Google Search just went from being an encyclopedia to an assistant. That’s the crux of everything Google announced in its recent I/O conference for 2026. The buzzword is “AI agents”, which now enter Google Search, its coding platforms, and even get a whole new standalone app for themselves. The idea...
Prompt Engineering Isn’t Enough — I Built a Control Layer That Works in Production
Most LLM failures in production aren’t random — they’re predictable.
I kept hitting broken JSON, silent failures, and outages that froze my entire app. Prompt engineering didn’t fix it.
So I built a control layer above the model — and took structured output reliability from 0% to 100% without changi...
Optimizing AI Agent Planning with Operations Research and Data Science
AI agents can quickly become expensive without a clear strategy for planning, skill coverage, and budgets. This article shows how to use operations research and data science to optimize AI agent cost and resource allocation. You will learn how to frame common agent problems—skill coverage, project a...
Gemini 3.5 Flash: frontier intelligence with speed
Google Gemini’s next-generation family offering: Gemini 3.5 is here! Gemini 3.5 Flash combines frontier intelligence with real-world action and supports high-speed agentic workflows, coding, and multimodal reasoning while maintaining the low latency expected from the Flash series. With Gemini 3.5 P...
Deploying a Multistage Multimodal Recommender System on Amazon Elastic Kubernetes Service
A practical walkthrough of building and deploying a multistage, multimodal recommender system on Amazon EKS, covering data pipelines, model training, Bloom filters, feature caching, and real-time ranking.
The post Deploying a Multistage Multimodal Recommender System on Amazon Elastic Kubernetes Serv...
Grounding LLMs with Fresh Web Data to Reduce Hallucinations
Why production LLM systems need live web search to overcome knowledge cutoffs and stale training data
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40 Advanced SQL Window Functions Every Data Scientist Must Know(with examples)
In the world of data science, SQL still remains the powerful tool for defining the data, data manipulation, data aggregation and data analysis. While basic SQL commands are very fundamental, and everyone knows about it. If you want to be the unique in the crowd then you should know advanced features...
Six Choices Every AI Engineer Has to Make (and Nobody Teaches)
The production trade-offs that only appear once your model is live.
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Why MCP servers keep losing to CLIs once the agent gets a terminal
The post One Flexible Tool Beats a Hundred Dedicated Ones appeared first on Towards Data Science.
I have been running local models as part of my daily workflow for some time, and what surprised me most is how often local turned out to be the better choice, not a compromise.
LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships
Most LLM evaluation systems rely on vague scoring and human judgment disguised as metrics. I built a lightweight evaluation layer in pure Python that turns LLM outputs into reproducible decisions by separating attribution, specificity, and relevance—so hallucinations are caught before they reach pro...
Recursive Language Models: An All-in-One Deep Dive
Exactly how does it differ from ReAct, CodeAct, Self-Loops, and Subagents?
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The Next AI Bottleneck Isn’t the Model: It’s the Inference System
Enterprise AI systems are entering a phase where inference design matters as much as model capability itself.
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