If I wrote about “how to paint great art” or “how to cook great meals” or “how to build great things” then it would be silly to say “have good taste”—even if that’s part of the answer. It won’t help anyone else to improve in any of those endeavors.

I’ll admit that I may just be immature at research as almost all my experience has either been attempting to replicate research or to put it into practice in production systems.

I know approximately nothing about approximately everything. Claude seems pretty good at those things. But in every case I’ve used Claude Code for something I do know about it’s been unsatisfactory as a solo operator. It’s not useless, but it is basically useless for anything serious unless you’re very actively guiding it.

I think it has a lot of potential value and will become more useful over time, but it’ll be most useful when we can confidently understand the limitations.

OMSCS allowed me to add MSCS to my resume, with additional networking and work experience details as a TA for the algorithms and Computational Photography courses. Suddenly I started getting a lot more calls back. About 6 months after graduation I had moved to the SFBay (to work for Udacity) and within 2 years I was an ML engineer at Apple where I remain today. I don’t think any of that would’ve happened without OMSCS.

Unemployment was _relatively_ high at that time, so individual consumers were eager to invest their own time & money to upskill and differentiate themselves. By 2018 unemployment hit record lows and suddenly it was _employers_ who were struggling to attract talent and wanted to differentiate themselves by offering upskill training as a benefit along with highly intentional training programs to organically grow the hard-to-hire talent from their existing workforce. This precipitated a shift from huge growth in the consumer side to growth in the enterprise business.

Contemporaneously, platforms like Udemy and Pluralsight commoditized content creation. Pluralsight bragged that it cost them $15k to launch a new course—orders of magnitude less than it cost us in house. Udacity pivoted away from high quality in house production to more partnerships with external content creators and identified the project grading and mentorship services as the largest cost drivers of ongoing course support costs.

As growth wasn’t tracking fast enough, Udacity closed most of the international offices—except India—then had two rounds of layoffs where the remaining content production was practically eliminated, and the mentorship and grading were commoditized by transferring the programs to the Udacity India office to administrate. All the hand-picked and trained graders and mentors were eliminated.

Then COVID hit. (I was gone by then.) I heard Udacity raised a debt round, but I think they were stuck against headwinds from the past few years. Eventually they were acquired for an “undisclosed sum”.

So what could have brought in more business? IMO, focusing on what was working for us, not trying to pivot into what worked for someone else. The problem I think is that we weren’t on track to make a reasonable return on all the money raised. We were trying to swing for the fences, even if it meant eventually striking out.

The anti-pattern I’ve seen from some folks is that they never want to propose solutions because then it’s someone else’s fault if those fail. These folks often demonstrate minimal ownership of any decisions, so they don’t feel bad complaining about all the problems they see. Not only is that unhelpful, it can actually be very toxic for the team. (As you mentioned.)

So when I’m saying “bring solutions” what I’m really asking for is some shared ownership of the choices and consequences—I’m asking folks to act like the main character in the story. And don’t worry, I own the consequences of the mistakes in my team to my leadership—this isn’t about throwing them under the bus. (Getting this to work well requires a lot of trust both ways.)

I’m surprised that a fixed size k doesn’t experience degrading performance in long context windows though. That’s a _lot_ of responsibility to push into that indexing function. How could such a simple model achieve high enough precision and recall in a fixed size k for long context windows?