RightNow-AI/AutoMegaKernel

Score8.5

Popularity76.0

Risknone

TierGold

Score breakdown

Usefulness8.0

Novelty9.0

Momentum8.0

Maturity8.2

Open-source/build8.4

Evidence8.0

Workflow potential10.0

Setup ease6.4

Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.

Why it matters

Useful for AI kernel and GPU-systems engineers who want an open research harness for provably-correct megakernel synthesis — AMK is the agent harness that compiles a HuggingFace Llama-family model into one provably-correct, self-retargeting persistent megakernel with measured int8 wins over CUDA-graphed cuBLAS bf16 at batch-1 decode on inference-class GPUs (L4, L40S, A10G, RTX 5090); for AI infras

Who should use it

AI kernel and GPU-systems engineers who want an open research harness for provably-correct megakernel synthesis — AMK is the agent harness that compiles a HuggingFace Llama-family model into one provably-correct, self-retargeting persistent megakernel with measured int8 wins over CUDA-graphed cuBLAS bf16 at batch-1 decode on inference-class GPUs (L4, L40S, A10G, RTX 5090)AI infrastructure teams running voice / realtime / agentic-loop workloads that are bandwidth-bound at single-stream / low-batch decode — AMK's win regime is exactly that, and the README states plainly that it does NOT claim to beat throughput-optimized serving at high batch (that is compute-bound and not this fight)AI coding-agent power users (Claude Code / Codex) who want to drive GPU kernel synthesis through one structured edit surface — AMK ships MCP server + Claude Code skill/commands/subagent/workflow + Codex AGENTS.md, and the 10-minute unattended autoresearch loop self-improves the megakernel 1.47× over its own starting scheduleEngineering teams who need a verifiable safety property — the deadlock-freedom-by-construction validator had zero false-accepts across 7,160 adversarial schedules, an unsafe agent-proposed schedule is REJECTED at validation time instead of hanging the GPUEngineering teams that need a real trained-checkpoint end-to-end test — AMK imports `HuggingFaceTB/SmolLM2-135M` and reproduces HuggingFace's own greedy `generate` token-for-tokenEngineering teams evaluating research vs production — AMK is the open research harness on GitHub (clean MIT, free for any use); Forge is the separate internal advanced kernel generator for enterprises (contact `[email protected]`)Self-retargeting evaluation — the same source built and ran a correct megakernel on sm_120 / sm_80 / sm_90 with the nvcc gencode derived from the live deviceEvaluation: `uv run pytest` runs 98 tests (78 on CPU, 20 CUDA auto-skip without GPU), `uv run amk generate toy --gpu rtx5090 --prompt-ids "1,2,3" --max-tokens 32 --verify` reproduces multi-token generation against eager greedy decode, `uv run python examples/run_hf_model.py` runs the SmolLM2-135M end-to-end, `uv run pytest tests/test_cuda_perf.py` reproduces the 10-minute self-improving run on RTX 5090

Who should skip it

Move on from RightNow-AI/AutoMegaKernel if the licensing terms, language support, or platform requirements do not fit your project.

About this signal

RightNow-AI/AutoMegaKernel is tracked by RepoRadar as a agent harness for provably-corre in the MIT agent harness that compiles a HuggingFace Ll section. It was first seen on 2026-06-25 and last updated on 2026-06-25. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. The standout signals for RightNow-AI/AutoMegaKernel are workflow potential (10.0) and novelty (9.0), while setup ease (6.4) trails — that balance shapes where it fits best. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.

How this item is evaluated

RepoRadar assigned RightNow-AI/AutoMegaKernel a composite score of 8.5 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 76.0 and never affects the composite score or tier. The risk label of 'none' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.

Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.

Risk explanation

No inherent user-impacting risk is flagged from the captured evidence.

Evidence links

github.com

Closest alternatives / related signals

automegakernelrightnow-aimegakernelpersistent-kernelgpu-kernel-synthesishuggingface-llamasmollm2-135msingle-stream-decode