“You don't have enough eyeballs for the amount of code being written so your focus needs to be on product intent, risk assessment and evaluating evidence rather than reviewing code”
This is where Replay shines, giving runtime evidence to your agent.
Give your agent a way to verify its work. This can dramatically improve the quality of output.
With this process in place, Claude Code was able to write 90%+ of its code itself. When generating software at a faster rate than you can review it, the bottleneck shifts to proving that it works. @bcherny and team invested enough in verification, we've been able to move fast while maintaining stability.
Code reviews aren't going away, but you need to think of it differently. One human reading through line-by-line is no longer going to work as a gate, it needs to evolve into a multi-layered verification system, built with fast deterministic feedback loops for your agents.
This is where e2e tests, feature maps (as @poteto has been advocating), property tests and multi-agent adversarial reviews come in. These all provide opportunities to catch errors early and fix them before things get out of hand.
You don't have enough eyeballs for the amount of code being written so your focus needs to be on product intent, risk assessment and evaluating evidence rather than reviewing code. Delegate the code checking work to your automated tests, scale your approvals based on risk, and turn your test failures into living evaluations. This approach helps maintain speed without losing reliability.
Let the agents do what they do best and let you focus on what makes you irreplaceable: judgement, direction, and ownership. We tried to capture how we handle code review and verification in this writeup. Hopefully it's helpful.
Replay retweeted
CASTING CALL: I had a team drop out, so I need 2 devs on short notice to join an episode of Web Dev Challenge in Portland on Oct 21–23
TimescaleDB and sensor data challenge. if that sounds like fun, grab a friend and slide into my DMs ASAP! 💜
The vision of self-improving software is something we are obsessed with, and this post is a fantastic articulation of it. 💥
We ran a self-healing software loop on our own repo for two weeks.
152 PRs created. 80 merged. 5.4 bugs fixed per day. No human authors.
The finding that mattered most: automated fixes only get merged when they include proof the bug was reproduced and eliminated - not AI assurances.
It turns out that @fullstory + Replay QA + @obvious is a killer combo for a self-driving, self-healing product.
Our autonomous QA agents were busy during the month of September, finding over 13,000 bugs across a range of categories on web apps built by our customers. Here's the breakdown...
Accessibility: 3,808
Glitches: 3,624
Network Performance: 2,705
Testing: 1,330
SEO: 520
User Experience: 436
React Rendering: 435
Layout Shifts: 398
Security: 193
Each bug report includes a video of the session, expected behavior and actual behavior, robust root-cause analysis of why it failed, and an agent-ready prompt with a suggested fix with all the context required. Teams using Replay QA connect it to their issue tracker so the bugs appear where the team already works.
How does Replay QA find and diagnose bugs? Learn more at replay.io/how-it-works
Great turnout for the @obvious Frontier Build event in SF. Stoked to be collaborating with them to bring a whole new way of software creation: autonomous & self-healing.
Obvious brings the software factory... we bring the autonomous QA for the high-volume output.
Thanks to House of AI for hosting this. What an awesome space to build in! house-of.ai/
You don't need to have a software factory to set up Replay QA for always-on app testing (but if you have a factory, you're gonna need it to keep up with the torrent of output).
learn more at replay.io






