Notes from Sai Dutta Abhishek Dash — AI infrastructure & security engineer — on LLM gateways, code-security agents, self-hosted systems, and the open-source tools he ships.
Kaggle's free 2x T4 accelerator became hard to get this month. I traced why: 53 YouTube tutorials with 612,813 views teaching people to run LLMs on Kaggle around the clock, 80% of that reach landing in a single 16-day format-cloning wave. Here is the full anatomy of how a shared resource gets eaten.
Part 5 closes the AI Pipeline deep dive: the full stack laid bare as a chain of seven monopolies, the evidence on whether AI is producing real value, what every layer's kill switch means, why nothing about the current arrangement is stable, and what a country that owns none of it can do.
Part 4 of the AI Pipeline deep dive: AI compute hubs exist in only about 30 countries, and India is the world's biggest consumer with almost no ownership. 20% of global data, 3% of data centers, the 1989 Mohali fire, a fab starting two decades behind, and the first credible counter-moves in decades.
Part 3 of the AI Pipeline deep dive: the three clouds and five labs that buy everything parts 1 and 2 make, NVIDIA's software moat, the custom-silicon rebellion, and the full four-year timeline of export controls, mineral curbs, and counter-moves between the US and China, with Russia as the cautionary tale.
Part 2 of the AI Pipeline deep dive: the ASML EUV machine that prints the world's chips, the Zeiss mirrors polished to below one atom of roughness, TSMC's 70% foundry grip, the HBM oligopoly, and the single suppliers (including an MSG company) hidden inside the machine.
Part 1 of the AI Pipeline deep dive: before AI is software, it is sand, quartz, rare earths and gallium from a handful of mines, drawn into chips by three American software companies on two proprietary architectures. The ground floor of the most concentrated supply chain humans have ever built.
A complete voice AI stack, speech to text, a 2B language model, and text to speech, running fully local on a 2013-era CPU server with no GPU. I benchmarked every stage, audited the marketing claims, and the "150 tokens per second" pitch came out 13.6x slower in reality. Here is what a local voice assistant actually costs and does.
I audited every claim on the Spark-X2.5 model cards and stress-tested both the 4B and 1.7B on agentic coding tasks over two Tesla T4s. The 4B is a genuinely usable small coding agent. The 1.7B is an assistant, not an agent. And both models have a thinking-verbosity tax worth knowing about.
I ran the first head-to-head of the two open "System One" decision models, Laya and Jev, on 751 byte-identical inputs: same states, same questions, same seed, question hashes verified before comparing. Jev wins most suites for $0.008. Laya wins where it counts for a free model. A fourth column with Qwen 1.5B under constrained decoding completed the picture.
