Location estimation (figuring out where you are) based on indoor WiFi / BLE is one example. Compared to 15 years ago, we have (IIRC - I don't work in this space) super-precise timing API from the modem, and there has been work on the reflections issue (the two big problematic things that non-RF people typically miss).
I think it would be great if YC turned discussions like this into well edited written articles. I know there’s talk about producing more text content to help startups.
So as a founder how can you tell if you are about to jump into a tarpit?
1) do a lot of research on the problem and see what has been done in the space in the past and who is working on the problem now. If you find lots of failure - dig in and try to understand what the core failure modes were. 2) work on something that people will pay you for, even a very ugly early product. Income is a strong validation. 3) reconsider your idea if it requires the incineration of mountains of cash to get people’s attention.
But at the end of the day Tarpit is really a descriptive heuristic that VCs can find to be useful but not absolute.
kristopolous•1d ago
Youtube wasn't the first video streaming service but it was one of the first for the DSL era when people could watch video without lengthy waits.
AI companies repeatedly failed until enough things, specifically data and compute were at enough scale to deliver.
Advancements in battery technology made electric cars practical bucking the trend of decades of failed EV car companies.
So many things - contactless payment, touchscreens, even LCD panels, these were lousy and impractical for decades.
Attempts at mass adoption of handheld computers, now called smartphones, started in the 1980s. Without high speed mobile networks, high density color LCD screens, reliable geolocation, these things were necessary to make the handheld pocket computer something that everybody has.
Even online grocery delivery services, now common place, had its start in the catastrophic collapse of WebVan in the 1990s. Cell phones, the gig economy, mature e-payments, these were all needed.
You always need to look for the context change and how that can untar some tarpits.
dgs_sgd•1d ago
kristopolous•1d ago
Hn is a discovery/recommendation site as is Reddit. Amazon makes a lot of margin on theirs and arguable it's part of the major value add for Spotify and Netflix.
Almost everybody looks at food and accommodation reviews and people bring up IMDb and rotten tomatoes when considering whether to watch a movie.
Search engines and llms make decisions on what to surface, those are a kind of recommendation as well.
So although I understand the sentiment, it's not really a great example - there's plenty of successful executions beyond the dreaded "for you recommendations" engagement bait slop on social media feeds. You're using the successful executions dozens of times a day without noticing it.
fakedang•2h ago
Nope, HN is just an online forum. I can't tailor what I see on HN to my tastes, and there's a subset of posters who get preferential treatment on the frontpage (YC companies), so nope, HN is not a recommendation site.
anself•6h ago
bee_rider•4h ago
fc417fc802•2h ago
Would probably be worth it even if just to have a consistent UI across services.
bee_rider•2h ago
bjelkeman-again•57m ago
nottorp•47m ago
stevage•4h ago
saulpw•3h ago
Now, if someone made a "Recommendation-Engine-in-a-Box", where someone who wanted to make a recommendation app for themselves would supply the content and could tweak the algorithm and the design, I could see that being successful in this market :)
fc417fc802•2h ago
I guess SaaS aimed primarily at founders makes it a meta startup? The snake is eating its tail.
petesergeant•2h ago
I spent 2024 building an awesome TV series recommendation platform. It worked by matching you to professional critics who shared your tastes, by basically crawling Rotten Tomatoes and getting an LLM to grade the reviews out of ten. The recommendations were awesome, and having a personalized Rotten Tomatoes where you could read about and research the show using reviews by people who felt the same way as you did about stuff was freakin' cool.
However, getting people to actually sign up and use the app without a massive marketing budget was very, very difficult. The stickiness to get people to go back to it is difficult. Asking people to input their preferences in the first place is hard. People also simply didn't believe the recommendations, and wouldn't take chances on shows; the computer can recommend The Detectorists to as many people as it wants, but there's a high number of people who would love the show but will dismiss it looking at the cover image and having a quick read of the synopsis.
The recommendation part isn't super hard, the getting people to use a B2C app is super hard.
nradov•6h ago
ijustlovemath•5h ago
nine_k•3h ago
You just have to have a colossal inventory, and a reasonably good algorithm.
bryanrasmussen•1h ago
mritchie712•1d ago
api•6h ago
It probably won’t be different this time unless something has changed. “I’m just that good, I will out execute everyone before me” is probably BS. The people before you were probably not lazy or dumb, it just wasn’t time.
cjohnson318•3h ago
fnord77•3h ago
kens•2h ago
fnord77•2h ago