Magnus Aschan

🚀 Skribent, producent, föreläsare & professionell geek. Jag berättar om hur teknik och vetenskap gör världen bättre. To infinity and beyond!

[Magnus Aschan @MagnusAschan]

🤯

[Anthropic @AnthropicAI]

New Anthropic research: Natural Language Autoencoders.

Models like Claude talk in words but think in numbers. The numbers—called activations—encode Claude’s thoughts, but not in a language we can read.

Here, we train Claude to translate its activations into human-readable text.

[Magnus Aschan @MagnusAschan]

Här är Sveriges största promptbibliotek för yrkesverksamma. 100+ prompts, även flöden (med flera prompts som till exempel hjälper dig gå igenom en hel marknadsföringskampanj) och teman.

https://t.co/hZYX472fDi

[Magnus Aschan @MagnusAschan]

Det är få som väljer @Copilot om de får välja AI-verktyg fritt på jobbet. Men oftast är det bara det som finns, officiellt. Leder det här till mer #shadowAI? https://t.co/Z1QkAnj3Ab

[Magnus Aschan @MagnusAschan]

🤯 UI går in i nästa fas

[Cheng Lou @_chenglou]

My dear front-end developers (and anyone who’s interested in the future of interfaces):

I have crawled through depths of hell to bring you, for the foreseeable years, one of the more important foundational pieces of UI engineering (if not in implementation then certainly at least in concept):
Fast, accurate and comprehensive userland text measurement algorithm in pure TypeScript, usable for laying out entire web pages without CSS, bypassing DOM measurements and reflow

[Magnus Aschan @MagnusAschan]

https://t.co/5P3i1XgJwx

[Magnus Aschan @MagnusAschan]

Du är nyfiken på Claude, men var börjar du? I min guide till exempel! 👇

[Magnus Aschan @MagnusAschan]

Jag ser tre stora skiften i alla branscher där vi jobbar med generativ AI, säger jag i en intervju i @dagensjuridik (tillsammans med en massa andra kloka saker såklart). 🤓

https://t.co/SeimuZGmDW

[Magnus Aschan @MagnusAschan]

🧪 Gemini-modellerna är intressanta att följa nu, 3.1 Pro innebär trots det lilla versionshoppet stora förbättringar inom vissa områden. Jag testade och skrev ihop några rader. #gemini #aisverige

Läs på: https://t.co/h414b961Up

[Magnus Aschan @MagnusAschan]

Can relate. 👇

[Nikunj Kothari @nikunj]

https://t.co/3mnuYn7jfP

[ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️ @DanielMiessler]

Just remember:

The smarter and more capable your AI gets....with more and more tools, and more and more autonomy, the nastier it'll be when it gets prompt injected.

[Dario Amodei @DarioAmodei]

The Adolescence of Technology: an essay on the risks posed by powerful AI to national security, economies and democracy—and how we can defend against them: https://t.co/0phIiJjrmz

[Andrej Karpathy @karpathy]

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.

[Magnus Aschan @MagnusAschan]

https://t.co/QwqnXWCPQ6

[Magnus Aschan @MagnusAschan]

🦀 Personliga AI-assistenter som Clawdbot lovar att ta hand om din e-post, boka dina möten och styra dina appar. Men innan du ger en AI nycklarna till ditt digitala liv finns det några saker du bör ha koll på. Tekniken är mind-blowing men, det är även säkerhetsriskerna. 👇

[Magnus Aschan @MagnusAschan]

Det är alltid intressant att följa vad Moxie Marlinspike, skaparen av Signal, gör. Nu har han lanserat AI-tjänsten Confer baserat på samma principer som Signal. Jag har börjat testa och skrev ihop en artikel.

https://t.co/rrZeKYiW82

[Magnus Aschan @MagnusAschan]

@businessbarista Claude

[Magnus Aschan @MagnusAschan]

Börjar bli exakt 🤟

[Clone @clonerobotics]

Building a fully actuated, human-level robotic hand by strength, speed, and range of motion is hard. Making it incredibly durable is even more challenging.

Clone has pioneered anthropomorphic hands with 27 degrees of freedom, human-level grip strength and speed, and the most durable artificial muscles in the world.

The Hand in this demo is missing middle flexor muscles, but here’s an update on the neural joint controller.

[Vardagshacker @vardagshacker]

🤯 Paretometoden – 80 procent resultat med 20 procent insats

🤓 Paretometoden hjälper dig att kapa bruset, välja bort uppgifter som ger låg effekt och lägga fokus på de få aktiviteter som verkligen driver resultat.

🛠️ Läs mer och lär dig hacket på: https://t.co/3VcxEe0QHI

[Sakine @Sakine]

...inte kan man få nog av Rushdie-citat? Här kommer en jag snubblade över först nu: ”Vi har de friheter vi kämpar för och förlorar dem vi inte försvarar.” (The Guardian, 1997.)

[Magnus Aschan @MagnusAschan]

Wow 👇

[OpenAI @OpenAI]

Sound on.