ardata · HackerTrans

قبل 7 أشهرdiscuss

risk? certainty. it's pretty much guaranteed. the most capable models are already behind closed doors for gov/military use and that's not ever changing. the public versions are always going to be several steps behind whatever they're actually running internally. the question is what the difference will be between the corporation and pleb versions is

ardata

قبل 7 أشهرdiscuss

for backtesting trading strategies this could be useful... i've had sims give different results across machines and never knew if it was real or fp drift. how is the performance on real-world sized datasets?

ardata

قبل 7 أشهرdiscuss

I've built trading bots that run monte carlo sims on historical data... numpy works but gets slow on large backtests, and pytorch feels like overkill when I just want fast array math without managing GPU memory. If this can drop in and handle the heavy lifting automatically i could see use for it

ardata

قبل 7 أشهرdiscuss

Does the guess cache sync across machines at all, or is it purely local? I jump between my laptop and desktop for AoC and it'd be annoying to resubmit a 'too low' answer I already tried elsewhere.

ardata

قبل 7 أشهرdiscuss

Ran clean for me:

=== Prima Veritas OSS — Hash Check (iris) ===

normalized → MATCH Expected: EF28EA082C882A3F9379A57E05C929D76E98899E151A6746B07D8D899644372F Actual: EF28EA082C882A3F9379A57E05C929D76E98899E151A6746B07D8D899644372F

kmeans → MATCH Expected: DA96D0505BCB1A5A2B826CEB1AA7C34073CB88CB29AE1236006FA4B0F0D74C46 Actual: DA96D0505BCB1A5A2B826CEB1AA7C34073CB88CB29AE1236006FA4B0F0D74C46

Hashcheck PASSED — outputs match golden hashes.

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Next step is probably benchmarking this against sklearn? Accuracy comparison and performance hit from all the rounding operations. Anyone here working in maritime auditing, medical data, or other regulated stuff - would you actually use something like this? Trying to figure out if crypto- verifiable analytics is solving a real problem or just a cool technical exercise.

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