Thanks for sharing! I make videos and often have the same problem as well.
Being able to semantic search over your library is useful, but does it solve the review problem? I feel like you would still need to watch the footage back before you know what you're working with.
I don't really understand how engineers at Uber are hitting $1500/month. Are they forced to pay API costs?
My company provides employees with API keys and soft limits, but as soon as you approach ~$400/month they ask that you get a Claude/Codex Max subscription instead. Curious if it's not the same case at Uber.
> As further evidence that enterprise agents represent product-market fit for these companies, consider their open job listings.
PMF is one interpretation, but it could also be read as desperation.
In my opinion, we've been at PMF for quite a while now. The November inflection point that's often referenced definitely changed how we interface with models, but as far as coding goes, I feel like Cursor had proven itself useful for at least a year prior to that.
The demand has always been there, the outstanding question is still - how do you build a business on top of these products? None of the frontier models have emerged as uniquely capable, but open weight models are now catching up in capability as well. The explosion in go-to-market roles feels more like an attempt to lock customers into contracts so that they don't consider alternatives.
I assume the hope is that during this 12-month contract they will develop real integrations, something deeper than just a CLI harness. If you've ever worked in procurement or dev tooling at a reasonably sized company, you'll know that this is exactly what teams try to avoid.
It's anyone's guess what will happen this time, but I'm excited to see how the IPOs go.
> Antigravity also offers models other than Gemini as well. When you say Antigravity, you think of a platform whereas when you say Gemini you think of the model.
I the other reasons you mentioned could be solved while keeping the Gemini name, but this is a fair point. I didn't realize they offered 3rd party models!
> When you say Antigravity, you think of a platform whereas when you say Gemini you think of the model
Yea I guess if their goal long-term is to be something more akin to Cursor that makes sense, but Anthropic seems to be doing just fine using "Claude" in their naming scheme.
How does anyone internally at Google justify these decisions?
Even if there are competing implementations, in terms of brand recognition, I feel like “Gemini” is more closely associated with Google than “Antigravity”. Why pick the more obscure option?!
Perhaps they felt the sentiment on Gemini CLI was beyond repair, but surely there must be some voice on the inside saying “developers will never adopt our products if we keep killing them”.
I read the piece before coming to the comments and had a similar feeling as OP - hence my comment. If AI wasn't used then my apologies, I didn't mean to diminish your work.
I agree with the premise of your post, just felt it was a bit long and the section headers read a little weird.
The app looks very slick! Just read through the docs and a couple of the Looms.
This might be a dumb question, but what are people using these MD knowledge bases for? What has this unlocked for you, that you weren't doing before?
I kind of understand the wiki use case in large code bases, with lots of changes happening, but on a personal level it's not as clear to me. Is it just another place to store ideas? Is the hope that AI resurfaces or connects relevant bits later?
This seems like a way to optimize your personal life, but I'm not sure what the end goal is.
This was a great comment. I don't know if it's common knowledge, but this really helped clarify how the shift happens.
I also remember half coding and half prompting a few months back, only to be frustrated when my manual changes started to confuse the LLM. Eventually you either have to make every change through prompting, or be ok with throwing away an existing session and add back in the relevant context in a fresh one.
> One thing we know for sure is LLMs write code differently than we do.
Kind of. One thing we do know for certain is that LLMs degrade in performance with context length. You will undoubtedly get worse results if the LLM has to reason through long functions and high LOC files. You might get to a working state eventually, but only after burning many more tokens than if given the right amount of context.
> The worst outcome I can imagine would be forcing them to code exactly like we do.
You're treating "code smells" like cyclomatic complexity as something that is stylistic preference, but these best practices are backed by research. They became popular because teams across the industry analyzed code responsible for bugs/SEVs, and all found high correlation between these metrics and shipping defects.
Yes, coding standards should evolve, but... that's not saying anything new. We've been iterating on them for decades now.
I think the worst outcome would be throwing out our collective wisdom because the AI labs tell us to. It might be good to question who stands to benefit when LLMs aren't leveraged efficiently.
