I personally enjoy the news feed part of it. It cleanly summarizes the major global news stories across several sources into 3 bullet points, and it doesn't show you any ads disguising as stories (eg product reviews, tips, etc)

If AI can improve/speed up writing code, why shouldn't it improve/speed up code review?

Where do you draw the line, and where does someone take final responsibility? I don't have an issue with allowing AI to do everything with limited to no human verification for hobby projects. I have an issue with allowing AI to write and ship medical device software with no human review. What's the middle ground?

You still take the full responsibility, I don't think leveraging AI reviews removes any layer of responsibility. The challenge is building the feedback loop (or the agent that builds the feedback loop) to make the review process actually catch what it needs to catch.

I think the line is wherever you feel comfortable with pushing the changes yourself. You feel like adequate testing had been done, basing it on previous testing periods or how successful AI runs have been.

Whether you or AI wrote the code, you decide when it's safe enough to put your name on it. Like it's always been!

If AI does something good, you take responsibility. If it does something bad - "the agent messed up". /s

You can probably still find the leaked code online. It’s not exactly a bastion of software engineering. Plenty of folks have reviewed it deeply.

Is n to the 1.9992 practically speaking subquadratic? Technically, yes, but is there a practically useful result here?

> A galactic algorithm is an algorithm with record-breaking theoretical (asymptotic) performance, but which is not used due to practical constraints. Typical reasons are that the performance gains only appear for problems that are so large they never occur, or the algorithm's complexity outweighs a relatively small gain in real-world performance. Galactic algorithms were so named by Richard Lipton and Ken Regan, because they will never be used on any data sets on Earth.

https://en.wikipedia.org/wiki/Galactic_algorithm

It's more that it demonstrates that it's possible at all. We now know that the floor isn't an exponent of 2, which makes pursuing further improvements way more valuable.

Yes because now it opens the door for future algorithms to chip away at that exponent where as in the past it may have seemed that an exponent of 2 was the floor.

I don't really get it, seems like a gate keeping exercise for the academic community.

Of course OpenAI and others will hype any LLM led/assisted research results, and the media will find juicy headlines to write about it. But in the end they are publishing findings, and the academic community can gauge the developments and decide what they want to do with them. To me that seems like research working as expected.

The academic community gatekeeps to ensure members of the community are not stealing from each other or are otherwise committing unethical acts.

There are typically processes to ensure rigor in the findings and to weed out crap.

It doesn't always work, but it tends to work much better than any other community

There might be a paradox here. We need closed communities to weed out slop. But we need open communities to weed out corruption.

We need (and have) clopen communities. (Or, as a biologist might say, semi-permeable)