awb

> I don't think any new languages will easily beat the existing ones, simply because of the mass of training data that is available.

Seems like a solvable problem though by generating synthetic data that’s guaranteed to be accurate through linters, compilers and tests.

If it’s ~9 figures to train a frontier model, it seems like training on a new language could be a rounding error if it was a priority.

I could see the appeal of a new agentic-friendly language that’s focused on minimizing tokens. The standard library could be massive with no concern of making the language easily readable or learnable.

If it was a poison pill then why make the severance $8M instead of the majority (or all) of the value of the company?

The demand curve for speed and intelligence seems pretty steep to me.

If you look at the hiring marketplace, being just marginally better than your peers can be very lucrative.

If you’re competing on speed or capability as a company (or as an employee), you’re probably going to be willing to pay for the frontier.

Employees can quit too.

In both scenarios, it really depends on how many alternative options for income you have available.

When you’re paying someone’s bill/salary, it changes the dynamic.

Just like someone in customer service might act differently with coworkers vs customers.

Exactly.

It’s the concept of a management chart as an inverted pyramid with each layer holding up and supporting the layer above them. If you imagine a promotion as working your way down the corporate pyramid, then it’s easier to see how the managers at the bottom are carrying more weight and deserving of higher pay.

As opposed to a pyramid where it’s visually represented as the broader management layers supporting the layers above them.

In a pyramid, it looks like the CEO has a cushy, overpaid job. In an inverted pyramid it looks like they have the weight and responsibility of the company on their shoulders.

Some hypothesize that flashbacks might be the brain searching for relevant useful memories, or hallucinating if it can’t find any. Or, perhaps emotions or physical issues cause your brain to function differently and it’s not an adaptive trait.

Time slowing down does seem useful in the event you can actually affect your circumstances.

But how do you measure intelligence or problem solving without language? It seems like an unavoidable and non-trivial parameter.

The difference is the point of sale. With VS Code, you purchase your AI compute elsewhere (Anthropic, OpenAI, etc.), and then use it through the free VS Code interface.

With Warp, you purchase your AI compute through Warp (who then pays Anthropic, Open AI, etc. based on the model you choose).

> On the Build plan, you pay for what you use and credits roll over month to month.

Here’s where I got it from, but I see how it’s ambiguous. “You pay for what you use” sounds a bit like the BYOK (bring your own key) “add-on credits” pricing model you’re referring to.

But in the pricing table, they refer to monthly “AI credits”.

> Pricing model for a terminal. What a time to be alive.

You’re really paying for AI compute, not the terminal.

Their old Pro plan at $15/mo (paid annually) had 2,500/mo AI requests per month, use it or lose it.

The new Build plan at $20/mo has 1,500 AI requests, but they roll over. (Edit: apparently they don’t)

> No bones about it: this plan will be more expensive for some users and less expensive for others.

> We get that there’s a lot of whiplash in the AI devtools pricing market, and sympathize. While we expect some churn from this change, we are trying to do it in as minimally disruptive a way as possible.

I’ve found Warp to be very useful, but you’re really paying for AI compute, not the terminal. And the AI compute space is getting very competitive.

A few issues:

* The casino takes a rake, so you lose money every hand, but you only win when the fish bets and loses. You’re also expected to tip the dealer

* Everything is on camera and dealers remember players, so there will be a lot of witnesses and evidence

* Seats often open one at a time, so you’d potentially lose money at other tables waiting to play together. Or, you all show up at once and ask to start a new table together, which would get suspicious.

* If you don’t know the fish’s cards, there’s still a chance you lose and lose big

> Because they would be smart enough to know the societal damage they caused by this revelation and they did it anyways...

That seems like a human-centric perspective.

Maybe they’re a cooperative, altruistic society with an innate desire to help, and maybe had been helped by others before. To not teach us about the imminent dangers of the universe might seem unconscionable to them.

Or maybe they’re a highly ordered society with an innate common goal and see nothing wrong with asking other entities to join their mission.

Sure, some humans may view their contact as intrusive or harmful, but that doesn’t mean they automatically would as well.

If I had to bet, I’d bet you’re right, but the universe is a big place and who knows what societies might be out there that would feel totally foreign to us.

