Exploring AI Learning, the specifics of Large Language Models, including architecture, prompt engineering, fine-tuning, RAG, and their application in text-based tasks.
An AI operating system sounds, at first, like the sort of phrase that gets shouted over synth music in a keynote and forgotten by lunch. Then you read Meta and KAUST’s Neural Computers paper and realize they mean it literally. Not “an assistant for your desktop.” Not “a smarter shell.” An AI operating system where … Read more
Self-Distillation is one of those ideas that sounds suspicious on first contact. Train a model on its own raw outputs? No verifier, no teacher, no reward model, no reinforcement learning, no execution sandbox? That usually sounds like a fast route to elegant nonsense. Which is why this Apple AI research paper is so interesting. It … Read more
KV Cache Compression: Recall vs. Memory Needle-in-Haystack benchmark · Llama-3.1-8B-Instruct · context up to 104k tokens Best recall 0.997 TurboQuant = full precision Memory at 3.5-bit 6x smaller KV cache GPU speedup 8x on H100 at 4-bit Needle-in-Haystack recall score KV cache size (bits) Tested on Llama-3.1-8B-Instruct · Needle-in-Haystack benchmark · context up to 104k … Read more
Standard residuals Fixed, uniform weights Embedding h₁ Layer 1 Layer 2 Layer 3 Each layer only sees the accumulated sum Attention Residuals Learned, input-dependent Embedding h₁ Layer 1 Layer 2 Layer 3 Layer 3 selectively attends to any earlier layer Residual connections are one of those rare ideas in deep learning that became so successful, … Read more
If you build with LLMs, you know the moment. The model sounds polished, calm, and certain, then it gives you a made-up answer with the confidence of a senior consultant. That gap between fluency and truth is where AI hallucinations become costly. They waste time, erode trust, and in real workflows, they can quietly contaminate … Read more
Introduction Most LLM agents today are brilliant goldfish. They reason well. They plan decently. They can browse, search, shop, code. Then the episode ends, and it is as if nothing ever happened. No scars. No instincts. No accumulation. That is the quiet ceiling on current agent systems. Not reasoning depth. Not model size. Memory. In … Read more
Most “AI welfare” debates jump straight to consciousness. That’s the flashy question, but it’s not what Anthropic is doing in the Claude Sonnet 4.6 System Card. Their “model welfare” section is not a claim that Claude is sentient. It’s an evaluation program: track behavioral signals that may matter once you treat a model as a … Read more