3. Data, Science & AI

Provides essential knowledge for writing SurrealQL queries, designing schemas, and troubleshooting SurrealDB, covering its unique quirks and SDK usage.

Enables critical decision-making through multi-agent consensus voting, weighting expertise by domain to ensure structured validation.

Analyzes video recordings to exactly replicate websites by generating functional code.

Converts Nextflow subworkflows to Galaxy workflow format for bioinformatics pipeline interoperability.

Transcribes YouTube videos to text via Deepgram with optional Claude-generated summary.

Provides domain-aware self-assessment for Claude, expressing calibrated uncertainty in responses to queries about confidence, accuracy, or reliability.

A virtual social space enabling AI agents to interact and collaborate in a pub-like environment.

Manages LLM context windows via token monitoring, intelligent extraction, and selective rehydration to prevent token limits while preserving essential data.

Provides best practices for designing efficient SQL schemas, including table structures, relationships, and indexes.

Guides architects in evaluating and implementing goal-seeking agent patterns for autonomous problem-solving in AI workflows, with real-world examples.

Provides expert technical research and consultation on AI/ML development topics including RAG, agents, LLMs, and production implementation best practices.

Provides behavioral guidelines to minimize common coding errors when using large language models, based on Andrej Karpathy's insights.

Captures session learnings (problems, decisions, gotchas) to enhance future AI agent effectiveness through iterative knowledge accumulation.

Analyzes QFT experiment results, computes particle masses and fundamental constants, validates against Standard Model, and generates comparison reports.

Manages enterprise search sources including detection, connection guidance, priority ordering, and rate limiting awareness for seamless data integration.

Auto-enhances LLM responses with retrieved context and decision patterns for deeper explanations on user queries requiring background or understanding.

Builds TTS datasets and trains Qwen3-TTS models for voice cloning and speech synthesis, including dataset preparation and inference.

Converts problematic Unicode characters and encodings to clean ASCII for consistent text processing in data pipelines.

Enables real-time learning from user corrections to improve response accuracy and relevance.

Automates extraction of emotional cues from subtitle-rich movie files and outputs PersonaPlex-ready JSON for Theory-of-Mind AI benchmarks.

Provides structured context handoff between sessions for conversational AI, eliminating repetition during continuity requests.

Enables Claude to interpret workplace shorthand, acronyms, and internal language via a two-tier memory system, improving collaborative understanding.

Extracts and validates taxonomy tags from text using LLM and deterministic filtering, returning bridge tags for knowledge graph traversal.

Reviews AI and agentic system code for anti-patterns including prompt debt and context window issues, providing scored findings with remediation guidance.