3. Data, Science & AI

Guides implementation of Adaptive Rejection Sampling (ARS) for log-concave distributions, covering envelope construction, adaptive updates, and performance verification.

Guides dataset reorganization across directories while enforcing file size and directory content constraints for efficient data partitioning.

Retrieves and verifies top-performing model rankings from dynamic ML leaderboards (MTEB, HuggingFace) as of a specific date.

Guides training of FastText text classification models with optimized size while maintaining required accuracy thresholds.

Guides building and training Caffe framework for CIFAR-10 image classification, including source compilation and legacy framework compatibility.

Guides high-performance portfolio optimization implementation via Python C extensions for financial computations, accelerating matrix operations and risk metrics on large datasets.

Guides recovery of SQLite database data from corrupted, encrypted, or inaccessible Write-Ahead Log (WAL) files using specialized analysis techniques.

Analyzes chess board images to detect piece positions and calculate optimal moves using AI-driven analysis for puzzle solving and position evaluation.

Guides setup of HuggingFace model inference services via Flask APIs, including model downloading, endpoint creation, and REST API development for machine learning models.

Extracts text, letters, or symbols encoded as movement commands in G-code files for 3D printing, CNC, and laser cutting applications.

Counts tokens in datasets using specified tokenizers, supporting filtering and aggregation for NLP tasks and AI model preparation.

Guides accurate token counting in datasets from HuggingFace, including schema analysis and tokenizer usage to prevent data errors.

Reconstructs PyTorch models from state dictionaries, checkpoints, or partial artifacts, enabling architecture inference and recovery without original source code.

Guides implementation of adaptive rejection sampling (ARS) for generating random samples from log-concave distributions in statistical computing.

Guides DNA insertion primer design for site-directed mutagenesis using Q5 kits, including verification and common pitfalls for plasmid insertion via PCR.

Extracts text from PDFs (including scanned documents via OCR) for AI/ML processing, RAG systems, and document analysis.

Guides MCMC sampling for Bayesian models using RStan or PyStan, including hierarchical models and diagnostics.

Extracts text-based game commands from video recordings using OCR and frame analysis, with domain-specific validation and deduplication.

Provides CLI tools for biological cell segmentation using SAM, mask-to-polygon conversion, and microscopy data processing with CSV outputs.

Enables Bayesian network structure recovery, parameter learning, causal inference via do-calculus, and sample generation from modified networks.

Comprehensive guide for building Solana blockchain-interacting AI agents using Solana Agent Kit, LangChain, and Vercel AI integration.

Comprehensive guide for integrating Pyth Network's decentralized oracle into Solana DeFi applications, covering price feed mechanisms and real-time data streaming.

Provides Solana-specific integration with CoinGecko API for real-time token prices, DEX data, OHLCV charts, and market analytics to build trading applications.

Analyzes datasets to extract actionable insights and patterns for data-driven decision-making.