walkerhughes/terminal-bench
Measuring and evolving with the frontier of agent work
math, ml, bjj
Measuring and evolving with the frontier of agent work
Temporary end-to-end validation of terminal-bench cross-platform CI
Helpful tools for working with Claude Code & Codex.
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.
OAuth to a MCP server distributed as a Claude Code plugin.
Harbor is a framework for running agent evaluations and creating and using RL environments.
Monte Carlo Tree Self-refine implementation for enhanced LLM accuracy in Q&A tasks based on MCTSr paper, based on Trelis Research implementation.
MCP server to interact with the Harbor Hub.
A benchmark for LLMs on complicated tasks in the terminal
This package implements Agent Skills (https://agentskills.io) support with progressive disclosure for Pydantic AI. Supports filesystem and programmatic skills.
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.
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.
Evals for both single-step and full-trajectory traces in multi-turn agent workflow.
Template repo for using uv and Claude Code for development. Includes pytest, ruff, mypy, coverage, CI, and pre-commit hooks.
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.
Config files for my GitHub profile.
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].
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.
repo for MSDS697 Distributed Data Systems
Exploration of data generation techniques with Variational Autoencoders in PyTorch, incorporating custom loss functions and deploying decoder with GCP.
Python package published to PyPI for quantitative yield curve analysis.