HN user
I fed ChatGPT the map with no other context, just “tell me about this function”. It did a bit of work finding the Jacobean etc and eventually worked out the implications of what it was seeing. It then proceeded to check the arithmetic 4 times, and then decided to do a manual verification using an ad hoc symbolic checker in case its SymPy had been tampered with.
I saw it and completed it as “top posts” “new posts” “hot posts” “my posts”.
Once a spec becomes sufficiently large and detailed and complicated, it becomes very difficult to ensure it is internally consistent. That's why I start every project with a METASPEC.md so that Claude can break up the task of writing SPEC.md into manageable steps.
They aren't saying that other models have the same overall level of capability. They are saying that the specific capability that the US Government tested is also available in other models.
20 years ago everyone was sold css entirely on the premise that, once the standards were adopted by all of the browsers, we would all be writing purely semantic html with completely orthogonal and swappable css. And today literally no one designs web sites that way - html today is mostly specific to presentation. It feels like pretty dramatic technological failure to me.
For Vim it isn’t replacing mouse necessarily. It’s giving you another way to navigate the cursor around the buffer by giving you absolute references rather than relative motions.
“Fed up” as a phrase comes from feeding livestock up to their fill. It’s very similar to how you would say “filled up”. So the upness comes from raising the level up to the limit.
For a long time I thought that the AdSense business model was ultimately doomed because I assumed that people hate ads as much as I do. It turns out I was just wrong about what most people are willing to put up with.
While I'm really enjoying this paper, I think you are way overstating the significance here. This is mathematically interesting, and conceptually elegant, but there is nothing in this paper that suggests a competitive regression or optimisation approach.
I might have misunderstood, but from the two "Why do X when you can do just Y with EML" sentences, I think you are describing symbolic regression, which has been around for quite some time and is a serious grown-up technique these days. But even the best symbolic regression tools do not typically "replace" other regression approaches.
I’ve been building a toy for exploring elliptic functions, modular forms, and elliptic curves. Sorry mobile support is not there yet.
Well, that wasn’t my conclusion at all to be clear!
Very cool concept and execution, well done.
I don't quite understand what is going on with the "spotlight" UI concept - I can click around on the characters and it highlights an area and it also reloads the landscape local to the character that I clicked on, so I can sort of traverse the similarity landscape this way. But I feel like I might be missing some part of the visual metaphor?
I think your assumption about inlining is essentially correct. As far as I know postgres was the last major rdbms to have an optimiser fence around CTEs.
I've been working on a side project for ~10 years (very intermittently) that involves a tricky combination of mathematics, classical AI algorithms, and programming language design, and I've gone though this very slow but rewarding journey to work out how all of the pieces should fit together properly.
In the last year or so I've been able to prototype it and accelerate the development quite significantly using Claude and pals, and now it is very close to a finished product. One one hand there's no doubt in my mind that the LLM tools can make this sort of thing faster and let you churn through ideas until you find the right ones, but on the other hand, if I hadn't had that slow burn of mostly just thinking about it conceptually for 10 years, I would have ended up vibe coding a much worse product.
Openclaw has 20k commits, almost 700k lines of code, and it is only four months old. I feel confident that that sort of code base would have a no coherent architecture at all, and also that no human has a good mental model of how the various subsystems interact.
I’m sure we’ll all learn a lot from these early days of agentic coding.
I think the total volume idea is more flawed than you realise. Pretty much everyone would be able to achieve greater volume, on any exercise, just by decreasing the weight, so your high rep caveat is covering up for quite a lot. This is true mathematically for an Epley style model for example.
To add to this, I find talking to it about code quality or architecture issues can work quite well. Just treating it like another developer. Saying, “I’m not happy with the way the project is going because of X, and Y” and then making a plan for how to get things back on track. Maybe putting a complete rewrite on the table, or maybe just having it record the agreed code style principles in CLAUDE.md, etc
I think I have almost the opposite intuition. The fact that attention models are capable of making sophisticated logical constructions within a recursive grammar, even for a simple DSL like SQL, is kind of surprising. I think it’s likely that this property does depend on training on a very large and more general corpus, and hence demands the full parameter space that we need for conversational writing.
Concrete Mathematics is probably the best single book that you could read to prepare you for some the problems beyond the first 50. It’s extremely fun, and also mathematically serious. A large portion of PE problems are exactly in the cross sections of number theory, combinatorics, and computation that is covered in this book.
Well, one might use picard to find a musicbrainz release id, so that beetz has something to grab on to when importing.
It's just a good set of models to use to think about all sorts of different mathematical systems, kind of like a unified vocabulary. Beyond undergraduate level, category theory these days plays a huge role within many vast fields - e.g., algebraic geometry, algebraic topology, or representation theory.
I do this at the moment in my hand rolled personal assistant experiment built out of Claude code agents and hooks. I describe my workouts to Claude (among other things) and they are logged to a csv table. Then it reads the recent workouts and makes recommendations on exercises when I plan my next session etc. It also helps me manage projects, todos, and time blocked schedules using a similar system. I think the calorie counter that the OP describes would be very easy to add to this sort of set up.
Can you explain why? Genuinely curious as a lover of case/match. My only complaint is that it is not general enough.
In my experience, complicated rules like this are extremely unreliable. Claude just ignores it much of the time. The problem is that when Claude sees a failing test it is usually just an obstacle to completing some other task at hand - it essentially never chooses to branch out into some new complicated workflow and instead will find some other low friction solution. This is exactly why subagents are effective: if Claude knows to always run tests via a testing subagent, then the specific testing workflow can become that subagent’s whole objective.
This would mean moving to 100% weighted exams, and there's good reasons why there has been a general trend away from that over recent decades. For one thing, some students simply perform better under pressure than others, independent of their preparedness and knowledge of the material.
Mind you, I don't really have any alternative suggestions.
This perspective is the inspiration for much of lattice theory. When you consider implication as an ordering, then "x and y" becomes max(x, y), "x or y" becomes min(x, y). True becomes the top term, False becomes the bottom. One of the neat implications is that much of what we think of as being propositions in boolean algebra also work in the wider setting of Heyting algebras i.e., any lattice that also has implication.
Sometime very soon we’ll cross a threshold where most people can do most of their coding through a tool like Claude and be more productive. It will feel like coding still, breaking and building things, but they will get more done in the same time. Everyone will switch.
This ain’t what you asked but I’m using Claude Code with a pro subscription and I get about an hour use out of it before I run out of tokens. Then I spend 4 hours thinking about how to set up my context for the next session.
I’d say this is firmly about knowing how colour mixing works, and not about memorising.
I think both approaches are useful. AI2027 presents a specific timeline in which a) the trajectory of tech is at least somewhat empirically grounded, and b) each step of the plot arc is plausible. There's a chance of it being convincing to a skeptic who had otherwise thought of the whole "rogue AI" scenario as a kind of magical thinking.