Weekly AI & Engineering Digest — Jul 27, 2026
by Vamshi • 7/26/2026Five 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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Nightly re-indexing leaves a ten-hour staleness gap and costs 100x more than embedding the delta. CDC ingestion, semantic caching, and the SKIP LOCKED pattern.
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Why Your RAG System Fails in Production: 5 Counter-Intuitive Truths About Retrieval
by Vamshi • 7/20/2026It's almost never the LLM. Precision beats recall, BM25 beats state-of-the-art embeddings on identifiers, and CPU-first retrieval is a perfectly valid architecture.
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Weekly AI & Engineering Digest — Jul 20, 2026
by Vamshi • 7/19/2026Rebuilding Claude Code's 6-layer harness in CrewAI, why small models alone won't cut your inference bill, and the four agent loop patterns — plus Kimi K3's 2.8T drop.
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The Hidden Mechanics of vLLM: 5 Surprising Realities of LLM Inference Optimization
by Vamshi • 7/17/2026Speculative decoding, continuous batching, and the KV cache memory tax — the mechanics that decide whether serving an LLM is affordable or ruinous.
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The Hidden Physics of AI: 5 Counter-Intuitive Secrets to Running LLMs 80% Cheaper
by Vamshi • 7/14/2026The prefill/decode split, the 4-bit sweet spot and the quality cliff below it, and why reflexively reaching for an H100 is often a multi-thousand-dollar mistake.
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Weekly AI & Engineering Digest — Jul 13, 2026
by Vamshi • 7/12/2026KV caching rethought with LMCache and 14x faster TTFT, the 4-stage production routing pipeline, and Fable-as-Advisor at 92% quality for 63% of the cost.
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The Efficiency Gap: 5 Counter-Intuitive Lessons from the Frontier of GPU Scaling
by Vamshi • 7/11/2026GPU utilization is a costly liar. Bypassing the Prometheus tax, fractional GPU allocation, deployment-aware routing, and the scale-to-zero mandate.
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Hardcoding every query to a flagship model is a 40–85% budget leak. Classifier-based routing, latency budgets, meta-models, and self-learning bandit routers.
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