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Base44, the vibe coding platform acquired by Wix, is rolling out its own custom AI model (Base1) trained on user interactions to improve latency and cost, as AI startups seek greater defensibility beyond relying on frontier models.
A user discusses building a small autocomplete model (25M parameters) as a learning project, mentions hardware constraints (32GB VRAM), data requirements (~100M tokens), and seeks advice on datasets and data formatting for autocomplete-style training.
The article argues that AI defensibility comes from owning the full feedback loop—custom models post-trained on proprietary data, tuned to specific workflows, and evaluated by user-defined standards—rather than renting frontier APIs from suppliers who can change terms. It emphasizes model customization as key to differentiation and margin control.
Demonstrates running a custom Qwen model (Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MTP-GGUF) on dual Nvidia RTX PRO 6000 Blackwell GPUs at 195 tokens per second using Hugging Face Inference.
DavidAU releases a custom 40B parameter model based on Qwen 3.6, expanded and fine-tuned with Claude 4.6 Opus distill and Deckard datasets, featuring optimized GGUF quantizations for improved precision and uncensored capabilities.