Learnings

<p>Tips for introverts at work </p>

<p>Speed up a Heroku App with Fly: </p>

<p>Optimizing Your Web Performance: Separating the Signals from the Noise: </p>

<p>Fast and secure standalone server for resizing and converting remote images: </p>

<p>Python's pathlib is slow! </p>

<p>Book Speed Up Your Django Tests: </p>

<p>Optimize and resize images in AWS S3 in real-time with ImageKit: </p>

<p>If you want to have high performance animations, avoid properties that trigger layout or paint </p>

<p>Preloading fonts in Chrome with 'preload' link directive: </p>

<p>Leaky Abstractions - When Copy-Paste-Delete is faster than Cut </p>

<p>"You have two hands. Observability and experimentation: It stuck with me as it also makes the point that when you're only using one type to solve a performance problem you're working one-handed." </p>

<p>btilly on HN: "Whatever scale you hit on a single machine, you can probably gain 1-2 orders of magnitude of performance by switching to a faster language and carefully optimizing. If you switch to distributed, you'll LOSE at least an order of magnitude performance due to the overhead of RPCs, but are able to scale indefinitely afterwards." </p>

<p>postgres-checkup – automated PostgreSQL health checks: </p>

<p>Adding CloudFlare to S3 bucket: </p>

<p>Secrets of a NRQL Wizard: New Relic Query Language </p>

<p>Speeding up SQL queries by orders of magnitude using UNION: </p>

<p>Pyroscope: an open source continuous profiling platform </p>

<p>Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python </p>

<p>Log all queries of Postgres on docker-compose: </p>

<p>Great introduction to memory (RAM) issues: </p>

<p>Understanding time.perf_counter() and time.process_time(): </p>

<p>How we used Postgres extended statistics to achieve a 3000x speedup: </p>

<p>Reading a Postgres EXPLAIN ANALYZE Query Plan: </p>

<p>How to Modernize a Django Index Definition with Zero Downtime: </p>