ยท
AI & ML interests
open source, infinite games. (they/them)
Recent Activity
reacted to Quazim0t0's post with โค๏ธ 1 day ago ๐งฉ Verified neural units, now for memory & storage
Following up on my neural-aarch64-units (small MLPs that emulate CPU datapath slices, verified bit-exact over their entire finite input domain โ N/N), I applied the same discipline to memory and storage. Three new repos:
๐ท neural-ddr โ verified units emulating DDR5 logic: DBI (256/256, 512/512), ADDR_MAP (4096/4096), CMD_DECODE (32/32), WR_CRC (512/512), and on-die ECC ODECC (256/256, 3328/3328). Composed into a bridge that presents DDR5 behavior over real DDR3/DDR4 RAM โ flip a bit in every stored byte, ECC corrects all of them.
๐ค https://huggingface.co/Quazim0t0/neural-ddr ยท ๐ป https://github.com/quzi93/neural-ddr
๐๏ธ neural-storage โ a self-healing vault on a neural-verified GF(2โธ) core (LOG/EXP compose to a multiply verified over all 65,536 pairs). Content-addressed dedup + Reed-Solomon so any k of n shards rebuild the whole, plus a whole-drive โ self-healing .pt imager.
๐ค https://huggingface.co/Quazim0t0/neural-storage ยท ๐ป https://github.com/quzi93/neural-storage
๐ฟ neural-cd-preserve โ scan a disc into a self-healing .pt that detects (per-shard SHA-256) and repairs bit-rot, restoring bit-exact even from a damaged copy. Beyond the RS limit it's flagged LOST, never silently wrong.
๐ค https://huggingface.co/Quazim0t0/neural-cd-preserve ยท ๐ป https://github.com/quzi93/neural-cd-preserve
Build your own: golden finite function โ enumerate the domain (decompose big/linear ops like CRC/ECC/GF into bit/byte slices) โ train a small MLP โ verify must be bit-exact on 100% of inputs or it's rejected โ compose. Every repo ships the training + exhaustive-verification scripts.
Honest by construction: dedup removes redundancy, erasure coding adds it, ECC corrects faults โ none of it pretends to beat entropy. Runs on modest/older hardware. ๐ค reacted to Quazim0t0's post with ๐ฅ 1 day ago ๐งฉ Verified neural units, now for memory & storage
Following up on my neural-aarch64-units (small MLPs that emulate CPU datapath slices, verified bit-exact over their entire finite input domain โ N/N), I applied the same discipline to memory and storage. Three new repos:
๐ท neural-ddr โ verified units emulating DDR5 logic: DBI (256/256, 512/512), ADDR_MAP (4096/4096), CMD_DECODE (32/32), WR_CRC (512/512), and on-die ECC ODECC (256/256, 3328/3328). Composed into a bridge that presents DDR5 behavior over real DDR3/DDR4 RAM โ flip a bit in every stored byte, ECC corrects all of them.
๐ค https://huggingface.co/Quazim0t0/neural-ddr ยท ๐ป https://github.com/quzi93/neural-ddr
๐๏ธ neural-storage โ a self-healing vault on a neural-verified GF(2โธ) core (LOG/EXP compose to a multiply verified over all 65,536 pairs). Content-addressed dedup + Reed-Solomon so any k of n shards rebuild the whole, plus a whole-drive โ self-healing .pt imager.
๐ค https://huggingface.co/Quazim0t0/neural-storage ยท ๐ป https://github.com/quzi93/neural-storage
๐ฟ neural-cd-preserve โ scan a disc into a self-healing .pt that detects (per-shard SHA-256) and repairs bit-rot, restoring bit-exact even from a damaged copy. Beyond the RS limit it's flagged LOST, never silently wrong.
