How does AI impact skill formation?
Linked is an insightful article commenting on a new anthropic paper
Linked is an insightful article commenting on a new anthropic paper
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.
Linked is an article with just observations of what it’s like to code with LLMs. In this case, with Claude. For me the observation are insightful and match my experience pretty well
The best ChatGPT that $100 can buy
Linked is a local LLM that looks very interesting. Although I dont know yet what it is or how I would use it.
The ultimate all-in-one guide to mastering Claude Code.
Linked is one of various Claude tips collections. From setup, prompt engineering, commands, hooks, workflows, automation, and integrations, to MCP servers, tools, and the BMAD method—packed w...
By Nicolas Hulscher, MPH
Linked article makes a pretty depressing claim. Some bias in my brain immediately went to look for evidence that the paper was. It credible… who wrote it, are they scientists, how big was the sample size, etc. Funny how I didn’t like the claim, and instead of believing it I looked for ways to…
A visual introduction to big O notation.
Linked is a BEAUTIFUL and instructive explanation of the basics of so-called Big-O notation
Welcome to PyViz! The PyViz.org website is an open platform for helping users decide on the best open-source (OSS) Python data visualization tools for their purposes, with links, overviews, comparisons, and examples.
Linked is a big ole catalog of all the different visualization tools, packages and frameworks in python. Handy resource if you are experiencing analysis paralysis!
Linked is (one of many I am sure) tool that creates pretty amazing images from prompts. And there's a pretty capable free tier!
Learn Python typing (type hints) with interactive online
exercises!
Linked is a useful tool to level up on your python.
Amazing ai generated videos
Linked is a service that generates quite respectable short promotional videos. I tried it on the syllabus to my course and it created something fairly amazing. I mean it’s still obviously ai generated. After your amazement subsides you may still not want to use it but it’s worth checking out.…
Beautiful, easy data visualization and storytelling
Flourish is a pretty amazing tool. Can’t wait to try it myself. Just click on the links and try it out.
Ruff is an extremely fast, modern linter with a simple interface, making it straightforward to use. It also aims to be a drop-in replacement for other linting and formatting tools, like Pylint, isort, and Black. It's no surprise it's quickly becoming one of the most popular Python linters.
The linked article goes in depth about ruff. Everyone loves ruff. Ruff is written in rust. Everyone loves rust. I think that last part is weird. Rust is a cool and interesting language but it is also pretty low level. Sure, for the user, rust programs tend to be very fast. But for the programmer,…
Large language models (LLMs) such as OpenAI's GPT series have shown remarkable capabilities in generating fluent and coherent text in various domains. We compar
I often say that ideas are cheap. And by that I mean that everyone has a million decent ideas. The art/trick/skill is to know how to turn that one idea into something that people want, use, benefit from, and maybe are willing to pay for. That's why I don't often worry about "who had that idea…
No course has ever been this engaging and effective in teaching hard-to-master STEM skills!
Another online robotics development environment.
Train a computer to recognize your own images, sounds, & poses.
A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required.
A web based tool, free to use, designed for education and teaching. Not heavy duty for professional use.
The first real AI developer. Contribute to Pythagora-io/gpt-pilot development by creating an account on GitHub.
Interesting. There are more and more of these coming out. For now I doubt they work very well, in practice. However the trend is clear and they will get better and better. Are our jobs at risk? Only if we don’t learn how to use these tools to up our game. A graphic designer or photographer that…
A framework for rapid development and deployment of production-ready RAG systems - SciPhi-AI/R2R
I’m not recommending this as I have not tried it. But it could be something useful for my project and yours.
Consensus is a search engine that uses AI to find answers in scientific research.
yada yada