- test should be considered part of the program
- the same for properties
- PBT and runtime checks should be able to be derived from props
- var name should be considered part of the doc. Docstring should not repeat what is already implicated by the var name.
Maybe I’m wrong, but I believe dependent types are a great fit for low-level programming because correctness there is relatively stable and easy to express formally. High-level features are different beasts. How do you formalize something like “Send no more than three notifications per day, except for urgent events”, let alone verify it? Things close to the business end are unstable in both meaning and priority. There is a case to be made for decoupling types and properties here. We can’t capture all the invariants we want, but we can choose to put some of what we can in types, some in properties, and we can leverage many existing tools to give us sufficient assurance. This is not to say dependent types are not worth it at high levels, though. I just think they need a friend in a separate proof system, ideally built into the same language.
@statusfailed I disagree with both of these claims. You can have what is essentially raw assembly + a type system. I have friend working on intrinsically typed / correct by construction compiler optimization and being able to write correct by construction MIPS is part of this.
I need a word to describe: "I saw a bug. I don't understand the bug. LLM came up with a fix I don't understand either. But it fixed the bug."
I really don't want to waste reviewer's time and make them think that I'm an idiot when attaching a gen-ai fix in a bug report.
I wonder what proxy they will come up to model the readability of codebase (not just code, but also a readable architecture design). Using Fable to evaluate Opus's code is definitely not good, because Fable has no issues with overused jargon and putting changing log in comments.
I think today's time is the turning point for AI labs to optimize for human communication rather than some "task"-related benchmaxxing in model training. The cost-benefit structure has made trading off readability for unreadable code golf not reasonable anymore.
I am resistant to use LLM to help me read a book. I have fear that if I don't read it from cover to cover, I'll get lazy and disrespect the knowledge and the author.
I just tried. It's good and efficient. I should probably do that more often. It's a skill to learn.
this critique is unfair because "computer science" as a field is less of a single field and more like at least 3 loosely overlapping domains:
1. (computer) science: the study of the actual machines we have: CPUs, caches, memory, networks, operating systems, databases, compilers, and algorithms. this is mostly the world of the von neumann machine, and roughly what an undergraduate CS curriculum centers on.
2. theoretical (computer science): complexity theory, information theory, turing machines, quantum computing, lambda calculus, and type theory. this is the mainstream mathematical theory of computation, and probably the part most criticisms of “computer science” are aimed at.
3. (theoretical computer) science: proof theory, category theory, philosophy, linguistics, and to a large extent just general mathematics. not many really know about it, and it's also just generally really hard because you start needing to know graduate level mathematics. topology, abstract linear algebra, functional analysis, differential geometry, ... are very often used. "computers" in this case don't necessarily mean "equivalent to turing machines" anymore, and often times are intentionally weakened to obtain better structural properties (ala Lafont's interaction combinators). furthermore, it is the upstream of most domain shifting ideas in computer science. to say that "computer science" only studies things like turing machines ignores an incredibly important and unusually generative part of the field
borrow checking? linear logic, via girard.
types? proof theory, via gentzen and curry–howard.
monads and algebraic effects? category theory and universal algebra, via lawvere, moggi, wadler, and many others.
so complaints about “computer scientists” usually need several asterisks. the field is not just intellectually fragmented, it's also sociologically fragmented! these communities often publish in different venues, use different standards of evidence, require different mathematical backgrounds, and are sometimes housed in entirely different departments.
Computer science is a weird field. It’s literally an entire field based around the operation of a particular machine. It's the equivalent of saying "piston-based internal combustion scientist" instead of mechanical engineer.
There are so many other ways to build a "computer".
Underrated LLM use case: if you have an expert conversation in almost any context you can feed a transcript/notes to an LLM and ask some variant of “What didn’t they tell me” to explore around space at higher level than you’d have access to w/o the expert’s seed context.
I don't understand why the migration target is Python (Python is my main language btw). I'd at least choose a language with ADT. OCaml? Scala? The conclusion might mean that Haskell was the wrong choice at the very beginning. LLM only makes correcting this error easier.
Andrew's post about the Bun Rust rewrite unfortunately starts off way too emotional.
But this is a fantastic point made inside the article
(Sadly it comes after many paragraphs of personal attacks, but it is a very valid point)
124 Followers 2K FollowingWe all underuse agents. Improving your productivity @Branchmore by finding token and other waste. Ex-Dir Eng @PermisoSecurity, CTO @Printivity. https://t.co/Di7LM3QgiI.
2K Followers 1K FollowingCreature Director. Building 🤖 Eric Robot-790: a fun companion robot with personality, imagination, memory and an inner dialog.
MIT / Media Lab / DEC R&D.
84K Followers 2K FollowingBren Professor of Computational Biology @caltech. Blog at https://t.co/FFQzhEsmhi. Tweets represent my views, not my employer's. #methodsmatter
6K Followers 2K FollowingThe cat is the Otocolobus Manul, https://t.co/Xswt7Vp2F1 . Manul is the perfect privacy mascot. All views & opinions are my own & personal.
13K Followers 125 FollowingIcelander. Eudaimonist. Individualist. Comonad enthusiast. Co-creator of the Unison programming language. Co-author of Functional Programming in Scala.
774 Followers 10 FollowingFUN OCaml is a 2 days open source conference and hacking event for OCaml enthusiasts and professionals
January 25+26, 2026 in Bengaluru, India
9K Followers 325 FollowingWe apply formal methods to improve the safety, reliability, and correctness of computing systems for auto, space, and blockchain.
713 Followers 21 FollowingMulti-classed Software Engineer/Constructive Mathematician. Sometimes plays video games sort of fast. Puts topoi in your computer. https://t.co/iFlKRVeGpN
2K Followers 355 FollowingChief Product Officer @ @erscio
I've written a lot of Rust, and now I make AI do that. Previously, @oxidecomputer, @cloudflare, and others
520 Followers 254 FollowingHardcore ML for silicon and analog/mixed-signal R&D at Apple—design, verification, adaptive optimization • ex-CNRS • Physics PhD
26K Followers 0 FollowingPure Mathematician+pianist. Grieving involuntary childlessness. Author: Unequal: The mathematics of when things do and don't add up, out 2025
885 Followers 233 FollowingI'm a software engineer, father and wishful amateur surfer. If you've come seeking my political views; you've found the wrong Fareed.
2K Followers 1K FollowingLLM theory and interpretability.
Professor at Saarland University @LstSaar @SIC_Saar.
Previously PhD at Stanford @stanfordnlp.
Hiring PhD students and Postdocs