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.
CUDA graph replay, KV-cache quantization, prompt vs semantic caching, the MemoryTax study on agent memory, and how AI-found PHP flaws forced an emergency patch across Europe.
Read PostCodeRabbit finds a confidence bug + stored XSS in agent code, 1,000-agent swarms, emergent misalignment, EBRMs beat 5 LLMs at Sudoku for $4, plus 6 GPU parallelism strategies.
Read PostKV 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).
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