Results for “munger”

Build production-quality Model Context Protocol (MCP) servers in C#/.NET using the official 1.x NuGet packages, covering transports, tools, prompts, resources, and common pitfalls.

Plan, debug, tune, evaluate, export, or deploy public Nemotron embedding and reranking retrieval recipes using the current checkout.

💻 MacBook Optimizer

Expert guidance for Mojo, the programming language by Modular that combines Python's usability with C-level performance. Helps developers write high-performance AI/ML code, optimize numerical computations with SIMD and parallelism, and gradually port Python code to Mojo for orders-of-magnitude speedups.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Apply Miller's Law and chunking strategies to group information into meaningful units, making interfaces easier to scan, understand, and recall.

Use when building a cursor trail whose spacing stays constant at any hand speed by emitting motes per distance travelled (not on a timer)—sub-segment placement, ring-buffer ordering, idle breath, 3D screen anchoring, coasting, touch and reduced-motion fallbacks—for wisps, sparks, embers, comet tails, plankton, or dust.

Interact with MCP servers directly from the command line, including listing, calling tools, managing authentication, and generating client code.

Enables decoupled pub/sub messaging between ViewModels using CommunityToolkit.Mvvm Messenger, covering weak/strong references, request/reply patterns, channel tokens, and lifecycle management.

Diagnoses Qdrant production issues using metrics and observability tools, covering optimizer problems, memory spikes, and slow queries.

Guides setting up and using Atheris for coverage-guided fuzzing of Python code and C extensions, including Docker setup, harness writing, and corpus management.

Build, refactor, or review macOS menubar apps that use Tuist and SwiftUI, with a focus on Tuist-first workflows, strict architecture boundaries, and reliable local launch scripts.

Use when implementing ML functionality with production-grade patterns and safeguards.

Use when running HMMER master or worker daemon services that front `phmmer`, `hmmsearch`, and `hmmscan` against cached databases.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Failure diagnosis, DAG mutation, and escalation engine for the WinDAGs meta-DAG. Receives failure information and quality vectors from the Evaluator. Classifies failures on four dimensions. Follows a five-level escalation ladder. Applies seven mutation types with saga compensation. Enforces BC-EXEC-002, BC-EXEC-003, BC-FAIL-002, BC-FAIL-005. Activate when operating as the Mutator role in the meta-DAG, when diagnosing node failures, when restructuring a DAG at runtime, or when deciding escalation level.