I can maybe see this argument being valid for OSS - as Carmack says, by nature it should be "no strings attached".
I don't think that's all anti-AI activists care about though. Honestly, I would say most activists don't talk about the use of OSS? The most prominent anti-AI sentiment seems to come from creatives. Artists, musicians, designers, etc.
They didn't publish their works with the same notion as OSS developers, but it was scraped up by corporations all the same. In many cases, these works were protected by copyright law and used anyways.
To me that feels like the equivalent of training on "private repos", which Carmack would call a violation [1].
The wave of LLM-style writing taking over the internet is definitely a bit scary. Feels like a similar problem to GenAI code/style eventually dominating the data that LLMs are trained on.
But luckily there's a large body of well written books/blogs/talks/speeches out there. Also anecdotally, I feel like a lot of the "bad writing" I see online these days is usually in the tech sphere.
I had a similar reaction to OP for a different post a few weeks back - I think some analysis on the health economy. Initially as I was reading I thought - "Wow, I've never read a financial article written so clearly". Everything in layman's terms. But as I continued to read, I began to notice the LLM-isms. Oversimplified concepts, "the honest truth" "like X for Y", etc.
Maybe the common factor here is not having deep/sufficient knowledge on the topic being discussed? For the article I mentioned, I feel like I was less focused on the strength of the writing and more on just understanding the content.
LLMs are very capable at simplifying concepts and meeting the reader at their level. Personally, I subscribe to the philosophy of - "if you couldn't be bothered to write it, I shouldn't bother to read it".
Not having taste also scales now, and the majority of people like to think they're above average.
Before AI, friction to create was an implicit filter. It meant "good ideas" were often short-lived because the individual lacked conviction. The ideas that saw the light of day were sharpened through weeks of hard consideration and at least worth a look.
Now, anyone who can form mildly coherent thoughts can ship an app. Even if there are newly empowered unicorns, rapidly shipping incredible products, what are the odds we'll find them amongst a sea of slop?
I think this advice is pretty apt for small to medium sized companies. We're all invested in the company succeeding, but you don't want to become known as the person that always says "no".
At large companies, I've rarely found a reason to speak out on a project. Unless it has a considerable effect on my team/work (read: peace of mind), it just doesn't make sense to be the person casting doubt. There's not much ROI for being "right".
If you manage to kill the project before it starts, no one will ever know how bad of a disaster you prevented. If the project succeeds despite your objections, you look like an idiot. And if it fails - as the author notes, that doesn't get remembered either.
As a senior IC, the only real ROI I've found in these situations is when you can have a solution handy if things fail. People love a fixer. Even if you only manage to pull this off once or twice, your perception in the org/company gets a massive boost. "Wow, so-and-so is always thinking ahead."
A basic example I saw at my last company was automated E2E testing in production. My teammate had suggested this to improve our ability to detect regressions, but it was ultimately shot down as not being worth the investment over other features.
A few months later, we had seen multiple instances of users hitting significant issues before we could catch them. My teammate was able to whip out the test framework they had been building on the side, and was immediately showered with praise/organizational support (and I'm sure a great review as well).
I feel like both of these examples are insights that won't be relevant in a year.
I agree that CC becoming omniscient is science fiction, but the goal of these interfaces is to make LLM-based coding more accessible. Any strategies we adopt to mitigate bad outcomes are destined to become part of the platform, no?
I've been coding with LLMs for maybe 3 years now. Obviously a dev who's experienced with the tools will be more adept than one who's not, but if someone started using CC today, I don't think it would take them anywhere near that time to get to a similar level of competency.
It's funny that you mention moving outside the city when Zohran's tax plan is centered on bringing the corporate tax rate in-line with our neighboring state.
I'll also caveat that any parallels you might see in Seattle don't really apply to NYC. Besides the low car ownership rates, wealthy individuals choose to in NYC for it's convenience and culture, which really are unique in the US.
Being able to semantic search over your library is useful, but does it solve the review problem? I feel like you would still need to watch the footage back before you know what you're working with.