bumbledraven

Indeed.

The term dates back at least to 2020, years before the release of ChatGPT.

https://research.google/pubs/a-promising-path-towards-autofo...

If you're unconvinced, try comparing a few random passages to see which one you prefer, or just check the beginning of Chapter 1. You could also show a few translations of a passage to an LLM and ask it which one best conveys the nuance of the original French.

- dealing with imperfect information

- bluffing

- thinking ahead with an enormous number of possible moves (especially when you consider deck construction)

- tracking board state

- mentally implementing "triggers"

• Deep Blue played Be4, declining a pawn capture to achieve a long-term positional advantage and fueling Kasparov’s suspicions of human intervention.

• AlphaGo played P10, a brilliant 5th-line shoulder hit that live commentators initially dismissed as a blunder or misclick.

https://franky07724-57962.medium.com/amazing-coincidence-in-...

> Builders value getting the work done as quickly and efficiently as possible. They are making something—likely something with parts beyond the frontend—and are often eager to see it through to completion. This means Builders may prize that initial execution over other long-term factors.

> Crafters are more likely to value long-term factors like ease of maintainability, legibility, and accessibility, and may not consider the project finished until those have also been accounted for.

> In my view, the more you optimize for building quickly, the more you optimize for homogeneity.

It begins:

> In David Crawshaw’s recent post “The agent principal-agent problem” there’s a lot of insight beneath the headline “Code review is broken.” Worth reading carefully.

> Toward the end, David reflects on what he calls the old “cowboy” development culture at Microsoft in the 80s/90s. Not much has been written about that era, mostly because there was no social media, no laptops everywhere, no phones recording daily engineering life.

> A few thoughts from someone who lived it.

Conclusion:

> A lot of software quality did not come from pre-commit review gates.

> It came from tight teams, deep ownership, brutal integration pressure, system-wide stress, and developers who fully understood the machinery they were standing on.

Searching for `“can anyone recommend”` to get unfiltered recommendations is an interesting hack, but I feel it's not too reliable. At reddit, you could ask this, and an unknown percentage of responses could be shills or bots.

I'm also skeptical of how much the suggested `@reddit` differs from just `reddit`. The description says it's for social media handles, but reddit is a platform, not an individual user's handle. I suspect google looks for the user's intent to see results from a particular site or social media platform and uses that signal to influence the ranking, and I doubt '@' has much of an effect on that process.

> The results Google omits tend to be less trafficked and less search-optimized, which frequently means they’re more substantive and written for readers rather than algorithms

Really? I call BS. Every time I've looked at the omitted results they've been very similar to ones I've already seen.

I stopped reading after that.

> We discovered simple adversarial strategies that beat superhuman Go AIs, and find that adding defenses helps but does not eliminate the problem. Our cyclic adversary beats the state-of-the-art KataGo AI more than 97% of the time at superhuman settings. This strategy is simple enough to be replicated by an amateur human player and transfers to other superhuman Go AIs.

This reads like AI slop.

Isn’t that basically Caltech? They had a 3% acceptance rate in 2023, the lowest in the nation. https://www.usnews.com/best-colleges/rankings/lowest-accepta...