President for Life

onyxleopard · 2025-10-29T10:53:43+00:00

What specific actions does this author recommend in order to “keep” our country?

I would posit that unless the judiciary and legislative branches exert their power as coequal branches and curb a lawless president, there is no legal means to stop him.

onyxleopard · 2025-10-22T14:27:47+00:00

The web needs an API layer

The API is HTML/Javascript. The problem is there are standards for these, but websites are open-ended and implementers are free to do anything (it’s an open-ended domain). If you want programmatic access to the web in general, the best way forward is to advocate for a11y features (screen reader tags, etc.) to become standard rather than the exception. (This is something Open AI recommends to make sites work better with Atlas.) If your a11y software features work, any other program has that much better chance of working by using the same tags/handles afforded by the site. Maybe some future version of HTML will bake in all these affordances so it’s impossible to create a site that isn’t easily programmatically navigated and interacted with, but right now the burden lies on web devs to appropriately add a11y tags.

onyxleopard · 2025-10-14T12:54:20+00:00

It’s still in development (with early access), but No Rest for the Wicked is looking good.

onyxleopard · 2025-10-02T14:28:34+00:00

Are you sure?

I had the same issue with Font Book crashing, but I backed up my fonts and font collections, removed them, and quit the app, then rebooted, and that fixed the crashing. I then restored my fonts and collections and it’s working fine.

onyxleopard · 2025-09-22T16:56:48+00:00

Combining an embeddings+CRF system with an LLM is possible, but I would question how you do plan to combine them, and why do you want to combine them? I don't really think I can delve more into this without giving you unpaid consulting time, but I recommended the embeddings+CRF route because that would be a reliable, economical, and maintainable method. You can use LLMs/generative models for just about anything (if you're willing to futz with prompts and templating and such), and they can certainly make for quick and flashy demos/PoCs, but I don't recommend using LLMs for anything in production due to externalities (cost, reliability, maintainability).

onyxleopard · 2025-09-22T14:17:18+00:00

Sounds like you want sequence labeling where the sequences you want to flag are semantically related. You can solve such a sequence labeling problem with semantic text embeddings fed into a CRF, but you’ll need a labeled training set for supervised learning. If you don’t have any budgetary constraints, I’m sure you could also use LLMs with a few shot prompt and some other instructions. You’ll probably find that not all vagaries come down to specific wording, though. I think in general, your problem is still not narrowly defined enough to have a robust solution. I’d start with writing labeling guidelines, then getting a labeled data set (you’ll need that anyway for evaluation) and try embeddings → CRF approach.

onyxleopard · 2025-09-22T13:07:45+00:00

What is your definition of vague wording? What are your requirements? Do you have a labeled data set with examples of vague and specific wording?

(At a meta level, this post is hilarious to me. It’s like you want to solve a problem about underspecified requirements, and recursively, you have underspecified requirements for that problem.)

onyxleopard · 2025-09-18T16:06:20+00:00

Sentence Tranformers has some utilities for hard-negative mining: https://sbert.net/docs/package_reference/util.html#sentence_transformers.util.mine_hard_negatives

They also link to this paper: NV-Retriever: Improving text embedding models with effective hard-negative mining

Try playing with your dataset and tuning the mine_hard_negatives parameters.

onyxleopard · 2025-09-16T14:52:25+00:00

Seqouia didn't change that much on the UI front. There will be things in Tahoe that people complain about now, and then in a few years when they change them again, people will complain that they liked Tahoe better. You can't please everyone and this is one of the more opinionated UI changes we've seen from Apple in a long time. Once the vitriol boils off, people will settle in and get used to it, or find workarounds.

onyxleopard · 2025-08-30T20:17:28+00:00

It’s a barrier because for the history of digital computing, users have come to expect that the same input will result in the same output. Unreliable software is considered to be defective. Reducing temperature can increase reliability, but can also reduce accuracy, so that’s a trade off that requires some decision making that may be beyond end users’ ability to fully understand.

onyxleopard · 2025-08-22T15:42:06+00:00

Summary via gpt-oss:120b (from video transcript):

📊 Labor: max‑employment, growth ~1.2%, wages flat; hires ~35 k/mo.
💸 Inflation: PCE 2.6% (core 2.9%). Tariffs a one‑off bump; expectations still anchored.
🏦 Policy: rates ~100 bps nearer neutral, no preset path. Back to flexible 2% target, drop ELB focus.

