Hi, this is Vamshi. I am a full stack developer with deep expertise on Frontend. I have been working on Applied AI, augmented experiences and I want share my learnings through this blog. More details about me can be found on my about page.
KV cache economics, the LLM security threat map, 5 models on one GPU, and a 7B model beating a 32B via distillation — six deep dives from Daily Dose and ByteByteGo this week.
Read PostA scheduled agent reads 18 AI newsletters from Gmail every Monday and publishes a digest here. The five failure modes that quietly cost me content before I caught them.
Read PostLLMs are stateless by default, and that is fatal for autonomous agents. A look at tiered memory, declarative intent, and the evaluation metrics that actually predict agent survival.
Read PostThe four-step RAG pipeline is a lie. The 15 load-bearing layers, cascade reranking, and why LLM context — not your vector database — is 99% of the bill.
Read PostHow ChatGPT's agent loop cuts token costs, 6 algorithms that auto-optimize LLM prompts, and how DoorDash, Instacart and Uber Eats each wired LLMs into search differently.
Read PostNine agents burned a 5,000-request quota in 90 seconds. Traffic-light throttling, predictive circuit breakers, and why Kubernetes namespaces fail the AI isolation test.
Read PostPostgres won the performance argument, serverless pricing floors broke the pay-as-you-go promise, and the Three-Tool Trap is still quietly killing RAG reliability.
Read PostEmbedding models have a lexical blind spot. Why Reciprocal Rank Fusion beats score normalization, and why you probably don't need a heavyweight vector database at all.
Read PostFive quantization techniques for 70B models on one GPU, the inference optimizations production stacks need, and building a custom RAG eval metric from scratch.
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