data-analysis
341
hl-build-standalone-app
Build a standalone HailoRT inference application.
hailo-ai
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
data-engineering
341
hl-build-pipeline-app
Build a complete GStreamer pipeline app for real-time video processing on Hailo-8/8L/10H.
hailo-ai
data-ai
machine-learning
341
hl-build-vlm-app
Build a Vision-Language Model application for Hailo-10H.
hailo-ai
data-ai
machine-learning
341
hl-model-management
HEF resolution, model download, and config management.
hailo-ai
data-ai
machine-learning
341
hl-build-vlm-app
Build a complete Vision-Language Model application that uses the Hailo-10H VLM for image understanding.
hailo-ai
data-ai
data-analysis
334
csv-data-summarizer
Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
coffeefuelbump
data-ai
data-analysis
333
displaying-timelines
Displays chronological events and activity through timelines, activity feeds, Gantt charts, and calendar interfaces. Use when showing historical events, project schedules, social feeds, notifications, audit logs, or time-based data. Provides implementation patterns for vertical/horizontal timelines, interactive visualizations, real-time updates, and responsive designs with accessibility (WCAG/ARIA).
ancoleman
data-ai
data-analysis
333
visualizing-data
Builds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.
ancoleman
data-ai
data-analysis
333
mprove-query-data
Query chart, dashboard, report or build a new one. Answer question using data.
mprove-io
data-ai
data-engineering
333
transforming-data
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
ancoleman
data-ai
data-analysis
333
building-tables
Builds tables and data grids for displaying tabular information, from simple HTML tables to complex enterprise data grids. Use when creating tables, implementing sorting/filtering/pagination, handling large datasets (10-1M+ rows), building spreadsheet-like interfaces, or designing data-heavy components. Provides performance optimization strategies, accessibility patterns (WCAG/ARIA), responsive designs, and library recommendations (TanStack Table, AG Grid).
ancoleman
data-ai
machine-learning
333
ai-data-engineering
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
ancoleman
data-ai
data-engineering
333
ingesting-data
Data ingestion patterns for loading data from cloud storage, APIs, files, and streaming sources into databases. Use when importing CSV/JSON/Parquet files, pulling from S3/GCS buckets, consuming API feeds, or building ETL pipelines.
ancoleman
data-ai
data-engineering
333
streaming-data
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
ancoleman
data-ai
machine-learning
333
embedding-optimization
Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
ancoleman
data-ai
machine-learning
333
implementing-mlops
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
ancoleman
data-ai
machine-learning
333
model-serving
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
ancoleman
data-ai
machine-learning
333
mprove-build-malloy-model
Build Mprove Model based on Malloy sources
mprove-io
data-ai
data-analysis
332
nw-interviewing-techniques
Mom Test questioning toolkit, JTBD analysis, interview conduct, assumption testing framework, and hypothesis design
nWave-ai
data-ai
data-engineering
332
walkeros-create-transformer
Use when creating a new walkerOS transformer. Example-driven workflow for validation, enrichment, or redaction transformers.
elbwalker
data-ai
data-engineering
332
walkeros-understanding-transformers
Use when working with walkerOS transformers, understanding event validation/enrichment/redaction, or learning about transformer chaining. Covers interface, return values, and pipeline integration.
elbwalker
data-ai
data-engineering
332
nw-command-design-patterns
Best practices for command definition files - size targets, declarative template, anti-patterns, and canonical examples based on research evidence
nWave-ai
data-ai
data-engineering
332
nw-data-architecture-patterns
Data architecture patterns (warehouse, lake, lakehouse, mesh), ETL/ELT pipelines, streaming architectures, scaling strategies, and schema design patterns
nWave-ai
data-ai
data-engineering
332
nw-database-technology-selection
Database comparison catalogs, RDBMS vs NoSQL selection criteria, CAP/ACID/BASE theory, OLTP vs OLAP, and technology-specific characteristics
nWave-ai
data-ai