This is very cool and makes me smile because I used to use a simplified version of this as a take home project in engineering interviews.
One usecase I find particularly interesting is predictions. People often predict the future like “in 2 years we will have AGI” etc. It would be fun to fact check these predictions on the exact date 2 years later. Pick top tech leaders or politicians and scrape all their predictions and make a leaderboard of who got it right or messed up. could be fun to try.
Thanks for the comment! Our approach has been very much depth-first rather than breadth-first. We’re laser-focused on the private credit industry for now and plan to stick with B2B for the foreseeable future. The biggest takeaway for us while building Arc Intelligence has been that accuracy is everything—and hitting those high accuracy levels really comes down to staying focused.
not sure if I'm understanding what you are comparing with. what is your alternative?
if we are taking authz as a need in your org and also a single point of failure for your entire services (as you said in your comment), are you suggesting you would instead build / host your own Zanzibar like authz service, and that your in-house solution would do better in terms of availability than this external solution?
leaving aside how much you'd lose on productivity, I would guess your in-house solution will break more unless you spend a lot of time making that a highly available service (and you'd depend on other SaaS for that as well, which may also break).
Also, if it's in house / on prem you can do a change freeze and actually mean it. With a SaaS that's a joke. You never know what the SaaS engineers might be doing. An in house system's downtime is more likey to be correlated with your other systems' downtime, so total downtime is going to be higher with a SaaS, especially since authz/authn is so critical to everything.
I don't understand why this is a SaaS and not just a software product you buy and run on prem. (In terms of business risk, buying this as an on prem product seems perfectly reasonable -- after this startup is bought by someone like Microsoft that can actually guarantee the features will stay around.)
Unlike Plaid, Finicity and Yodlee have direct integrations with some banks. Example: Silicon Valley Bank has direct integration with Finicity. SVB through Plaid breaks quickly (because they require some weird 2fa policy).
Let me know if I'm missing something but if Stripe is A) providing reliable connection to common banks Plaid misses and B) saving it's users from all the headaches of integrating with old school services like Finicity/Yodlee, then charging a premium sounds like fair game.
Plaid has direct integrations with many banks too -- Silicon Valley Bank is actually a Plaid partner for ACH processing (see https://www.svb.com/news/company-news/silicon-valley-bank-an...). Not sure when your bad experience with 2fa was but Plaid's connection to SVB has improved over the past ~6 months as we've begun to work together more closely and should continue to do so. [I work at Plaid]
Hate to argue, but I agree that Plaid's connection to SVB is indeed unusuable. I've been trying to use them for over a year and we ended up dropping SVB just this month. Chase is on OAuth and WAY better if you need TXN data.
A partnership for ACH is more related to importing stable routing and account numbers, then enabling initiating ACH transfers. Scraping transaction data is a completely different integration that seems to have been forgotten.
Sadly, I'd even wager SVB-Plaid data won't improve any time soon. Remember that SVB doesn't even yet allow external bank transfers on their own bank portal.
I hope you find extra motivation from the extreme contraints you forced on yourself in the longer term.
Making yourself live on 24k in NYC is not a good idea. Publicly sharing that is an even worse idea. I’d be surprised if this gave a positive signal to your investors, but it surely won’t to anyone you possibly employ/pay salary for.
If I have to make a guess it’s much more likely that their deep learning algorithms learned nudity is more engaging (without them using that as a feature).
This obviously doesn’t mean the platform isn’t responsible. I agree we need to have regulations that require these companies to ensure their algorithms are “inclusive”.
Unlike what the author thinks linkedin revenue isn't largely fueled by recruiter business. It is driven by where he thinks they are weak: linkedin marketing solutions, or in friendlier words "feed interactions". See the most recent microsoft earnings report:
And unlike what the author thinks linkedin navigated the current climate around social networks pretty well. Not only they've succeeded in keeping the network out of the political environment, and showed strong growth in user base and engagement (see above report), they also ranked as the most trusted social network consistently according to Business Insider Digital Trust study:
This breaks the site guidelines and will get your account banned here. Would you mind reviewing https://news.ycombinator.com/newsguidelines.html and sticking to the rules when posting to HN? We'd appreciate it.
You can't generalize but most often that is the case. Having said that I'm surprised nobody brought up maybe the most important factor: sunk investments.
If you have a brand name and credibility at stake, if you have customer liability, if you have built significant the features and infrastructure, if you hired the people that run and maintain those, if your revenue depends on current status of your product... We can keep going on but, you have to take extra actions and caution as you introduce change. That means additional complexity.
However keep in mind big companies can benefit from certain types of barriers to entry such as regulation that provide them efficiency over small companies. For example US immigration policy favors big companies, and IMO GPDR will do so as well.
One usecase I find particularly interesting is predictions. People often predict the future like “in 2 years we will have AGI” etc. It would be fun to fact check these predictions on the exact date 2 years later. Pick top tech leaders or politicians and scrape all their predictions and make a leaderboard of who got it right or messed up. could be fun to try.