Read source code together, write articles together, and enjoy spicy hot pot together.
The article argues that traditional documentation quickly becomes obsolete and proposes a self‑updating specification workflow where both developers and AI agents read and write the spec, eliminating hidden maintenance work and keeping the plan aligned with reality.
In an interview, Peter Steinberger shares his 13‑year entrepreneurial journey, burnout recovery, rapid AI‑driven project creation, the birth of OpenClaw, his views on AI‑assisted coding, security, and practical advice for developers seeking to work effectively with AI tools.
After fixing unlimited queries and pagination issues, this article reveals why async FastAPI still stalls under load, outlines the hidden bottlenecks in the request lifecycle, and provides practical rules and code examples for offloading heavy work to background workers, ensuring scalability, idempotence, and observability.
This article explains what backpressure is, why Node.js services silently run out of memory, and provides practical streaming and bounded‑concurrency patterns with code examples to keep memory usage stable under load.
FastAPI feels lightning‑fast on small datasets, but returning millions of rows can exhaust memory, block the event loop, and cripple the database; this article explains why that happens and provides concrete design rules—selective fields, pagination, cursor‑based queries, streaming, and chunked processing—to keep APIs stable at scale.
This guide explains how to harness Cursor's AI agents for software development by covering agent harness components, planning modes, context management, rule and skill extensions, long‑running loops, image handling, common workflows like TDD and Git integration, parallel execution, cloud delegation, and debugging strategies.
WebMCP, a new browser‑level Model Context Protocol co‑developed by Google and Microsoft, replaces fragile visual scraping with a structured API that lets websites expose tools directly to AI agents, dramatically improving token efficiency, reliability, and security while raising new implementation and safety challenges.
This guide enumerates ten frequent traps encountered when streaming JSON in Node.js—such as assuming one chunk per object, UTF‑8 split issues, missing newline delimiters, back‑pressure overload, and handling of large numbers—and presents reliable patterns like using NDJSON framing, StringDecoder, pipeline, and proper error handling to avoid data loss and memory spikes.
This guide walks you through installing Claude Code and Ollama, pulling and configuring various open‑source models, setting environment variables, and running Claude Code with both local and cloud‑hosted models, while covering context length, performance considerations, and tool‑calling examples.
AI is not the future of engineering but a tireless junior engineer that can speed up repetitive tasks, mechanical refactoring, and test scaffolding, while still failing at architectural decisions, performance reasoning, and async concurrency, so senior developers must guide and verify its output.
