Mike Kane

For every LLM problem there is an LLM solution.

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.

torch for #rstats v0.16.0 is now on CRAN. Main change is upgrading to libtorch 2.7, so we can support CUDA 12.8. See release notes in github.com/mlverse/torch/…

Remember when IT automation was a simpler time? A time when we just wrote a Bash or Perl script and let cron jobs handle the rest. Now, we've traded that simplicity for a vast landscape of tools, and a new kind of complexity has emerged and laughs at us.

🚀 Webinar 8/28 10 AM PT | Git, GitHub CI/CD & LLMs for clinical trials. 15+ pharma programmers reveal: ⚙️ Branching/PRs, 🤖 Actions‑driven QC, ✨ LLM edge‑case checks. Speakers: Ning Leng, Eli Miller, Ben Straub. Register ➡️ r-consortium.org/webinars/unloc… #rstats #opensource

NEXT WEEK! R Consortium webinar: Open Source Software Adoption in Japan's Pharma Industry Register for free here: r-consortium.org/webinars/open-… #rstats #pharma #Japan

Virtual R/Medicine data challenge - Deadline May 20, 2025 - $200 prize - Students or Professionals - Submit as an individual or a team! Analyze MMR vaccination rates! Examples, guidelines: rconsortium.github.io/RMedicine_webs… #rstats #opensource #RMed25

I run a law firm. Last week I spent 5 hours digging into TikTok’s terms of service. What I found absolutely terrified me. Here’s a breakdown of what you’re actually handing over when you install TikTok (with receipts):

Full video now available! Rix: reproducible data science environments with Nix Speaker: Bruno Rodrigues, Head of Stats and Data Strategy Departments, Ministry of Research and Higher Education, Luxembourg r-consortium.org/webinars/rix-r… #rstats #OpenSource

#RStats 4.4.3, released 40 minutes ago, is already available through rix by using today’s date!

doi.org/10.1093/jrsssc… 🚀 New research alert! Our paper in the Journal of the Royal Statistical Society Series C presents a new approach combining machine learning & Bayesian methods for personalized cancer prognostics. Discover how thoracic cancer immunity predictions are being enhanced with quantitative methods. Congratulations to our team: Duo Yu, Meilin Huang, me, and Brian P. Hobbs for getting this out.

Maybe my favorite scene ever from a TV show. Major Briggs tells his son about his vision. RIP David Lynch 🫡

AI coding replacing programmers is the modern version of calculators replacing mathematicians.

"You insist that there is something a machine cannot do. If you will tell me precisely what it is that a machine cannot do, then I can always make a machine that will do just that!" -- John von Neumann [emphasis added]

Julia's representation of vectors is a little bit different than either Python or R. It is true that the Julia statement: [1, 2, 3] is most like a R's c(1, 2, 3). It is also true that you can perform vector operations including: [1, 2, 3] + [1, 2, 3] [1, 2, 3] .+ 1 # to add 1 to each element (like in Matlab) However, Julia vectors can also accommodate different types. For example, the following is valid: [1, "a", 3]

In Julia, the statement [1, 2, 3] Is most similar to which of the following statements in R: A. c(1, 2, 3) B. list(1, 2, 3) #JuliaLang #RStats