crakhamster01

I think a lot of the commenters here may already have a negative perception of Platform teams. That they are a byproduct of big tech org inflation, a sign that the companies aren't serving their users, etc. The initial hook of "inventing work" then indulges these pre-conceived notions, and you end up with rants on how engineers have lost the plot.

Personally, I found the article well written. Platform teams ultimately want the overall company to succeed. Their leverage - and mandate - is through helping the engineering org ship safer, faster, and more efficiently. The signal recommendations mentioned by the author are spot on from my experience, and I appreciated reading their perspective.

> But in Japan, there are no large groups of foreigners in Japan that don't commit crimes at far higher levels than the extremely low Japanese baseline.

Can you share a source for this?

From the most recent numbers I've found - "foreign nationals" made up 5.5% of all arrests in 2024, while comprising just 3% of the population. But that number includes visitors/tourists as well, and that group accounts for two-thirds of all cases. [0]

Taken together, it seems like foreign residents at worse match the Japanese baseline, but are likely committing crimes less often.

[0] https://www.japantimes.co.jp/news/2025/12/19/japan/crime-leg...

Care to elaborate on how this parallels Japan? Otherwise this just reads like a random drive-by at best, and a culture-war talking point at worst.

> By some estimates, Japan’s foreign-born population could reach the OECD average of 10 percent by 2070.

Ignoring whether the new rules are reasonable, the most baffling part of all this is how small the foreign population in Japan is.

Things are undoubtedly harder for the average Japanese citizen these days, but foreigners make up just 3% of the population. Targeting this cohort, I can't imagine a single policy change that would positively impact the lives of non-foreigners. Even if you snapped your fingers and deported every foreigner out of Japan, the only noticeable impact would be a shortage of low-wage workers/convenience store staff, who are predominantly from SE and South Asian countries.

The foreign population growing from 3% today to 10% in 45 years should be a consideration for sure, but the amount of attention and media play immigration issues get in Japan is astonishing. I don't think the politicians are acting in bad faith, so my conclusion is that they simply don't know what else to do to improve things.

Having also worked with the tools you mentioned, is using commits in-place of "versions" + squash merging all that different?

  1. Adding commits means reviewers can easily diff changes to the PR
  2. Squash merging preserves only 1 commit per PR ends up on trunk

> I know personally the value the LLM adds to my workflow is considerably more than the $200/m I pay the frontier labs.

AFAIK the frontier labs make their money on enterprise, and they recently changed their terms so that most businesses can't use the $200/m plan anymore.

This is why companies like Uber - and mine :( - announced per employee AI budgets on the order of ~$1000/month.

When you're forced to pay API costs, the value prop of Kimi/Qwen becomes a lot more compelling.

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.

> I would wager your feelings conflict with their analysis

How their target audience feels isn’t separate from “analysis” - it’s the input.

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 realize this post is about the pelican test, but in regards to coding, has anyone tried out the advisor strategy with V4?[0]

e.g. Have V4 call out to Opus when it's uncertain, but otherwise handle execution.

The results with Sonnet/Haiku in the blog post seemed promising, so I'm curious how it would go with these latest open models.

[0] https://claude.com/blog/the-advisor-strategy

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.

Pasted it into Pangram AI and it classified the article as 100% AI generated, so take that as you will...

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].

[1] https://x.com/ID_AA_Carmack/status/2031769354401091988

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".

> taste scales now.

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.

> and now, what screen you’re on, what do you see?

There's a "follow me" feature to see what other users are doing. It's been around for several years.

There's undoubtedly a cohort of tourists that come to Japan with the "Disneyland" mindset, and I agree that some sort of government-level change is needed to curb abuse. But I would like to believe these folks are in the minority.

I think a greater proportion of the tourist population are individuals that visit Japan and maybe haven't done enough research, or are just unaware of norms here. Not understanding where to queue, how to order, navigate public transport, what to do at a temple, onsen, etc. This group isn't the 15% of "Best in Class tourists" Craig writes about, but rather the 75% that want to be respectful and don't know any better.

Many locals/expats will see this group and look down in disdain (or lament about them in a blog post...), but why don't more people just ask if they need help? It takes little effort to point someone in the right direction, and if it helps them better understand the country it's a win-win for both tourists and residents alike.

I feel like people love to talk about how considerate Japanese culture is, but don't care to practice it themselves when given the chance.

Haha, I tried to couch this by adding "too far", but I agree. Companies should let their teams try out relevant tools in their workflows.

My point was more of a response to the inflated expectations that people have about AI. The current generation of AI tech is rife with gotchas and pitfalls. Many companies seem to be making decisions with the hope that they will out-innovate any consequences.

I'm increasingly certain that companies leaning too far into the AI hype are opening themselves up to disruption.

The author of this post is right, code is a liability, but AI leaders have somehow convinced the market that code generation on demand is a massive win. They're selling the industry on a future where companies can maintain "productivity" with a fraction of the headcount.

Surprisingly, no one seems to ask (or care) about how product quality fares in the vibe code era. Last month Satya Nadella famously claimed that 30% of Microsoft's code was written by AI. Is it a coincidence that Github has been averaging 20 incidents a month this year?[1] That's basically once a work day...

Nothing comes for free. My prediction is that companies over-prioritizing efficiency through LLMs will pay for it with quality. I'm not going to bet that this will bring down any giants, but not every company buying this snake oil is Microsoft. There are plenty of hungry entrepreneurs out there that will swarm if businesses fumble their core value prop.

[1] https://www.githubstatus.com/history

Is any of this pushback having a material impact on the company? It seems like their stock is still hovering around all-time highs.