Stories, experiments, research and deep‑dives into the world of artificial intelligence
This pattern is for teams building RAG over millions of chunks, where float32 embeddings start to dominate RAM, SSD, and retrieval cost. This is…
Token cost optimization is no longer a side concern. In 2026, it is the control lever that separates scalable AI systems from budget black…
On that date, the AI Act’s general application phase starts: most obligations for high‑risk AI systems and transparency duties kick in for companies operating…
As generative AI applications move from fragile prototypes to high-scale production systems, the operational costs of LLM API calls can quickly spiral out of…
The decision of where to execute your large language models is no longer just an infrastructure line item; it is a core architectural and…
Many teams building production AI applications quickly realize that single-turn prompting inevitably falls apart when faced with intricate, open-ended tasks. We have spent the…
Stop acting as the feedback mechanism for your AI. Instead of traditional prompting (You → Prompt → Agent → Output → You Fix), design…
If you are comparing LLM architectures for business, the smart move is not to chase the model with the flashiest benchmark, the real job…
These are two open-weight models released in June 2026 just one day apart, both Mixture-of-Experts systems and both aimed at developers but under that…
Most teams use AI coding agents wrong. They throw a massive prompt at a single LLM, hit the context window limit, and end up…
Your RTX 4090 just became a 70B-model machine. Intel's AutoRound makes it possible — and this guide shows exactly how to quantize, export to…
Many teams eagerly wire up a multi-agent framework to automate their workflows and point it at a default US-based API, only to later realize…