MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
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satpalsrΒ
submitted a
paper to Daily Papers 2 days ago
satpalsrΒ
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paper 2 days ago
Post
120
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
Post
143
We're open-sourcing our infra with 10M+ frames of dataset!
We're releasing Stera, an open-source infra that turns an off-the-shelf device in your pocket into a high-fidelity multimodal data pipeline. It's built around four layers. Capture β Process β Evaluate β Export.
Stera Capture removes the need for bespoke/gated hardware and runs on an off-the-shelf iPhone. It fuses together synchronized RGB, IMU, Lidar-guided depth, and 6-DoF pose out of the box from ARKit and exports them to a raw MCAP file.
Dataset: fpvlabs/stera-10m
Launch Details: https://x.com/fpv_labs/status/2055262652033908832
We're releasing Stera, an open-source infra that turns an off-the-shelf device in your pocket into a high-fidelity multimodal data pipeline. It's built around four layers. Capture β Process β Evaluate β Export.
Stera Capture removes the need for bespoke/gated hardware and runs on an off-the-shelf iPhone. It fuses together synchronized RGB, IMU, Lidar-guided depth, and 6-DoF pose out of the box from ARKit and exports them to a raw MCAP file.
Dataset: fpvlabs/stera-10m
Launch Details: https://x.com/fpv_labs/status/2055262652033908832
merveΒ
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Space 7 days ago
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π₯π 2
106
#5 opened about 2 years ago
by 
victor
[Support] Community Articles
π₯π 2
106
#5 opened about 2 years ago
by victor
Future of Agentic Models
π₯ 2
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#18 opened 21 days ago
by 
MohamedRashad
AbstractPhilΒ
posted an update 18 days ago
Post
2742
By trying to disprove the Omega H2 battery I have discovered;
* Each topology formed by the H2 battery is deviant, none have a uniformly shared substrate of behavior. They are each uniquely independent per training set all with perfect recon.
* Image recon can be tracked and mapped, yielding a consistently mapped and response 16.77m vocabulary potential. In the current spectrum testing at around 5 million unicode bytes.
* The model scale shows patch size is related to how much data you want the model to represent within the model itself, and this has yet to see a capacity to this day. The MSE recons and yields - and the more data fed, the more they yield.
* The scaling principle shows that the model indefinitely scales upward and each level of the model can be iteratively captured upward to form deviant and uniformly consistent repeatable pathways of implicit codewise response, not just arbitrary bitwise recall. Meaningful implicit learned utility.
* Image recon patch size should match the slice of image you want to represent, as it uses patch smoothing per patch internally from identity.
* byte trigrams are channel-agnostic, they do not require a channel count just a formula for recall at nGram recall 99.6% for byte-by-byte representations. With those comes an adjacently capable codebook.
* sentencepiece preliminary tests show validity and reconstruction just like the byte trigrams, using the new byte trigram this would be arbitrarily convenient to recon a codebook for the structure.
* binary trees learn a uniformly potent and powerful gating mechanism that required further exploration, each of them produces direct responsive independent capacity and the responses are controllable.
* ternary experiments show the models are directly responsive to -1, 0, +1 behavior, so the quantization is very much a valid potential.
* preliminary tests with the H2O1 series of batteries show the models are responding similar to natural universal elements in the universe itself
* Each topology formed by the H2 battery is deviant, none have a uniformly shared substrate of behavior. They are each uniquely independent per training set all with perfect recon.
* Image recon can be tracked and mapped, yielding a consistently mapped and response 16.77m vocabulary potential. In the current spectrum testing at around 5 million unicode bytes.
* The model scale shows patch size is related to how much data you want the model to represent within the model itself, and this has yet to see a capacity to this day. The MSE recons and yields - and the more data fed, the more they yield.
* The scaling principle shows that the model indefinitely scales upward and each level of the model can be iteratively captured upward to form deviant and uniformly consistent repeatable pathways of implicit codewise response, not just arbitrary bitwise recall. Meaningful implicit learned utility.
* Image recon patch size should match the slice of image you want to represent, as it uses patch smoothing per patch internally from identity.
* byte trigrams are channel-agnostic, they do not require a channel count just a formula for recall at nGram recall 99.6% for byte-by-byte representations. With those comes an adjacently capable codebook.
* sentencepiece preliminary tests show validity and reconstruction just like the byte trigrams, using the new byte trigram this would be arbitrarily convenient to recon a codebook for the structure.
