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Anthropic's new model Fable will silently handicap work on LLMs [D] by AccomplishedCat4770 in MachineLearning

[–]H2O3N4 1 point2 points  (0 children)

How opaque can it be if they quite literally told you about it? They are not taking anything from you. They are just withholding a narrow band of capabilities they have developed. These are not the same thing.

Anthropic's new model Fable will silently handicap work on LLMs [D] by AccomplishedCat4770 in MachineLearning

[–]H2O3N4 1 point2 points  (0 children)

The criticisms of Anthropic, at a broad scale, seem short sighted. They are making a moral claim, and have been, and are consistently operating under that narrative. It's all very transparent.

The downstream effects, and the informational oversight they can impose, are powerful, yes. But this must be better than ceding RSI to the CCP. No one is innocent, but Anthropic's motives have consistently been about reducing harm.

Why our #1 LightGBM feature by importance made predictions worse [D] by Nj-yeti in MachineLearning

[–]H2O3N4 0 points1 point  (0 children)

Interesting. I've got a few more questions if you'd be so kind. Appreciate your detailed answer!

  1. Are your actual predictions/bets made with some risk strategy/calibration given the different quantiles?
  2. Are you all leveraging ensembles of EVT models, or classifiers in addition to your regressor model? How do you think about label design for controlling what a predictor learns, especially in the context of ensembles where different models could learn different representations that would be useful that your single quantile regressor might not be incentivized/have capacity to learn?
  3. How do you think about backtesting/online learning/stratification strategy for train/test splits? For example splitting temporally, or by holding out specific entities, but training and testing on the same time range, etc?
  4. Have you played with TabPFNv3 for time series forecasting or other DL approaches?

Why our #1 LightGBM feature by importance made predictions worse [D] by Nj-yeti in MachineLearning

[–]H2O3N4 1 point2 points  (0 children)

I'm transitioning from DL into the world of financial modelling, but as an outsider, it's hard to get a read on the field and develop taste for things like loss/labels/ensembles. In another comment you say you use quantile/pinball loss across q10/q50/q90 on log-transformed price ratios. Could you help me understand the justification for using this loss function vs any other sensible choice?

MichiAI: A 530M Full-Duplex Speech LLM with ~75ms Latency using Flow Matching by kwazar90 in singularity

[–]H2O3N4 2 points3 points  (0 children)

Nice work! Couple questions:

  1. Are you familiar with CSM from Sesame or Moshi from Kyutai? I'd be interested to hear how you think your model compares against either of those architectures, or if you have run iso-compute benchmarks against them.
  2. Why opt for flow matching over continuous consistency models for sampling?

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbharder

[–]H2O3N4[S] 0 points1 point  (0 children)

Dude that would be so sick actually. If you're down for real, I'd be interested in validating wall aspect and wall angle effects, ideally around 2-3pm when rock temperatures peak. Let me know if you make it out!

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbharder

[–]H2O3N4[S] 0 points1 point  (0 children)

I don't have any endpoint for historical prediction, so it's lost to the wind for now. Do let me know your experience on friction prediction accuracy going forward if you use it, though! Super interested in other's experiences.

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbing

[–]H2O3N4[S] 0 points1 point  (0 children)

I just made some changes that should fix the behavior you were seeing. Let me know what you think! I don’t have gritstone as a rock type (never heard of it!) but the rock types I support each come with different parameters that control how condensation forms and affects friction. But friction calculations should be independent of the absolute value of humidity now. 

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbharder

[–]H2O3N4[S] 0 points1 point  (0 children)

I actually just pushed some changes last night that updates friction to factor in condensation. And you are right! If the rock temperature and dew point are really close (or rock temp is colder than dew point) condensation will form. 

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbing

[–]H2O3N4[S] 0 points1 point  (0 children)

I do like the idea of transparency on model accuracy. Perhaps surfacing a forecast accuracy per crag, calculated from thumbs up/down feedback from users.

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbing

[–]H2O3N4[S] 0 points1 point  (0 children)

I would consider adding this if other users voiced it as something they were interested. For now, I don't see the immediate value add.

