5 Developer Projects You Can Build with Feynman: From Research to Code

TL;DR

• Automate the translation of academic papers into executable software repositories.
• Audit scientific claims against their corresponding GitHub codebases for accuracy.
• Scale agent evaluation and safety testing with standardized sandbox workflows.
• Accelerate the transition from research to code using Feynman's interactive AI pipelines.

Transforming academic theory into practical software is traditionally a slow and manual process. Developers spend weeks deciphering mathematical notations, hunting down undocumented configurations, and fighting dependency errors just to reproduce a single baseline. We need a better way to move from research to code. The moment you introduce AI agents into this workflow, the entire dynamic changes. You can automate literature reviews, plan architectures, and generate functional prototypes directly from academic PDFs. Because these AI models can parse complex technical documents, they bridge the gap between static papers and executable scripts.

You can orchestrate this entire pipeline using Feynman, a specialized research agent framework. Once you install the CLI and run the feynman setup command, you unlock access to an interactive REPL and a suite of science-focused tools. That means you can build powerful internal developer tools that automate the hardest parts of machine learning engineering.

Here are five practical developer projects you can build using this ecosystem.

01. Turn Research Papers into Working ML Prototypes

Use case: Take a research problem, study relevant papers and implementations, then turn the findings into an actual prototype.

Code: Feynman -> Paper Research -> Method Extraction -> Experiment Planner -> Code -> Benchmark

The gap between reading a paper and having working code is filled with undocumented hyperparameters and ambiguous architecture descriptions. Most papers are never reproduced at all because the implementation details are too vague. That is why building an automated extraction pipeline is such a valuable project. You can leverage tools like ArXivist to convert scientific papers into fully executable, reproducible codebases.

ArXivist works by extracting a Scientific Intermediate Representation from the paper, which means it maps out every architectural decision and equation into a structured format. From there, your Feynman workflow can feed this structured data into an architecture planner. Once the plan is ready, you can synthesize the actual codebase and run a synthetic benchmark to verify the logic.

How do you transform research to code efficiently?

You can automate the heavy lifting by leveraging multi-agent frameworks like PaperCoder. PaperCoder decomposes the task into planning, analysis, and generation stages. So instead of writing everything manually, you prompt the system to construct a high-level roadmap and design the system architecture first.

# Example Feynman command to start a guided implementation recipe
feynman recipe "fine-tune a small model for math reasoning"

02. Build a Paper-to-Code Reproducibility Auditor

Use case: Compare a research paper against its GitHub implementation to find missing methods, mismatched configurations and unsupported claims.

Code: Paper + Repo -> Feynman -> Code Analysis -> Paper/Code Comparison -> Audit Report

When authors release code alongside their papers, the code often fails to match the claims made in the text. This mismatch creates massive headaches for engineers trying to adopt new techniques. Because independent verification of published research has become both harder and more important, you can build an automated reproducibility auditor. You can use frameworks like Veritas to extract a paper's claims and judge each claim against the evidence from experiment runs.

This project requires parsing both the PDF and the GitHub repository simultaneously. Feynman handles this effortlessly through its file analysis commands. The system can map the reported metrics to the actual evaluation scripts. If the repository code does not grade itself accurately, your auditor will flag the discrepancy.

What is the best way to verify paper to code reproducibility?

You should rely on evidence-grounded reproduction tools like VeriRepro. VeriRepro builds an inspectable chain from paper claims to repository evidence and sandbox execution. It intentionally splits the orchestration and deterministic policy layers.

• PASS: Execution completed and every available scientific comparison passed.
• FAIL: A required input or environment stage failed.
• PARTIAL: No hard failure occurred, but evidence is insufficient to establish truth.

# Run an automated audit using Feynman's CLI
feynman audit 2401.12345

03. Automate the Journey from Literature to Experiments

Use case: Start with a research topic, identify promising techniques, generate experiment configurations and track results across multiple runs.

Code: Topic -> Feynman -> Paper Retrieval -> Technique Extraction -> Experiment Runner -> Results Store

Moving from a broad topic to a concrete experiment requires sifting through hundreds of publications. This process is incredibly tedious, which means researchers often settle for sub-optimal baselines. You can solve this by building a pipeline that automates the literature review and configures the initial experiments. Feynman provides a /lit slash command specifically designed to run structured literature reviews with consensus mapping and open question generation.

Once the literature is mapped, your system can extract the exact training pipelines and evaluation protocols needed. You can store these configurations in a database and queue them for execution. This workflow drastically reduces the time spent on manual setup.

// Example configuration schema for the experiment runner
export interface ExperimentConfig {
  topic: string;
  extractedTechniques: string[];
  baseModel: string;
  evaluationMetrics: string[];
}

04. Let AI Research Your Next System Architecture

Use case: Give it an engineering problem. It studies papers, open-source systems and benchmarks before producing a practical architecture and implementation plan.

Code: Problem -> Research -> Papers + Repos -> Trade-offs -> Architecture -> Implementation Plan

Designing a new system architecture requires understanding the trade-offs of existing solutions. You usually have to read multiple whitepapers and dig through open-source repositories to figure out what actually works in production. Since this takes weeks of deep focus, delegating the initial research phase to an AI agent is a massive productivity multiplier. Feynman's flagship deep research workflow is perfect for this exact scenario.

When you run the /deepresearch command inside the Feynman Docs REPL, the agent searches the web, reads academic papers, and cross-references findings. It then synthesizes a structured research report with inline citations. You can use this report as the foundation for your implementation plan.

Can AI generate reliable architecture plans?

Yes, provided the AI uses a structured reasoning process. The key is to force the agent to evaluate trade-offs rather than just suggesting the most popular framework. Because the agent can process system benchmarks and repository issues concurrently, it often spots edge cases that human architects might miss.

# Kick off a deep research session for system architecture
feynman deepresearch "Scalable vector database architectures for real-time RAG"

05. Build an AI Agent Safety & Evaluation Lab

Use case: Give it an AI agent, research relevant evaluation methods, generate targeted tests for failures and analyze the agent's behaviour.

Code: Agent -> Research -> Test Generation -> Sandbox -> Evaluation -> Safety Report

As large models evolve into autonomous agents, they introduce new risks related to tool use and long-horizon decision making. Evaluating these agents safely requires dedicated infrastructure. You can build a comprehensive evaluation lab that tests agents in isolated environments. Projects like the Agent Safety Eval Lab demonstrate how to evaluate tool policies, traces, and safety outcomes in a reproducible mock pipeline.

You can take this a step further by integrating SAfactory, a scalable infrastructure for agent evaluation and trajectory collection. SAfactory allows you to inject attacks and perturbations during execution to systematically expose failure modes. Your Feynman pipeline can research the latest jailbreak techniques, generate new test cases, and feed them into the SAfactory sandbox.

# Example command to launch an evaluation sandbox in SAfactory
python launcher.py \
  --mode docker \
  --agent-config env/custom_agent.yaml \
  --enable-evaluation \
  --job-name safety-audit-run

Building an automated lab ensures your agents remain compliant as you iteratively optimize their policies. And then, you can use the collected trajectory data for reinforcement learning.

Moving from research to code no longer requires months of manual trial and error. By leveraging Feynman's orchestration capabilities alongside specialized tools like ArXivist and VeriRepro, you can build developer tools that automate the hardest parts of software engineering. Whether you are auditing scientific papers or building safe autonomous agents, the ability to seamlessly bridge the gap from research to code is your ultimate competitive advantage.

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