
Source Audit: KTransformers and Heterogeneous CPU-GPU MoE Inference
Auditing kvcache-ai/ktransformers: dissecting CPU-GPU offloading, AMX/AVX-512 kernels, and how heterogeneous memory architectures run 671B MoE models.
LATEST INVESTIGATIONS

Auditing kvcache-ai/ktransformers: dissecting CPU-GPU offloading, AMX/AVX-512 kernels, and how heterogeneous memory architectures run 671B MoE models.

Auditing MemoriLabs/Memori: analyzing relational schema interception, ACID durability, and why standard SQL databases challenge dedicated vector stores.
Auditing Hugo Duprez’s Rust/Wasm Pixel Snapper: why deterministic grid snapping and color quantization solve diffusion pixel art flaws where LLMs fail.

Auditing Anthropic’s 100,000-conversation productivity report against METR randomized developer trials and 2026 multi-agent benchmarks under equal token budgets.
An empirical audit of Microsoft Fara-7B and FaraGen: why synthetic trajectories and critical points matter for on-device computer-use agents.
A source audit of Google DeepMind’s native multimodal image models, evaluating unified visual tokenization, typographic precision, and diffusion transformer scaling.
An architectural audit of karpathy/llm-council: examining 3-stage anonymized peer review, Borda count aggregation, and synthesis bottlenecks.
Source audit of structured input schemas, JSON mode constraints, and prompt orchestration across multi-agent pipelines with Gemini and open LLM APIs.
Source audit of hybrid graph-vector memory architectures for autonomous agents, evaluating multi-hop relationship traversal and persistent episodic storage.
Analysis of evolving engineering skills when transitioning from writing code syntax to designing agent harnesses, interface contracts, and invariant verification.
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