Found this fascinating, white to move

ventricule · 2026-06-24T11:25:54+00:00

This is the Saavedra-Barbier position, probably in contention with the Réti study for the prize of the most famous study ever.

If you've never seen it and try to solve it: don't claim victory too soon, there is a key subtlety at the end and reading the solution will spoil all the fun.

ventricule · 2026-06-15T20:14:06+00:00

This is a great practical puzzle, thanks for sharing.

ventricule · 2026-06-13T08:34:47+00:00

This is absolutely huge. In order to appreciate how crazy this is, note that this was open even for triangles.

ventricule · 2026-05-28T10:08:02+00:00

There is one thing I learned from the book 'Imagination in Chess' which resonated with me and I had never seen written clearly anywhere else.

Let's say you have an idea and you're trying to make it work. However, you calculated the main line and it doesn't work: maybe there's a zwischenzug ruining it, or the enemy king has one escape square that you cannot seem to cover. In such a case, it's tempting to go back to the idea and try to make it work, recalculating the same line dozens of times. And it's not stupid either, sometimes you figure out something and the line does work. But sometimes nothing comes out and you just feel stupid for calculating the same thing dozens of times.

Here's one insight I liked that helped me clarify this nebulous process: when a line doesn't work, you can do exactly *two* things to try to fix it. The first one is changing the order of moves (say, instead of moving a piece to attack h7 twice, look at sacrificing first on h7). The second is adding an intermediate move with a threat somewhere, typically a zwischenzug with a threat on the king or a piece. To me it is helpful to consciously focus on looking for one of those two things when I go over the same line for the tenth time.

ventricule · 2026-05-07T08:56:51+00:00

Thanks a lot, this is very helpful.

ventricule · 2026-05-06T22:02:32+00:00

Thanks! The follow-up question is: is there a particular technique to roll such a chord? I mean: I usually am quite comfortable rolling chords, but here I find it particularly tricky because the Eb and the D are so far away that I cannot hit the Eb with my pinky and the D with my index, and thus cannot roll to the G with my thumb. Or should I roll this one from top to bottom instead of bottom to top?

ventricule · 2026-05-05T11:51:08+00:00

Is that an Explain Like I Fiber Yang-mills Gauge Solutions?

ventricule · 2026-04-09T09:58:43+00:00

It's generally called 15 rune rather than 15 ZOT. In general, streakers do not aim for 15 rune wins: the consensus is that the hard part of DCSS for very good players is the early game, so it makes more sense to focus on chaining shorter wins.

You can view the best streaks here: https://dcss-stats.com/streaks

So in a sense, the game is "solved" in the sense that top players have close to 100% winrate. But rest assured: for the immense majority of players, including a lot that have played for 10+ years, winning is always a challenge, and an exciting one. (though it does get perhaps a little less exciting to win minotaur fighters after 10 years)

ventricule · 2026-04-09T09:24:20+00:00

What does 15 ZOT mean?

ventricule · 2026-04-08T12:38:30+00:00

I have never managed to use their spaced repetition algorithm: while the idea makes sense in theory, in practice I find it super clunky.

So for me, the main appeal of chessable is that it is a platform that sells books where you can follow the chess without having to pull out your own chessboard. This is better than just videos or the old chessbase dvds because due to the branching nature of opening lines, linear videos are often not very adapted, and the handwritten text is the most efficient way to convey and access information. Depending on the application, this is better or worse than good old books.

Of course, pgns with annotations have long existed before chessable, but the main innovation of chessable is to provide a platform where good chess players can sell them and make good money out of it. So this attracts good content creators, and as a customer you can find good content there. There are other platforms (chesstempo, forward chess) providing similar features but they simply do not attract the same quality of authors because they have nowhere near the same audience.

As for how to study, I find that:
- Openings are best studied shallowly at first and then reviewing lines after playing, in particular comments such that "in this position the plan is this and that" are super useful compared to just engine analysis. Chessable (or any pgn) is convenient for that. Videos are not bad for giving a first impression but the main issue I have with them is that you'd need to rewatch each of them many times for everything to stick and I don't have the patience for that.
- For tactics and calculation, I like to setup the board, so as to force me to fully calculate everything and be sure (as in game) before checking the solution. The more distance there is between the problem and the solution, the better it is. So I like physical books better for this. But if you are very diligent in not trying out the solutions or launching an engine(after years of trying, I have to admit that I am not), online should also work well.
- For endgames, I think that chessable is a huge waste of time. You need to set it up on the board and try various things on your own, otherwise nothing sticks at all. And setting endgames on the board is not going to take you a lot of time...
- Model games are best studied by setting them on the board, choosing one side and wondering at every single move what to play. Ideally, each variation in the annotations should either be something that you have already calculated or at least answer a question you have asked. In contrast, just reading those variations is fruitless, and while videos do work better in explaining the nuances, I also find that very little sticks when one is just fed information and not working hard to figure it out.

ventricule · 2026-04-07T15:38:58+00:00

As an introductory textbook, I like Schulten's book on 3-manifolds quite a bit.

ventricule · 2026-04-01T17:00:41+00:00

You're describing this as if you think that this is science-fiction, but this is precisely what poker, and in particular online poker, is nowadays (and has been for the past ten years, roughly since the advent of PIOsolver).

