This all sounds good on paper but I have an honest question: How do you develop judgement as a junior or mid level engineer to critique AI if you yourself never learned that skill by making mistakes in the first place?

I’m not saying that all code should be typed by hand in 2026 but there are certain subtle things you learn only when you get into nitty gritty details especially related to security.

Also today’s AI is notoriously bad at ownership. When you ask it to give you a concrete answer, it will still give you options with pros & cons of each so that ultimately you own the decision and not it. So how do you decide between the two (or more) when you never learned to do it yourself?

Something I appreciated when going through Anthropic interview process was that I only dealt with humans. They could have been showcasing how even their interviews are done with Claude or something, but no, the whole loop was interacting with real humans, and nothing was on the topic “how do you work with LLMs”. It was about how me, the human, think and approach situations, and how I handle working with others (humans), etc. Which made a lot of sense to me, and I overall appreciated their process (other than it’s very time consuming, requires lots of prep). But it’s interesting to see that AI labs customers feel the need to showcase how much of their processes are now around agentic stuff

> When the cost of building goes to zero

Where are they getting all those free tokens from?!? I don't like this rhetoric. It still costs money to write code, only now that wealth gets transferred to Anthropic instead of to individual contributors.

Curious to know:

- does the company provide the harness and specific model for this? Or the interviewee use whatever they have access to? If they don't do a good job, is it the fault of the harness or the model or the interviewee not knowing how to fully utilise the harness and model or alternative harnesses and models?

- if every interviewee uses different harnesses and models, how does the company ensure that it's a fair comparison between interviewees for the same role?

- will the interviewer go through all lines in the code generated or let AI do it?

I'm very skeptical of that graph. What does it mean for code to be AI-generated vs human-generated?

If I merge a PR that was opened by Claude do all the changes in it automatically count as AI-generated? How about the individual commits? If I'm reviewing locally and make manual changes but then have Claude create the commit it appears to be AI-generated but might not have been.

The point is that these metrics are easy to game and I've definitely wasted time and tokens refining code with Claude that would have been easier to just edit by hand. It can be kind of fun and when the goal is just "use AI" I don't find it surprising that graphs showing 100% switch to AI-generated code could be defensibly generated without really saying anything about how much manual intervention is happening or how efficient the process is.

I've been considering different ways to measure code understanding and fluency. I'm still using traditional 'easy' problems on a coding pad to probe this, but I'm finding a new generation candidates that don't prepare for this, and may be able to have a full career with just a higher level of code understanding. But they might not ever develop the ride-a-bike with one hand level of fluency unless they grind pointless leetcodes.

So I'm increasingly uncertain about what and how to test. My default for now is still to rely on ability to write basic code fluently, but I'm open to changing this perspective.

I really want to know how this existing repo AI-assisted live coding test works, with example problems.

It seems the standard data structure puzzle type thing won't be feasible if you are using an LLM.

Also the latency for these agentic coding/prompts seems like it would make the interview a bit awkward.

Anyone been conducting or taking interviews with this kind of thing with thoughts to share?

I'm not sure if it is a good idea when you are referencing your "rebuilding" after you reduce headcount by 14% . If this isn't some kind of entendre then it's actually extremely effective.

https://xcancel.com/brian_armstrong/status/20516167591451857...

Regulatory compliance as attempted by Coinbase does not pay, and of course they had many fines for non-compliance already.

The whole point of Bitcoin is sanctions evasion. Tether moved from Deltec Bank (CIA) in the Caribbean to Lutnick's Cantor & Fitzgerald and El Salvador. That is where the action is.

So promoting the AI bullshit is for investors because that is what they want to hear. They are not going to vibe code financial transactions and get another $500M EU fine.

As an aside, it is interesting that the new "taste" talking point was already on Coinbase in mid-July.

Writing for investors in the AI Era

- We have embraced everything about the AI Era at our company

- Arbitrary topic about how that changes something

I am really looking forward to the day where AI is normalized during interviews. Right now the duality of interviews is basically:

- You get a leetcode question and if you're lucky is an easy medium that you can solve, if you're really lucky you already solved it and can pretend you are approaching the problem the first time. Good luck if you get a hard question and you never saw it before.

- You get a home assignment, in a framework you might not know but you're expected to be fluent with it, then waste 1 hour setting up the project structure, and one more hour to find out how the framework expects you to define the CORS allow list. You are expected to deliver the project in 3 hours.

The good I see in AI is that it completely removes the need to study just for interviews, and you can also delegate all the project setup to the AI. Then you can focus on what you would test (e2e? integration? what are the boundaries? what do we mock?), how to keep the documentation, how to structure your code. You have an expensive endpoint, do I make it sync or add an async jobs framework?

Imagine you're an expert in C++ interviewing for a Django position and the interview consists of fixing a big in a repo. The bug is that a function without type hinting is modifying what is expected to be a list, but the caller is passing a tuple. Trivial after a week you work in python and you have your environment set up for type warnings, also trivial with AI and definitely not an interesting problem that shows expertise with software engineering in general.

We also did this in our last interview at work, and it was a really good indicator to see if someone just copy pasted code, or understood it after it was generated. Some candidates had a unit test fail and couldn't debug it for his life, even if he "wrote" all the code himself. Others simply did not understand the architecture they wrote, and assumed that a function defined with "async" and awaited would run in parallel from the code that called it (as if you spawned a thread)

We gave away the most fun part of being a SWE to the clankers

I would do the opposite and would likely use the same framework that some AI companies are doing to evaluate candidates. By either not allowing them to use AI assistants in any part of the interview process (Anthropic and OpenAI does this.) or give candidates a strict token limit (100k tokens) until the candidate runs out of tokens.

They have to earn it, as the tokens are not free.

Given that deskilling and over-reliance in AI assistance will continue to happen, putting a hard token limit <100k tokens in the interview process serves as a great filter to prevent the vibe-coders and "tokenmaxxers" out and forces a higher bar for quality, with clean code and reasoning across well maintained software with less tokens rather than increasing the slop.

Do you want a candidate that knows when to use AI and carefully uses tokens with in their limits, or do you want a candidate generating incomprehensible AI slop to be tokenmaxxing out your company limits and then draining your company bank account?