{"slug":"let-s-science","label":"Let's Science","item_count":3,"day_count":3,"source_count":2,"first_seen":"2026-07-03T06:32:31+00:00","last_updated":"2026-07-06T08:35:12+00:00","generated_at":"2026-07-22T00:07:23.024637+00:00","sources":["search_agent_engineering_news","search_llm_ops_news"],"days":[{"date":"2026-07-03","items":[{"title":"codebase-memory-mcp speeds AI coding agent queries - Let's Data Science","url":"https://news.google.com/rss/articles/CBMilwFBVV95cUxOVGtKZERvYXNfbmEzcG5XNDlOWnY2TFY2am9mbVNzUWxqU0hvdjVHMUlHak91dVhWSHNvZTd3U3c1LVZiRVFXaHU5b3RRcnpzbk1kVWpUYll5M2Ixa2IxVEdlakJNa05reFo4RUZSVWZTa094em9iYjBqZlg5ck51MDgyX0xSSzc3SHlOVEtWdDJZTjliSlZR?oc=5","source":"search_agent_engineering_news","type":"news","summary_1line":"codebase-memory-mcp speeds AI coding agent queries Let's Data Science","why_it_matters":"Matches feed focus: agent.","sid":"a100d2bc462a761c","published":"2026-07-03T06:32:31+00:00","editor_note":"First post in the series: a memory tool aimed at speeding up AI coding agent queries."}]},{"date":"2026-07-05","items":[{"title":"WattGPU Predicts LLM Inference Power Without Profiling - Let's Data Science","url":"https://news.google.com/rss/articles/CBMinAFBVV95cUxPWGQ5ZGpORXlHMk9fc0JpTlJHeFNrQjNxY01YTmthVEl3Um5oWHZ4NjIzLUJvaFpVeEdEU0tZTGpPNWZWTzVvbUx6UzdTMk5hc1dGcjFMN19vUVNuMDluZHNrVUJ6UTZyRmNYSDF1ZmUwNTNQaHlBZklFVlNibEFpclRSTDdIMmlkMllFWk10bDhCdkZtS2pEZUpCUXQ?oc=5","source":"search_llm_ops_news","type":"news","summary_1line":"WattGPU Predicts LLM Inference Power Without Profiling Let's Data Science","sid":"c0e88935b7925bb1","published":"2026-07-05T17:56:40+00:00","editor_note":"Second post: a method to predict LLM inference power draw without profiling."}]},{"date":"2026-07-06","items":[{"title":"Inference Chips Differ for LLM Serving Workloads - Let's Data Science","url":"https://news.google.com/rss/articles/CBMilAFBVV95cUxNWjlBbGk4VkRqN2tTaDNNcDc3djFyVXNvWHl3Q01tUXlZbUZaeEZOQlUtT3NPOHRQbmVsX1FCUVl0YTI3VDBFMElPVnR0RFdENVhlREdFMG91NzJDVXNjUmwxWF9zVnoyTlVvVms2YnJrU0JLeTdneWFmOFgySXpPOEYxSUU5bE1Ud1VFemVVazY5V3Ex?oc=5","source":"search_llm_ops_news","type":"news","summary_1line":"Inference Chips Differ for LLM Serving Workloads Let's Data Science","sid":"50a2c68ea36708c1","published":"2026-07-06T08:35:12+00:00","editor_note":"Third post: a comparison of inference chip options for LLM serving workloads."}]}],"editorial":{"tldr":"In early July, Let's Data Science published three technical posts on LLM-serving infrastructure: a memory tool for speeding up AI coding agent queries, a method to predict LLM inference power draw without profiling, and a comparison of inference chip options for serving workloads.","stale":false,"whats_new":"The latest post (Jul 6) compares how inference chips differ for LLM serving workloads, following the Jul 5 piece on predicting inference power draw and the Jul 3 piece on a coding-agent memory tool.","why_it_matters":"Each post is a direct lever on serving cost and latency for platform engineers: agent-query memory affects token spend, power prediction affects capacity planning, and chip choice affects the serving budget itself.","take_for_builders":"If you're tuning LLM-serving cost, check WattGPU's power-prediction approach before building a profiling pipeline, and weigh the inference-chip comparison against your own workload mix before a hardware purchase.","beats":[{"kicker":"AGENT TOOLING","tone":"neutral","headline":"codebase-memory-mcp aims to speed up AI coding agent queries","sids":["a100d2bc462a761c"]},{"kicker":"POWER","tone":"neutral","headline":"WattGPU predicts LLM inference power draw without profiling","sids":["c0e88935b7925bb1"]},{"kicker":"CHIP CHOICE","tone":"neutral","headline":"Inference chips differ meaningfully for LLM serving workloads","sids":["50a2c68ea36708c1"]}],"open_questions":["Does codebase-memory-mcp's speedup hold up in production agent workloads, or only in the presented benchmark?","How accurate is WattGPU's power prediction against actual profiled measurements across different GPU types?","Which specific inference chips does the comparison favor for cost-sensitive LLM serving, and under what workload assumptions?"],"generated_at":"2026-07-22T00:05:43+00:00"}}