just asked my agent to research it and explain everything back to me in a video.
And I loved that experience.
I could just watch and listen. The combination of visuals, audio, and explanation made it much easier for me to build an understanding of the topic.
It got me thinking that we've been looking at agent-generated video mostly as a content creation problem.
But I think there's a much bigger opportunity here.
Video could become one of the most natural ways for agents to communicate with humans.
Not every interaction needs to be a video. Text is still great for back-and-forth communication.
But when an agent has done the work and wants to present what it learned, why shouldn't it just explain it to me through video?
I think we're going to see a lot more of this.
And personally, I'm pretty excited about that future.
Synonyms are probably the most underrated asset in search.
Companies spend millions indexing vectors for internal search engines so USA returns "UNITED STATES". That can be solved with synonyms.
Use vectors when
Consider vectors if users ask questions instead of dropping keywords on the search bar, and be prepared to pay more, and see more noise in the search results.
On an enterprise context is much easier to tune the system and train the people.
LLMs are also very good at query expansion (e.g adding the synonyms to the query, so you don't have to add them to the search engine).
So as an expert user you can ask your agent to expand the query on certain ways.
users are exploring, very unlikely on internal search engines.
On enterprise systems users are trained and will tend to search for existing keywords. Most of the time they know what they are looking for, so vectors only add noise.
For this reason I'm struggling hitting any goal I set. Quality > quantity
the big trap is using AI to increase your recall instead of your precision. I fell.
Day 4/28 of @llermaly's replies challenge: 39/30 today, 166 total, 4-day streak. Follow along: llermaly.github.io/replies-c…
My current way to provide feedback and grade generated media: build an app, go evaluate there, come back to the chat.
You can make T3 Code embed a review panel in the chat instead.
I'm sold.
I just tell my @bot to find people, or topics and it filters all the noise.
Is this the end of the scrolling?
Nice use case for @bot: help finding people to connect with.
My account was inactive for a couple of years and my interests have changed.
The algorithm wasn't recommending many people who align exactly with me, but Grok Bot was able to find interesting posts on point.
There are plenty of benches for 3D animation, coding, even writing, but I found a big predictor of model intelligence was its ability to create video stories where it has to pay attention to a lot of details:
script coherence, character consistency, cameras, expressions, etc.
So I'm creating a bench to discover if:
1. Effort levels affect output quality
2. Some models underperform in code but overperfom on this tasks
3. Adding hyperframes skills improves weak models performance