* binary trees learn a uniformly potent and powerful gating mechanism that required further exploration, each of them produces direct responsive independent capacity and the responses are controllable.
* ternary experiments show the models are directly responsive to -1, 0, +1 behavior, so the quantization is very much a valid potential.
* preliminary tests with the H2O1 series of batteries show the models are responding similar to natural universal elements in the universe itself
- 9 replies
spanofzeroΒ
posted an update 19 days ago
Post
92
Productivity PSA
save tome learning and use an easy Ai of all ais and consider a friend James Murdza worldly hospitable attitude whos YT series will catch you up fast,
Today is no different as hes introduced me
to BackGrounder.dev
https://youtu.be/KFu0GTrV31g?si=jdM7DY9q49EM5FYA
Sandbox built in Multi Gfree chat code creat makes sense
and saves money can BYOapi just great for quick sandbox dev checks or just safety wise NO Regrets Code Insider Knowledge
save tome learning and use an easy Ai of all ais and consider a friend James Murdza worldly hospitable attitude whos YT series will catch you up fast,
Today is no different as hes introduced me
to BackGrounder.dev
https://youtu.be/KFu0GTrV31g?si=jdM7DY9q49EM5FYA
Sandbox built in Multi Gfree chat code creat makes sense
and saves money can BYOapi just great for quick sandbox dev checks or just safety wise NO Regrets Code Insider Knowledge
π© Report: Spam
1
#19 opened 20 days ago
by 
ccocks-deca
Future of Agentic Models
π₯ 2
12
#18 opened 21 days ago
by MohamedRashad
π© Report: Spam
1
#19 opened 20 days ago
by ccocks-deca
Future of Agentic Models
π₯ 2
12
#18 opened 21 days ago
by MohamedRashad
Future of Agentic Models
π₯ 2
12
#18 opened 21 days ago
by MohamedRashad
Future of Agentic Models
π₯ 2
12
#18 opened 21 days ago
by MohamedRashad
Future of Agentic Models
π₯ 2
12
#18 opened 21 days ago
by MohamedRashad
Post
486
π Introducing Goldener: The Python Data Orchestrator for more efficient ML
Machine Learning workflows often rely on randomness: selecting/splitting data for training, batching it variably, and monitoring real-world performance.
Nowadays, foundation models give access to the semantics of data. Goldener leverages this semantic to make the entire ML lifecycle more efficient!
π Check it out: https://github.com/goldener-data/goldener
π¨ Give it a try: pip install goldener
Machine Learning workflows often rely on randomness: selecting/splitting data for training, batching it variably, and monitoring real-world performance.
Nowadays, foundation models give access to the semantics of data. Goldener leverages this semantic to make the entire ML lifecycle more efficient!
π Check it out: https://github.com/goldener-data/goldener
π¨ Give it a try: pip install goldener
AbstractPhilΒ
posted an update 23 days ago
Post
189
Today, I'll be determining the codebook capacity and utility potential for the larger batteries; Fresnel, Johanna, Grandmaster, Freckles, and Johanna-F variants, which should give a good indication of which models are capable of handling codebooks and which are more errant. The earlier all use SVD while the later do not. The differences are noted per and the behavior divergent.
I anticipate the D=16 will be more errant, and the final-state variants of those could very well be much more difficult or costly to inference as their axis bends are likely considerably harder to track. However, I'm confident that enough bounces will give the yield required so I'll set up some high-yield noise barrages to determine how much of them we can in fact extract from Johanna, and then set up similar barrages for images to map the internals of Fresnel and Grandmaster.
Grandmaster will be tricky, as it was an experimental Johanna-256 finetuned series meant to map sigma noised image inputs to recreate Fresnel behavioral output. Noised image goes in -> Fresnel-grade replication comes out in high res.
This allowed preliminary Dall-E Mini-esque VAE generation and will be explored further for the stereoscopic translation subsystem, to allow image generation in the unique format of diffusion that I was working out. I anticipate this system to be more than capable at making monstrosities, so I won't be posting TOO MANY prelims on this one, but the high-capacity potential of these noise makers are meaningfully powerful. Getting uniform codebooks in-place for these models will allow full transformer mapping downstream instead of just guess working the MSE piecemeal, which the earlier versions and variants were doing.