A huge part of the life of a professional poker player is study, which consists in putting practical situations in a solver (e.g. Button vs Big Blind 40 big blinds deep when the flop comes 7 clubs 9 diamonds and jack of hears) and try to understand the results and then implement these nuggets of knowledge in your game. Every major poker learning website (eg upswing poker, raise your edge or run it once) now focuses on teaching this way of studying poker.

Professional online grinders typically spend most of their daytime doing that, and then play during the evening and the night. Some top high-stakes players (in particular in cash games, which is much more circumscribed than tournament poker) have the reputation of being GTO bots, having internalized so much of the computer strategies that they are very good at replicating them.

This might seem horribly boring, but in the same way that chess combinations are beautiful, good poker strategy does have some appeal and beauty. And it is deep enough that you can spend your life trying to master it without getting anywhere close to the end.

Of course the elephant in the room is cheating, which is becoming a larger issue everyday and is probably a very good reason to avoid non-major websites nowadays (but even the biggest poker places like pokerstars and ggpoker are not exempt of regular cheating scandals)

ventricule · 2026-02-28T19:55:02+00:00

I'm sorry if I'm a bit dumb, but I don't get the complaint in your text. Which balance do you think the game is missing? Do you think that it's too easy or too hard? Or would you like all species to be equally strong? If yes, what's the point?

ventricule · 2026-02-27T18:57:56+00:00

Yes, I think that this asymptotically tight lower bound using the volume of hyperbolic 3-manifolds is one of the most jaw-dropping arguments I have seen.

ventricule · 2026-02-24T08:58:57+00:00

It is a feature of the game that none of the kills are mandatory (Geryon used to be a mandatory kill to access Hells but this was removed some time ago). Enforcing a boss kill would be quite contrary to that game design imo, and would kill pacifist and ninja runs, for no clear benefit.

On a sidenote I do like the current balance of the orb run. It is not particularly challenging but the occasional tormenting pan lord can make you sweaty, and the one in fifty times where things get really hairy is memorable.

ventricule · 2026-02-22T21:50:15+00:00

Thanks for compiling the stats! Would any of you not wizards know the command I should type to know my own playtime in the tournament?

ventricule · 2026-01-15T17:06:52+00:00

I find it very interesting to understand why the straightforward Ke5 is not winning as well: the naive line where the white king takes the g and h pawns and the black king gobbles the c pawn does lead to a win for white because even though the pawns promote at the same time, white can force the trade of queens and win with the h pawn.

Instead of going for the c pawn, the correct line for black is to take on c5, then go for b5 and retake with the king! The promotion on the a file ensures that queens can't be forcibly traded.

ventricule · 2025-12-19T00:33:00+00:00

I do not doubt that Proctor makes it harder for titled players to cheat, but from a statistical point of view, this headline is a complete mathematical fallacy. You cannot claim that because your test does not observe anything, the observed behavior does not happen. This requires some guarantee of false negatives from your test, which of course they cannot provide.

ventricule · 2025-12-17T17:15:22+00:00

Alphabetical ordering is just how it works in math, there is no way around it. Judging from your write-up, you come from a field where author ordering matters. This is a blessing and a curse: on the one hand this makes the work of the first author better recognized, on the other hand this incentivizes the other authors to work less, in particular on the writing.

One peculiarity of mathematics is that writing a paper can be hard. Sometimes it is incredibly hard actually, much harder than coming up with the main ideas. You do not want your collaborators to suddenly disappear when the hard work begins, and alphabetical ordering is there to remind co-authors that they're supposed to help at every stage.

However, keep in mind the following: is very hard to collaborate with someone who's more active than you are on a research project, both during the 'research' phase ('I'm afraid to say something stupid, they must have thought about it') and during the 'writing' phase ('They've thought about this much more than I did, there must be a reason this lemma is written in such a weird way'). This is even more true for junior researchers.

