The original title is about building a research-grade AI project as a solo developer, sharing their stack, tools, and workflow.

Short summary

A solo developer shares their stack and workflow for building research-grade AI projects without a lab or team. The approach centers on reproducibility through config-driven experiments, Weights & Biases for tracking, DVC for data versioning, and disciplined checkpoint management. The article includes code examples for training loops, experiment logging, and testing, making it a practical reference for individual AI practitioners.

• •Config-driven experiments with YAML files for reproducible runs
• •Weights & Biases for experiment tracking; DVC for data versioning
• •Model checkpoints versioned with JSON manifests linking config, data hash, and metrics
• •Practical testing and tmux/session management tips for solo AI development

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