So they’re watermarking requests according to your environment variables and maybe changing a string format if you’re in a certain time zone? Am I missing something here? Where’s the five alarm fire?
> The worst case of these were the few companies that set up token leaderboards, which is perhaps the dumbest way possible to encourage learning how to use LLMs well
My company does something dumber now. A leaderboard of how many lines of code you shipped, weighted by how complex they were (assigned by a heuristic). You can imagine the incentive this creates. I wish we just measured tokens
I’m confused: is it just markdown files in git? Or does the hybrid graph+semantic layer matter? If the latter is true, the title is just clickbait right
I tried semaglutide and while it was effective for losing weight, it made working out impossible (felt exhausted and very sick as soon as hr went up) and it made hangovers awful. Is retatrutide any different?
The author of stringzilla, Ash Vardanian, has a bunch of really cool talks/lectures/demos. Highly recommend checking him out if you like that kind of thing :)
I don’t think OP’s point has anything to do with AI companions.
The big benefit of moving compute to edge devices is to distribute the inference load on the grid. Powering and cooling phones is a lot easier than powering and cooling a datacenter
My issue with this is that a simple design can set you up for failure if you don’t foresee and account for future requirements.
Every abstraction adds some complexity. So maybe the PoC skips all abstractions. Then we need to add a variant to something. Well, a single if/else is simpler than an abstract base class with two concrete implementations. Adding the 3rd as another if clause is simpler than refactoring all of them to an ABC structure. And so on.
“Simple” is relative. Investing in a little complexity now can save your ass later. Weighing this decision takes skill and experience
I am seeing this at my work right now. They are about to start using token consumption as _part_ of the performance review process. Obviously this is a coarse and problematic proxy for productivity.
OTOH, it’s an attempt to address a real problem. There are people who are in fact falling behind (I’m talking literally editing code in notepad), and we can either let them get PIPped eventually, or try to bring them along. There is a real “activation energy” required to learning new tools, and some people need an excuse/permission. Not saying that token count is a GOOD signal, but I haven’t heard many better ideas
Very bad hire. I’ve gently said as much to my manager and skip. But for some reason hiring is hard and firing is hard, and we’re a small team, so I’ve been told to just lower my standards. Yeah, I know
AI pull request descriptions are my current pet peeve. The ones I have seen are verbose and filled with meaningless fluff words (“optimized”, “performant” for what? In terms of what?), they leak details about the CoT that didn’t make it into the final solution (“removed the SQLite implementation” what SQLite implementation? There isn’t one on main…), and are devoid of context about _why_ the work is even being done, what alternatives were considered etc.
My first round of code review has become a back and forth with the original author just asking them questions about their description, before I even bother to look at code. At first I decided I’d just be a stick in the mud and juniors would learn to get it right the first time, but it turns out I’m just burning myself out due to spite instead.
I burnt out helping a junior on my team for the past few months. It was just terribly obvious she was feeding my responses directly into a chatbot to fix instead of actually understanding the issue. I can’t really even blame her, there isn’t much incentive to actually learn
I think this is well put. Cohesive philosophy, even if flawed, is a lot easier to work with than a patchwork of out-of-context “best practices” strewn together by an LLM
Strawman. It’s entirely possible for two things to be true at once: border laws are worth enforcing, and the current approach of flooding ICE with untrained goons explicitly targeted with white supremacist recruiting material is not going to end well
The crux is in the “and it gets implemented” part. Teams have a limited bandwidth, so what gets implemented absolutely is a zero sum game, that’s why backlog prioritization exists. In order for your idea to get implemented, you have to advocate for it, and convince others to do the same.
Writing great code and delivering useful side-projects can make you a 2-5x engineer. If you want to be a 10x engineer, you have to scale your impact beyond what you can do alone
Edit: maybe your great idea is actually something that you can implement on your own, such as a test suite or a tool. You still need to change other peoples behavior. You need to convince people to try it.