Besides obvious time-traveling debugging, imagine jumping to exact time in execution by a side effect (like "who drew this pixel?", "who wrote those bytes?") or automatically identifying where test runs diverge, leading to failures vs. successes.

As someone who's battled flaky tests throughout my career, I see this as a game-changer. Once an intermittent test failure is recorded, there are no excuses – you have everything needed to find the root cause.

I watch this space closely because it could reshape debugging tools.

On a similar note, other helpful communication tools besides the well-known STAR include BLUF (Bottom Line Up Front) and the Minto Pyramid: a top-down structure starting from the conclusion.

untools.co/minto-pyramid/

For example, you can create a leaderboard like the one at lmarena.ai, where candidates are rated through head-to-head or more complex comparisons.

It lets you balance teams by predicting win probabilities and draw chances. OpenSkill also shows how to add features like time decay by gradually increasing uncertainty during inactivity.

Every player's rating is defined by a Gaussian distribution with mean (skill) and standard deviation (uncertainty). For estimates, players are compared using their skill minus uncertainty.

If you want to get a taste of Lean, I highly recommend these two tutorials:
- Proofs about Programs (20 min) - prove if a recursion will halt: busy-beavers.tigyog.app/proofs-about...
- Natural Number Game (3-6 hours) - build natural numbers math from scratch: adam.math.hhu.de#/g/leanprove...

Here's an excellent post about using Metaphone/DuckDB to search French wines without knowing how to spell them: vikramoberoi.com/posts/using-....

From the article, I also learned about Jaro–Winkler distance, which differs from Levenshtein by penalizing differences at the beginning of the string.

A list of tools/libraries related to LD_PRELOAD: github.com/gaul/awesome...

The tricks it enables without recompiling include making random numbers non-random, freezing or fast-forwarding time, monitoring file access, automatically uncompress unput files (via zlibc/uncompress), making builds/tests faster by disabling disk syncs (via eatmydata), and more.

It reduces errors when converting physical positions to digital values. If the value increases or decreases consecutively, but reading multiple bits isn't synchronized, we get the correct value or its immediate neighbor - no other numbers could be constructed from binary parts of two neighbors.

Related rabbit holes:

- Cuckoo Filter (when you need deletion support)
- Avalanche effect and Chi-Square distribution tests for hash functions

You could have a list of 'maybe' prohibited words on the client without exposing them, even then reverse-engineered. Or a maybe-blob of pwned passwords?

My favorite real-world example is Akamai, which used Bloom filters to avoid "one-hit wonders" — only caching content after maybe seeing it twice. Or you could put it in front of any heavier operation.

The best explainer I've found with visualizations and tips for choosing parameters: samwho.dev/bloom-filters/

It's hard to pick the first gem, but for me, it has to be the Bloom filter! It's my reminder of the existence of a tradeoff: precision often isn't worth the cost. It's a game-changer when "definitely no" or "maybe yes" is good enough.