Results for “pre-pr”

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 Oracle PL/SQL stored procedures to PostgreSQL PL/pgSQL, preserving method signatures and type-anchored parameters while applying Oracle-compatible sorting and leveraging orafce.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning using 100+ featurizers, including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa.

Reduce SIEM alert fatigue by systematically tuning detection rules in Splunk and Elastic, using statistical baselines, whitelists, and precision/recall metrics.

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.

Predicts chromatin state across 919 tracks (histone marks, DNase, TF binding) for DNA sequences via the hosted Genomic Intelligence API, producing a report and JSON results.

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

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

Define el enfoque, las herramientas y el pipeline de análisis antes de escribir código, eligiendo entre SQL, Python o un enfoque híbrido según la pregunta y los datos.

Upserts a validated MDX book corpus into Supabase via Drizzle, hydrating books, chapters, sections, and chunks tables while preserving stable bookmark anchors and only re-embedding changed content.

Automates the preparation of Easy difficulty diagram specifications from a CSV report and chapter files, generating reports, execution plans, and spec files for MicroSim generation.

Predict continuous values from temporal sequences using aeon's time series regressors, covering convolutional, deep learning, distance-based, feature-based, hybrid, interval-based, and shapelet-based approaches.

Takes raw whole-genome sequencing FASTQ files or a pre-existing VCF through variant calling, quality control, and polygenic risk score computation using the PGS Catalog.

Manage AWS S3 buckets and objects: create buckets, upload/download files, configure lifecycle policies, set up static websites, manage permissions, and implement cross-region replication.

Analyze PyTorch profiler and vLLM trace files with Perfetto SQL evidence. Use when working with torch profiler traces, merged_trace files, vLLM profiling logs, Perfetto trace analysis, GPU kernel hotspots, synchronization waits, rank imbalance, prefill/decode bottlenecks, or optimization reports.

Create interactive, publication-quality visualizations with 40+ chart types, hover, zoom, pan, and web embedding. Choose between Plotly Express and Graph Objects for dashboards, exploratory analysis, and presentations.

Build and train graph neural networks with PyTorch Geometric, covering node/link/graph classification, message passing layers, heterogeneous graphs, and custom datasets.

Investigates insider threat indicators including data exfiltration attempts, unauthorized access patterns, policy violations, and pre-departure behaviors using SIEM analytics, DLP alerts, and HR data correlation.

Parse FCS (Flow Cytometry Standard) files v2.0-3.1, extract events as NumPy arrays, read metadata and channels, and convert to CSV or DataFrame for flow cytometry data preprocessing.

Load pre-trained models from Hugging Face Hub, run pipeline inference, generate text, and fine-tune models on NLP, vision, audio, and multimodal tasks using the Transformers library.

Generate high-quality sentence and text embeddings for semantic similarity, clustering, and retrieval using 5000+ pre-trained models. Supports multilingual and domain-specific embeddings for RAG and semantic search.

Tracks institutional investor ownership changes and portfolio flows using 13F filings data to identify stocks with significant smart money accumulation or distribution.

Limpia y transforma texto en DataFrames usando expresiones regulares: extrae emails, teléfonos, fechas y precios, valida formatos como CUIL o CBU, y maneja datos inconsistentes con opciones de fuzzy matching.

Extract public web data via HasData APIs, including search engine results, structured data from ecommerce, travel, jobs, and local business platforms, with support for web scraping, pre-parsed APIs, and async jobs.

Reads and preprocesses 5-minute stock candlestick CSV data, then clusters the time series using tslearn's TimeSeriesKMeans, including data cleaning, percentage change calculation, model training, saving, and representative sample extraction.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning, covering 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa, with support for QSAR modeling and virtual screening.

Execute Python code in a safe sandboxed environment with 100+ pre-installed libraries for data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, and automation.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Prevents silent decimal mismatch bugs across EVM chains by querying token decimals at runtime, caching by chain and token address, and using safe normalization for bots, dashboards, and DeFi tools.

Handle files and binary data in n8n workflows correctly, covering the $binary vs $json split, reading/writing binary, preserving binary across transforms, and the agent-tool binary boundary.

Covers MariaDB-specific behavior of the Connector/C++ sql:: API, including driver and connection setup, 1-based indexes, placeholder syntax, ResultSet iteration, raw pointer ownership, SQLString, autocommit, and exception handling.

Calculates IV Rank and IV Percentile for any ticker to determine whether implied volatility is high or low relative to its 252-day history, and provides trading signals based on IV zones.

Parse and analyze Windows forensic artifacts including MFT, registry hives, prefetch files, event logs, LNK files, and jump lists using Eric Zimmerman's EZ Tools suite and KAPE.

Analyze whole-slide pathology images with Python: load 160+ slide formats, preprocess H&E stains, segment nuclei, construct spatial graphs, train ML models, and process multiplex immunofluorescence data (CODEX, Vectra).

Ranks weekly token unlocks by Absorption Ratio (unlock value / 7d avg volume) instead of supply percentage, with per-event cliff vs linear classification, recipient category, and a one-line market read.