I'm straying from the CLS specifically for this series because CLS creates adjudicated pools of bias orbiting the INCORRECT orbiter some SVAE. The orbital target IS the soft-hand accumulated bias with the sphere-norm, so having a competitor isn't going to be a good option.
I anticipate the D=16 will be more errant, and the final-state variants of those could very well be much more difficult or costly to inference as their axis bends are likely considerably harder to track. However, I'm confident that enough bounces will give the yield required so I'll set up some high-yield noise barrages to determine how much of them we can in fact extract from Johanna, and then set up similar barrages for images to map the internals of Fresnel and Grandmaster.
Grandmaster will be tricky, as it was an experimental Johanna-256 finetuned series meant to map sigma noised image inputs to recreate Fresnel behavioral output. Noised image goes in -> Fresnel-grade replication comes out in high res.
This allowed preliminary Dall-E Mini-esque VAE generation and will be explored further for the stereoscopic translation subsystem, to allow image generation in the unique format of diffusion that I was working out. I anticipate this system to be more than capable at making monstrosities, so I won't be posting TOO MANY prelims on this one, but the high-capacity potential of these noise makers are meaningfully powerful. Getting uniform codebooks in-place for these models will allow full transformer mapping downstream instead of just guess working the MSE piecemeal, which the earlier versions and variants were doing.
I'm straying from the CLS specifically for this series because CLS creates adjudicated pools of bias orbiting the INCORRECT orbiter some SVAE. The orbital target IS the soft-hand accumulated bias with the sphere-norm, so having a competitor isn't going to be a good option.
- 7 replies
AbstractPhilΒ
posted an update 25 days ago
Post
131
My recent study in a nutshell shows a few important elements and everything else is technical.
* There are most definitely invariant architectural geometric states that persist and can be taught.
* They are not coincidental and the process works effectively on multiple data types and processes, not just noise. Noise is just fast to test with.
* Systems like SVD, Eigh, Conv, and the like - HELP align those systems for larger structures to produce amplified stability, but are not required for smaller structures, and the tests show even attention gets in the way at the smallest.
* Batched arrays, stacks, queues, and so on - all improve performance depending on the task.
* An SVAE battery is resolution agnostic, meaning with simple processing and logic you can scan space and record meshes fairly optimally to record large amounts of inference data.
* Batteries when trained on one specific task often can be directly used for other tasks once a codebook is fitted with the necessary data. Meaning a battery trained on gaussian noise can be fed imagenet snippets and downstream the MSE rates from the 64 battery array can be consumed for statistics aggregation to a fair degree of accuracy without actually training the array on images themselves.
* The battery codebook is a pointwise rigid map within the battery and can be used for pairwise learning when using the H2, H2a, and H2b batteries.
So this is, the evolved state of the geometric vocabulary in some ways, and a completely new and unexpected systemic development in others. They stack, you can reuse them, so small you can swap them at runtime with no time loss, they align rapidly, and downstream tasks can consume their information.
There are many untested avenues that I need to make a full writeup for because quite frankly it's messy currently and Claude is only making it more messy instead of cleaner.
* There are most definitely invariant architectural geometric states that persist and can be taught.
* They are not coincidental and the process works effectively on multiple data types and processes, not just noise. Noise is just fast to test with.
* Systems like SVD, Eigh, Conv, and the like - HELP align those systems for larger structures to produce amplified stability, but are not required for smaller structures, and the tests show even attention gets in the way at the smallest.
* Batched arrays, stacks, queues, and so on - all improve performance depending on the task.
* An SVAE battery is resolution agnostic, meaning with simple processing and logic you can scan space and record meshes fairly optimally to record large amounts of inference data.
* Batteries when trained on one specific task often can be directly used for other tasks once a codebook is fitted with the necessary data. Meaning a battery trained on gaussian noise can be fed imagenet snippets and downstream the MSE rates from the 64 battery array can be consumed for statistics aggregation to a fair degree of accuracy without actually training the array on images themselves.
* The battery codebook is a pointwise rigid map within the battery and can be used for pairwise learning when using the H2, H2a, and H2b batteries.
So this is, the evolved state of the geometric vocabulary in some ways, and a completely new and unexpected systemic development in others. They stack, you can reuse them, so small you can swap them at runtime with no time loss, they align rapidly, and downstream tasks can consume their information.
There are many untested avenues that I need to make a full writeup for because quite frankly it's messy currently and Claude is only making it more messy instead of cleaner.
- 2 replies