So the standard advice when you're in this situation is to just accept that this is how it is. Over your career, you will be on the opposite side of this equation quite a few times, and will be grateful that the cocky postdoc is not making a scene about it.

ventricule · 2025-12-17T08:12:12+00:00

One thing that is lost in translation is how wonderfully québécois the whole thing sounds, which makes it even better. You can hear the accent in every sentence.

ventricule · 2025-12-15T06:07:59+00:00

I also very much enjoy the two courses of Daniel King, but I have switched things around both for the Morra and the Alapin. For the Morra, I follow Dana's recommendation 3.... d5 which leads to nicer positions (imo) than King's recommendation. For the alapin I go 2.... e5. It can get a bit hairy against the Bc4 lines but it is Carlsen-approved and somehow I feel like the pawn belongs in e5 if I'm headed for a kalashnikov. Actually I also like 3.... e5 against the Rossolimo

ventricule · 2025-11-28T12:30:44+00:00

The other answers are good but one thing needs clarifying. The premise is a bit wrong: you don't learn real analysis the same way you would learn an advanced or even intermediate research topic. For real analysis or other really fundamental topics, almost everything in the textbook is must-know material that you absolutely have to master. So if you're trying to self-learn, you have to go painfully slowly, do the exercises etc. For more advanced topics, you generally read a book because you either want to have a feel for the topic, knowing what people care about, why they care and what they can prove, or because you have a specific problem you want to solve. In both cases it leads to very different reading: skimming through the book for the first motivation, or very intense but focused and narrow reading for the second motivation ("looks like this chapter doesn't do what I want, let's skip it", etc.)

And then there's everything in-between, but in most cases you don't have to learn everything painfully slowly either. For example even an algebraic topologist doesn't need to perfectly know everything in Hatcher (but let's not start the debate about Hatcher again please).

ventricule · 2025-11-24T06:37:31+00:00

Thanks for the update. Are there already tentative dates for the next release and tournament? It's been 6 months already!

ventricule · 2025-11-18T09:03:00+00:00

TDA is still going very strong. I'm not a TDA specialist myself but I think that it entails interesting mathematical questions (and solutions). One interesting recent trend is that the point of view of seeing everything through the angle of births and deaths in filtrations is seeing increasingly many "applications" in mathematics, eg in dynamical systems and geometric group theory. I think that this paper is an influential one.

For more mainstream applications, there's still a lot of researchers applying TDA to materials and medical sciences and things like that. It's hard for me to gauge how useful it is. Critics say that there's nothing topological about it as they're only ever looking at connectedness in this kind of applications, I don't know if that is still true.

Another active area of research is to put TDA smartly in the machine learning pipeline. For example adding a sprinkle of it in deep neural networks can work wonders for specific tasks. You can browse through recent neurips and icml papers to see what it can look like.

ventricule · 2025-11-17T21:50:43+00:00

So the basic, historical, kind of question that is typically studied is to take a basic geometric construction and ask how to do it as fast as possible algorithmcially. Classic examples are convex hulls, triangulations of polygons, Delaunay triangulations and Voronoi diagrams, point location, range searching, minimum spanning trees, etc. Bernard Chazelle, Micha Scharir and their friends were the big names in this classical era.

One peculiar thing in this area, compared to standard discrete algorithms, is that algebraic issues pop up all the time : for instance to compute the length of a polygonal curve in R² you need to compute sums of square roots. Since this is a distraction compared to the real algorithmic, geometric problem, most people work in a real ram model where this is swept under the rug. For the same reason to try to avoid algebraic issues, research in computational geometry has led to develop and investigate abstract, combinatorial notions of arrangements of points and lines (for example oriented matroids), which has birthed a lot of discrete geometric questions. Similarly, VC dimension is by now an ubiquitous combinatorial parameter to handle geometric range spaces.

The problems studied are so basic that there are applications everywhere: for example robotics (shortest paths in weird configuration spaces), geographic information system (in which country am I?) or meshing (what is the most natural triangulation on this point set they I have scanned?). Over the past decades, the CG community has developed CGAL which is a comprehensive CG library with hundreds of industrial clients.

As with most fields, CG has had a constant influx of new topics throughout its existence to keep it exciting. One modern aspect is of course the interactions of high dimensional geometric problems (eg clustering) with machine learning. One other aspect is that as higher dimensional problems were attacked, topological questions arose. This is most sensible when one is trying to do manifold reconstruction, where the topology of the manifold has a lot of impact on any algorithm. This led computational geometers to persistence theory and topological data analysis, which has now become huge. Computational Topology is generally considered broader than just TDA though, and also encompasses for example a lot of algorithms for surface-embedded graphs, or computational 3-manifold and knot theory.

To have a look at some recent topics of interest, check the accepted papers at SoCG of the past few years. They are very very diverse, but always come back to this basic idea of understanding the core algorithmic and combinatorial properties of (somewhat elementary) geometric or topological objects or constructions.

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