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zaydzaari/README.md

Zayd Zaari

High school student in Morocco · AI tooling & applied machine learning

Applied ML · AI systems · Reproducible experiments

I build practical AI tools and explore how models generalize to unfamiliar data. My work brings together software engineering, self-hosted inference, and controlled experiments. I’m looking to deepen my research skills through mentorship and collaboration.

Research · LinkScribe · ORCID

Research in focus

Question: does training on more regions change the advantage of multispectral imagery over RGB, independently of total training-data volume?

107

Configs

642

Models

2,568

Evaluations

Figure 2 from the forest-loss study: model performance versus number of training regions. Open the original image for full detail.

Figure from the study repository: performance as geographic training diversity changes. Open the image for the full-resolution figure.

What the evidence supports

Multispectral models outperformed RGB at every tested region count. The 95% bootstrap interval for the interaction slope included zero, so the study found no clear evidence that this advantage changed systematically with geographic training diversity.

Research status: manuscript in preparation; not yet published.

Experimental design & reproducibility
  • Fixed total budget: 48 training patches per model, redistributed across regions.
  • 1, 2, 4, or 8 training regions, enumerating all 107 subsets of the eight-region roster.
  • RGB and multispectral inputs use paired, deterministic patch sampling.
  • Two modalities and three seeds: 107 × 2 × 3 = 642 models.
  • Two validation regions for model selection; four separate regions for final testing.
  • Frozen configurations, normalization artifacts, checkpoint hashes, and final results are documented in the repository.

Methods and reproduction guide

Engineering evidence

AI tooling / local inference

English transcripts from public video links, served to AI agents through an authenticated FastAPI queue. Speech recognition runs locally with whisper.cpp on an ARM VPS.

Python · FastAPI · SQLite · FFmpeg

Architecture & demo

Device control / distributed systems

Google Home controls for Windows. A .NET agent connects outward to Cloudflare and executes a defined set of actions without exposing a remote terminal.

C# · .NET · Cloudflare · TypeScript

Architecture & setup

LinkScribe / From URL to transcript

LinkScribe sends a media URL through a FastAPI queue, processes audio locally with FFmpeg and whisper.cpp, and returns English text to an AI agent.

Also built: RemoteCraft, a Minecraft server dashboard, and Lumen, a native Rust music player for Windows and Linux.

What I want to learn next

How to design stronger model evaluations, distinguish data effects from model improvements, and make experiments easier to reproduce. I’m interested in research mentorship where I can build on both the software and experimental sides of my work.

Tools used: Python, PyTorch, rasterio, FastAPI, SQLite, Rust, C#, Linux, GitHub Actions.

Research methods · ORCID · All repositories

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