DietrichGebert/ponytail
Ponytail enables AI agents to generate more concise, efficient, and cost-effective code by enforcing a "lazy senior dev" methodology, prioritizing existing solutions and minimal implementation.
Awesome AI Agents › AI Assistants
Feynman is an open-source command-line AI research agent aimed at scientific and technical research work rather than general coding. It is installed via a shell script on macOS and Linux, a PowerShell script on Windows, or npm, and after installation a `feynman setup` step connects it to a model provider, with support for local models and editor integrations such as Zed. Once configured, a user runs natural-language queries directly (for example asking what is known about a topic) or invokes a set of dedicated workflows: `/deepresearch` runs a planned, multi-step investigation with several parallel research passes and citation checking; `/lit` produces a literature review summarizing consensus, disagreements and open questions on a topic or research group; `/review` critiques a paper or draft with severity-graded feedback and a revision plan; `/audit` compares a paper's stated claims against its public code; `/replicate` produces a replication plan that only proceeds once the user chooses a runtime environment; `/recipe` ranks machine-learning training recipes together with datasets and reference code; `/compare` builds a source comparison matrix across a topic; and `/draft` assists with writing a paper. The project positions itself as an agent purpose-built for the research process end to end, from initial literature discovery through critical review and reproducibility checking, distinguishing it from general-purpose coding or chat agents by the specificity of its built-in research workflows. It is documented at a dedicated docs site and has a DeepWiki-generated reference, and offers a skills-only bundle for use inside other agent CLIs such as Codex and OpenCode instead of running as its own standalone process.
https://github.com/Companion-Inc/feynman
Ponytail enables AI agents to generate more concise, efficient, and cost-effective code by enforcing a "lazy senior dev" methodology, prioritizing existing solutions and minimal implementation.
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