> ## Documentation Index > Fetch the complete documentation index at: https://numpyts.dev/llms.txt > Use this file to discover all available pages before exploring further. # AI Disclosure In 2026, I see two competing narratives around AI in software development: 1. AI tools are pervasive and can significantly accelerate software development, provided they are used thoughtfully. 2. The community is quickly growing disillusioned with "AI slop" and sloppily-built projects, causing a backlash against AI-generated code. To navigate this landscape, I believe in radical transparency about how AI is used across projects, including this one. `numpy-ts` was built with the assistance of AI tools (e.g., Claude Code). The contribution graph below tells the story clearly: GitHub contribution graph showing dupontcyborg (393 commits, 1M+ lines) and claude (107 commits, 137K lines) **Claude was used primarily during the prototype and early implementation phase** (Nov 2025 - Jan 2026) to accelerate the initial buildout of numpy-ts's 450+ API functions. This included generating first-pass implementations of well-specified NumPy functions, test scaffolding, and documentation. **Architecture, optimization, and WASM acceleration work was done manually** (Jan 2026 onward). This work requires systems expertise and cannot be meaningfully delegated to AI. The WASM microkernels, memory management strategy, and tree-shaking architecture were all designed, and mostly implemented, by hand. In my experience building numpy-ts, AI tools excelled at: * Boilerplate code generation * Implementing well-specified algorithms (e.g., `np.dot`, `np.sum`, etc.) * Writing test scaffolding and generating test cases * Building and maintaining benchmarks * Drafting documentation and usage examples On the other hand, it was pretty bad at: * Architecting complex systems (e.g., the WASM acceleration layer) * Performance optimization and low-level code decisions * Debugging and correctness assurance, especially for edge cases Trust is built on transparency. It's no different for open-source projects. So you should know that in this project: 1. Every line of code is human-reviewed. AI-generated code was never merged without review by me ([@dupontcyborg](https://github.com/dupontcyborg)), a full-time software engineer with >10 YOE. 2. AI was a productivity tool, not an author. The same way a project might use code generation, linters, or scaffolding tools, AI accelerated the tedious parts so more time could be spent on the hard problems. 3. I make mistakes, and lots of them. Nothing more to add here :) I'm just as tired of the AI-slop as everyone else. But it's not binary: it's not "AI-generated = bad" and "human-generated = good". Nuance is hard but important. I hope this disclosure helps to make that nuance a little easier.