161–170 of 228 posts
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I can never remember what they are, though. To avoid this problem, I think I wrote them down on a post-it, but I had too many post-its on my desk so I got rid of them all, and now I can't remember.
The two most difficult ones are naming things, cache invalidation, and off-by-one errors. HTH. ;)
The commit that fixes this issue: https://github.com/gitster/git/commit/684dd4c2b414bcf648505e... (Surprise, the root cause is a cache )
It's amazing how often exploits come down to optimizations. The general form being "the domain logic is X, and is secure, but we faked around it in this one case to make it faster, and it turns out we made a bad assumption while doing so". Meltdown fits this description too.
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Wait for a macOS security update that includes it. If you don’t want to wait, macports and homebrew will both be patched much faster.
Isn't git distributed with the Xcode command line tools?
Tangential.. are there bug bounties available for vulns in open source projects?
More independent projects sometimes does it to, for example curl.
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I can never remember what they are, though. To avoid this problem, I think I wrote them down on a post-it, but I had too many post-its on my desk so I got rid of them all, and now I can't remember.
The two most difficult ones are naming things, cache invalidation, and off-by-one errors. HTH. ;)
three most difficult things in CS:
2) Naming Things
1) Cache Invalidation
4) off by one errors
3) Concurrency
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Difficult problems in programming: (1) cache invalidation (2) off-by-one errors
Fortunately, many off-by-one errors can be caught with more ergonomic tooling. For the simplest example: compare the old C-style for loop vs a Python style for-each loop.
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well played. I think that just got added to my standard vocabulary. Caching has caused more errors and bugs that I've had to deal with than I can recall. My favorite was an off by one error where we returned nicely cached info -- just for the previous user who came through our system! :facepalm: That was a bad one.
That's because essentially, "state" and "caching" are the same thing on some level. And the problem with state is that you have to make sure all your state transitions don't cause bugs. What we know as a "cache" is essentially creating new state representing existing state, with all new transitions...
On the other hand, the whole point of normalization in databases is to avoid redundancy and have "single source of truth".
I find the concepts of normalization and denormalization applicable and helpful outside databases as well, though a different terminology is often used.