Results for “mem”

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas replacement. For larger-than-RAM data prefer dask or vaex. Part of the AlterLab Academic Skills suite.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Guides optimization of Qdrant query latency by tuning segments, memory, quantization, and search parameters.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Scale pandas and NumPy workflows to larger-than-memory datasets using parallel and distributed computing.

Computação paralela/distribuída. Escale pandas/NumPy além da memória disponível, DataFrames/Arrays paralelos, processamento multi-arquivo, grafos de tarefas, para datasets maiores que RAM e workflows paralelos.

Perform efficient pandas DataFrame operations for data analysis, manipulation, and transformation with production-grade patterns.

Scales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file processing, distributed ML, or integration with existing pandas code. For out-of-core analytics on a single machine prefer vaex; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Provides guidance on using the ZVec in-process vector database for efficient similarity search and embedding storage in agent memory systems.

Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on massive datasets, visualizing big data, or building ML pipelines that do not fit in memory. For distributed clusters prefer dask; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Generates a single-page team management dashboard with tabs for members, details, and activity log, including charts and CSV export.

Process tabular data with Polars' expression API, lazy evaluation, and parallel execution for fast in-memory analysis and pandas migration.

Open, create, read, analyze, edit, or validate Excel/spreadsheet files (.xlsx, .xlsm, .csv, .tsv) with full formula support and professional formatting.

Manages and analyzes Clerk authentication resources, including users, organizations, and sessions, with commands for user growth, auth health, and membership review.

Process in-memory tabular data with a fast, expression-based DataFrame library that supports lazy evaluation, parallel execution, and Apache Arrow semantics.

Read, extract, merge, split, rotate, encrypt, and create PDF files. Supports markdown-to-PDF conversion, table extraction, form filling, and OCR for scanned documents.

Scales pandas and NumPy workflows to datasets larger than memory using parallel and distributed computing, with support for dataframes, arrays, bags, and custom task graphs.

Process in-memory datasets with Polars' expression API, lazy evaluation, and parallel execution, including pandas migration patterns and I/O for CSV, Parquet, and JSON.

Identify, collect, and analyze ransomware attack artifacts to determine the variant, initial access vector, encryption scope, and recovery options.

Syncs key-value preferences and state across multiple agents using a shared TiDB Zero database, with optional auto-provisioning of a free ephemeral database.

Translates natural language data intents into syntactically valid Perfetto SQL queries and executes them against a local trace file. Use this skill to extract slice, thread, or memory data from Android Perfetto traces using trace_processor.

Provides a fast in-memory DataFrame library for datasets that fit in RAM, with lazy evaluation, parallel execution, and an Apache Arrow backend for ETL pipelines and analytics.

Write correct, efficient Kusto Query Language queries with coverage of syntax, joins, dynamic types, datetime pitfalls, regex, serialization, memory management, and advanced functions.

Extract embedded configuration from Agent Tesla RAT samples including SMTP/FTP/Telegram exfiltration credentials, keylogger settings, and C2 endpoints using .NET decompilation and memory analysis.

Provides battle-tested PyTorch training recipes for LLMs, vision, diffusion, and biomedical domains, covering training loops, optimizer selection, LR scheduling, mixed precision, and debugging.

Executes malware samples in Cuckoo Sandbox to observe runtime behavior including process creation, file system modifications, registry changes, network communications, and API calls. Generates comprehensive behavioral reports for malware classification and IOC extraction.

Track investment theses across their lifecycle — from screening idea to closed position with postmortem. Register theses from screener outputs, manage state transitions, attach position sizing, review due dates, and generate postmortem reports with P&L and MAE/MFE analysis.

Diagnose and observe an Oxla distributed analytical database using system catalog tables, Prometheus metrics, runtime log-level changes, and troubleshooting workflows for slow queries, node health, and memory/OOM pressure. Also covers debugging Oxla's external data sources, including the Redpanda/Kafka ingestion path.

Use for writing, reviewing, debugging, testing, or optimizing Python NumPy ndarray code. Trigger on array construction, shape/axis reasoning, dtypes and casting, broadcasting, indexing, copies/views, ufuncs, reductions, vectorization, random Generator, linear algebra, FFT, masked/structured arrays, memory layout, or NumPy interoperability. Do not use for pandas/Polars table semantics, JAX/CuPy-only arrays, symbolic SymPy, or pure Python sequences without a NumPy boundary.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.

Use essa skill para processar e analisar grandes conjuntos de dados tabulares (bilhões de linhas) que excedem a RAM disponível. Vaex excels em operações DataFrame out-of-core, avaliação lazy, agregações rápidas, visualização eficiente de big data e machine learning em datasets grandes. Aplique quando usuários precisarem trabalhar com arquivos CSV/HDF5/Arrow/Parquet grandes, realizar estatísticas rápidas em datasets massivos, criar visualizações de big data ou construir pipelines de ML que não cabem em memória.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.