There's even more options still, especially if you go further back toward more traditional methods. Static word vectors like GloVe or fasttext (optionally more modern equivalents like WordLlama or Model2Vec). Then there's sklearn-style stuff too. Those can be really small/fast but have more accuracy-level tradeoffs.

- Zero-shot encoders like tasksource or GliNER

- Natural language inference: https://huggingface.co/blog/dleemiller/nli-xenc-ways-to-use

- GRPO training

- GEPA prompt tuning Qwen 0.6B (or GEPA, then GRPO)

- Use an embedding model and train a classifier (MLP, logistic, svm)

- Use a larger LLM to generate a synthetic dataset (beware of lack of diversity, mine "seed text" from real sources first)

- Synthetically generate "hard examples" where more than one category may be valid and DPO tune your preferred responses

I think people tend to fixate on the worker-to-worker differences inside of unions. Yes, that is the most visible part of a union when in place, and at least in the US has valid arguments about meritocracy.

What is missed when limiting the scope to just that is the population-level abuses of workers that no amount of meritocracy will fix. When corporations engage in collusion against workers (now common and nearly unpunished in the US) the top-level wages are suppressed industry wide.

The whole pay band alignment that comes out of that undermines the meritocracy argument, and doesn't even begin to address the wage-fixing that has gone almost unchecked in tech for decades[1,2]. As a merited employee, you might have more options to where you can go, but it won't protect you from predatory hiring/layoff cycles and it certainly won't guarantee that you'll receive a truly competitive wage.

On paper, meritocracy sounds great. I have worked many places in tech and never once observed it, personally. Best case, if you have warmed a seat for enough years, then you advance that way. Worst, your employer knows they can just take advantage of you because you're willing to work without a dangling carrot.

As before, either the government frees itself from corruption and enacts justice or unions will come back. That is point we are at.

[1] https://www.npr.org/sections/alltechconsidered/2015/01/16/37...

[2] https://conversableeconomist.com/2025/10/31/the-silicon-vall...

They also seem to have adopted a no-remote hire policy and are in an extreme high CoL location. It’s a truly awful mix for trying to attract outside talent. I don’t know why they even bother.

So here we walk around the circle one more time again, voicing our anxieties, talking past each other, waiting for the next opportunity for commentary to come in half an hour.

They just got fired for "piercing the veil". They committed the sin of bringing attention to the invasion of privacy.

In the US with our broken system of capitalism, it’s the only way we can tether these companies to reality. Left to their own devices, I’m not convinced they would actually compete with each other on price.

Buy nobody like to talk about how “moat” building is fundamentally anti-competitive, even in name.

Funny that self proclaimed capitalists hate the system in practice. Commodity pricing is what truly terrifies them.

* We need to completely deregulate these US companies so China doesn't win and take us over

* We need to heavily regulate anybody who is not following the rules that make us the de-facto winner

* This is so powerful it will take all the jobs (and therefore if you lead a company that isn't using AI, you will soon be obsolete)

* If you don't use AI, you will not be able to function in a future job

* We need to lineup an excuse to call our friends in government and turn off the open source spigot when the time is right

They have chosen fear as a motivator, and it is clearly working very well. It's easier to use fear now, while it's new and then flip the narrative once people are more familiar with it than to go the other direction. Companies are not just telling a story to hype their product, but why they alone are the ones that should be entrusted to build it.

Isn't this fundamentally what MBAs do with their time? Keep going with this analysis, because it goes much deeper... In my experience, BI is often a house of cards. A lot of times it's just narrative crafting, just like we're all encouraged to do when we write our resumes.

Can you embellish a story? Can you invent a convincing political narrative? As far as I can tell, that's the fundamental unit of US corporation.

Anyone who likes good noise cancellation, which is a lot of people.

Back in the day we just called it ML. But now you have to stop for a minute to read and determine what they’re talking about, because “AI” is primarily a marketing term.

An important aspect of AI is that it needs to be seen as moving forward all the time. Plateaus are the death of the hype cycle, and would tether people's expectations closer to reality.