Hopefully there’s a similar “don’t make me think” mantra that comes to AI product design.

I like the trend where the agent decides what models, tooling and thought process to use. That seems to me far more powerful than asking users to create solutions for each discreet problem space.

For reference, here are the current guidelines: https://news.ycombinator.com/newsguidelines.html

As a long-time user I’ve seen the most change around “What to Submit”:

> Off-Topic: Most stories about politics

> If they'd cover it on TV news, it's probably off-topic

The guidelines haven’t changed but it feels like the enforcement of it has.

For example, the US government shutdown is currently on the HN top 20: https://news.ycombinator.com/item?id=45434146

While a mainstream newsworthy story, I fail to see how it “gratifies one's intellectual curiosity” or is “Anything that good hackers would find interesting.”

Judging by the lack of responses and my own experience: no.

Most subagent examples are vague or simplistic.

Fun fact: It would take ~278 hours to take a look at 1M websites for 1s each.

It would be interesting to analyze this dataset in terms of colors, layout, features, fonts, photos, etc. to be able to statistically measure the uniqueness or creativity of a given web design.

Interesting read and thanks for sharing.

Two observations:

1. Natural language appears to be to be the starting point of any endeavor.

2.

> It may be illuminating to try to imagine what would have happened if, right from the start our native tongue would have been the only vehicle for the input into and the output from our information processing equipment. My considered guess is that history would, in a sense, have repeated itself, and that computer science would consist mainly of the indeed black art how to bootstrap from there to a sufficiently well-defined formal system. We would need all the intellect in the world to get the interface narrow enough to be usable, and, in view of the history of mankind, it may not be overly pessimistic to guess that to do the job well enough would require again a few thousand years.

LLMs are trying to replicate all of the intellect in the world.

I’m curious if the author would consider that these lofty caveats may be more plausible today than they were when the text was written.

Hyper-niche products come with some inherent risk that it’s not always profitable to maintain or develop them long-term.

With a mass market product leader you’re sacrificing a bit of customization for long-term stability.

PageRank worked well for Google for a long time. This sounds like an adaptation of that that’s interesting to consider.

Luxury goods and staple goods have distinct optimizations, both viable for generating profits and economic utility.

A high end soup and an affordable soup might be serving two different markets.

IMO, it depends. I’ve worked with hundreds of startups over the last 20 years. The most significant trend I’ve seen is cultural cohesion. Some startups have a young, energetic culture. Some have an experienced, veteran culture. Some have a quirky, trendy culture.

But overall many companies seem to value and attract more of the same culture they already have in place. So to a degree, I’d say yes ageism exists in certain companies that don’t value experience as highly as other qualities.

As an engineer, if you’ve had a skilled Project Manager you’ve receive detailed, thoughtful “prompts” on how to complete a project that leads to a shared understanding. But with unskilled Project Managers you might receive a vague “prompt” that leads to ambiguity and misalignment. And Project Managers can go through training or read books on how to effectively “prompt” (aka communicate) more effectively.

When I say that your native language is now a programming language, I mean that you are now the Project Manager, and the code is generated automatically. But because the code is auto-generated, the focus shifts towards the “prompt” as the first and potentially only step in generating code.

I’m not debating the potential accuracy of anyone’s interpretation, just commenting on the degree of simplicity of those interpretations.

It’s a Razor, not a law, so it isn’t going to be 100% accuracy or predictive. But to suggest that the Razor can be used in conjunction with a long, complex explanation is a contradiction.

Actually, Occam’s razor would suggest that their words be taken at face value. A hidden agenda is not the simplest explanation.

Many years ago there were guides on how to write user stories: “As a [role], I want to be able to do [task] so I can achieve [objective]”, because it was useful to teach high-level thinkers how to communicate requirements with less ambiguity.

It may seem simple, but in my experience even brilliant developers can miss or misinterpret unstructured requirements, through no fault of their own.

Your native language is now a programming language.

We’ve seen tech completely eliminate jobs like phone switch operators and lamp lighters.

And it’s decimated other professions like manual agriculture, assembly line jobs, etc.

It seems like people are debating whether the impact of AI on computer-based jobs will be elimination or decimation. But for the majority of people, what’s the difference?