If only they put half the effort spent lobbying taxes, putting hurdles for space development instead, the whole world could benefit from not turning earth’s orbit into an american clusterfuck

hehe you have the word ass in your private key, what are the odds

also python has built-ins for sum, min, max, and math.prod - which account for a lot of use cases of reduce in a more readable way.

But if you’re really crunching numbers, it’s just better to go with something like numpy, pandas, polars, …

yeah, I often some address like the simpsons family, or 85 Albert Embankment, Vauxhall, Lambeth, London, UK for Mr. Bond

I don’t hate it, but I prefer not to use it mostly for readability

I’d either rent a PO box or use those Amazon pickup locations

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I don’t know you, but it doesn’t sound like a high standards issue to me, sounds like a lack of process. I’ve been a thorough reviewer before AI, at least thorough in the ways that mattered, not nitpicking formatting. And I’ll tell you an automated reviewer today can catch more things before I have time to confirm the first item I find. There are still false positives, but it’s still more thorough than I have time to be. Bc of that it really helps including a round of automated review before looping in a human, regardless of who/what wrote the code.

The main kind of review problems AI still struggles with are the project direction ones: “does it make sense to implement this/like this?”, “should this be a new package instead?”, and things involving tacit knowledge that often goes undocumented “last time we did this, someone had to access prod on a Sunday” - so that’s what I focus my reviews on. And the other area is if you’re writing UI code, whether it’s a web app or a game, it’ll also struggle to determine what “feels” good to use, so it’ll need a human earlier in the loop.

This kind of specification applies to the agent behavior or the agent behavior in a project. You don’t write it on every conversation, you write/review it once for the project or agent and let the harness include it on every conversation. You can even ask the agent to scan the codebase and pull desirable patterns out of it for future sessions.

The entire point of using an agent is to not have to write everything yourself, so that when you write “implement feature X”, the feature gets implemented in a way that makes sense in that project, is reviewed, refactored, and tested by agents, and it’s good enough to bring in a human reviewer.

See, that’s the issue. People letting it “drive itself” get worse results. You should be the one holding the standards and guiding the model, otherwise you will be frustrated.

browsers offload inactive tabs from memory anyway, unless you’re actively switching among 15+ tabs, they won’t be using as much ram

My honest opinion is that it’s bad because a lot of people using LLMs have no standards and push the first thing that seems to work. Be mad at who’s at the driving wheel, not the car.

You absolutely can generate crap with agents/LLMs, and like a humans writing, the first draft will probably be subpar or maybe complete garbage. Every new session is a clean slate, that’s why putting effort in the documents guiding it is so important.

I don’t think there was a lot of people working on the rewrite. Most PRs are from bots. Original estimates for a manual rewrite were a small team working for a year or so, which puts total costs over $1M. Even doubling the token cost estimates, it was still cheaper than doing it manually by a factor of 2x-3x

I’ve seen plenty of code in my life, from humans and AI.

For the past year or so, these agents can code just fine most of the time, as long as they are given enough context (or have the tools to get it). Regardless of how many downvotes I get here, they really are capable of generating decent code. I’m sorry you couldn’t make it work yet.

It was higher sure, but anthropic also has some of the most expensive models out there. That cost could be 5x-10x less just by going with cheaper model providers (if one were to pay the API costs, not the case for bun).

bun 1.4 was released 3 weeks ago, btw

unironically, a better prompt does yield better results - shocking, I know

well, that depends entirely on your prompt/process, it can be done

fuck; I’m so used to dark patterns like these that whenever I read “cookie” my instincts flip and look for the less noticeable / most hidden buttons instead. It took me a few seconds to even realize this was a dark pattern.

Other common ones to be on the lookout:

• Filled out buttons that favor the company, while outlined buttons (or even just links) that favor the user;
• Toggles phrased as negatives, so “enabling” looks like “disabling” and vice-versa;
• Wording to disguise user intent: “Confirm choices” or “Save preferences” instead of “Reject cookies”, like above;
• Sneaking in optional checkboxes among mandatory ones at the end of a form;
• Small and low contrast elements to minimize important information that favors the user;
• Emotional manipulation: “this offer expires in X minutes”; “only Y left”.

yes, I get that and it’s a bad default, but not RCE like the post says