I loved Christmas Lemmings so much back in the day! The snowfall visualization and the little Santa lemming clearing it. I made a much less impressive snowfall demo a while back based on that (minus the clearing lemming, because I always wanted to watch the snow pile up). https://anderegg.ca/projects/flake/
By leaving out scale or prior models they are effectively manipulating improvement. If from 3 to 4 it was from 10 to 80, and from 4 to 4o it was 80 to 82, leaving out 3 would let us see a steep line instead of steep decrease of growth.
Light is the opposite of hard (ie hard close). It makes more sense (to me!) to use explicit vs implicit close? Clicking outside the popover means an implicit close.
I used a lot of solvers in the early 2000s in my Operations Research master after my econometrics study. While now working on software (web) that uses python I’m thrilled to see these deep dives on this subject!
I love the subject and reading this brought back a lot of memories. Also the realization that translating constraints to a model (variables, structure etc) is 90% of the work and the most difficult part.
I'm intrigued by bits like Outside Margin Comments[1] - `$onMargin minCol 20 maxCol 45` - text before column 20 and after column 45 is treated as a comment - 1970s.
>the realization that translating constraints to a model (variables, structure etc) is 90% of the work and the most difficult part.
LLMs can help a lot there. I've been wanting to write an LLM => Constraint model adapter that does it for you. It's such low hanging fruit, I wonder if anyone else would benefit from it though.
Indeed, it seems like an obvious thing to do. But just as you noted, it's not very clear LLMs really can improve over Prolog in terms of expressiveness and practicality given that Prolog already was designed for natural language parsing and is a concise formalism based on predicate logic (and ultimately propositional and first order logic) with constraint domain theory embeddings such as for arithmetic. Prolog syntax is also the starting point for most constraint solvers, and Prolog evaluation is also often referred to as basis for generalization into constraint solving. Though I'm not sure this generalization bears much value tbh when the break-through successes in constraint solving were particular domain-specific techniques (SAT solvers, interval propagation, arc consisteny/finite domain propagation, etc).
They're already very good at it—I myself have been using OR-Tools's CP-SAT solver for a large bin packing problem at work (via https://github.com/ankane/or-tools-ruby) & Chat-GPT was a big help working out the details of some of the constraints and objectives.
I think that I would. Using natural language to describe the problem and constraints would be much better than figuring out mid project that the variable structure I've chosen does not allow to express a particular constraint. Defining the right structure is just Art at this point.
I would say the most difficult part is to run it in production with minimal issues. Scaling them and making them robust to changes in data takes a long time.
I’m following /r/stripe and there are more horror stories over there. If you read carefully and between the lines it is often pretty logical that a ban/freeze is given (shady business, 35k per day within a week, etc)
The good thing is that Stripe is also monitoring that subreddit. And they often reply and mention [email protected] as a quick and easy way to get someone to speak to you. Maybe you can try that next time?