๐ค https://huggingface.co/Quazim0t0/neural-cd-preserve ยท ๐ป https://github.com/quzi93/neural-cd-preserve
Build your own: golden finite function โ enumerate the domain (decompose big/linear ops like CRC/ECC/GF into bit/byte slices) โ train a small MLP โ verify must be bit-exact on 100% of inputs or it's rejected โ compose. Every repo ships the training + exhaustive-verification scripts.
Honest by construction: dedup removes redundancy, erasure coding adds it, ECC corrects faults โ none of it pretends to beat entropy. Runs on modest/older hardware. ๐ค View all activity Organizations
sequelbox/Mitakihara2-DeepSeek-V4-Pro
Viewer
โข Updated โข 22.7k โข 260
โข 4
sequelbox/Titanium4-DeepSeek-V4-Pro
Viewer
โข Updated โข 23.5k โข 298
โข 7
sequelbox/Titanium4-DeepSeek-V4-Pro-PREVIEW
Viewer
โข Updated โข 4.92k โข 70
โข 1
sequelbox/Tachibana4-DeepSeek-V4-Pro
Viewer
โข Updated โข 17.2k โข 303
โข 20
sequelbox/Tachibana4-DeepSeek-V4-Pro-PREVIEW
Viewer
โข Updated โข 1.2k โข 108
โข 17
sequelbox/Superpotion-DeepSeek-V3.2-Speciale
Viewer
โข Updated โข 57.8k โข 119
โข 7
sequelbox/Raiden-Mini-DeepSeek-V3.2-Speciale
Viewer
โข Updated โข 16.1k โข 87
โข 7
sequelbox/UML-Generator-Dataset-DeepSeek-V3.2
Viewer
โข Updated โข 2.71k โข 66
โข 6
sequelbox/Tachibana3-Part2-DeepSeek-V3.2
Viewer
โข Updated โข 9.38k โข 54
โข 4
sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus
Viewer
โข Updated โข 9.34k โข 40
โข 3
sequelbox/Titanium3-DeepSeek-V3.1-Terminus
Viewer
โข Updated โข 27.7k โข 57
โข 2
sequelbox/DES-Reasoning-DeepSeek-V3.1
Viewer
โข Updated โข 4.03k โข 63
โข 1
sequelbox/DAG-Reasoning-DeepSeek-R1-0528
Viewer
โข Updated โข 4.08k โข 41
โข 12
sequelbox/Mitakihara-DeepSeek-R1-0528
Viewer
โข Updated โข 16.9k โข 54
โข 6
sequelbox/Celestia3-DeepSeek-R1-0528
Viewer
โข Updated โข 91k โข 49
โข 35
sequelbox/Celestia3-DeepSeek-R1-0528-PREVIEW
Viewer
โข Updated โข 13.4k โข 22
โข 7
sequelbox/Titanium2.1-DeepSeek-R1
Viewer
โข Updated โข 31.7k โข 82
โข 8
sequelbox/Tachibana2-DeepSeek-R1
Viewer
โข Updated โข 27.3k โข 48
โข 5
sequelbox/Titanium2-DeepSeek-R1
Viewer
โข Updated โข 32.4k โข 44
โข 3
sequelbox/Raiden-DeepSeek-R1
Viewer
โข Updated โข 62.9k โข 144
โข 52
sequelbox/Raiden-DeepSeek-R1-PREVIEW
Viewer
โข Updated โข 5.8k โข 33
โข 6
sequelbox/Tachibana2-DeepSeek-R1-PREVIEW
Updated โข 13
โข 1
Viewer
โข Updated โข 103k โข 33
โข 4
sequelbox/Tachibana-QVQ-PREVIEW
Viewer
โข Updated โข 9.31k โข 13
โข 4
Viewer
โข Updated โข 176k โข 37
โข 4
Viewer
โข Updated โข 10.7k โข 53
Viewer
โข Updated โข 126k โข 34
โข 10
Viewer
โข Updated โข 26.6k โข 64
โข 2
Viewer
โข Updated โข 104k โข 61
โข 10
Viewer
โข Updated โข 178k โข 83
โข 8