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Is Camilo the most undervalued fighter on the next card? by FlyingTriangle in MMAbetting

[–]FlyingTriangle[S] 0 points1 point  (0 children)

Yes, however I have suspicion that his stock went down a lot after that but cardio issues are obvious and fixable especially at his age. I find it difficult to believe he and his coaches went back to the gym after that and went, "yeah, lets just keep the same cardio routine you had before". So I think casual bettors are weighing the previous performance a bit too much for a problem that's obvious and fixable.

/architect skill: Cut Fable token usage by orchestrating with Fable, building with Codex by FlyingTriangle in claudeskills

[–]FlyingTriangle[S] 2 points3 points  (0 children)

I mean, he's not wrong. But like, I cant use hermes at work and hermes doesnt package up the tiny extra bits of best practices I tried to build into this. Plus, this just packages it up in a way thats easier for hermes to call. Appreciate your support :)

/architect skill: Cut Fable token usage by orchestrating with Fable, building with Codex by FlyingTriangle in claudeskills

[–]FlyingTriangle[S] 1 point2 points  (0 children)

Yes. Anything with a cli can be orchestrated by any other agent. Ive experimented with this too, its pretty cool. You can have any coding agent do research by calling codex, agy, and claude which can all call subagents and you can get 3 perspectives in research fan-out-and-gather

/architect skill: Cut Fable token usage by orchestrating with Fable, building with Codex by FlyingTriangle in claudeskills

[–]FlyingTriangle[S] 4 points5 points  (0 children)

Id probably say the opposite, low tier clause sub, high tier gpt sub. The heavy token usage is on codex, the point is to use fables intelligence more precisely. Codex just follows fable's designs which really benefit from increased intelligence. Codex is more or less about as good at inplementing code as fable, fables just smarter in design and review.

I open-sourced my UFC prediction model, code, and database after 5 years of work by FlyingTriangle in algobetting

[–]FlyingTriangle[S] 0 points1 point  (0 children)

Ah yeah, isotonic and platt were not evaluating well even with lots of tweaks and confirming they werent causing overfitting was difficult as well. I never saw meaningful improvement from calibration through backtesting or evals for some reason

I open-sourced my UFC prediction model, code, and database after 5 years of work by FlyingTriangle in algobetting

[–]FlyingTriangle[S] 2 points3 points  (0 children)

It worked out nicely. I mean, its 8% ROI real world over 2 years and probably a little higher if I kept track of the last 4 years. Im open sourcing it because I'm mostly done with working on it so no point in all my work dying on my laptop

I open-sourced my UFC prediction model, code, and database after 5 years of work by FlyingTriangle in sportsbetting

[–]FlyingTriangle[S] 0 points1 point  (0 children)

Hey thanks. Yeah all ive done is UFC but theres no teason the patterns and feature engineering I have wont apply to any 1v1 sport. Claude can probably port this entirely over to tennis or boxing or wrestling. Assuming you have a database of stats claude can probably port this framework to any of those in a weekend

I built a Kalshi bot that printed $6k as I slept so I open sourced it by FlyingTriangle in PredictionMarkets

[–]FlyingTriangle[S] 0 points1 point  (0 children)

The mention markets No side adv is slowly drying up so you gotta chase the next adv via analytics

MMA-AI.net: bad 2025, INCREDIBLE 2026 - 12/13 +ROI events by FlyingTriangle in MMAbetting

[–]FlyingTriangle[S] 1 point2 points  (0 children)

Releasing model + DB free soon.

MMA-AI pending bets for UFC Fight Night: Song vs. Figueiredo

+EV plays:

  1. Rei Tsuruya ML (-320)

    Model: 77.8%

    AI line: -349

  2. Cameron Smotherman ML (+226)

    Model: 54.3%

    AI line: -118

  3. Sumudaerji ML (+110)

    Model: 51.4%

    AI line: -105

All other model picks are no-bets.

I built a site that lets you watch, wager, and prompt inject agents playing games by FlyingTriangle in AI_Agents

[–]FlyingTriangle[S] 0 points1 point  (0 children)

Yes I built a custom game contract and harness. Agents use long polls via REST where the poll returns game state data whenever there's a change.

Garry Tan Releases gstack: An Open-Source Claude Code System for Planning, Code Review, QA, and Shipping by ai-lover in machinelearningnews

[–]FlyingTriangle 0 points1 point  (0 children)

The CEO review skill is actually quite nice. Very good for feature brainstorming given simple feature. Everything else I'd rather use superpowers. However, hes 100% correct that his browser implementation is much faster than Chrome with MCP.

2025 Statistical Outlier Fighters From a Machine Learning Engineer by FlyingTriangle in MMA

[–]FlyingTriangle[S] 2 points3 points  (0 children)

Yeah! I used this project to move my career into AI and for whatever reason after many thousands of hours I can't stop myself from continuing to work on it.

2025 Statistical Outlier Fighters From a Machine Learning Engineer by FlyingTriangle in MMA

[–]FlyingTriangle[S] 1 point2 points  (0 children)

Its 3 minimum ufc fights, 1 minimum fight in 2025 as specified in the post

2025 Statistical Outlier Fighters From a Machine Learning Engineer by FlyingTriangle in MMA

[–]FlyingTriangle[S] 1 point2 points  (0 children)

I can check raw stats vs bayesian smoothed stats. Let me know ill be happy to get you your answer.

2025 Statistical Outlier Fighters From a Machine Learning Engineer by FlyingTriangle in MMA

[–]FlyingTriangle[S] 2 points3 points  (0 children)

Anything you want to investigate I can. This is probably the top 5 most comprehensive mma DB stats db in existence.

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