It seems like a good path forward is to somewhat try to replicate the idea of "once you can do it yourself, feel free to use it going forward" (knowing how various calculator operations work before you let it do it for you).
I'm curious if we instead gave students an AI tool, but one that would intentionally throw in wrong things that the student had to catch. Instead of the student using LLMs, they would have one paid for by the school.
This is more brainstorming then a well thought-out idea, but I generally think "opposing AI" is doomed to fail. If we follow a montessori approach, kids are naturally inclined to want to learn thing, if students are trying to lie/cheat, we've already failed them by turning off their natural curiosity for something else.
I agree, I think schools and universities need to adapt, just like calculators, these things aren't going away. Let students leverage AI as tools and come out of Uni more capable than we did.
AI _do_ currently throw in an occasional wrong thing. Sometimes a lot. A students job needs to be verifying and fact checking the information the AI is telling them.
The student's job becomes asking the right questions and verifying the results.
This is not necessarily true. Hypothetical, if most breakthroughs are coming from PHDs and they aren't making any PHDs, then that pool is not necessarily larger.
I recently interviewed for Anthropic, 6 rounds, recruiter was great, said they were putting together an offer letter. I met one of the managers, then another came back from vacation... and then they decided not to give me an offer.
I asked for feedback, and the recruiter sounded frustrated (about the internal process), because they had a moving bar on what was wanted from the hiring managers. I know I hadn't completely aced one of the interviews (they had me do a second one), and apparently they thought it was good enough on initial review, but when coming back to review it again it was not good enough.
It seems like they are going through growing pains as a company.
I had an interview at a big tech company last year where I had a verbal offer letter which then got rescinded when a VP went over my interview feedback and decided I didn't come across as enough of a team player!
That feedback occurred immediately after another interview where I failed because I didn't show enough individual drive, I had talked too much about working in my team.
Probably not even about whether it was good enough at all, just random managers making random decisions by how they were feeling the day they interviewed you. Any time a company has a LOT of applicants, like any big tech company does, the less you should take any info at all from a given rejection or even an offer. It’s kinda random.
You’ll typically get better info only when a company is small and has a role in low demand and they only had a couple of people apply. This situation is pretty rare.
I posted about this in another thread of this comment section; submit a data privacy request for all and any data pertaining to the hiring process and interview, it won't negatively impact your chances in the future.
Hey, really appreciate your interest! Funny enough, I’ve actually been thinking about expanding the platform to cover other types of real estate.
The biggest difference with hotels is that they operate more like businesses—you’re dealing with things like daily rates, fluctuating occupancy, and a lot more operational complexity. That makes the valuation process very performance-driven and dynamic.
With something like multi-family or storage, it’s more about stable leases, consistent rental income, and lower day-to-day volatility. The underwriting approach shifts accordingly—less focus on real-time performance, more on long-term cash flow and cap rates.
I’d love to hear more about what you’re working on or what you’d want to see in a tool like this. Sounds like we’re thinking in a similar direction.
Unfortunately I do not have a windows device to test with. I have a suspicion as to what’s causing it (background blurs) and I plan to get rid of it this week
I work a lot with smaller investors, in real estate, private money lending, etc. It's sometimes hard to do due diligence on someone, and after having a couple bad deals and realize over 30 people were scammed, I wished there was a simple review site where you could see someone's past reviews.
Site is 80% there, hoping to enter beta in the next month.
I like this idea a lot -- feels like there's a lot of room to grow here. Do you have any sort of historical price tracking/alerting?
And/or also curious if there is a way to enter in a list of items I want and for it to calculate which store - in aggregate - is the cheapest.
For instance, people often tell me Costco is much cheaper than alternatives, and for me to compare I have to compile my shopping cart in multiple stores to compare.
> For instance, people often tell me Costco is much cheaper than alternatives, and for me to compare I have to compile my shopping cart in multiple stores to compare.
A few years ago, I was very diligently tracking _all_ my family's grocery purchases. I kept every receipt, entered it into a spreadsheet, added categories (eg, dairy, meat), and calculated a normalized cost per unit (eg, $/gallon for milk, $/dozen eggs).
I learned a lot from that, and I think I saved our family a decent amount of money, but man it was a lot of work.
Glad you guys mentioned Costco -- I happen to have written a blog post on exactly that: https://popgot.com/blog/retailer-comparison Surprisingly, Costco does not win most of the time, and especially if you are not brand loyal. Costco has famously low-margins, but it turns out that when you sort by price-per-unit they're ok, but not great.
@mynameisash I'm curious what you learned... maybe I can help more people learn that using Popgot data.
One thing to call out is that costco.com and in-person have different offerings (& prices) -- but you probably know that already.
I just dusted off my spreadsheet, and it's not as complete as I'd like it to be. I didn't normalize everything but did have many of the staples like milk and eggs normalized; some products had multiple units (eg, "bananas - each" vs "bananas - pound"); and a lot of my comparisons were done based on the store (eg, I was often comparing "Potatoes - 20#" at Costco but "Potatoes - 5#" at Target over time).
Anyway, Costco didn't always win, but in my experience, they frequently did -- $5 peanut butter @ Costco vs $7.74 @ Target based on whatever size and brand I got, which is interesting because Costco doesn't have "generic" PB, whereas Target has much cheaper Market Pantry, and I tried to opt for that.
My family’s favorite experience has been that Costco usually doesn’t have the cheapest option but it has a good value option.
Our main example is something like pasta. Our local grocery stores all carry their own brand of dirt cheap pasta but it’s not as good as the more expensive pasta at Costco. Comparable pasta at the local grocer would be more expensive.
For items that are carried at both stores, Costco is usually no cheaper than the regular retail price and rarely much more expensive.
I'm curious if we instead gave students an AI tool, but one that would intentionally throw in wrong things that the student had to catch. Instead of the student using LLMs, they would have one paid for by the school.
This is more brainstorming then a well thought-out idea, but I generally think "opposing AI" is doomed to fail. If we follow a montessori approach, kids are naturally inclined to want to learn thing, if students are trying to lie/cheat, we've already failed them by turning off their natural curiosity for something else.