Eventellect builds algorithmic systems for live event ticketing. We’re on a mission to fix ticketing for fans and teams -- eliminating inefficiencies, replacing opaque pricing, and getting the right tickets directly to the right fans at the right time. We work directly with both sports teams and other major live events, directly shaping revenue, attendance, and fan experience at scale.
We’re hiring for:
Full-Stack Engineers (Senior+)
Own and build complete systems. Work on real-time pricing, inventory management, and internal/external tooling. Looking for strong fundamentals, ownership, and good judgment.
Data Platform Engineers (Staff / Principal)
Own the data stack and internal data products. Design core data models, pipelines, and standards that support analytics, modeling, and operations. High-impact role with real ownership.
What we’re looking for:
Strong builders who thrive in messy real-world problem spaces, take ownership, and ship.
Email me directly at sgrondahl [at] eventellect [dot] com (Subject line should start with "HN")
> what has the war on drugs really ended up accomplishing?
I haven't studied this extensively, and I know that it's widely accepted that the war on drugs was unsuccessful, but I would caution against conflating "it didn't completely solve the problem" with "it had no positive impact at all." As a parent comment mentioned, periods of prohibition are generally associated with lower consumption overall (across both legal and illegal channels).
We're not talking about a war on drugs that "didn't completely solve the problem", we're talking about one that made the problem ten times worse and fucked up society in the process.
It doesn't matter if it had any positive impact, the negative impact outweigh them so much it's a downright crime against humanity that imprisoned millions of people in a system that constitutes modern day slave labor.
Do you have a source for that cost attribution? Based on my time there (slightly more recent than yours), that breakdown feels roughly correct. However, I've been trying to find similar information and have found it difficult even for public universities.
The institution publishes its budget. 10 seconds of DDG search found this page: https://vpf.mit.edu/about-vpf/publications Looks like tuition is becoming less important over the Covid period and grad+ug is down to 9% of revenue.
They have a nice presentation with various charts and such which is where I get this info myself. I don’t know if it’s online. I forget how I got it when I was in school but now the people who come visit from the development office bring it with them because they know I’m interested.
Fairmarkit is the intelligent sourcing platform that empowers organizations to more efficiently purchase the goods and services they need. By equipping procurement and supply-chain teams with automation and data, Fairmarkit promotes competitive bidding while reducing manual work within existing processes. Fairmarkit has been recognized with awards by organizations such as Gartner and IDC, and is backed by strategic investors like GGV Capital, Insight Partners, 1984.VC, and Newfund.
> You acknowledge and agree that the DCF (or, as applicable, its licensors) retains ownership of all legal right, title and interest in and to the Media and all intellectual property rights therein. The rights that you have in and to the NFT are as described in this License. The DCF reserves all rights in and to the Media not expressly granted to you in this License.
If you would have opened it, you would have seen that the spreadsheet has exactly what you're asking for... reimbursement rates by DRG/CPT code and insurer. You're right that in many cases the rates paid by different insurers vary significantly.
No it doesn't. That's the charges hospitals charge for patients under those plans
What I am asking for is completely different.
I want to know what insurers pay out by procedure code by provider. There's a reason why insurers lobbied against providing this information under this law.
Looks like this one is free and also covers a wider breadth of ML topics (though perhaps doesn't cover NNs in as much detail -- I don't have a paid copy of nnfs).
Eventellect builds algorithmic systems for live event ticketing. We’re on a mission to fix ticketing for fans and teams -- eliminating inefficiencies, replacing opaque pricing, and getting the right tickets directly to the right fans at the right time. We work directly with both sports teams and other major live events, directly shaping revenue, attendance, and fan experience at scale.
We’re hiring for:
Full-Stack Engineers (Senior+) Own and build complete systems. Work on real-time pricing, inventory management, and internal/external tooling. Looking for strong fundamentals, ownership, and good judgment.
Data Platform Engineers (Staff / Principal) Own the data stack and internal data products. Design core data models, pipelines, and standards that support analytics, modeling, and operations. High-impact role with real ownership.
What we’re looking for: Strong builders who thrive in messy real-world problem spaces, take ownership, and ship.
Email me directly at sgrondahl [at] eventellect [dot] com (Subject line should start with "HN")