Nicknamed "Tiny"
A full-stack, self-improving AI brain built entirely in Python. Not a wrapper. Not a fine-tune. A custom recurrent neural architecture that runs on any CPU, learns from your data, and gets smarter every single day — with zero cloud dependency and zero API costs.
Every other AI product you've used starts smart and stays the same. TinyCPUDevAI starts at zero and earns every point of its intelligence score through real training. No seeded data. No fake metrics. No cloud dependency.
Based on "Were RNNs All We Needed?" (Feng et al., 2024). The MinGRU update rule eliminates the reset gate, reducing parameters by ~33% while enabling parallel prefix-scan training. Log-space gate computation prevents gradient saturation at long sequence lengths.
Before every response, Tiny retrieves the most relevant past conversations using MinGRU embedding similarity — with TF-IDF fallback when no model is loaded. It also indexes its own source code, so it can accurately answer questions about its own implementation.
A formula-driven 0–100 score that the AI must genuinely earn: score = 100 × tanh(depth×0.45 + quality×0.30 + breadth×0.15 + endurance×0.10). The tanh asymptote ensures the score never mathematically reaches 100. No free points.
TinyCPUDevAI ships with a twelve-module meta-cognitive intelligence layer that monitors, optimises, and protects the training process autonomously.
Monitors training loss trajectories and automatically adjusts learning rate, batch size, and curriculum pace based on plateau detection and gradient statistics.
Continuously checks corpus integrity, model weight norms, and loss divergence. Detects poisoned samples and weight collapse — automatically rolling back to the last clean checkpoint.
Uses do-calculus-inspired analysis to distinguish genuine model improvements from confounding factors like data distribution shifts. Provides unbiased estimates of intervention effects.
Orchestrates multiple parallel scraper threads with load balancing, priority queuing, and adaptive rate limiting. Dynamically reallocates bandwidth to the highest-quality sources.
Detects and quarantines adversarially crafted training samples using gradient alignment analysis. Identifies samples whose gradients are geometrically inconsistent with the honest majority.
Transfers learned representations across programming languages. Structural similarities between Python, Rust, and Go are exploited to bootstrap learning in new languages from existing weights.
TinyCPUDevAI ships with a complete multi-tier knowledge lifecycle management system. Hot, warm, and cold storage tiers. Content-addressed deduplication. Semantic compression. Delta snapshots. All managed autonomously.
Tiny is finishing training. Be the first to get access when we launch.
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