Hybryd VCS. · The AI Advantage

Hybryd VCS.

🌀 Hybrid VCS: The Stuff-You-Actually-Care-About Layer

Most of us treat Git like a sacred temple for code… and then dump everything else into chaos.

📁 Models, datasets, configs, screenshots, research links – all the assets that actually decide whether an AI system works – usually end up in random folders, Google Drive, or “final_final_v7.zip”.

That’s the gap Hybrid VCS is trying to close.

🧠 What Hybrid VCS Does

🧩 Unifies versionsInstead of juggling Git for code and mystery storage for everything else, Hybrid VCS ties together:

💻 Code (still in Git)
🧪 Experiments (states, hyperparams, metrics)
🧱 Artifacts (models, datasets, exports)
🔖 Context (notes, feedback, web content via a browser extension)

All of that is tracked as one timeline, so “What produced this model?” is a query, not a crime scene investigation.

🛠️ How it changes your day

⏪ Reproducible checkpoints“Give me the exact config + data + code for the last good run” stops being a fantasy.
🧾 Honest experiment historyYou see what you actually tried, not what you remember you tried.
🤝 Handoff without painSharing a project with your future self or a teammate doesn’t require a 2‑hour voice note and 5 drive links.

🏠 Local‑first on purpose

Hybrid VCS is designed to be:

🧱 Local-first – runs on your own machine, your disks, your rules.
🔒 Privacy‑friendly – no hidden cloud pipeline you have to “trust”.
😌 Boringly reliable – Git where it makes sense, a database where that makes sense.

If you’re building AI systems where artifacts + context matter as much as the code, a hybrid version control layer stops being a luxury and starts being basic hygiene.

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