The problem is Anthropic doesn't have the compute to deploy this model to scale to everyone yet. Dario didn't believe that they needed as much compute, OpenAI is going to have much more compute unlocked this year and especially next year.
lol does sound like and ad, but is true. Also forgot about hooks use hooks too! I just use voice to text then had claude reword it. Still my real world ideas
Most software engineers are seriously sleeping on how good LLM agents are right now, especially something like Claude Code.
Once you’ve got Claude Code set up, you can point it at your codebase, have it learn your conventions, pull in best practices, and refine everything until it’s basically operating like a super-powered teammate. The real unlock is building a solid set of reusable “skills” plus a few agents for the stuff you do all the time.
For example, we have a custom UI library, and Claude Code has a skill that explains exactly how to use it. Same for how we write Storybooks, how we structure APIs, and basically how we want everything done in our repo. So when it generates code, it already matches our patterns and standards out of the box.
We also had Claude Code create a bunch of ESLint automation, including custom ESLint rules and lint checks that catch and auto-handle a lot of stuff before it even hits review.
Then we take it further: we have a deep code review agent Claude Code runs after changes are made. And when a PR goes up, we have another Claude Code agent that does a full PR review, following a detailed markdown checklist we’ve written for it.
On top of that, we’ve got like five other Claude Code GitHub workflow agents that run on a schedule. One of them reads all commits from the last month and makes sure docs are still aligned. Another checks for gaps in end-to-end coverage. Stuff like that. A ton of maintenance and quality work is just… automated. It runs ridiculously smoothly.
We even use Claude Code for ticket triage. It reads the ticket, digs into the codebase, and leaves a comment with what it thinks should be done. So when an engineer picks it up, they’re basically starting halfway through already.
There is so much low-hanging fruit here that it honestly blows my mind people aren’t all over it. 2026 is going to be a wake-up call.
(used voice to text then had claude reword, I am lazy and not gonna hand write it all for yall sorry!)
The collapse in production costs from AI video is going to change the volume and quality of what gets made. We’re headed for a world where studios and small teams alike can produce work that would have required a Game of Thrones budget not long ago. The pipeline for high end series and films is about to get a lot bigger, and the pace of experimentation is going to jump
Netflix is basically strangling the creative potential of GenAI before it can even breathe. Their new “guidelines” read like a corporate legal panic document, not a policy for innovation. Every use case needs escalation, approval, or a lawyer’s blessing. That’s not how creativity works.
The irony is rich they built their empire on disrupting old Hollywood gatekeeping, and now they’re recreating it in AI form. Instead of letting creators experiment freely with these tools, Netflix wants control over every brushstroke of ai creativity
Sign:
The approximation with denominator b^b underestimates the exact value.
Digit picture in base b:
(b - 1)^3 has base-b digits (b - 3), 2, (b - 1).
Dividing by b^b places those three digits starting b places after the radix point.
Examples:
base 10: 8 + 9^3 / 10^10 = 8.0000000729
base 9: 7 + 8^3 / 9^9 = 7.000000628 in base 9
base 8: 6 + 7^3 / 8^8 = 6.00000527 in base 8
num(b) / denom(b) equals (b - 2) + (b - 1)^3 / (b^b - b^2 + b - 1) exactly.
Replacing the denominator by b^b gives a simple approximation with relative error exactly (b^2 - b + 1) / b^b.
two years ago, I opened a PR asking for an LLM commit feature, and they flat-out said they weren’t doing it. Meanwhile, Cursor was eating their lunch and lapping them twice. I couldn’t believe how complacent and out-of-touch they were—it was pure laziness dressed up as “product focus.” And let’s not forget the ancient bugs rotting in their backlog that they refuse to fix. It’s like they actively don’t care about their users.