I don't even necessarily ask it to fix the bug — just identify the bug
Like if I've made a change that is causing some unit test to fail, it can just run off and figure out where I made an off-by-one error or whatever in my change.
I routinely leave codex running for a few hours overnight to debug stuff
If you have a deterministic unit test that can reproduce the bug through your app front door, but you have no idea how the bug is actually happening, having a coding agent just grind through the slog of sticking debug prints everywhere, testing hypotheses, etc — it's an ideal usecase
AI detectors in general are unreliable, but there are a few made by serious researchers that have only 1-in-10000 false positive rate, e.g. https://arxiv.org/pdf/2402.14873
Having worked in a bigcorp, I've read my fair share of management-speak, and none of it sounds quite as empty as the allegedly AI text.
The AI sounds like someone conjuring a parody emulation of management speak instead of actual management speak.
More broadly — and I feel this way about AI code at well as AI prose — I find that part of my brain is always trying to reverse engineer what kind of person wrote this, what was their mental state when writing it?
And when reading AI code or AI prose, this part of my brain short circuits a little. Because there is no cohesive human mind behind the text.
It's kind of like how you subconsciously learn to detect emotion in tiny facial movements, you also subconsciously learn to reverse engineer someone's mind state from their writing.
Reading AI writing feels like watching an alien in skinsuit try to emulate human face emotional cues — it's just not quite right in a hard-to-describe-but-easy-to-detect way.
While reading the text, my mental AI alarm bells were going off, sent it all to pangram.com and it flags both the layoff post and his campaign website text as being 100% AI generated
The other pangram mention elsewhere in this comment section is also me -- I'm totally unaffiliated with them, just a fan of their tool
I specify the accuracy and false positive rate because otherwise skeptics in comment sections might otherwise think it's one of the plethora of other AI detection tools that don't really work
I started with basic Newton-Raphson solver too but found cases where it diverges but Excel somehow doesn't, so concluded that Excel has some kind of extra logic to handle more cases, so I also bolted on more fallback logic.
I work on an Excel-compatible spreadsheet startup (rowzero.com) and had to implement these.
One tricky part is RATE involves zero-finding with an initial guess. The syntax is:
RATE(nper, pmt, pv, [fv], [type], [guess])
Sometimes there are multiple zeros. When doing parity testing with Excel and Google Sheets, I found many cases where Sheets and Excel find different zeros, so their internal solver algorithm must be different in some cases.
My initial solution tended to match Sheets when they differed, so I assume I and the Google engineers both came up with similar simple implementations. Who knows what the Excel algorithm is doing.
Of course, almost all these edge cases are for extremely weird unrealistic inputs.
Searched around and found it. It was actually Ashton Kutcher's interview with Eric Schmidt.
Kutcher mentions the establishing shots, and I'd forgotten also points out the utility for relatively short stunt sequences.
> Why would you go out and shoot an establishing shot of a house in a television show when you could just create the establishing shot for $100? To go out and shoot it would cost you thousands of dollars.
> Action scenes of me jumping off of this building, you don’t have to have a stunt person go do it, you could just go do it [with AI].
I saw a famous actor-director (can't remember who, but an A-list guy) said it would be super valuable even if you only use it for establishing shots.
Like you have an exterior shot of a cabin, the surrounding environment, etc — all generated. Then you jump inside which can be shot on a traditional set in a studio.
Getting that establishing shot in real life might cost $30K to find a location, get the crew there, etc. Huge boon to indie films on a budget, but being able to endlessly tweak the shot is valuable even for productions that could afford to do it IRL.
Where would you make make the cut that takes advantage of object store parallelism?
That is, at what layer of the stack do you start migrating some stuff to the new strongly consistent system on the live service?
You can't really do it on a per-bucket basis, since existing buckets already have data in the old system.
You can't do it at the key-prefix level for the same reason.
Can't do both systems in parallel and try the new one and fall back to the old one if the key isn't in it, because opens up violations of the consistency rules you're trying to add.
Our startup, rowzero.io, easily handles tens of millions of rows and is a subset of Excel. Leave us feedback in the app if there are any missing features you need.
"China opens world's longest undersea tunnel"
vs
"China opens longest undersea tunnel"
It's a little unclear if it's the longest undersea tunnel in the world, or just in China