Joined December 2025
Hedge-fund titan Ken Griffin is donating $2 billion to start a Carnegie Mellon University campus in Miami, part of the largest-ever individual gift toward higher education on.wsj.com/4AGpvMq
171
295
208
2,488
1,212,251
The fact that you can't even make out on a bench in Central Park without someone recording you on their iPhone and then posting it to social media as some kind of bit is everything that's wrong with society and I am not joking. The surveillance state (administered by an actual state or by the millions of people with phones who want to embarrass people on their TL) is essentially the least romantic thing imaginable in both the love sense and the living sense. It is anti-romantic and anti-life. Part of the reason why the before world was fun was the fact that you didn't have this kind of thing. The ability to do something without it being documented and recorded is the difference between being free vs an animal locked in a cage. There is a direct correlation between everyone constantly being afraid that someone is going to record them and post it somewhere and society becoming more neurotic, less fun, more boring, and less alive. The self-administered surveillance state is the enemy of life.
Rumored lawyers caught making out in Central Park goes viral and divides the internet trib.al/FWTTAUH
158
1,582
93
19,201
1,742,766
maybe this is controversial, but i believe what Cursor shipped here is a wrong solution to routing intelligence more generally, any attempt to do model routing at request level, while may yield some small gains, is fundamentally flawed and doomed to fail here's why - the complexity of a task only reveals itself when you start working on it. this is the same reason why we humans are often wrong when asked to give cost estimates upfront the correct solution is have a smart model (often a tech lead in human teams) do some planning and understanding, and hand over the implementation to another agent with appropriate level of intelligence and reasoning effort when the task is delegated to a less intelligent model, the smarter model also needs to continuously monitor the execution and examine outcomes to ensure things are on the right track this is a system that proved to work really well in firstmate and helped me save a lot of tokens. routing should work at the boundary between agents and subagents, not per each LLM request
Introducing Cursor Router, our intelligent model router that selects the right model for the task at hand. Router delivers frontier-quality results at 60% lower cost.
170
66
33
1,600
208,122
rip retweeted
"our apps let your agents work like a full fortune 500 company with our proprietary and customized personas" - brought to you by the team that has never worked in a F500 company "our ai coding harness and ai operating system lets you write faang quality code at scale that is secure and production ready " - brought to you by the team that has never worked in faang and has 400 active P0s and SEV1s
9
9
2
171
16,911
Really don’t understand the tech obsession with trying to automate the process of booking flights… I think I travel reasonably frequently and it’s really not a big deal to click around for a couple of mins to book a flight…
75
16
11
1,029
63,817
rip retweeted
alex bronzini-vender
@alexbronziniFeb 20
7
31
20
801
80,539
rip retweeted
THE UNBRIDLED GREATNESS OF TRAINS WILL ENDURE FOR A MILLION YEARS
1,509
8,040
2,149
57,834
10,126,352
A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.
1,630
5,569
1,457
40,709
7,860,265