so I have done a lot of transcripts, coding, one versus the other (gpt4 vs ultra). Often simple prompts like refactor this code or convert this python to typescript.

My experience is that Gemini ultra understands the code better, but doesn’t always give me as complete of results (they seem to limit output length more)

Beyond that it is very smart. I’ve had to tell me this code packs 12 bit integers into different parts of an array using the following encodinv. Which most people would not figure out from the code as written. It then will say you can actually do that with this neat little translate function that you never knew about.

It will then get the code very slightly wrong. if I tell it not to use the cool function, it will actually get the code, right.

GPT4 has no idea what the code is doing but can clean it up a bit.

so it’s like ultra is too clever by half sometimes.

That said, I have fed thousands of lines of code into both of them and asked them to refactor it, and neither one of them made more than one error. All code otherwise compiled and worked first try.

this is code that can’t possibly be in their training sets, it’s basically handwritten python that was written, based on an old x86 binary that nobody has the source to anymore. so the code is basically garbage, and what it is doing doesn't, say, appear on GitHub in a nicer form.

Both gpt4 and Gemini ultra were able to make the code, look like clean, idiomatic, python, or typescript without any work on my part. except for the one bug each. which, for 8000 to 10,000 lines of code is not bad.

The GPT4 inserted bug was more insidious. It changed (the equivalent of) (uint8)'a' to (uint8)'a' - '0' for no reason when converting some code to typescript. Not sure if that is representative of anything

if I do the same with any of the other “top” models ( from can ai code, etc), most of them can’t even generate correct working code for all the input, let alone good code. Most aren't even close.

Lots of comments about it barely beating GPT-4 despite the latter being out for a while, but personally ill be happy to have another alternative, if nothing else for the competition.

But I really dislike these pre-availability announcements - we have to speculate and take their benchmarks for gospel for a week, while they get a bunch of press for unproven claims.

Back to the original point though, ill be happier having google competing in this space, I think we will all benefit from heavyweight competition.

This is the dagger that will make online schooling unviable.

ChatGPT already made it so that you could easily copy & paste any full-text questions and receive an answer with 90% accuracy. The only flaw was that problems that also used diagrams or figures would be out of the domain of ChatGPT.

With image support, students could just take screenshots or document scans and have ChatGPT give them a valid answer. From what I’ve seen, more students than not will gladly abuse this functionality. The counter would be to either leave the grading system behind, or to force in-person schooling with no homework, only supervised schoolwork.

I feel like this post was intended to inflame or shock the reader with the writers stance on password policy. But anyone who has been in security for more than 1 month knows that regular password rotation has not been a recommendation for over 5 years. Both NIST, and MS have been trying to get the world to move to long, never rotated password, so long as those passwords are dictionary checked.

Every company (all 3 of them) I have worked at within the last 10 years, the IAM team has already implimented, or was working on implimenting a system that removed regular rotation, special chars and number requirements, and relied on three things: Length, a dictionary check at the time of pass creation, and routine dictionary attacks against the credential store. This started 10 years ago, for someone to make the same claims now, is not a shock.

Please note that if you are unable to impliment such an IAM system, especially the inability to dictionary check the credentials against known lists (seclists' github is great for this), then length plus regular rotation is still the recommendation

Can you honestly say you'd prefer to be debug thousands of lines of SQL versus the usual <100 lines of Python/pandas that does the same thing? It's no contest. A histogram in pandas: df.col.hist(). Unique values: df.col.value_counts(). Off the top of your head, what is the cleanest way of doing this in SQL and how does it compare? How anyone can say that SQL is objectively better (in readability, density, any metric) other than the fact that they learned it first and now are frustrated that they have to learn another new tool baffles me.

I learned Pandas first. I have no issue with indexing, different ways of referencing cells, modifying individual rows and columns, numerous ways of slicing and dicing. It gets a little sprawling but there's a method to the madness. I can come back to it months later and easily debug. With SQL, it's just madness and 10x more verbose.