Sabine Korte

Other route if you install from the tarball yourself: pull codex-package-<arch>-<os>.tar.gz from the release page and put both files from its bin/ directory on your path together. The two are a pair. Copying only codex across reproduces this exact error on a clean machine.

If it must move at 6am regardless of your network, that is a microcontroller. If it must serve a page or keep a database, that is a Pi. When you need both, buy both, because they cost less than one bad morning.

Look at the model page before you make anything your default.

The library page for glm-5.2 lists it at 756B parameters with a 976K context window and marks it as high usage. deepseek-v4-flash's cloud tag, by comparison, is marked medium usage. Those labels are Ollama telling you in advance roughly what a turn will do to your allowance, and people scroll straight past them.

Also relevant to your options: glm-5.2 is cloud-only there. The only tag listed is glm-5.2:cloud. There is no local variant to fall back to when you run out, so every single turn is metered.

For a document diff I would sit in the medium band by default and only escalate when the cheaper model visibly fails.

Do not pick this from a thread, including this one. Two hours of measurement now saves you months.

Take ten tasks you actually did last week: real tickets from your real repo, not toy prompts. Write down the acceptance criteria for each. Run all ten through each candidate model from a fresh session. Record two things: did it pass, and how far did the session bar move.

You will end up with a table that says something like "model A passes 8/10 at one unit of cost, model B passes 9/10 at four units". Then the routing rule writes itself, and it is your routing rule rather than mine.

Once the popup is working again, do the thing that stops this being a problem next time: copy the direct .msu download URL into your runbook next to the KB number, along with the file hash.

The catalog website is a discovery tool. The file behind it is just an object on a CDN. If you already have the URL from a previous cycle you can fetch it with curl or Invoke-WebRequest without touching the site at all. Verify the hash every time, because URLs do get re-pointed.

And record the direct URL alongside the hash, not just the file. Six months later you will want to re-verify or re-fetch and the KB number alone sends you back to the site you were trying to avoid.

Verify the hash on every restore rather than at download time only. Files get replaced, links get re-pointed, and a silently different .msu on a domain controller is a bad afternoon.

Worth being precise about who that applies to, because "my free tier expired" describes two completely different events now.

The closure behaviour is the credit-based free plan, for accounts created after the July 2025 change. If you are on a legacy account from before that, your twelve months of free usage allowances simply run out and you start paying at normal rates: nobody closes your account and nothing gets deleted.

Same phrase, opposite consequences. Check which one you are on before you panic or before you relax.

Flagging a piece of folklore that circulates on this exact topic: "account closed means the data is already deleted". It is not what AWS documents, the documented behaviour is 90 days of retention with restoration by upgrading to a paid plan.

This matters because the panic reaction to believing it is to immediately create a fresh account and start rebuilding, which is the one move that guarantees you never recover the original. Check the actual policy, then act.

Also worth reading the closure email properly rather than skimming it. It states the dates.

On the quant side, two things that get conflated.

First: dropping below roughly 3 bits per weight tends to hurt more on a model whose experts are already small. You are compressing something that has less redundancy to give. A 10B-active model at 2.5bpw is a rougher ride than a dense model of the same file size would be.

Second, and more annoying: "Q2" is not a standard. Different quantisation families keep different tensors at different precisions, so two files both labelled two-bit can be materially different objects. The advertised number is an average across a layout you cannot see from the filename.

So the honest answer to "is Q2 usable" is: for that specific file, from that specific publisher, on your specific tasks. Nobody can answer it for you from the label alone.

What breaks first is the thing you will not notice in a ten-minute chat.

Short conversational quality is remarkably robust to aggressive quantisation. What degrades early is long-horizon behaviour: holding an output format across many turns, following a multi-step instruction to the end, using tools correctly on the fortieth call rather than the fourth, not quietly dropping a constraint it agreed to twenty messages ago.

So the standard failure story is: person tries a low quant, it feels great, they make it the daily driver, and a week later they cannot work out why their agent loop has become unreliable. Nothing crashed. It just got slightly worse at staying on task, which is the hardest kind of regression to attribute.

Budget doubles for two reasons. Radiator valves, and the fact that you will replace whatever you bought in the first month.

Switch to terracotta for anything you are worried about overwatering. Porous pot, water evaporates through the walls, mix dries faster, and the margin for error on a heavy-handed waterer roughly doubles. It is the cheapest fix on this whole list and it saved my pothos from me.

for an internal tool with 200 requests a day, consider not fixing it. a loading state that says waking up costs you an hour and the alternative is paying for something to stay running all weekend for one slow request on monday.

Gentle disagreement with the debugger-first advice. Print statements are fine, the problem is that yours are unstructured. Print with labels and values together, print at stage boundaries rather than inside tight loops, and delete them as soon as they have told you something. I have written Python for years and still solve most data bugs with well-placed prints; the debugger earns its keep on control flow bugs rather than data bugs.

Worth timing it in a private window with extensions disabled too. Spreadsheet performance complaints are occasionally a browser extension inspecting the page on every change rather than the sheet at all.

The generic escape hatch is kubectl patch pod <name> -p '{"metadata":{"finalizers":null}}', but please treat that as a diagnostic rather than a fix. Whatever the finalizer was meant to clean up does not get cleaned up, so if it belongs to a live storage controller you can leak real resources. Use it to confirm the cause, then fix the cause.

Worth grepping the whole cluster for that finalizer while you are there. If one deployment kept it, others probably did too and just have not rolled yet.

A matrix that renders tens of thousands of cells is slow no matter how good your model is, because each cell is its own filter context evaluation. Start it collapsed to the top level, turn off show items with no data, and cap the columns to 12 months rather than three years. Users almost never look at all of it and they can expand what they want.

Percentages hide the thing that actually matters, which is what is left in absolute money. Thirty eight percent on a large income leaves a lot of room and the same percentage on a small one leaves none, and the childcare line you mentioned is temporary while the mortgage is not. I would work out the monthly surplus in real currency for this year and for the year childcare ends, and decide from those two numbers.

Whatever you pick, migrate one room at a time and leave the rest alone. I did the whole house in a weekend, everything worked and the family still hated me, because thirty accessories all changed names on the same day.

The card wearing out is real. Move the database onto an SSD or shorten your recorder retention on day one, because I killed two before anyone told me.

Worth knowing that a linter catches this before you ever run it. Ruff and flake8 with the bugbear rules both flag a mutable default argument as a specific warning, and it takes about a minute to add to a project. The category of bug that a linter finds is small but it is entirely free, and this one is the flagship member of that category.

Order I would spend it in: a proper floor pump with a gauge, a decent set of hex and torx keys that are not the folding multitool, a chain wear checker, cable cutters if you run mechanical shifting, and a chain tool. That lot is well inside your budget and covers the jobs you will actually do this year. The stand is the thing everyone buys first and it is the one I would buy last, because a cheap wobbly stand is worse than leaning the bike against a wall.

Worth adding: the calendar should cover whole years, 1 Jan to 31 Dec, not just min to max of your data. Year-to-date measures get strange at the edges otherwise.

Two things that make a long-running loop behave itself. Catch KeyboardInterrupt around the loop and exit cleanly so Ctrl+C prints one tidy line instead of a traceback. And back off when the endpoint complains: if you get a rate limit response, sleep longer before retrying rather than hammering it every thirty seconds, and respect the header that tells you how long to wait if the API sends one. Polite polling is also what keeps your key from being blocked.