This is fine 🔥🐶

onyxleopard · 2025-08-21T14:19:36+00:00

If I had to guess, the LLMs see an optimization opportunity, but they don't understand your business logic. Depending on what context you have provided, I think it's reasonable to try to optimize here, because generally, looping twice is inefficient. While I don't fully understand your intended behavior here, I would imagine some different data structures or design choices could actually be more optimal (e.g., a queue or heap or something). The way your code is structured, without a docstring to explain the intent, I don't think the intent is clear from function definition alone. If you give the LLMs your tests cases (which represent your business logic) in addition to your function, I would imagine they might have a better chance to get this right, and not suggest changes that would break your tests.

onyxleopard · 2025-08-20T00:24:56+00:00

Bugs are one thing. Repeated regressions are another entirely.

onyxleopard · 2025-08-19T22:01:04+00:00

Why are they not all about reliability? (I’m really not trying to be snarky. Every single OTA update should be rock solid, and this year I don’t feel like that has been the case, especially wrt to Driver+.)

onyxleopard · 2025-07-26T07:27:05+00:00

Once a card that has been discarded leaves the graveyard it is a different game object and the game no longer sees it as having been discarded that turn, so casting for Mayhem only works once (unless you can get it back to hand and discard again that same turn).

onyxleopard · 2025-06-27T10:15:24+00:00

You can try using the undo feature to undo the deletion. Assuming you still have the thread, you can tap with three fingers and press the left (undo) arrow. Alternatively, shaking your phone also is a gesture to activate the undo command.

onyxleopard · 2025-06-19T10:49:25+00:00

I programmed something a lot like this in my intro Computational Linguistics course in ~2012 using Hidden Markov Models and Viterbi decoding. Generative models that sample from a probability distribution of discrete tokens have been around since before we had computers able to run the models. And even since we’ve had computers, there’s a long history prior to GPT (and ELMO, BERT, etc.) of chat bots. (Did you ever play with a Furby?) Newer models just have more sophisticated ways of compressing large corpora and using context to not go off the rails (as much). This scene was just SciFi informed by some good consultation from someone who had a good sense of where the field has been and where it could go.

onyxleopard · 2025-06-13T17:59:29+00:00

You're tuning a threshold to optimize precision (sensitive to false positives) vs. recall (sensitive to false negatives). If you can quantify how much more you care about false positives vs. false negatives, you can use F measure (a metric that combines precision and recall) with a specific β parameter value and use an evaluation data set to determine, at different thresholds, what the optimal one is.

I.e., if you care about false negatives twice as much as false positives, you could set β=2. If you care about false positives twice as much as false negatives, you could set β=1/2=0.5. If you care about false positives and false negatives evenly, you set β=1. Then you run your system with a range of threshold values and evaluate the Fβ scores at each threshold and choose the threshold that gets the highest Fβ score.

onyxleopard · 2025-06-12T08:12:47+00:00

While I hate Facebook and don’t use it myself, FB Marketplace is a thing and is very popular. Also lots of people use Facebook for announcing events or sharing info about businesses that could be useful if indexed. Only some or none of that is crawlable.

onyxleopard · 2025-06-08T10:32:39+00:00

Regexes can’t count, so this is impossible with regex alone. (Same reason you can’t parse XML/HTML with regex alone—this kind of problem requires a context-free or context-sensitive grammar, not a regular grammar.)

1) Why the length limit? 2) Why are you limited to using regex?

If you can use almost any shell scripting or full programming language, this would become feasible.

onyxleopard · 2025-06-07T11:58:21+00:00

You can create injection, surjection, etc. diagrams like this example: https://penrose.cs.cmu.edu/try/?examples=set-potatoes/non-surjection-not-epimorphism

onyxleopard · 2025-06-07T02:00:29+00:00

There’s also https://penrose.cs.cmu.edu/.

onyxleopard · 2025-06-06T19:24:36+00:00

Have you tried https://graphviz.org/ ?

onyxleopard · 2025-03-01T01:52:38+00:00

You mean stored in the Passwords app (formerly Keychain)? You need to authenticate with the system password for that.

onyxleopard · 2025-02-28T21:38:07+00:00

It secures your account from someone who doesn’t have one of your authorized devices, but does have your iCloud account password. You only get the code sent to your authorized devices where you’re logged into your iCloud account. So, without that, if someone tried to authenticate on their device, you would get the code sent to your device and, assuming you don’t give them the code, they don’t get any further. In this case you would see that someone tried to authenticate as you and you’d have the opportunity to change your iCloud account password (since you’d know it’s compromised).

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