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 transfer cuts model handoffs 25x, Netflix's GenRec reranks with an LLM, chunked prefill keeps vLLM responsive, plus why LLMs cave under pressure and forget the middle.
Read PostDoorDash's agent tool gateway, MPP agent payments, why LLMs hallucinate, and GPU execution internals — plus Sonnet 5.5, GPT-6.1 Sol, and Gemini 4 Argon all shipping this week.
Read PostJev's actual mechanics: SGLang scoring, Opik-as-judge, NVIDIA/Stanford's rival CLM. Plus MoE serving internals and this week's Anthropic/OpenAI price war.
Read PostAgent harnesses can 2x inference cost for a 1pt accuracy gain. Plus RAG retrieval internals, LLM memory architecture, LLM-as-judge evals, GPU VRAM math, and GRPO fine-tuning.
Read PostDeepSeek's V4.1-Flash cuts KV cache 4x, OpenAI's 10K agents cracked Navier-Stokes, Anthropic found 151M Claude distillation exchanges. Plus 6 deep-dives on routing and serving.
Read PostGPT-6 Astra's benchmarks and safety classification, attention-mechanism internals, embedding compression, RAG retrieval failures, how Shopify beat GPT-5.6 with a 0.8B model.
Read PostvLLM vs SGLang vs Ollama, GraphRAG map-reduce search, a one-line Qdrant fix for multivector memory, and why agent skills work as runbooks, not facts (8,100 trials).
Read PostA cheaper model can double your turn cost, vLLM's 23x batching trick, Google's split TPU 8t/8i chips, and the 3-layer stack teams are shipping to sandbox AI agents.
Read PostKV 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.
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