[What the heck is a time-travel engineer?] Mark Erikson, Redux maintainer and senior time-travel engineer here at Replay, joined Jeff Auriemma for part two of their conversation. This episode covers Replay QA: what it is, why we built it, and the technology underneath.
When Mark gets going on a topic, the energy is infectious.
Link to the post with the video and transcript in the comments.
Full video and transcript: replay.io/blog/mark-erikson-…
This GitHub repo tracking software factories is 💥💥💥. Well done @varun1505
We been building our own directory of activity in this space but this is next next level.
github.com/varun1505/awesome…
"Replay QA gave me much more confidence before shipping the app." - @KaiteeShiks
Most QA tools tell you tests passed. Replay shows you what actually happened.
Been wondering what a Senior Time-travel Engineer does day to day (I mean, who isn't!? ), allow our very own Mark Erikson tell you himself. It's a fascinating telling of the journey from debugging devtools to Replay QA.
Hint: there are no Flux Capacitors involved.
Watch it on Jeff Auriemma 's Youtube Channel:
youtube.com/watch?v=n1FEL724…
“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.






