AI Automation
Workflows that ship. Pipelines, scrapers, agents glued to APIs, and the operational discipline that keeps them running past the demo.
Building Real-Time Industrial Digital Twins with AI
Modern digital twins must move beyond static dashboards to active, predictive systems that simulate and anticipate factory operations using real-time streaming data.
Real-Time Fluid Monitoring for Data Center Cooling Efficiency
Omen AI is using real-time optical spectroscopy to detect bacterial growth and component wear in data center liquid cooling systems, preventing costly, multi-hour system shutdowns.
Scaling E-commerce Item Knowledge with LLM-Centric Architectures
JD.com's Oxygen AIIC platform uses a 'Semantic Search then Discrimination' architecture and human-AI collaboration to manage tens of billions of SKUs, achieving 94.2% precision in automated item knowledge production.
Building an Autonomous PR Outreach Agent with OpenAI Agents SDK
Learn to build a multi-agent system in Python using the OpenAI Agents SDK to automate product research, journalist identification, and the creation of personalized PR pitches.
The Agentic AI Engineer: Eval-Driven Development Loops
The Agentic AI Engineer automates the agent development lifecycle—spec, build, evaluate, diagnose, and optimize—using a multi-agent system to remove the human bottleneck from production-ready AI agent maintenance.
Automating ETL Pipeline Recovery with RL Agents
A reliable, safety-first architecture for ETL pipeline remediation that uses deterministic anomaly detection, Q-learning for action selection, and an external safety layer to reduce MTTR by 99.85%.
AI-Driven Multi-Document Correlation for Financial Compliance
Moving from isolated document validation to cross-document intelligence using graph-based entity correlation and probabilistic risk modeling significantly improves fraud detection and reduces false positives in enterprise compliance.
AI EngineerFord Rehires Veteran Engineers to Correct AI Quality Failures
Ford rehired 350 veteran engineers after over-reliance on automated AI quality systems led to disappointing results, successfully reducing warranty costs and improving vehicle quality.
Building Scalable Multi-Agent Systems with A2A and Agent Registry
The Agent2Agent (A2A) protocol and Agent Registry solve agent sprawl by providing a standardized, discoverable way for AI agents to communicate, replacing hard-coded URLs with a centralized, governed directory.
Google Cloud TechBuilding a Personal AI Research OS
Transform a fragmented 'Second Brain' into a living research system by using a file-based index and a three-layer architecture (Raw, Index, Wiki) instead of complex vector databases.
AI EngineerAgentic Aggregators for Electric Bus Fleet Management
Agentic systems can optimize electric bus fleets by balancing grid flexibility and operational constraints, but profit-oriented configurations risk extracting value from public transport operators.
Scaling Enterprise AI: Agent Registry and ADK
Google Cloud's Agent Development Kit (ADK) and Agent Registry provide a governed, scalable architecture for orchestrating AI agents and tools, enabling enterprises to transform legacy APIs into secure, reusable MCP-compliant services.
Google Cloud TechBuilding AI-Powered Apps: A Low-Code Guide for Small Teams
Small teams can modernize legacy applications by leveraging 'vibe coding' and managed database AI features like hybrid search and vector embeddings, allowing them to implement semantic capabilities without needing a team of AI experts.
Scaling AI and Vibe Coding: What's New in Google Cloud Run
Google Cloud Run is evolving into a comprehensive platform for AI agents, 'vibe coding,' and high-scale microservices, introducing features like spend caps, GPU support, ephemeral sandboxes, and dedicated worker pools.
Netris Automates Data Center Networking for AI Neoclouds
Netris provides hardware-accelerated network automation to help emerging cloud providers (neoclouds) deploy GPU clusters faster by replacing manual configuration with deterministic, vendor-agnostic software.
Scaling Enterprise AI: HP's Strategy with OpenAI Frontier
HP Inc. is scaling its AI adoption by using OpenAI Frontier as a unified operating model to govern, deploy, and evaluate AI agents across customer support, security, and software development workflows.
Scaling Cyber Defense: From Vulnerability Discovery to Patching
OpenAI's Daybreak initiative shifts the focus of AI-powered cybersecurity from merely finding vulnerabilities to automating the end-to-end patching process, supported by new models, developer plugins, and open-source partnerships.
Meta's New AI Creator Studio App
Meta is transitioning its Creator Studio into a standalone AI-powered companion app to help creators manage performance and engagement without leaving the Facebook ecosystem.
Designing Agentic Loops with Claude Code
Move beyond manual prompting by structuring repetitive AI tasks into persistent, stateful loops that handle verification, memory, and iterative execution.
Engineering Reliable AI Vision Pipelines
Building a production-ready vision pipeline requires separating transcription from reasoning, implementing classification gates to filter junk, and acknowledging that the biggest risk is a confident, polished, but incorrect output.
Building a Local Multimodal Search Engine with Gemma 4
Build a local-first, multimodal search engine by using Gemma 4 to describe media assets into text, then indexing those descriptions in Qdrant for unified, high-accuracy retrieval.
OpenAI's Patch the Planet Initiative for Open Source Security
OpenAI has launched 'Patch the Planet,' a collaboration with security firm Trail of Bits, to provide open source maintainers with expert security reviews and AI-assisted tooling to identify and remediate vulnerabilities.
Building an Autonomous Visual Testing Agent for Mobile Apps
Move beyond brittle pixel-diffing by using local vision-language models to autonomously navigate and validate mobile app flows without hardcoded coordinates.
Patch the Planet: Scaling Open Source Security with AI-Assisted Workflows
OpenAI's 'Patch the Planet' initiative pairs frontier AI models with human security experts to identify, validate, and patch vulnerabilities in critical open-source infrastructure, reducing the burden on maintainers.
Scaling AI-Native Operations: Lessons from Omio
Omio transformed its travel booking platform by integrating LLMs into both customer-facing conversational interfaces and internal engineering workflows, resulting in an 80% reduction in development effort.
Building a Python Intelligence Layer for Automated Signal Detection
Moving beyond simple data collection, this intelligence layer uses async processing and AI to transform raw web data into actionable business signals, automating the transition from information to decision-making.
Building Custom Internal Tools with AI
Stop overpaying for bloated SaaS. Use a structured, AI-assisted workflow to build lean, custom internal tools that do exactly what you need and nothing more.
Building End-to-End Forecasting Pipelines with TimeCopilot
TimeCopilot provides a unified interface for forecasting that integrates statistical models, foundation models, anomaly detection, and LLM-driven interpretation into a single workflow.
Building a One-Click AI Record Summary in Salesforce
Streamline Salesforce workflows by using Einstein Prompt Builder and Screen Flows to create a zero-code AI summary button for complex records.
New Usage Analytics and Spend Controls for ChatGPT Enterprise
OpenAI has introduced granular credit usage analytics and flexible spend controls for ChatGPT Enterprise, allowing administrators to track consumption by user, product, and model while setting tiered budget limits.
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