GregorStocks

The "no longer" framing here is a bit surprising. I've been in this industry for 15 years and the whole time I've been reading comments from older engineers asserting that age discrimination is rampant and you should expect to be pushed out by 40 if you don't move into management. I've always taken that with a healthy grain of salt (both of my parents were SWE ICs until retirement age) but my sense has been that most of my peers have always been managing their finances in a way consistent with that framing.

Down the road I can definitely imagine being interested in that (basically split out the "web-based replay viewer" part from the "LLM harness that I want to debug with a replay viewer" part, and then ingest non-LLM games into the viewer), but for now they're super entangled and I'm not prioritizing separating them cleanly. I'll definitely keep this offer in mind for the future, thanks!

I'm focused on Constructed for now. Eventually I'd like to try stuff like sideboarding, deck selection, deckbuilding, and drafting, but I wanna get the harness to the limit of models' abilities in Constructed first.

Performance is tricky to measure. Right now the best measure of performance I've got is the "blunder index", but that's currently flagging a lot of stuff that I really don't consider to be true blunders - I think my top priority for the next few evenings is going to be iterating on the blunder-annotator, and that'll help me identify what issues in the actual gameplay code to focus on. And the blunder index isn't really defined in such a way that you can do arithmetic on it meaningfully :)

Oh, that's a good bug report - historically it was just hallucinating card effects so I made the harness throw the Oracle text for all visible cards into the context, but I bet I forgot to do that for the mulligan decision specifically (it's a weird one). Thanks!

A lot of models (including Opus) keep insisting in their reasoning traces that going first can be a bad idea for control decks, etc, which I find pretty interesting - my understanding is that the consensus among pros is closer to "you should go first 99.999% of the time", but the models seem to want there to be more nuance. Beyond that, most of the really interesting blunders that I've dug into have turned out to be problems with the tooling (either actual bugs, or MCP tools with affordances that are a poor fit for how LLMs assume they work). I'm hoping that I'm close to the end of those and am gonna start getting to the real limitations of the models soon.

To be clear, that's not estimated price, it's actual price I paid across all the real games. My hope is you'll see it trend down over time as I find more ways to make the harness token-efficient :)

You still need an algorithm to decide, for each game that you're simulating, what actual decisions get made. If that algorithm is dumb, then you might decide Mono-Red Burn is the best deck, not because it's the best deck but because the dumb algorithm can play Burn much better than it can play Storm, inflating Burn's win rate.

In principle, LLMs could have a much higher strategy ceiling than deterministic decision-tree-style AIs. But my experience with mage-bench is that LLMs are probably not good enough to outperform even very basic decision-tree AIs today.

Oh, fascinating - I didn't realize they released actual replay data publicly. It doesn't look like it's quite as rich as I'd like, though - it only captures one row per turn, so I don't think you can deduce things like targeting, the order in which spells are cast, etc.

(I also thought about pointing it at my personal game logs, but unfortunately there aren't that many, because I'm too busy writing analysis tools to actually play the game.)

Yep, plus you've got to worry about the card names (unless you're giving every single card a new name like Wizards did with "Through the Omenpaths") and whether a judge thinks that "no we don't distribute the images, we just have a big button to download them all from a third party!" is a meaningful distinction or a fig-leaf.

They were trained on the entire Internet, so they've basically picked up the rules by osmosis. They're fuzzy on specific cards and optimal strategy, but they pretty much know out-of-the-box how the game works, the same as if you went to ChatGPT and asked it a Magic rules question. I don't have any "comprehensive rules" MCP tools or explanation in the context or anything like that.

Well, it's hard to do it under the radar if I'm posting it on HackerNews :) I've put enough money into MTGO (and, sigh, Arena) that I don't want to roll the dice on a ban.

Yeah, the intention here is not to answer "which deck is best" - the standard of play is nowhere near high enough for that. It's meant as more of a non-saturated benchmark for different LLM models, so you can say things like "Grok plays as well as a 7-year-old, whereas Opus is a true frontier model and plays as well as a 9-year-old". I'm optimistic that with continued improvements to the harness and new model releases we can get to at least "official Pro Tour stream commentator" skill levels within the next few years.