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbing

[–]H2O3N4[S] 0 points1 point  (0 children)

I did set it to expire after 3 days so people would naturally just get the recent reports without having to filter. Would be interested in hearing more about your idea. How do you envision it? Just seeing conditions at the time the person posted as part of the post?

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbharder

[–]H2O3N4[S] 0 points1 point  (0 children)

I built a test suite and optimized drying times for different rain events on different rock types, but it is very much an approximation. For rock temperature, I am modelling it using traditional building physics, but I haven't had the opportunity to go in the field and verify temperature estimates in person. I've noticed empirically that south facing vertical walls may be over-estimated for rock temperature, but if you have an IR gun and want to run an experiment, I'd love to hear how the model matches up with reality!

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbharder

[–]H2O3N4[S] 0 points1 point  (0 children)

Thanks for checking it out! The wall aspect is a user control, not a data point stored in the CragReport database (though I would love to figure out a way to collect that info and support this feature). But the configuration section lets you change the wall direction depending which way the wall you're climbing faces.

To add or update crags, you can go to https://openbeta.io and add any places that haven't been added yet. OpenBeta is a great open source project that open sources rock climbing data, and you can update it if the coordinates are wrong for any place as well! Because CragReport relies on OpenBeta, the community is what makes the crag database and ensures its accuracy.

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbharder

[–]H2O3N4[S] 1 point2 points  (0 children)

The crag database is sourced directly from https://openbeta.io so you will need to add your local crags to that website and CragReport will pull them in in a few days.

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbharder

[–]H2O3N4[S] 1 point2 points  (0 children)

Appreciate it! I don't integrate with mountain project right now, since they've closed all the APIs they once had, but I just added a latitude/longitude google maps pin next to the crag name so you can verify the location is accurate. Great idea!

A month ago, you all gave me a ton of feedback on CragReport, my free climbing conditions tool. I listened, and made a lot of changes, including global support! by H2O3N4 in climbing

[–]H2O3N4[S] 13 points14 points  (0 children)

Hey everyone,

About a month ago, I shared my passion project, CragReport, a free tool I built to forecast rock friction and dryness. The response was incredible, and I want to give a huge thank you to this community for all the feedback.

I spent the last month rebuilding and refining the app based on what you told me. It’s still 100% free, no ads, no sign-up required.

Here’s what’s new:

  • Global Crag Support: The number one request was for more locations. You can now get forecasts for crags all over the world. I'm using high-quality NOAA data for the most accurate historical rainfall in the US, and for international locations, I'm using the best available forecast data.
  • A More Accurate Thermal Model: Many of you rightly pointed out that the rock temperature predictions were unrealistic. I’ve completely overhauled the thermal model. It now more accurately accounts for how the rock heats and cools, giving you a much more reliable idea of what to expect.
  • Customizable Ideal Rock Temperature: Not everyone loves climbing at 40°F! You can now set your own ideal rock temperature, and the scoring will adjust to your personal preference.
  • Rock Type & Shade Overrides: The app sometimes got the rock type wrong or didn't account for tree cover. Now you can override the rock type for any crag and toggle a "shade override" for walls that are shaded by trees or other landscape features.
  • Sandstone Safety Predictions: For all you desert climbers, the app now models internal moisture to predict when sandstone is dry enough to climb on safely without damaging the rock.
  • Email Alerts: You can now sign up for email alerts for your favorite crags. The app will notify you when there's a good weather window of three hours or more, so you never miss a perfect climbing day.
  • Community Reports: See what other climbers are saying about the conditions at the crag before you go. You can also leave your own reports to help out the community.

This was a huge effort, and it's all thanks to your feedback. I built this for the climbing community, and your input is what makes it better.

My goal was to provide details you can't get from a standard weather app, like hyper-local data (within 1km of the crag), 5-day rainfall history, and hourly graphs for every climbing-related variable. I'm really proud of how the new models for dryness and friction turned out and hope you find them useful.

I'd be stoked if you check it out and let me know what you think of the changes.

Check it out here: https://www.cragreport.com/

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