Software Engineering
Engineering practice, broader than any single framework. Architecture, testing, debugging, and the patterns that survive contact with production.
Optimizing AI ROI Through Trusted Throughput
Stop treating AI token usage as a leaderboard. Instead, optimize for 'trusted throughput'—the volume of high-quality, validated code that successfully clears automated tests, human review, and customer deployment.
AI EngineerBuilding Defensible AI: An Air-Gapped Fortress for Financial Data
To build AI systems that hold up in court, treat them as data pipelines rather than magic boxes, prioritize physical security over software configuration, and use semantic routing to optimize compute.
Formal Verification for AI-Generated Code with Lean4
As AI agents generate code at scale, traditional testing and human review fail to guarantee correctness. Formal verification using Lean4 allows developers to define specifications that machines prove mathematically, ensuring code is correct for every possible input.
AI EngineerScaling AI Agents Safely: A Roadmap for Engineering Teams
Adopt AI agents by prioritizing verification over prompting, treating skeptic feedback as a safety roadmap, and maintaining human-centric communication standards to avoid 'slop'.
From AI-Assisted to AI-Native: Frontier Development Habits
Productivity gains from AI aren't about the tools, but about shifting from 'vibe coding' (babysitting) to 'frontier development' (feeding agents), which requires intentional changes to team habits and codebase hygiene.
Strategies for Serving JAX Models in Production
Moving JAX models from notebooks to production requires choosing the right serialization and compilation strategy to avoid latency spikes caused by just-in-time compilation.
Google Cloud TechScaling JAX Models to Multi-GPU Systems
Scale JAX models across multiple GPUs by defining array layouts with Mesh and PartitionSpec, allowing the compiler to handle gradient synchronization automatically.
Building and Optimizing JAX Training Loops
Build high-performance JAX training loops by maintaining pure functions, keeping data on-device, and utilizing fused kernels like cuDNN attention to avoid GPU memory bottlenecks.
Scaling Agentic SDLC at Uber
Uber has shifted 70% of pull requests to AI agents by building a standardized infrastructure layer that manages model security, context retrieval, and automated validation, effectively moving the engineering bottleneck from 'how to build' to 'what to build'.
AI EngineerModernizing Legacy Codebases with AI Agents
Tackle legacy code by treating AI as a coworker: use a three-step 'plan, execute, verify' workflow, prioritize test-driven development, and enforce strict guardrails to prevent hallucinations and errors.
Google Cloud TechArchitecting Enterprise AI Agents for Regulated Environments
Enterprise AI agents fail in production because compliance requirements are bolted on as an afterthought. Instead, build systems using immutable event logs, segregated object storage, and human-agent parity to make auditability and evaluation inherent to the architecture.
AI EngineerBuilding Resilient Notification Systems with Temporal & Cloud Run
Imaxxing, a viral movie ticket monitoring app, uses Temporal's durable execution and Cloud Run's serverless scaling to handle spiky traffic and unreliable downstream data sources without losing state.
Google Cloud TechThe Defender’s Window: Securing Systems in the AI Era
AI-driven cyberattacks are accelerating, but defenders can gain the upper hand by using AI to automate vulnerability discovery, code hardening, and infrastructure remediation at machine speed.
Moving Beyond Line-by-Line Code Reviews with AI
Code reviews are failing because they are bottlenecked and often ignored. Instead of reviewing diffs, teams should review intent and evidence by capturing AI-session decisions, codifying recurring feedback into a registry, and automating verification.
AI EngineerApplying RAD Methodology to AI-Driven Development
Rapid Application Development (RAD) provides a proven framework for AI coding: plan lightly, prototype iteratively, and use spec-driven development to bridge the gap between AI-generated prototypes and production-ready software.
How to Audit and Secure Your AI Platform Accounts
If you suspect unauthorized access to your AI accounts, you can audit active sessions and force logouts through the security settings of ChatGPT, Claude, and Perplexity.
Architecting Secure, Serverless AI Apps on Google Cloud
Build scalable AI-powered mobile apps by combining Flutter for the frontend, Firebase for managed services, and Google Cloud for backend heavy lifting, while prioritizing security through model-level protections.
Google Cloud TechMoving Beyond Checklists: Operationalizing AI and SBOM Security
Security experts argue that frameworks like the OWASP Top 10 and SBOM guidance are not compliance checklists but foundations for cyber resilience, requiring active tabletop exercises and operational integration to be effective.
IBM TechnologyScaling AI Agent Adoption Across Engineering Teams
Moving from individual AI leverage to team-wide productivity requires treating agent integration as a leadership-driven infrastructure challenge rather than an individual task, focusing on harness engineering, self-healing systems, and psychological buy-in.
AI EngineerMultiplayer Agentic Engineering: Scaling AI Teams
To scale AI-powered development, move agents into isolated cloud sandboxes, make their work visible across all team interfaces, and implement codebase-specific benchmarking to remain model-agnostic.
AI EngineerBuilding Reliable AI Software with Verification Loops
AI-generated code often introduces 'verification debt' and security risks. To ship production-ready AI software, teams must implement a zero-trust, multi-layered verification regime that integrates into both inner agentic loops and outer CI/CD pipelines.
Refactoring Legacy Codebases in the Age of AI Agents
While AI models are rapidly improving, they cannot yet reliably 'one-shot' complex refactors. Building a clean, maintainable monorepo remains a high-ROI investment that accelerates development velocity and improves developer experience.
AI EngineerAgentic Code Quality: Managing Quality Through Constraints
As AI agents increase code volume, human review becomes a bottleneck. Quality must shift from manual oversight to automated, constraint-driven guardrails embedded throughout the development lifecycle.
Building Turbopuffer: Engineering for Performance and Scale
Simon Eskildsen, former Shopify Principal Engineer, shares how his obsession with 'napkin math' and low-level performance led to the creation of Turbopuffer, a high-performance vector database built on S3.
AI EngineerBuilding AI-Powered Products: Workflows, Agents, and Community
A deep dive into modern design engineering, exploring how AI agents and mixed-media workflows are enabling builders to experiment faster, ship code directly, and foster community through interactive, live-demo projects.
Dive ClubFighting AI Slop with Systemic Rigor
To ship AI-powered products at scale, you must stop relying on human code reviews and instead build 'sloppy' agentic tools that enforce invariants, type safety, and deterministic execution traces at the foundational layer.
Shifting from Writing Code to Reviewing AI Output
AI coding agents don't replace developer craft; they shift the primary responsibility from writing code to rigorous review, verification, and production safety.
Google Cloud TechScaling the Hugging Face Hub to 3 Million Models
Hugging Face maintains sub-second search and high availability at scale by decoupling metadata from binary storage, leveraging Apache Lucene for full-text search, and utilizing event-driven autoscaling to handle traffic spikes.
AI EngineerHow AI is Reshaping the Integrated Development Environment
AI-powered IDEs are shifting from simple text editors to context-aware partners that automate refactoring, debugging, and code generation by analyzing entire codebases rather than individual files.
IBM TechnologySoftware Factories: Balancing AI Autonomy with Human Oversight
Software factories scale agentic loops, but success depends on managing 'back pressure'—the limit of what can be reliably verified. You must choose between 'dark' factories (fully automated) and 'lit' ones (human-reviewed) based on the cost of failure.
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