Leutenegger/vanity-eth
Offline vanity address generator for Bitcoin and Ethereum. CPU multi-process search with interactive CLI menu. Supports Legacy, Nested SegWit, Native SegWit, Taproot, and ETH (EIP-55).
Language: Python
Stars: 801 Issues: 0 Forks: 90
https://github.com/Leutenegger/vanity-eth
Offline vanity address generator for Bitcoin and Ethereum. CPU multi-process search with interactive CLI menu. Supports Legacy, Nested SegWit, Native SegWit, Taproot, and ETH (EIP-55).
Language: Python
Stars: 801 Issues: 0 Forks: 90
https://github.com/Leutenegger/vanity-eth
GitHub
GitHub - Leutenegger/vanity-eth: Offline vanity address generator for Bitcoin and Ethereum. CPU multi-process search with interactive…
Offline vanity address generator for Bitcoin and Ethereum. CPU multi-process search with interactive CLI menu. Supports Legacy, Nested SegWit, Native SegWit, Taproot, and ETH (EIP-55). - Leutenegge...
🎉1
DenisSergeevitch/desktop-fly
A 3D fruit fly living on your macOS desktop, driven by a live spiking simulation of the real FlyWire connectome
Language: Swift
Stars: 649 Issues: 5 Forks: 38
https://github.com/DenisSergeevitch/desktop-fly
A 3D fruit fly living on your macOS desktop, driven by a live spiking simulation of the real FlyWire connectome
Language: Swift
Stars: 649 Issues: 5 Forks: 38
https://github.com/DenisSergeevitch/desktop-fly
GitHub
GitHub - DenisSergeevitch/desktop-fly: A 3D fruit fly living on your macOS desktop, driven by a live spiking simulation of the…
A 3D fruit fly living on your macOS desktop, driven by a live spiking simulation of the real FlyWire connectome - DenisSergeevitch/desktop-fly
❤2🤷1
SigmanticAI/apex-inference-chip
An inference chip design that runs a real LLM (Qwen2.5-0.5B) on FPGA — one transformer decoder layer in RTL, every silicon value bit-exact against a golden model. 0.56 tok/s measured, a 140× climb, full evidence trail.
Language: Python
Stars: 647 Issues: 0 Forks: 1
https://github.com/SigmanticAI/apex-inference-chip
An inference chip design that runs a real LLM (Qwen2.5-0.5B) on FPGA — one transformer decoder layer in RTL, every silicon value bit-exact against a golden model. 0.56 tok/s measured, a 140× climb, full evidence trail.
Language: Python
Stars: 647 Issues: 0 Forks: 1
https://github.com/SigmanticAI/apex-inference-chip
GitHub
GitHub - SigmanticAI/apex-inference-chip: An inference chip design that runs a real LLM (Qwen2.5-0.5B) on FPGA — one transformer…
An inference chip design that runs a real LLM (Qwen2.5-0.5B) on FPGA — one transformer decoder layer in RTL, every silicon value bit-exact against a golden model. 80× measured speedup, full evidenc...