He's the creator of Redis, few interesting quotes:

> I believe many programmers at this point have less impact they could have because they look at the code

> didn’t you say that you check all the AI generated code for Redis? And this is a good question indeed. Yes, I do, but this is, at this point, something I need to do but that I believe to be mostly pointless

> I would do, instead? Use all the time that the review is taking me to do more QA, to think at the next optimization idea and apply it, and to use LLMs to write a DESIGN.md file where each data structure is described in human language, with the ideas it contains, the implementation tricks, the design. That, in the future, is going to be much more useful. Do you want to modify sorted sets? You open the file, read the design, then you own the ideas. You can open your agent and ask it what to do with the right mental model.

> Fable and GPT 5.6 reviews to the sorted sets memory saving are going to spot ways more errors and subtle race conditions that my review is going to uncover.

Comments URL: https://news.ycombinator.com/item?id=50136995

Points: 1

# Comments: 1

1. Chat-esque LLM assistance while mostly hand writing code

2. LLM writes the code and you review line by line

3. Review mainly the high level design and tests, scanning the lower level implementation for stuff that jumps out

4. Relying on LLM generated reviews and inspecting what it highlights

5. Relying on agentic code/PR/fix loops

How long ago did you hit that level of trust, was it tied to a model release?

Are expectations, team norms etc forcing you to use LLMs beyond your trust in it?

Have the industry changes brought more excitement about or apathy towards your work?

Comments URL: https://news.ycombinator.com/item?id=50130686

Points: 2

# Comments: 3

- How long do you reckon it would take a human to do the task?

- What percentage of features/ feature-extensions you add would have been done at all if you didn't have agentic assistance? Is there a tension between llm productivity gains and 'because we can' feature additions?

- Are many of the tasks for throwaway v1 features, exploratory tries?

- How speced out is the work you give it, eg did this task have pages of specs, how long did the spec take to write?

- How deeply will you manually review the work and how much time will it take? Will you read all the code / review at a high level and interrogate with llm / have the llm review and self iterate?