Rather than watching Usher during the Superbowl halftime you should check out this new app I built!!
Get the performance of any NBA player in any stat category for any game. Understand how they played or how they have been playing for upcoming games.
Link: sa360 dot io
Composer 2.5 is outstanding. Cursor's has been a daily driver for a year but struggles with limits because it can't subsidize token cost..
But it can with Composer and now that it's on par, it's the main model I go to. Incredible work by the @cursor_ai team.
Cursor could well make an imporbable comeback by... offering the best bang-for-buck for coding models!
Charging 1/20th the price (Composer 2.5) vs Opus 4.7, with similar coding characteristics.
I expect Cursor to win back a lot of market share thanks to this.
@austin_rief Swim was easier than I expected.
Bike was as expected.
Run was much hard than I expected.
Essentially it gets harder the further you go.
Train the long bike to run. Plan fuel and train injesting fuel during long training.
Then expect it to suck on race day and enjoy it.
I've used @cursor_ai and @windsurf both for the past 12 months. Probably equally.
Cursor rate of improvement has been noticeably steeper than Windsurf. Not even close.
I guess that's what happens when you lose your whole leadership team to google...
My biggest takeaways from @bcherny:
1. Coding is now “solved” for most use cases. Boris hasn’t written a single line of code by hand since November, with 100% of his work now authored by Claude Code. At the same time, he remains one of the most productive engineers at Anthropic, shipping 10 to 30 pull requests daily while leading the team.
2. Anthropic has seen a 200% increase in engineer productivity since adopting Claude Code. As Boris notes, “Back at Meta, with hundreds of engineers working on productivity, we’d see gains of a few percentage points in a year. Now we’re seeing hundreds of percentage points.”
3. AI is moving beyond writing code to generating ideas. “Claude is starting to come up with ideas. It’s looking through feedback, bug reports, and telemetry, then suggesting features to ship.”
4. The next roles to be transformed are those adjacent to engineering. Product managers, designers, and data scientists will see similar transformations as agentic AI expands beyond coding. “Any kind of job where you use computer tools will be next.”
5. Build for the model six months from now, not today. One of Boris’s key principles is to design products for future AI capabilities, not current ones. “It’s going to be uncomfortable because your product-market fit won’t be very good for the first six months. But when that model comes out, you’ll hit the ground running.”
6. Watch for “latent demand.” Claude Code was built by observing what people were already trying to do, and then making it easier. Cowork emerged when they noticed people using Claude Code for non-coding tasks like analyzing MRIs or recovering wedding photos from corrupted drives.
7. Don’t optimize for token cost. Boris advises companies to give engineers unlimited tokens during experimentation phases. “At small scale, the token cost is still relatively low compared to their salary. If an idea works and scales, that’s when you optimize it.”
8. Underfund headcount on purpose. When Boris puts one engineer on a project, they’re forced to let AI do more of the work. Constraint drives creative use of AI tooling, not just faster typing.
9. The most successful people in the future will be generalists. “Try to be a generalist more than you have in the past. Some of the most effective engineers cross over disciplines. The people who will be rewarded most won’t just be AI-native—they’ll be curious generalists who can think about the broader problem they’re solving.”
10. Always use the most capable model, not the cheapest. A less intelligent model often burns more tokens correcting mistakes than a smarter one spends getting it right the first time. Boris runs maximum effort on Opus 4.6 for everything.
Here's the full conversation: https://t.co/4hHAEq0Nto
Right now everyone is trying to learn how to use coding agents.
Which makes sense. But I expect that soon the meta will shift back to teaching engineering foundations.
Have some interesting ideas here 😄
The goal is to preserve semantic optionality as long as possible, only collapsing meaning when the task demands it.
-- how to think about information and AI
I used to think my wide array of interests that adapted quickly was a bad thing.
Not until I read 'Suley You're Joking Mr.Feynman' did I understand that a wide array of interests, with agency can get you to unimaginable places.
This article embodies that as well.
The permission to explore is best matched with agency.
In months you'll be in places you would have never expected.
Keep exploring, keep learning and don't stop being curious.
World's most unaffordable housing markets
1. 🇭🇰 Hong Kong
2. 🇦🇺 Sydney
3. 🇺🇸 San Jose
4. 🇨🇦 Vancouver
5. 🇺🇸 Los Angeles
6. 🇦🇺 Adelaide
7. 🇺🇸 Honolulu
8. 🇺🇸 San Francisco
9. 🇦🇺 Melbourne
10. 🇺🇸 San Diego
11. 🇦🇺 Brisbane
12. 🇬🇧 London
13. 🇨🇦 Toronto
14. 🇦🇺 Perth
15. 🇺🇸 Miami
(Demographia)
Today, AWS CEO Matt Garman announced Nova Forge, a model builder which lets companies inject their own data during the pre-training phase.
"You [tell Forge]: 'Here's my corpus of corporate data, here's everything I need to know about my industry.' We then mix that in and finish pre-training the model. So you get a pre-trained model that totally understands your company and your data."
"I flirt with the idea that smart TVs should be illegal. I hate them so much." - @PalmerLuckey
Instead of building a TV, manufacturers feel like they need to be a services company, an app store, etc.
"I wouldn't be surprised to see @modretro make a modern technology display."
@DaronGreenblatt Really good read - I shot you a message on substack but loved the points on qualitative analysis of players handling future predictions!
We should chat. Message me!
Quickest examples are:
@AviSchiffmann with Friend
@im_roy_lee and Cluely
I don't agree with everything they do, but I hope they prove all the haters wrong.
It's crazy in tech how everyone can so obviously cheer for someone and then so quickly all jump to shit on them once they are perceived to be wrong once.
An industry that is built on taking big bets that will likely fail is also the first to laugh at those wrong bets.
Whenever I see it, it makes me want to see those people succeed even more.