What do you mean? Costs spiked with the introduction of the 1M context window I believe due to larger average cached input tokens, which dominate cost.
Nah, there's apparently a few caching bugs, one --resume and some noisy tool use. I have a little app that monitors and resets the context window at 70% usage based on 200k tokens and I'm about to run out of weekly allowance after just a couple days. Never happened before
I used them for repeated problems or workflows I encounter when running with the default. If I find myself needing to repeat myself about a certain thing a lot, I put it into claude.md. When that gets too big or I want to have detailed token-heavy instructions that are only occasionally needed, I create a skill.
I also import skills or groups of skills like Superpowers (https://github.com/obra/superpowers) when I want to try out someone else's approach to claude code for a while.
I don't really care if other people want to be on or off the AI train (no hate to the gp poster), but if you are on the train and you read the above comment, it's hard not to think that this person might be holding it wrong.
Using sonnet 4 or even just not knowing which model they are using is a sign of someone not really taking this tech all that seriously. More or less anyone who is seriously trying to adopt this technology knows they are using Opus 4.6 and probably even knows when they stopped using Opus 4. Also, the idea that you wouldn't review the code it generated is, perhaps not uncommon, but I think a minority opinion among people who are using the tools effectively. Also a rename falls squarely in the realm of operations that will reliably work in my experience.
This is why these conversations are so fruitless online - someone describes their experience with an anecdote that is (IMO) a fairly inaccurate representation of what the technology can do today. If this is their experience, I think it's very possible they are holding it wrong.
Again, I don't mean any hate towards the original poster, everyone can have their own approach to AI.
Yeah, I'm definitely guilty of not being motivated to use these tools. I find them annoying and boring. But my company's screaming that we should be using them, so I have been trying to find ways to integrate it into my work. As I mentioned, it's mostly not been going very well. I'm just using the tool the company put in front of me and told me to use, I don't know or really care what it is.
How is that the point of AI. The point is that it can chug through things that would take humans hours in a matter of seconds. You still have to work with it. But it reduces huge tasks into very small ones
> He discontinued the blood exchange after data showed “no benefits.” A suspicious person might note that a vampire would say exactly this after the media got too interested.
I don't think it's the media (clearly the younger generations are media friendly), it's probably pressure from the older vamps.
I felt the same way and came to the comments to see if anyone else smelled it. It's either AI-assisted writing or people are genuinely starting to write like how ChatGPT sounds.
First, the structure of this satirical post is headings and bullet points. Fine, whatever, a lot of people write this way.
Then there's the exhausting litany of super short sentence fragments.
> He published this. Openly. In a book. As a priest.
This is how airport novels and LinkedIn "thought leadership" clickbait is written, so ok, fine, I'll let it pass.
Then I started to notice a lot of: "It's not X. It's Y" or "this isn't just A. It's B."
> Feeding isn’t nutrition. It’s dialysis.
Before LLMs, people weren't writing this way. At the risk of sounding like a curmudgeon: it's insulting to read, like the reader is a 5-year-old.
When several of these smells pile up, I close the tab immediately and try to forget about it. This one was so egregious that I had to read the whole thing and then come to the comments to rant a bit.
The cost of replacement-level software drops a lot with agentic coding. And maintenance tasks are similarly much smaller time syncs. When you combine that with the long-standing benefits of inhouse software (customizable to your exact problem, tweakable, often cleaner code because the feature-set can be a lot smaller), I think a lot of previously obvious dependencies become viable to write in house.
It's going to vary a lot by the dependency and scope - obvious owning your own react is a lot different than owning your own leftpad, but to me it feels like there's no way that agentic coding doesn't shift the calculus somewhat. Particularly when agentic coding make a lot of nice-to-have mini-features trivial to add so the developer experience gap between a maintained library and a homegrown solution is smaller than it used to be.
The API price is 6x that of normal Opus, so look forward to a new $1200/mo subscription that gives you the same amount of usage if you need the extra speed.
The writing has been on the wall since day 1. They wouldn't be marketing a subscription being sold at a loss as hard as they are if the intention wasn't to lock you in and then increase the price later.
What I expect to happen is that they'll slowly decrease the usage limits on the existing subscriptions over time, and introduce new, more expensive subscription tiers with more usage. There's a reason why AI subscriptions generally don't tell you exactly what the limits are, they're intended to be "flexible" to allow for this.
It's explicitly called out as excluded in the blue info bubble they have there.
> Fast mode usage is billed directly to extra usage, even if you have remaining usage on your plan. This means fast mode tokens do not count against your plan’s included usage and are charged at the fast mode rate from the first token.
People want convenience more than they want security. No one wants permission grants to go away in minutes or hours. Every time the agent is stopped by permissions grant check, the average user experience is a little worse.
I agree that UX is the hard part. The point isn’t “pop-up permission dialog every minute.” It’s “remove standing power.” You can make short lived authority feel smoother with scoped permits, pre-approved workflows, clear revocation semantics, and defaults that renew narrowly. The non-negotiable part is that authority can be pulled instantly and cannot silently widen.
Convenience matters, but “always-on admin” is convenience paid for with failure.
Why do you think python without access to the library ecosystem is a good approach? I think you will end up with small tool call subgraphs (i.e. more round trips) or having to generate substantially more utility code.
Nice post! Can you share a bit more about what variety of tasks you've used agents for? Agents can mean so many different things depending on who you're talking to. A lot of the examples seem like read-only/analysis tasks. Did you also work on tasks where agent took actions and changed state? If yes, did you find any differences in the patterns that worked for those agents?
Sure! So there are both read-only and write-only agents that I'm working on. Basically there's a main agent (main LLM) that is responsible for the overall flow (currently testing GPT-5 Mini for this) and then there are the sub-agents, like I mentioned, that are defined as tools.
Hopefully this isn't against the terms here, but I posted a screenshot here of how I'm trying to build this into the changelog editor to allow users to basically go:
1. What tickets did we recently close?
2. Nice, write a changelog entry for that.
3. Add me as author, tags, and title.
4. Schedule this changelog for monday morning.
Of course, this sounds very trivial on the surface, but it starts to get more complex when you think about how to do find and replace in the text, how to fetch tickets and analyze them, how to write the changelog entry, etc.
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