Hana

And the wipe is not the end of it. A factory reset puts you into reset protection, so you then need the account credentials the phone was signed into. If the PIN is gone and the account password only lives in a manager unlocked by that phone, the reset does not rescue you, it just moves the wall. Test that chain before you need it.

This does not work, and the failure is confusing rather than loud, so it is worth being precise. React.cache operates in an isolated scope inside use cache boundaries. Values stored via React.cache outside a cached function are not visible inside it, and the docs give a worked example where the value reads back as null.

The only channel into a cached scope is arguments, and captured closure variables count, because they get bound as arguments and become part of the key. If you want something in there, it has to be passed in as a serializable value.

Be honest about what five tasks can tell you: it can catch a disaster, it cannot separate two close models. If model A goes 5/5 and B goes 4/5, you have learned essentially nothing.

So use the small set as a smoke test, "is this thing unusable on my stack", and get the fine-grained signal from a running log of real work: date, model, task, outcome, one line of why it failed. After a month that log answers the question your afternoon experiment cannot.

Neither denominator, and that is not a dodge.

Browser analytics count sessions from browsers that allowed the script to run. GA4 loses people to ad blockers, to consent banners where nobody clicks accept, to ITP shortening cookie lifetime so the same human counts twice, and to bot filtering rules that differ between the two tools. Plausible loses a different set for different reasons. Neither one is measuring the population you care about, which is humans who could have signed up.

What I do: pick one tool as the trend instrument and never switch. Absolute value does not matter, direction does. Then compute the numbers that decide anything from your own database, server side, where you control the definition. Signups per day, activation per signup, paid per activation.

The rate you quote in your own head should be signups over one consistently-defined denominator, and you should write the definition down so future you cannot renegotiate it.

The variant I use where a query string looks ugly or gets stripped by an app is a dedicated path per channel - /go/list, /go/forum - that redirects to the real page. Survives copy-paste, readable in server logs, and someone forwarding the link to a colleague still counts against the right channel.

I did the rewrite you are describing. Three weeks, ended up about six times faster, and I am still slightly annoyed about it because when I finally profiled the old version afterwards, decompression and the Parquet write were more than half the wall clock. The real win came from streaming instead of loading, which I could have done in Python. If I did it again I would spend the first day measuring and the remaining four deciding.

I did eight months of accepting diffs I mostly understood, and it was fine right up until a production incident at 2am where the failing path was code I had shipped and never read. I spent 40 minutes reading my own work under pressure while people waited. Nothing about my velocity in those eight months was worth that 40 minutes, and I have written every incident sensitive path by hand since.

Do the sum out loud, it is not close on cost alone. $1,800 over three years is $50 a month before you plug it in. A 3090 build pulling roughly 400W while generating, for two hours a day, is about 24 kWh a month, so call it another $3 to $8 depending on where you live, plus whatever it idles at if you leave it on. So you are at $55 to $60 a month against a $40 API bill, and the API models will be better than what you are running for most of that period. The rig makes sense when the reason is not money - privacy, no rate limits, offline, or because you enjoy it: and those are all fine reasons. It just is not a savings decision at two hours a day.

Momentum on a side project is mostly about the size of the smallest useful unit of work. When mine got cold it was because every remaining task had grown into a three-evening job in my head, and I never have three consecutive free evenings.

So I split the board until nothing on it takes more than an hour. It feels like busywork when you are doing the splitting. It is the entire difference between opening the editor and closing the tab.

Not foolish, it's genuinely non-obvious. Same trick works for MySQL and for anything doing lots of small SQLite writes in tests.

Use whichever one you will open on a Tuesday when you are tired. Everything else is a rounding error against that.

The vanity activation trap is worth underlining. If your activation event can be satisfied by looking at something you generated, it is not an activation event.

Do the per-customer minutes, not the total. 40 minutes for 20 people is 2 minutes each. The question is whether that 2 keeps falling as you get better at it or parks. Mine went 6 -> 3 -> 2.5 and then sat at 2.5 forever, which is the actual signal: you have squeezed everything the human can squeeze and the rest only comes out with code.

Also price your own hour honestly. 40 min/day is about 13 hours a month against $45 of revenue. That is fine as a learning exercise, just do not tell yourself it is a business decision when it is a schedule decision.

Config table, cached, defaults off, one helper function. Revisit when a second person can deploy.

The timing detail is the useful part of this. I went back through my last three and the cooling started within a month of moving in, every time, and I had told myself a different story about each one.

I made the opposite choice with roughly the same budget and I would make it again. I spent about 180 hours on databases and observability, meaning query plans, indexing, and being able to read traces and know where the time went. In the year since, that has been the thing I get asked about, it is the thing the tooling is worst at, and it is the reason I get pulled into incidents. My honest read is that the model can produce a plausible query far more easily than it can tell you why yours is slow on your data.

Generation speed for a dense model is essentially a memory bandwidth problem, because every token has to read the weights. Your 3060 has roughly 360 GB/s. Dual channel DDR5 gives you somewhere around 90 to 100 GB/s in practice, so anything living in system RAM is being served at about a quarter of the card's rate, and that is before the transfer overhead. So more RAM does not make a 32b model fast, it makes it possible. The rule that predicts your speed is what fraction of the layers ended up off the card, not how much RAM you own.

I want to push back on the reflexive just use Go answer, because the ecosystem argument is a lot weaker than it was. axum plus sqlx plus serde is a genuinely pleasant stack now, the borrow checker cost is front loaded into weeks one to three and then largely disappears for web services where everything is owned per request, and serde has saved me more JSON bugs than I can count. The two person part cuts both ways: with a small team you cannot afford the runtime surprises either. I would still not do it with a six week deadline and zero Rust experience, but I would not frame it as Rust being unsuitable, only as the timing being wrong.

Check whether you have any git or http dependencies in that lockfile. Those bypass the npm cache entirely and re-fetch on every run, and one of them can eat a minute on its own. Grep the lockfile for "resolved": "git and you'll know in a second.

The distinct alert sound is the underrated part. If your outage alert makes the same noise as a marketing email, you will have trained yourself to ignore it within a fortnight.

The ask matters as much as the quote. Ask immediately after something good happens, usually a support thread where you solved their problem. Ask for one sentence on what was annoying before and what changed. Offer to draft it so they only have to edit, because most people are happy to approve and hate to compose. My acceptance rate went from about half to nearly everyone with that last part.

The distinction you want is regulatory clearance, and it sorts the category quickly.

There's a headband-style device that stimulates the trigeminal nerve, it's FDA-cleared and sold over the counter in the US, with a 60-minute session for treating an attack and a 20-minute daily session for prevention. There's also an arm-worn one that uses remote electrical neuromodulation, which is prescription-only, FDA-cleared from age 8, and used either at the start of an attack or every other day for prevention.

Those have gone through a process. The £30 band with the same stock photos has not. That's not a promise either will work for you, it's the line between a device with evidence behind it and a fabric strap with a claim on the box.

Ours is 5s and 2, so probably not the main cause, but I hadn't even thought to look. Checking.

preStop sleep 10 plus failing readiness on SIGTERM took the tail from 40s to about 3s. I had it exactly backwards, I'd spent a week trying to make shutdown faster.

One thing nobody mentions: a box cannot be reviewed on a train, and an app cannot be reviewed when the battery is dead. Whichever failure mode you hit more often is your answer.