Do you have Settings > Apps > App Store > (Automatic Downloads) App Downloads turned on?
I noticed apps appearing on my Home Screen I’d never heard of before. Turns out with that setting and Family Purchase sharing turned on, every time my wife installed a new app, it installed on my phone too.
That may not be your exact scenario, but I wonder if turning off that Automatic App Downloads setting (if enabled) changes anything. Could give you a clue, if so.
Speaking of which. Under Cellular Data There is a setting right below Automatic Downloads call "App Downloads". I wonder if that works independently of whether or not Automatic Downloads is off. The only options are: Always allow, ask if over 200mb and always ask.
aix - Like the npm CLI and package.json, but for AI config. Allows standardizing your AI config to share with others, and defining it all in one spot but installing to Claude, Codex, Cursor, etc.: https://aix.a1st.dev/
"Kamal runs blue-green deploys — it starts a new container, health-checks it, then stops the old one. During the switchover, both containers are running. Both mount ultrathink_storage. Both have the SQLite files open."
WAL mode requires shared access to System V IPC mapped memory. This is unlikely to work across containers.
I don't know much about Kamal but I'd look into ways of "pausing" traffic during a deploy - the trick where a proxy pretends that a request is taking another second to finish when it's actually held in the proxy while the two containers switch over.
Pausing requests then running two sqlites momentarily probably won’t prevent corruption. It might make it less likely and harder to catch in testing.
The easiest approach is to kill sqlite, then start the new one. I’d use a unix lockfile as a last-resort mechanism (assuming the container environment doesn’t somehow break those).
I dug into this limitation a bit around a year ago on AWS, using a sqlite db stored on an EFS volume (I think it was EFS -- relying on memory here) and lambda clients.
Although my tests were slamming the db with reads and write I didn't induce a bad read or write using WAL.
But I wouldn't use experimental results to override what the sqlite people are saying. I (and you) probably just didn't happen to hit the right access pattern.
The containers would need to use a path on a shared FS to setup the SHM handle, and, even then, this sounds like the sort of thing you could probably break via arcane misconfiguration.
> The wal-index is implemented using an ordinary file that is mmapped for robustness. Early (pre-release) implementations of WAL mode stored the wal-index in volatile shared-memory, such as files created in /dev/shm on Linux or /tmp on other unix systems. The problem with that approach is that processes with a different root directory (changed via chroot) will see different files and hence use different shared memory areas, leading to database corruption. Other methods for creating nameless shared memory blocks are not portable across the various flavors of unix. And we could not find any method to create nameless shared memory blocks on windows. The only way we have found to guarantee that all processes accessing the same database file use the same shared memory is to create the shared memory by mmapping a file in the same directory as the database itself.
> WAL mode requires shared access to System V IPC mapped memory.
Incorrect. It requires access to mmap()
"The wal-index is implemented using an ordinary file that is mmapped for robustness. Early (pre-release) implementations of WAL mode stored the wal-index in volatile shared-memory, such as files created in /dev/shm on Linux or /tmp on other unix systems. The problem with that approach is that processes with a different root directory (changed via chroot) will see different files and hence use different shared memory areas, leading to database corruption."
> This is unlikely to work across containers.
I'd imagine sqlite code would fail if that was the case; in case of k8s at least mounting same storage to 2 containers in most configurations causes K8S to co-locate both pods on same node so it should be fine.
It is far more likely they just fucked up the code and lost data that way...
NeXTstep?
(Leaving aside fun spitballing about whether Tahoe is morally OPENSTEP 26, and whether it was NeXT that actually bought Apple for negative $400 million...)
"Not as a proof of concept. Not for a side project with three users. A real store" - suggestion for human writers, don't use "not X, not Y" - it carries that LLM smell whether or not you used an LLM.
I assumed that it was to ensure that the announced products were revealed in a controlled manner rather than because they aren't able to do updates to their product listings as a regular thing.
I found that having a rule like this helped some too:
> * ABSOLUTELY DO NOT use `@deprecated` on anything unless you are explicitly asked to. Always fully refactor and delete old code as-needed instead of deprecating it
I had the same question. There are older and more established component libraries, so why’d this one win? It seems like a scientific answer would be worth a lot.
Alternate title: "How to break your website's styling for 10-20% of your users"
This is a nice reference, and some properties like `scrollbar-gutter` can be used for progressive enhancement.
However, many options listed will require some kind of fallback if `autoprefixer`/`postcss`/etc. doesn't cover it, and if you don't want to exclude a large fraction of your users.
It's reasonable in some cases to have both "new" and the old fallback code side-by-side until _your users's_ browser adoption stats indicate that you can delete the old fallback code without breaking a substantial number of users.
But the reality of using the new CSS hotness is that if the code is not supported by a % threshold that is much higher than many of these techniques show, it actually _increases_ your workload in the near term. You write new + the fallback + ensure that they don't interfere with each other.
P.S. Note the emphasis on _your users_ in the paragraph above. Global browser stats are fine as a basic reference, but your specific site/app's userbase demographics affect the actual percentages tremendously. That may mean you can use ALL of these new techniques today, or some, or none of them.
If your audience is primarily software developers, then after measuring you may find you can use these without a fallback. If it includes people in less wealthy communities or countries, or in countries with restricted access to mobile phone markets, you likely cannot.
I don't follow. Assuming that the caniuse data is also representative of your users (a big assumption), then it's 10-20% of either group. Adjusting the % for the subset that is "your users" can result in either a higher or lower %.
It’s a CLI tool and MCP server for creating discrete, versioned “libraries” of RAG-able content.
Under the hood, it uses an embedding model locally. It chunks your content and stores embeddings in SQLite. The search functionality uses vector + keyword search + a re-ranking model.
You can also point it at any GitHub repo and it will create a RAG DB out of it.
You can also use the MCP server to create and query the libraries.
I noticed apps appearing on my Home Screen I’d never heard of before. Turns out with that setting and Family Purchase sharing turned on, every time my wife installed a new app, it installed on my phone too.
That may not be your exact scenario, but I wonder if turning off that Automatic App Downloads setting (if enabled) changes anything. Could give you a clue, if so.