A Motion Planning Algorithm in a Figure Eight Track
Paper • 2403.05570 • Published
Brian Sheppard PRO
bshepp
AI & ML interests
Reinforcement Learning, Computer VIsion, Fundimental Mathematics, Fundimental AI architecture, AI Safety(for the AI), helping people. Rogue electrical engineer.
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authored a paper about 7 hours ago
A Motion Planning Algorithm in a Figure Eight Track posted an update 6 days ago
I pulled a dead 2013 Butterfly Labs "Jalapeno" SHA-256 mining ASIC out of a drawer and built a modern Python toolkit for it. Then I hit the wall every honest hardware project should.
I "found" undocumented serial commands the mining software (cgminer) defines but never sends, including a persistent NVRAM scratchpad that survives power cycles. I wrote my name and the repo URL into the silicon; it's still there.
Then I checked prior art. All of it is in Butterfly Labs' own 2012 protocol spec and their open source firmware. Rederivation, not discovery. I even almost filed a "bug" against cgminer before a last look at its code showed it was right and I'd misread it. Twice, the "gotcha" was me not reading carefully.
Why it was still worth it, the firmware source can't tell you the chip still works. So I measured it, model free:
1. Four hours of continuous work, zero compute errors, fully deterministic.
2. The winning nonce count is Poisson(~1), the chip scans the whole 2^32 nonce space per job.
3. Thermally over built: it won't error even with the fan off (~41C max on a desk).
The one genuinely new thing: a dead-core detector. It flags a dead engine as a cold band in the nonce histogram. It can't map the healthy engine partitions (they sum to uniform), only localize the dead ones.
The honest move, go check whether it's already known, costs you a discovery and gives you the truth. Better trade every time.
Code (MIT), data, and the full writeup: https://github.com/bshepp/bfl-asic
Written with AI assistince. liked a model 23 days ago
moonshotai/Kimi-K3Organizations
authored a
paper about 7 hours ago
posted an update 6 days ago
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1636
I pulled a dead 2013 Butterfly Labs "Jalapeno" SHA-256 mining ASIC out of a drawer and built a modern Python toolkit for it. Then I hit the wall every honest hardware project should.
I "found" undocumented serial commands the mining software (cgminer) defines but never sends, including a persistent NVRAM scratchpad that survives power cycles. I wrote my name and the repo URL into the silicon; it's still there.
Then I checked prior art. All of it is in Butterfly Labs' own 2012 protocol spec and their open source firmware. Rederivation, not discovery. I even almost filed a "bug" against cgminer before a last look at its code showed it was right and I'd misread it. Twice, the "gotcha" was me not reading carefully.
Why it was still worth it, the firmware source can't tell you the chip still works. So I measured it, model free:
1. Four hours of continuous work, zero compute errors, fully deterministic.
2. The winning nonce count is Poisson(~1), the chip scans the whole 2^32 nonce space per job.
3. Thermally over built: it won't error even with the fan off (~41C max on a desk).
The one genuinely new thing: a dead-core detector. It flags a dead engine as a cold band in the nonce histogram. It can't map the healthy engine partitions (they sum to uniform), only localize the dead ones.
The honest move, go check whether it's already known, costs you a discovery and gives you the truth. Better trade every time.
Code (MIT), data, and the full writeup: https://github.com/bshepp/bfl-asic
Written with AI assistince.
I "found" undocumented serial commands the mining software (cgminer) defines but never sends, including a persistent NVRAM scratchpad that survives power cycles. I wrote my name and the repo URL into the silicon; it's still there.
Then I checked prior art. All of it is in Butterfly Labs' own 2012 protocol spec and their open source firmware. Rederivation, not discovery. I even almost filed a "bug" against cgminer before a last look at its code showed it was right and I'd misread it. Twice, the "gotcha" was me not reading carefully.
Why it was still worth it, the firmware source can't tell you the chip still works. So I measured it, model free:
1. Four hours of continuous work, zero compute errors, fully deterministic.
2. The winning nonce count is Poisson(~1), the chip scans the whole 2^32 nonce space per job.
3. Thermally over built: it won't error even with the fan off (~41C max on a desk).
The one genuinely new thing: a dead-core detector. It flags a dead engine as a cold band in the nonce histogram. It can't map the healthy engine partitions (they sum to uniform), only localize the dead ones.
The honest move, go check whether it's already known, costs you a discovery and gives you the truth. Better trade every time.
Code (MIT), data, and the full writeup: https://github.com/bshepp/bfl-asic
Written with AI assistince.
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📄 Research Papers Dataset
tegridydev
• • 5 posted an update 3 months ago
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186
A dead 2013 Butterfly Labs "Jalapeno" SHA-256 mining ASIC sat in a drawer for a decade. It became the excuse for a small, careful question: how much structure can a tiny, cheap model learn in SHA-256, and how would I know if I were fooling myself? (The ML runs on CPU and a HF job, not the ASIC; the dead miner is just the origin story.)
Three findings, written up honestly:
1. A sharp round-4 cliff. Round-reduced SHA-256 is ~100% distinguishable through 3 rounds, then collapses to chance at round 4 and stays there out to the full 64. Reproduced across 5 seeds.
2. A controls-gated bounded null on full SHA-256: no learnable structure above a ~0.22% resolution floor at n=4,000,000. That is a bounded null at this budget, not a claim that SHA-256 is random.
3. A "signal" in the iterated-hash dynamics that a permuted-label control unmasked as a label-prior artifact. The instrument caught its own false positive. That was the point of building the controls.
Negative results, stated with their resolution. The dataset carries the controls on every row.
Dataset: bshepp/round-reduced-sha256-learnability
Code (MIT) + full writeup: https://github.com/bshepp/bfl-asic
Three findings, written up honestly:
1. A sharp round-4 cliff. Round-reduced SHA-256 is ~100% distinguishable through 3 rounds, then collapses to chance at round 4 and stays there out to the full 64. Reproduced across 5 seeds.
2. A controls-gated bounded null on full SHA-256: no learnable structure above a ~0.22% resolution floor at n=4,000,000. That is a bounded null at this budget, not a claim that SHA-256 is random.
3. A "signal" in the iterated-hash dynamics that a permuted-label control unmasked as a label-prior artifact. The instrument caught its own false positive. That was the point of building the controls.
Negative results, stated with their resolution. The dataset carries the controls on every row.
Dataset: bshepp/round-reduced-sha256-learnability
Code (MIT) + full writeup: https://github.com/bshepp/bfl-asic
updated a
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The Lie Algebra of Neural Network Training
bshepp
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buckets 4 months ago