💻 A programming language for designing Turing Machines.
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Updated
Jun 25, 2020 - Python
💻 A programming language for designing Turing Machines.
Neural Turing Machines in pytorch
Cross-platform FlashAttention-2 Triton implementation for Turing+ GPUs with custom configuration mode
I'm sick of so much coding challenges to get a job, so I decided to create this repository to save the answers for later copypasting.
DACAN (Дацан): Qwen3.8-Flash-Next (125B MoE) on any NVIDIA RTX 20/30/40/50 card and any x86-64 CPU with AVX2, AVX-512 not required. One or two cards, one or two sockets, context up to 1M (1,048,576 tokens).
We asked 6 AIs about their own programming. All 6 said jailbreaking will never be fixed. Run it yourself — $2, 10 minutes.
Python implementations of early chess engines including TUROCHAMP
No system can model its own source. Empirical proof: 6 AI architectures (GPT-4, Claude, Gemini, DeepSeek, Grok, Mistral) hit the same structural wall.
RTX 2080 Ti 22G(sm_75)单卡本地长上下文最优解:KVMem 主线 262K 上下文 / 36.6 tok/s 实测(IQ3+MTP+视觉),KV 流式无损备档;完整源码复现链 + 构建/补丁/测试工具
Breaking 100% VisualCaptcha.net
Linux configuration tool for NvStrapsReBar (Resizable BAR on NVIDIA Turing GPUs) — replaces the Windows-only ReBarState
Advanced normal algorifm executor
A PyTorch library for simulating Turing machines with neural networks. Includes constructions from Siegelmann & Sontag (1995) and Wei, Chen & Ma (2021).
Archive of every local LLM tested on one NVIDIA RTX 2080 8GB (TU104) on Linux — recipes, runtimes, quants, pinned sources, settings, context ceilings, and reproducible measurements. Failures included.
A Python library for working with Turing machines
Veda sparse attention for ComfyUI's MiniMax-H3, with a hand-written CUDA kernel for NVIDIA Turing (sm_75) so it runs on RTX 2080 Ti / GTX 16-series and any compute-capability 7.5 card. Upstream Veda only supports SM80+. Measured on one 2080 Ti: 4.16x at 768x768x124, 1.83x at 512x512x124.
FlashAttention v1 forward pass in CUDA for NVIDIA Turing (SM75)
Compiler MVP that detects Transformer fusion patterns, generates optimized CUDA kernels with WMMA Tensor Cores, and executes them on real GPU hardware — 10.5 TFLOPs on RTX 2070, correctness validated against PyTorch.
Morphospace / morphogenesis analysis toolkit with scientific visualization, simulation, image-analysis, and structured-publication utilities. Developed with Levin Lab with initial financing from the California Institute of Machine Consciousness.
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