Nadia Brill

Worth adding that the label describes the model, not your prompt. A medium-usage model that thinks for four thousand tokens can cost you more than a high-usage one that answers in two hundred. The label narrows the search. It does not finish it.

Activated parameters set the floor, but thinking budget usually dominates in practice. A small-active model that reasons for four thousand tokens on a trivial edit costs you more than a large one that answers in two hundred. Measure per task, not per model.

Also worth flagging that deepseek-v4-flash is presented as a preview of the V4 series, so treat anything you conclude about it as provisional - preview endpoints get re-tuned under you.

Adding a gentle pushback on the framing rather than the conclusion: model families differ in how much low-precision damage they take, partly because of how they were trained and released. So a two-bit file from one family genuinely is not equivalent damage to a two-bit file from another.

The claim you will see repeated is that some of these models were "trained in low precision so quantising costs nothing". Sometimes there is something to that and sometimes it is folklore. Check the model card before you plan a hardware decision around it, and either way run your own eval - that is the only version of this argument that produces a number.

"Detectable is not harmful, and a data request got reported as a verdict" is a genuinely useful frame. Thank you.

We ended up split exactly like that: HTTP driver for the ~95% of traffic that is single reads, a pooled TCP client for the three endpoints that need a real transaction. Slightly annoying to have two clients, much easier than tuning one to do both.

Do not use perplexity as your proxy. It is easy to compute and it moves smoothly, which makes it feel scientific, but it is dominated by high-frequency tokens and can barely move while the behaviour you care about degrades badly.

Use task evaluations against work you actually do. Twenty items, pass/fail, scored the same way every time. It is less elegant and much more predictive.

Store-only has the nice side effect that you can compute the exact final size in advance from the object sizes, so you can show a real progress bar and set Content-Length instead of chunking blind.

This also covers the cases you cannot see from the client: the user restoring a tab, a retry from a flaky proxy, someone with a trackpad that double-fires. Any mutation that moves money gets a key on my projects, no discussion.

The generator diffs two schema snapshots. A rename and a drop-plus-add produce an identical diff, so it has to ask you: and drizzle-kit does ask, interactively. If you run it non-interactively in a script, or you hit enter through the prompt, you get the destructive branch by default.

Three rules that have kept me out of this:

• Generate on your machine, interactively, never in CI.
• Open the .sql before committing it. Every time. It is thirty seconds.
• Make the pipeline grep the pending migrations for DROP COLUMN and DROP TABLE and fail unless the PR has an explicit label. Destructive changes should require a human sentence somewhere.

New to this area. Is there any published list of which specific products got retested and passed? Every article I find names the failures and never the survivors.

Short version of what happened: a run of products came under question, several Korean brands pulled products or issued refunds, and then this July there was a fresh round of noise about Daiso's line after an independent tester published his own results. Daiso disputed those results and said it is going back through a state-accredited lab. So "Korean sunscreen is fake" is too strong, but "the testing pipeline had real problems" is fair.

Mild disagreement with the caution in this thread. Everyone lists ownership costs as if rent were fixed forever, and it is not, and in most places it has moved more than maintenance has. If you are staying five years with stable incomes, the risk of your rent rising is a bigger unknown than the risk of your boiler dying.

This is the line that ends most of these arguments. The throwing money away camp and the renting is smarter camp are usually both comparing the wrong two numbers.

Slight disagreement with "you don't need it, move on." You don't need it today. You will need something on that road the first time a customer asks for SSO with their company directory, and that request arrives with a contract attached and a two-week deadline.

What I'd do at 400 users: keep passwords and sessions, but put your user lookup behind one module so identity source is swappable. Don't buy the identity provider now. Just don't wire user_id assumptions through forty files, because that's the part that hurts later, not the protocol.

Eighty images at five minutes is nearly seven hours of retouching alone. I quoted a day for the whole job.

Per image also gives me a way to renegotiate without reopening the entire quote, which is the escape hatch I currently do not have.

Partial indexes per tenant solve the filter problem cleanly and stop scaling at a few hundred tenants, at which point you have a few hundred indexes and every autovacuum takes an age. Fine for enterprise-shaped customer lists, not for self-serve.

180MB for 2 million rows suggests something high cardinality is riding along. Usually a transaction ID or a full datetime stamp. Split datetime into a date column and a time column, drop any ID you do not actually join or count on, and watch the model shrink. Narrower columns compress better and scan faster.

On the one-month constraint specifically: presigned upload is an afternoon, including getting the CORS rule right, which is the only annoying part. I spent most of a week last year making streamed uploads through a serverless function behave, and then deleted it for presigned URLs anyway. Pick the boring one first.

Take the highest number a lender will approve as information about the lender, not about you. They are pricing their risk, which ends when the loan is repaid, not yours, which includes childcare and a freelance quarter with nothing in it.

I planted straight from the garden centre without washing the roots and lost my entire springtail culture within a fortnight. Nursery plants can carry systemic pesticides and the soil they come in is not tank soil. Now everything gets bare rooted, rinsed and grown out in a spare tub for a few weeks before it goes near an animal.

Run the arithmetic backwards from what you need to earn rather than forwards from what feels polite. Pick the annual figure you want, divide by the number of billable days you can realistically sell - for a solo shooter that is nowhere near 200, it is more like 90 to 120 once you count pitching, admin and the weeks nothing lands: and that gives you a day rate floor. Then a job like this is three to four of those days. Almost everyone starting out quotes about a third of what that maths says.

I would not do that here, bidirectional filtering on a date relationship causes ambiguity as soon as a second fact table appears, and it will not conjure up missing calendar rows anyway. The gaps explanation above matches the symptom exactly.

Longer term I would move off DATEADD for month comparisons and use an integer month offset column instead. Add MonthIndex = (Year * 12) + Month to the date table, then last year is just this month's index minus 12. It survives gaps, partial months and 4-4-5 fiscal calendars, none of which the built-in functions handle gracefully. Slightly more setup, far fewer mystery blanks later.

I shot a paid job entirely in jpeg because I had reset the camera and did not check. Mixed tungsten and daylight, white balance all over the place, and almost nothing I could do about it afterwards. Now the first thing I do in any unfamiliar situation is take one frame and look at the file type on the back screen.