It's infuriating to read, but even more so, it feels disrespectful. It's like they're implicitly saying, "I can't be bothered to respond to you, so just parse this AI slop".

I don't really know how to make a case against it since leadership is very pro-AI usage. Has anyone dealt with something similar and have suggestions?

To that end though, this line feels like it (intentionally?) undersells the engineering team -

> Very often, we found that the role of the engineers was to tell Claude “no.”

To me, this blog post highlighted the immense value that domain-specific knowledge still carries in engineering. I wouldn't be surprised if their team was comprised of former Facebook/Google/Github source control engineers. Individuals with real experience on these types of systems.

The dream for a lot of folks in the industry right now is that you could just staff a bunch of generalists on a problem like this, and through the power of Claude come up with a sufficient solution. I think the reality is that without the domain expertise to direct Claude, you would end up with something vastly different, and not really usable at Anthropic's scale.

Does anyone know why having 128GB of storage is useful? Just for installing more apps? I've had Google TV devices in the past, and don't think I ever came close to the 32GB storage limit. Maybe there's a gap I'm missing...

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.

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...

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.

  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

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.

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.

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.

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.

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

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”.

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 agree with the premise of your post, just felt it was a bit long and the section headers read a little weird.

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.

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.