San Francisco, CA
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The reason I still review every line of code despite encoding all the software engineering best practices in my agentic workflow, is because sometimes it’s the *exceptions* to these generalized rules that work best.
The agent isn’t discerning enough to make these calls (as yet)
I've been reviewing a lot of code written by Opus 5.5, and it still seems to make a lot of bad software engineering decisions. The kind that will accumulate/calcify into unmaintainable software over time. IMO even the agents will struggle to maintain vibecoded software.
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At Paris CDG, Terminal 2E, security check:
Gate K: no need to take your electronics out
Gate M: take all your electronics out and put it in a tray
Me: I wasn’t asked to do it in Gate K
Security: Gate K is Gate K, Gate M is Gate M
Never seen this in any other airport. 😕
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Cindy Sridharan retweeted
I came back to Google to work on AI infra. Instead we got pulled in to solving the problem of exploding use of agents across the company.
Announcing Agent Compute Engine: bringing planet scale agentic infra to Google services and now to Google Cloud.
cloud.google.com/blog/topics…
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I found this unpublished blog post from 6y ago (look at the hand drawn diagram lol).
I’d totally forgotten about this, but the key ideas here (embracing non-determinism as a first class citizen via “error kernel” centric design etc) work super well as guidelines for my agents.
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5 months and many experiments later, this was totally a case of “skill issue” on my part 😅
Like with code, agents don’t magically write great tests
If your unit tests are slop (as mine were), that’s because you don’t have proper testing guidelines for your agents (as I didn’t)
end-to-end testing > unit tests, in the vibecoding era.
A massive, almost entirely agent-coded refactor passed all unit and pre-merge tests but broke a critical feature.
It was only caught due to my own excessive paranoia making me run end-to-end tests before the prod deploy.
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We’ll still need end to end tests, and frankly, we’re going to need all the forms of testing I’ve been talking about for a decade.
But “unit tests are now slop” doesn’t have to be the foregone conclusion.
Unit tests can be very useful even if a human will never again read them.
Updated my testing taxonomy to include formal verification. Some book recs too.
Model checking, fuzzing, property based testing, sanitizers and static analysis are some of the most powerful tools one can use in the "pre-production" phase.
Also, are "types" a form of testing? 🤔
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Unfinished coding side projects are now way easier to complete than unfinished blog posts.
I’m perfectly OK with AI writing my code, even the docs.
But I flinch at the idea of AI writing my blog posts, which feels like an egregious violation of a fundamental ethnical contract.
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I started this series on testing nearly a decade ago, but never published the final part copyconstruct.medium.com/tes…
There’ve been so many great advances in the storage space in the ~7y I feel this is worth completing.
Have a draft from years ago I hope I can wrap up in early 2027.
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I’m literally that weirdo who wrote (all by hand) a 15K word blog post on:
- why one cant treat models as oracles
- how models gaslight you all the time
- what it takes to build confidence in the model’s output
copyconstruct.medium.com/how…
So yeah sure, I believe in magical woo-woo
I don't expect models to do things well automatically. I'm just pointing out that you believe in the magic too much. Including things "I gave it things to read" lol
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To create my claude.md, I had it read:
- my favorite programming books + blogs
- the best oss projects
- the code of the engineers I respect the most
Claude (when given proper instructions, constraints, and context) now writes better code than me, almost always.
the coding agent haters are getting dunning-kruger'd so hard right now
do you really think that your engineering ability is better than the models at this point?
it is very very likely that fable is better than you in every branch of software
it has been trained on all the code of the internet, it knows almost every trick in the book, all the design patterns of every domain, every single low-level detail, every bit-twiddling hack, it's read every single blog post from every cracked engineer that ever existed
do you really think you can beat it? do you really think you can be more creative than it?
unless you are 99th percentile expert in some niche domain of software, it simply is not possible. sorry.
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Many moons ago, I wrote about “post-commit” reviews.
In the agentic era, a workflow I find extremely useful is … *pre-commit* agentic reviews.
For large changes, the review agents take 10x the time it takes the coding agent to create a patch, but is totally worth it, IMO.
Blogged: Post commit reviews
Where I question if pre-commit mandatory code reviews are truly needed and what a post-commit review world would look like.
Caveat: post-commit review doesn’t necessarily mean post-deploy review.
bit.ly/2Oid8jG
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It’s disappointing to see people say all code is going to be slop and we should be ok with it, because humans weren’t writing the best code either.
Quality and reliability should be higher (as quantitatively measured), not lower.
Why’re we lowering the bar for ourselves? 😔
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Resharing this and urging everyone to read it for several reasons.
1. Everything Cindy writes is fantastic and serious engineering discourse
2. The examples here are concrete and clear and most importantly production services with real world consequences
How to effectively debug with agents when you no longer read or understand the code
In this post, I propose a debugging framework for agents that doesn’t rely on human knowledge of code to get to the right “root cause”
The TL;DR are the screenshots.
Link to post in the reply.
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How to effectively debug with agents when you no longer read or understand the code
In this post, I propose a debugging framework for agents that doesn’t rely on human knowledge of code to get to the right “root cause”
The TL;DR are the screenshots.
Link to post in the reply.
Writing a post about “how to effectively debug code when you no longer fully understand it”.
This is real bug I had to fix in ~9K LOC (100% AI generated).
None of the tactics I started with here helped “root cause” the issue, lol. Had to take a different approach to isolate it.
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It’s criminally long and took over a month to write.
I thought of making it shorter, but figured anyone who might care to read is just going to ask an LLM for a summary, so might as well leave it as it is.
copyconstruct.medium.com/how…
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I’ve found that agents are great at debugging any problem when provided with this two-tier framework for troubleshooting.
This requires enforcing discipline and imposing constraints, rather than give agents a free rein or vague end goals, but works better than unguided “loops”.
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Cindy Sridharan retweeted
Today @bitdriftio is really excited to open source blob-stream: a Kafka alternative for no fuss, low cost high volume streaming. I wrote in depth about what blob-stream is, why I made it, and how I made it.
blog.bitdrift.io/post/blob-s…
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People talk about the diminishing returns of writing unit tests in the agentic era, but what’s even less useful? Comments.
As a rule, drop all comments. If something is important enough to need a comment, make an exception for it and audit the comment so it actually makes sense.
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I need a personal agent for work where it:
- triages all incoming bugs
- manages slack comms
- gives me a summary and classification of all emails (8000+ unread), design docs, SEVs etc
I don’t need an agent for my personal life, where I just don’t deal with anything like this.
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Here’s an example where Twitter Engineering was way ahead of the curve.
Many companies didn’t have to deal with “Twitter scale”, but agentic architectures are fast changing the scale even small orgs are expected to handle re stateful load balancing, orchestration, storage etc.
This post on the evolution of client side (distributed w/o coordination) load balancing at Twitter is 🤯
- [service] "mesh" topology + P2C has cons
- the "random aperture" approach which scales unfairly
- "deterministic random aperture" scalable + fair
bit.ly/2FvTH2u
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