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!)

Edit: made an example repo for ya

https://github.com/ChrisWiles/claude-code-showcase

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

Exact relation: num(b) - (b - 2)denom(b) = b - 1

Therefore: num(b) / denom(b) = (b - 2) + (b - 1)^3 / (b^b - b^2 + b - 1) [exact]

Geometric expansion: Let a = b^2 - b + 1. 1 / (b^b - b^2 + b - 1) = (1 / b^b) * 1 / (1 - a / b^b) = (1 / b^b) * sum_{k>=0} (a / b^b)^k

So: num(b) / denom(b) = (b - 2) • (b - 1)^3 / b^b • (b - 1)^3 * a / b^{2b} • (b - 1)^3 * a^2 / b^{3b} • …

Practical approximation: num(b) / denom(b) ≈ (b - 2) + (b - 1)^3 / b^b

Exact error: Let T_exact = (b - 1)^3 / (b^b - b^2 + b - 1) Let T_approx = (b - 1)^3 / b^b

Absolute error: T_exact - T_approx = (b - 1)^3 * (b^2 - b + 1) / [ b^b * (b^b - b^2 + b - 1) ]

Relative error: (T_exact - T_approx) / T_exact = (b^2 - b + 1) / b^b

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.