I don't think you should feel bad about that!  This scenario was pretty complicated and difficult, and even if you didn't solve it I think "tried to solve it but didn't quite manage it" is more impressive than "didn't try at all"!

1. There is a problem I want solved.

2. No-one, anywhere in the world, has solved it for me.

3. Therefore, Silicon Valley specifically is bad.

Were whichever markets you're looking at open at this time? Most stuff doesn't trade that much out of hours.

I think this is just an unavoidable consequence of the bonus objective being outside-the-box in some sense: any remotely-real world is much more complicated than the dataset can ever be.

If you were making this decision at a D&D table, you might want to ask the GM:

• How easy is it to identify magic items?  Can you tell what items your opponent uses while fighting him?  Can you tell what items the contestants use while spectating a fight?
• Can we disguise magic items?  If we paint the totally powerful Boots of Speed lime green, will they still be recognizable?
• How exactly did we get these +4 Boots?  Did we (or can we convincingly claim to have) take them from people who stole them, rather than stealing them ourselves?
• How honorable is House Cadagal's reputation?  If we give the Boots back, will they be grateful enough that it's worth it rather than keeping the Boots?

I can't realistically explain all of these up front in the scenario!  And this is just the questions I can think of - in my last scenario (linked comment contains spoilers for that if you haven't played it yet) the players came up with a zany scheme I hadn't considered myself.

Overall, I think if you realized that the +4 Boots in your inventory came from the Elf Ninja you can count yourself as having accomplished the Bonus Objective regardless of what you decided to do with them.  (You can imagine that you discussed the matter with the GM and your companions, asked all the questions above, and made a sensible decision based on the answers).

ETA: I have finally tracked down the trivial coding error that ended up distorting my model: I accidentally used kRace in a few places where I should have used kClass while calculating simon's values for Speed and Strength.

Thanks for looking into that: I spent most of the week being very confused about what was happening there but not able to say anything.

Yeah, my recent experience with trying out LLMs has not filled me with confidence.  

In my case the correct solution to my problem (how to use kerberos credentials to authenticate a database connection using a certain library) was literally 'do nothing, the library will find a correctly-initialized krb file on its own as long as you don't tell it to use a different authentication approach'.  Sadly, AI advice kept inventing ways for me to pass in the path of the krb file, none of which worked.

I'm hopeful that they'll get better going forward, but right now they are a substantial drawback rather than a useful tool.

Ah, sorry to hear that.  You can still look for a solution even if you aren't in time to make it on the leaderboard!

Also, if you are interested in these scenarios in general, you can subscribe to the D&D. Sci tag (click the 'Subscribe' button on that page) and you'll get notifications whenever a new one is posted.

Your 'accidents still happen' link shows:

One airship accident worldwide in the past 5 years, in Brazil.

The last airship accident in the US was in 2017.

The last airship accident fatality anywhere in the world was in 2011 in Germany.

The last airship accident fatality in the US was in 1986.

I think that this compares favorably with very nearly everything.

How many of those green lights could the Wright Brothers have shown you?

You can correct it in the dataset going forward, but you shouldn't go back and correct it historically.   To see why, imagine this simplified world:

• In 2000, GM had revenue of $1M, and its stock was worth in total $10M.  Ford had revenue of $2M, and its stock was worth in total $20M.  And Enron reported fake revenue of $3M, and its stock was worth in total $30M.
• In 2001, the news of Enron's fraud came out, and Enron's stock dropped to zero.  Also, our data vendor went back and corrected its 2000 revenue down to 0.
• In 2002, I propose a trading strategy based on looking at a company's revenue.  I point to our historical data, where we see GM as having been worth 10x revenue, Ford as having been worth 10x revenue, and Enron as having been worth $30M on zero revenue.  I suggest that I can perform better than the market average by just basing my investing on a company's revenue data.  This would have let me invest in Ford and GM, but avoid Enron!  Hooray!
• Of course, this is ridiculous.  Investing based on revenue data would not have let me avoid losing money on Enron.  Back in 2000, I would have seen the faked revenue data and invested...and in 2001, when the fraud came out, I would have lost money like everyone else.
• But, by basing my backtest on historical data that has been corrected, I am smuggling the 2001 knowledge of Enron's fraud back into 2000 and pretending that I could have used it to avoid investing in Enron in the first place.

If you care about having accurate tracking of the corrected 'what was Enron's real revenue back in 2000' number, you can store that number somewhere.  But by putting it in your historical data, you're making it look like you had access to that number in 2000.  Ideally you would want to distinguish between:

• 2000 revenue as we knew it in 2000.
• 2000 revenue as we knew it in 2001.
• 2001 revenue as we knew it in 2001.

but this requires a more complicated database.