Walker Hughes

@walkerhughes · User

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math, ml, bjj

San Francisco, CA3 followers57 repositories

Repositories

walkerhughes/tastytrade-mcp

MCP built on the TastyTrade OpenAPI spec. Let agents access to your brokerage account, market data (options chains, prices, indicators), and order management for options trading.

★ 1PythonForks 1

walkerhughes/harbor

Harbor is a framework for running agent evaluations and creating and using RL environments.

★ 0PythonForks 0

walkerhughes/mctsr

Monte Carlo Tree Self-refine implementation for enhanced LLM accuracy in Q&A tasks based on MCTSr paper, based on Trelis Research implementation.

★ 1PythonForks 1

walkerhughes/pydantic-ai-skills

This package implements Agent Skills (https://agentskills.io) support with progressive disclosure for Pydantic AI. Supports filesystem and programmatic skills.

★ 0PythonForks 0

walkerhughes/llm-sft-grpo

Teaching LLM agents to play (easy) games via multi-turn SFT + GRPO training. Increased win-rate by 47% over baseline on 0.5B param model.

★ 2PythonForks 0

walkerhughes/agent-sandboxing

Sandboxed agent with human-in-the-loop checkpointing. Containers exit when waiting for user input and resume with full context for $0 in idle container costs.

★ 0TypeScriptForks 0

walkerhughes/claude-code-uv-template

Template repo for using uv and Claude Code for development. Includes pytest, ruff, mypy, coverage, CI, and pre-commit hooks.

★ 0MakefileForks 0

walkerhughes/multistage-retrieval-pipeline

Full-stack hybrid RAG application with keyword search (Postgres FTS), semantic re-ranking (pgvector), and evals in CI. Chat about episodes of the Dwarkesh Patel podcast.

★ 0PythonForks 0

walkerhughes/yield-curve

Daily AI-generated newsletter on macroeconomic environment and major trends. Automates data ingestion pipeline via GitHub Actions and stores data in Google BigQuery [not maintained].

★ 0PythonForks 0

walkerhughes/debate_duel

An arena for head-to-head debates between teams of AI agents on hot topics. Agents plan, research, and present their arguments to an LLM Judge, determining a winner and updating teams' ELO rankings.

★ 0PythonForks 0

walkerhughes/Autoencoders

Exploration of data generation techniques with Variational Autoencoders in PyTorch, incorporating custom loss functions and deploying decoder with GCP.

★ 0Jupyter NotebookForks 0

walkerhughes/yc-pypi

Python package published to PyPI for quantitative yield curve analysis.

★ 0PythonForks 0