Hello. I really liked stackoverflow >5 years ago. There were many people asking about easy-to-medium problems to be solved, and it was a great way for me to learn C/C++/Bash/awk/sed/cmake/Linux/whatever by solving real-life(!) mediocre problems and also helping people in the process and also being criticized and corrected at the same time, from which I learned triple as much.

Now stackoverflow is dead. My almost 150k reputation means nothing. Finally they added a "advice" type of questions which is way way too late.

Now I lurk over reddit for typic-specific type of questions, but reddit is more a social network then help-me-with-programming-problem site, and the "help me" part is anyway so easy to solve with an AI. It was great back then - the feeling of learning something new and at the same actually helping and actually feeling like an expert in something. I learned the C programming standard by heart by answering really niche questions about C program behaviors. This was really fun for me. I enjoyed the specificity of stackoverflow - the idea of being "exact", answering only the question asked.

There are just no questions nowadays on stackoverflow. There is a void now. And AI. There are topic-specific Discord chats, as a modern replacement of IRC, but I always assumed they are used by developers, not for noobs. I know the times without AI will never come again, but the internet, the thing connecting people across the whole globe, just feels more empty, more robotic and like an advertisement nowadays.

Most JavaScript developers have been burned by Date at least once: a report that's off by a day, an invoice that lands in the wrong month, a timezone bug that only appears in certain regions.

A writeup on the main failure modes with production examples:

• new Date('2026-07-21') parsed as UTC, displayed as local: date shifts by a day west of UTC
• constructor months are 0-based, so new Date(2026, 7, 21) is August
• every set* method mutates in place, including across shared references
• "add one month" and "add 30 days" are not the same operation and can diverge by days near month boundaries
• JSON.stringify drops timezone context silently

Each section also covers what safe Date patterns look like for code that can't migrate yet, and where Temporal fixes the design rather than just adding a wrapper.

I explain rotations and quaternions. I then implement the latter into my physics engine by replacing the matrix version of angular velocity code with a quaternion version. Lastly, I benchmark the results.

Most SQL performance tooling requires either a $400/month monitoring agent or asking models and hoping the advice applies to your database.

The interesting architecture decisions:

• Heuristic engine runs first (<200ms), LLM is optional and additive — if LLM fails, you still get structured findings
• All dialect logic lives in one dialect_config.py — DDL templates, optimizer syntax, LLM system prompts, maintenance commands for all 5 DBs
• Schema-aware mode: paste DDL, get confirmed recommendations with real table names instead of placeholders
• Every analysis gets a permanent shareable URL

Source: https://github.com/AutoShiftOps/querytuner

Live: https://querytuner.com

Two nested loops can do the same work, have the same O(n²) complexity, and still run around 15× apart.

A visual explainer on why: the RAM model, the memory wall, cache lines, locality, L1/L2/L3, prefetching, data layout and false sharing.

It also covers how the timing difference between a cache hit and miss became the side channel behind Spectre and Meltdown.

The talk host explains how ASTs work and shows how to build them step by step.

Pretty cool seeing that people were already thinking about memory safety decades ago. Some of these ideas that feel modern today were being explored back in the 80s and 90s.
Makes you wonder how different things would look if languages like Modula-3 had taken off.

Regular Indexing works on a column level; it indexes the entire column value, but if you want to index each value in one row, then the GIN indexing technique is used.

GIN can index each element of JSON stored in a JSONB field

How Postgres creates a GIN file physically, what is the format of that file, how that file is retrieved in RAM while fetching records, what data types are used, and how Postgres calculates the exact address of an element of JSON are all explained in the article