You can’t outwork the whole world. There’s always going to be someone somewhere willing to work as hard as you. Someone just as hungry. Or hungrier.
Assuming you can work harder and longer than someone else is giving yourself too much credit for your effort and not enough for theirs. Putting in 1,001 hours to someone else’s 1,000 isn’t going to tip the scale in your favor.
What’s worse is when management holds up certain people as having a great “work ethic” because they’re always around, always available, always working. That’s a terrible example of a work ethic and a great example of someone who’s overworked.
A great work ethic isn’t about working whenever you’re called upon. It’s about doing what you say you’re going to do, putting in a fair day’s work, respecting the work, respecting the customer, respecting coworkers, not wasting time, not creating unnecessary work for other people, and not being a bottleneck. Work ethic is about being a fundamentally good person that others can count on and enjoy working with.
So how do people get ahead if it’s not about outworking everyone else?
People make it because they’re talented, they’re lucky, they’re in the right place at the right time, they know how to work with other people, they know how to sell an idea, they know what moves people, they can tell a story, they know which details matter and which don’t, they can see the big and small pictures in every situation, and they know how to do something with an opportunity. And for so many other reasons.
So get the outwork myth out of your head. Stop equating work ethic with excessive work hours. Neither is going to get you ahead or help you find calm.
[The Outwork Myth — It Doesn't Have To Be Crazy At Work, 2018]
Frontier LLMs are doing too much when it comes to editing code.
I'm excited to share this work on the Over-Editing problem which refers to models modifying code beyond what is asked of them.
The main findings are:
- Many frontier models Over-Edit with GPT 5.4 being the biggest culprit
- Reasoning models have a higher natural tendency to Over-Edit compared to their non-reasoning counterparts
- RL is the best approach to train models to perform minimal code editing while preventing catastrophic forgetting compared to SFT, DPO and Rejection Sampling.
Blog and details below!
With agentic slop, we are trading software reliability for shipping velocity and calling it progress. It isn't. Systems are more fragile than ever, and engineers building them no longer trust their code to hold up in real-world edge cases.
I am pro-AI, but this will backfire - big time.
@AdvaitRaykar already on the company card :)
just moving between codex and claude makes you realize how much openai is generous with limits on their $20 tier
@mschoening Just spent the last 25 mins talking with grug and it is fun!! I knew I didn't need phd level intelligence 😅
PS. Following up on my email sent on last Tuesday. Please check it when you are free.