Engineering guides
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Page 2 of 18AI coding agents and pull requests: a workflow that keeps humans responsible
AI coding agents can open useful pull requests, but speed does not remove ownership. A strong workflow makes scope, tests, permissions, and review evidence visi
Model routing for AI agents: small model, frontier model, or fallback?
A multi-model agent should not send every request to the most expensive model. Route based on task risk, context size, latency, and the cost of being wrong.
Local AI agents on a laptop: a realistic privacy and speed guide
Local agents are appealing because code and documents can stay on the machine. They are not automatically private or fast, so the design needs clear boundaries
MCP server security: a practical allowlist for AI tool access
Model Context Protocol makes tools easier to connect, but a connected tool is a new permission boundary. Use an explicit allowlist, narrow inputs, and reviewabl
Google Gemini agent for work: what developers should learn from it
Google’s new workplace agent direction points toward systems that connect models to documents, inboxes, business tools, and developer environments. Developers c
Gemini managed agents explained: sandboxes, tools, and governance
Managed agents promise to handle longer tasks with tools and background execution. The important design question is what the agent may read, change, and approve
Agentic security autofix: when an AI patch is safe to review
Agentic autofix is attractive because it can explore a codebase, propose a patch, and rerun analysis. The engineering risk is accepting a plausible fix without
GitHub AI security detections: what developers should verify before merging
AI-assisted security findings can widen coverage, but an alert is still a signal that needs triage. Here is how to review AI detections in pull requests without
Node.js 22.23 security release: a practical LTS upgrade guide
A Node.js LTS security release deserves a calm, repeatable response. Upgrade the runtime, read the security notes, and test the behavior around TLS, HTTP client
Node.js 26.11 upgrade checklist: what backend teams should test
Node.js 26.11 is a current release, so the useful question is not whether every project should upgrade today. It is which runtime changes, security fixes, and d
AI-generated code review: a practical checklist before merging
A practical review process for AI-generated code that checks behavior, security, tests, dependencies, and maintainability before a pull request is merged.
Rate limiting explained: choose the right algorithm for your API
Compare fixed windows, sliding windows, token buckets, and leaky buckets, then choose a rate-limit design that is fair, observable, and safe to retry.
Docker for Node.js: a production checklist beyond the first container
A plain-language Docker checklist for Node.js applications covering image size, permissions, health checks, signals, environment variables, and reproducible builds.
REST vs GraphQL: which API style should a small team choose?
A practical REST versus GraphQL comparison for teams choosing an API style, with guidance on caching, validation, versioning, errors, and operational cost.
Node.js event loop explained: timers, promises, and real request order
A practical explanation of the Node.js event loop, including timers, promise callbacks, I/O, and the ordering mistakes that cause surprising backend behavior.
AI agent memory: files, databases, embeddings, and task history
AI agent memory is not one feature. Compare files, databases, embeddings, and task history for reliable long-running workflows.
Open-model licenses: the checklist developers should use before shipping
Open weights do not automatically mean unrestricted commercial use. Use this developer checklist before putting an open model into production.
AI model routing: how to balance cost, latency, and quality
A practical guide to routing AI requests across models using workload, latency, quality, and budget signals instead of guesswork.
AI agent observability: the metrics that reveal runaway workflows
AI agents need more than logs. Track turns, tools, retries, approvals, cost, and outcomes to find runaway workflows before users do.
Open models and AI infrastructure: who pays when the model is free?
Open AI models may be free to download, but hosting, fine-tuning, evaluation, and support still cost money. Here is the real economics.
AI inference pricing: why cost per token is not the full bill
Token prices are only one part of an AI bill. Learn how retries, context, latency, storage, and reserved capacity change inference cost.
AI agents and inference economics: why every tool call has a cost
AI agents can multiply inference, tool, and storage costs. Learn how to budget agent runs and keep autonomous workflows reliable.
AI training data provenance: what developers should check before using a dataset
A practical dataset-provenance checklist covering licenses, source records, consent, transformations, and reproducibility for AI developers.
Reserved versus on-demand AI compute: which model fits a growing product?
Reserved AI capacity can improve reliability, while on-demand compute preserves flexibility. Here is how developers should compare both options.
OpenAI's multi-chip strategy: NVIDIA, AMD, Trainium, and Cerebras
OpenAI's mix of NVIDIA, AMD, AWS Trainium, and Cerebras shows why AI companies are diversifying chips by workload instead of betting on one supplier.
OpenAI and Snowflake's $200 million partnership: why enterprise data is the prize
What the OpenAI-Snowflake partnership means for enterprise agents, governed data access, model choice, and the hidden engineering work behind useful AI features.
The new Microsoft-OpenAI agreement: cloud access, revenue share, and developer impact
A simple explanation of the amended Microsoft-OpenAI relationship, including Azure's role, revenue sharing, model access, and what developers should verify.
Why open AI training data may matter more than the next model headline
Why datasets, documentation, licensing, and evaluation are becoming strategic AI infrastructure, and what developers should check before using an open dataset.
Why OpenAI is adding Cerebras for low-latency inference
Why OpenAI is adding Cerebras systems to its inference mix, how low latency differs from raw model quality, and what the change means for real-time AI applications.
OpenAI and Amazon's Trainium deal: what the 2-gigawatt commitment means
A plain-English explanation of OpenAI's expanded AWS relationship, Trainium capacity, agent workloads, and the questions worth asking.