Results for “bench”

Benchmarks Raspberry Pi 5 Docker performance and storage speeds, with setup instructions for Docker on Raspberry Pi.

CI with Apache Bench. CI integration.

Spike with Apache Bench. spike tests.

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality.

Add a new cuTile GPU kernel operator to TileGym, covering dispatch registration, backend implementation, exports, tests, and benchmarks.

Helps find and analyze competitors to imitate using a five-filter method, focusing on profitability and feasibility while eliminating personal bias.

Routes prompts to any LLM model across multiple providers via CLI tools or APIs, with auto-discovery of new models and benchmark data.

Curation-Bench Protocol (skill-grounded)

Performance regression detection using the browse daemon. (gstack)

Run config-driven performance benchmarks against a deployed NVIDIA RAG Blueprint server, including profiling and load testing, with a unified report.

Write reliable, maintainable Go tests using table-driven tests, subtests, benchmarks, fuzzing, and golden files following TDD methodology.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

Optimize AI agents through automated evolution cycles using LLM-driven mutation of prompts, skills, and memory against measurable benchmarks.

Create new skills, modify and improve existing skills, and measure skill performance through iterative evaluation and benchmarking.

Access AI-ready drug discovery datasets, benchmarks, and molecular oracles from Therapeutics Data Commons for therapeutic machine learning and pharmacological prediction.

Provides startup metrics frameworks and stage-specific benchmarks for SaaS, Marketplace, Consumer, and B2B models, including investor-ready dashboard generation.

Improves AI skills for activation, clarity, and cross-model reliability through benchmarking, salience tuning, and regression triage.

Measure performance baselines, detect regressions before and after PRs, and compare stack alternatives using browser, API, and build benchmarks.

Write reliable Go tests using table-driven tests, subtests, benchmarks, fuzzing, and coverage, following TDD with idiomatic patterns.

Automates security and compliance checks using OPA policies, Trivy vulnerability scanning, AWS CIS benchmark verification, and Kubernetes remediation scripts.

Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.

Hardens the Docker daemon by configuring daemon.json with user namespace remapping, TLS authentication, rootless mode, and CIS benchmark controls.

Reference for Benchling REST API v2 endpoints, covering authentication, pagination, error handling, and CRUD operations for sequences, entities, containers, and notebook entries.

Benchmarks inference latency and computational runtime of transformer models and MLX operations across Apple Silicon and NVIDIA GPU backends, with configurable input lengths and batch sizes.

Benchmarks a text-to-text transformer across GLUE, SuperGLUE, CNN/Daily Mail, SQuAD, and WMT, reporting GLUE average, BLEU, ROUGE-2-F, and Exact Match scores.

Benchmarks medical AI agents on synthetic EHR tasks, measuring success rates for data retrieval, temporal aggregation, and threshold-based conditional logic with exact-match scoring.

Queries Hugging Face benchmark leaderboards to find the best AI models for a task, filters by device constraints, and returns a ranked comparison table with scores.

Reduce per-token latency on Jetson vLLM servers by appending speculative decoding configuration, with guidance on when to enable and how to benchmark the improvement.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) using standardized prompts and metrics. Supports HuggingFace, vLLM, and API backends.

Benchmarks LLM bias by scoring responses to automatically generated open-ended questions across sensitive attributes, producing a composite fitness score from 0 to 5.

Evaluates the correlation between a zero-cost NAS metric (epsilon) and actual training accuracy across different neural architecture search spaces, testing the metric's ability to rank architectures without training. It probes whether output dispersion from constant weight initializations can serve as a reliable.

Calculates SaaS health metrics like ARR, MRR, churn, LTV, and CAC from raw business numbers, benchmarks them against industry standards, and provides prioritized actionable advice.

Evaluates social bias in question-answering models using the BBQ benchmark, measuring accuracy and a bias score across ambiguous and disambiguated contexts to reveal reliance on stereotypes.

Apply CIS Docker Benchmark v1.8.0 security best practices to harden Docker containers for production, covering daemon configuration, image building, runtime controls, and auditing.

Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution on local Docker, Slurm HPC, or cloud platforms.

Solves single- and multi-objective optimization problems with NSGA-II/III, MOEA/D, and other evolutionary algorithms, including constraint handling, Pareto front analysis, and benchmark problems.