Mastering Web Performance: From Novice to Expert

Implement spec-driven development to ensure clean code over spaghetti code by providing precise constraints for AI, such as 80-line functions and at most three parameters, while investing in observability.

Identify and fix memory leaks in web apps by diagnosing root causes, tracking leaks, and recognizing patterns in source code, while optimizing end-user device resources.

Identify two types of web performance problems, including one-time performance and memory leaks. Apply fixes like splitting components and JavaScript files, removing unused code, and using the Whip Bundle Analyzer.

recognize memory leaks in web apps by watching for production slowdowns after long sessions, as the UI becomes sluggish and memory usage rises with increasing node size and listeners.

Identify memory leaks with Chrome DevTools using the performance timeline to detect memory bloat and frequent garbage collection; enable memory checks, collect garbage, and run interactions to analyze blue line.

Use the performance monitor to visualize cpu usage, gas heap size, dom nodes, and event listeners. Activate metrics you want to analyze and observe changes as you interact with app.

Open the task manager to access resource details, then use desk manager to identify which assets, especially JavaScript, drive the largest memory usage.

Explore how collections affect memory: shallow size in arrays and streams, how forgotten references cause leaks, and strategies like clearing arrays or resetting lists to avoid lingering objects.

Explore two strategies for fetching API data from a form array with an autocomplete in angular, comparing value changes versus option selected handling to boost performance and reduce unnecessary requests.

Case study reveals a web app using a TensorFlow hand-gesture model, showing a memory leak from repeated loading that slows performance and inflates memory.

Mastering web performance in the era of the dark factory, this lecture exposes the five levels of AI coding and a three-layer defense to ship fast with safety.

Assess how AI coding tools impact productivity, using METR's study to reveal that 20% feel faster while tasks take 19% longer due to reviewing and steering output.

Compare two real-world stories from StrongDM, one success and one cautionary, and show how digital twin universes test AI-generated code with human intent.

Learn to distinguish behavioral tests from implementation tests, focusing on outcomes over internals, and apply this in real-world scenarios like multi-pod deployments, race conditions, and user-facing behavior.

An examination of Amazon's ai-driven code failures reveals outages and lost orders, prompting senior sign-off on ai-generated changes to curb fragility and speed without understanding.

Uncover dark code, where functional yet incomprehensible code accumulates and debugging becomes archaeology. Apply behavioral testing to elevate safeguards and move up the levels without burning down the house.

Explore the three-layer defense in the software lifecycle: spec-first development, self-describing code with documentation and observability, and the comprehension gate before shipping.

The new engineering paradigm emphasizes organizational design and disciplined ai-assisted development, starting from a spec, generating in-code docs, passing a comprehension gate, and requiring senior sign-off.

Automate bug fixes with multiplayer full stack session recording and the MCP server, enabling AI-powered, context-rich diagnoses linked to code, reducing debugging time and improving collaboration.

Explore the 2024 state of software security by Veracode, examining security debt in software, especially in the public sector, with focus on third-party dependencies, remediation timelines, and prioritizing critical flaws.