Back in my day, you could also just Google the problems and find the solutions. What mitigated cheating at UVA was the honor code and each professor's faith and trust in our integrity. That culture was enough to not cheat.
Imo, the fix should be to work on culture. Cheating should always be a tempting choice, so that the student may challenge their integrity, which is a muscle that can atrophy.
One aspect that still bothers me is that you claim the just-say-no-engineer "was a critical role during ZIRP." I might be in the minority here, but I don't hold that same stance. I wonder if I am alone in that?
Ever read a quantum circuit? It's all so foreign, so let me introduce to you a simple one (no speedup) called rank-select, described through an interactive story.
LLMs fall victim to "garbage in, garbage out." Claude can solve open problems if you know what you're doing, but it can also incorrectly convince you it's right if you don't know what you're doing.
A PhD teaches you how to think, how to learn, and how to question the world. That's a vital set of skills no matter what tool exists.
Rousseau said "mathematical precision has no place in moral calculations," so I was tempted to see how far I could go. Started it during the holidays, but finally came around to putting a bow on it :)
The human still needs to think, of course. But, I can get to my answer or my primary source using a tool faster than a typical search engine. That's a super power, when used right!
Huge fan of minimizing Kolmogorov complexity [1]. There's a balance, but typically if the same behavior can be described in less code, then the simplicity will yield dividends.
QA Wolf | AI lead / Multiple Roles | Remote (International) | Full-time | https://www.qawolf.com/
QA Wolf gets engineering teams to 80% automated E2E coverage fast, and keeps it there.
We are growing quickly and building the dream team of engineers to develop our test creation, running, and maintenance platform. The stack is Node.js, GraphQL, React, Prisma, Go, and Kubernetes.
We are looking for a master of AI engineering who stays familiar with the latest best practices and knows the trade-offs of different patterns.
You will own problems end-to-end: collect relevant details, spec and scope solution(s), communicate progress, ship and own the results.
You:
- love coding and want to work hard
- are a self-starter, curious, and ship projects undirected
- have a proven track record of delivering challenging technical projects
- have an eye for design and can make good judgement calls with ambiguity
- are an expert in one or more technical areas
- have meaningful contributions to open source projects
ntfy.sh for a wide range of things connected to git hooks or GitHub actions, since I use git for personal things - gives me a second pair of eyes on things asynchronously
Saw the title, jumped to the diagrams, and thought I knew where this was going, but I was way off. I made the exact opposite conclusion from a quick glance at the diagram - that debt gives you more freedom. It allows you to go in the red. That a safe, debt-free life leads to less volatility and therefore less ups. Then I read the article, and couldn't hold both opposing ideas in my head.
It answered "How many frogs does a horse have?" correctly, with perfect reasoning. No model I've tested has ever answered that correctly without 3-4 hints.
[ my public key: https://keybase.io/nishant; my proof: https://keybase.io/nishant/sigs/JG92mBiH_49Ra4Tz6m7tRn-fsu8kxamrPvLRgN0eomg ]