AI Coding2025
Equips AI coding assistants like Claude Code and Cursor with 75+ executable tools, an MCP server, reusable skills, and a Python library to build on Databricks—Spark pipelines, jobs, dashboards, Unity Catalog resources, and ML workflows—from your editor.
AI Client2026
Generates daily LLM-powered decision dashboards for A/H/US stocks by combining multi-source market data, real-time news, technical signals and agent-style strategy reasoning; deploys via GitHub Actions or Docker and pushes reports to multiple channels.
AI Agent2026
Provides a framework to build, evaluate, and run AI SRE agents that investigate and remediate production incidents on your infrastructure. Includes a CLI, synthetic + end-to-end benchmark suites, and 40+ connectors for observability, infra, and LLM providers so teams can train agents and run investigations locally or in cloud.
AI Deploy2026
Local LLM inference server for Apple Silicon that exposes an OpenAI-compatible API and a macOS menubar app. Uses continuous batching and a two-tier KV cache (RAM + SSD in safetensors) to persist context across restarts, enabling practical multi-model serving and fast local coding workflows.
AI Infra2026
Acts as an OpenAI‑compatible local and cloud gateway that routes requests across 100+ LLM providers with smart routing, load balancing, retries and fallbacks. Adds policies, rate limits, semantic caching and observability for reliable, cost‑aware inference in Docker, Electron or npm installs.
MLOps2026
Runs durable, checkpointed SQL workflows inside PostgreSQL so long-running data and AI pipelines can resume after crashes without external orchestrators. Provides a SQL DSL, in-process background worker, and Postgres-backed state—useful for embeddings, ETL, scheduling, and fan-out jobs when you can install extensions.
AI Model2026
Identifies and surgically removes the internal activation directions that trigger refusal behavior in large language models, with one-click options on a HuggingFace Space or a local Python API. Combines multiple extraction methods (SVD, whitened SVD, sparse autoencoders), reversible steering, and analysis-informed verification to quantify capability and refusal trade-offs.
AI Agent2026
Automatically evolves Hermes Agent skills, prompts, tool descriptions and code using DSPy + GEPA — mutating text via API calls, evaluating trace-based failures, and selecting variants that pass tests and human PR review. No GPU training required; runs cost roughly $2–$10 per optimization.
AI Agent2026
Lightweight, Markdown-only skill pack that lets LLM agents autonomously run ML research workflows—literature survey, idea discovery, cross-model review loops, experiment automation and paper writing—designed for Claude Code, Codex CLI, Cursor and local model setups.
AI Agent2026
Gives the pi terminal AI agent an autonomous experiment loop: propose code changes, run benchmarks, record metrics, auto-commit improvements and revert regressions. Ships a live widget/dashboard, MAD-based confidence scoring, hooks and backpressure checks — made for iterating on speed, bundle size, training loss and build times inside a terminal workflow.
AI Agent2026
Turns a single research idea into runnable experiments and a conference-ready paper by orchestrating an LLM-driven end-to-end workflow (literature → design → code → sandboxed runs → analysis → writing). Provides human-in-the-loop checkpoints, domain-specialist executors, and multi-layer citation verification.