Is this accurate? Or is this still an ongoing issue?

Whether to lean into or push back against using AI in the technical was a major point of discussion for us when building the hiring pipeline. Ultimately we decided it would be fighting against the current to try to prevent candidates from using AI and so we decided to assume they would and build questions in to evaluate their efficacy.

I'm also not sure it's fair to say we invest no time just because we use AI. We hop on a call with each candidate after they submit the technical and ask questions about their process, how they decided scope, and try to figure out how much awareness they have of what they coded.

I came in, set Claude up, gave it read access to CI artifacts, had it build out some tooling to monitor the rolling pass/fail rate over the last 30 days, and let it loose. It identifies the worst offending flaky tests, forms hypotheses on whether it's a testing issue or a production issue, then tries to divide-and-conquer until it gets minimal reproduction steps. If it's not able to create deterministic reproduction then it'll make a best guess at fixing the issue and grind away at test re-runs all night until it can try to figure out if it fixed the issue with statistical confidence instead.

It's not perfect. I have to throw away some of the bad solutions, but shaved 20 minutes off their pipeline and improved pass rate by 35% in a handful of weeks. Very minimal oversight on my part - just letting it run while I'm asleep and reviewing PR proposals during the day between meetings.

We have an initiative to make an entire web application significantly more accessible in response to some government mandates. Tight deadline, tons of grunt work, repetitive patterns, some small nuances on edge-cases. The team was able to create a set of skills for doing the conversion logic, slowly build up and address all the edge cases, and are now able to work several magnitudes more quickly in modernizing the app.

A team had punted repeatedly on updating Jest to the latest version because it inherently came with a breaking change to JSDOM which made some properties unable to be spied upon. Took like 20 minutes to have Claude one-shot the entire conversion when they'd ignored it for months because it just felt too finicky prior to agents. In general, everything to do with testing infrastructure is easy to push forward with confidence.

Uhm, we have an active interview pipeline where we give a take-home technical assessment. After we got a few submissions, and manually evaluated them, I fed our analyses in and our grading rubric and had it generate assessments for incoming candidates following the rubric. After checking a few pretty carefully it became clear that it was good enough to trust - the take home wasn't groundbreaking and the problem space was understood enough to be able to identify obvious issues if there were any.

I was given a small team of semi-technical people who were being used to fetch numbers from DBs for product/marketing/sales and perform light data analysis on them. A lot of their day to day was just paper pushing SQL queries into Excel spreadsheets and then transforming them into PowerPoints with key takeaways. They didn't have any experience writing code. I had Claude build a gameified playground for them where I gave them a VSCode dev container, a SQLite DB full of synthetic data emulating what they'd encounter IRL, and a Jupyter notebook filled with questions they'd need to answer by writing code to interrogate the database and form insights. In a couple of weeks I was able to get them to the point where they were comfortable writing basic Python scripts with the help of Claude and they're now off automating all their paper-pushing workflows with deterministic scripts. When they're done we're going to move them to higher value work by having them do sleuthing against our data and surfacing proactive insights to propose to Product rather than just reactively fetching data and building reports.

I was asked to quickly build a prototype for some basic AI functionality we thought we might want to add to one of the products. I was able to go from "I have no idea what I should build" to "here's a prototype we can put in front of clients and see if this idea has any merit" in about 14 hours. Just riffing with Claude from product idea to functional/technical specs, implementation plan, then full working prototype was one shot, and then a tight iteration loop for a couple of hours with me guiding it on personal aesthetic choices to give it enough final polish. Obviously I wouldn't ship this code into production, but it's really nice not having any sunken cost biases when demoing a prototype. If customers don't like it? Great, I lost one day and half the time I was multi-tasking while Claude implemented specs. Even better - I had Claude write a script to extract all the conversations I had with it and include those in the prototype repo. Then I filmed a quick demo video of my process, shared that with the engineers, and they're able to review my Claude conversations to get inspiration for how to modify their own agentic coding strategies.

I just don't believe for an instant that they're anywhere in the same ballpark of capabilities as running Opus or similar. My time is the most valuable resource. Opus would need to be SIGNIFICANTLY more costly and unstable for me to start entertaining local models for day-to-day development.

Perhaps whatever work you're doing makes this trade-off more sensible, but I struggle to see how that could be true. I'm averse to running Sonnet on a large amount of software engineering problems - let alone Qwen.

Typo in the first sentence of the second paragraph is sad though. C'mon, proofread a little.

Can you explain what you mean by this?

https://finance.yahoo.com/quote/HPP/

Check the 5-year on Hudson Pacific. They're down 96% and dropping. They own a significant number of downtown commercial properties in SF and LA. They're completely underwater, their spaces are barely half full, and they can't lower rents without violating their bank loan covenants.

Of course, if the commercial landscape hadn't shifted in a way nobody could predict then, yes, they'd likely have continued to print money for the foreseeable future. Instead, they're left holding a very heavy bag and will take it to the grave.

I can appreciate this technology might negatively impact other countries more heavily, but, for me, it's easily the most exciting tech I interact with and I'm rooting for it whole-heartedly. I'm at around 1000 miles logged on Waymo and am part of their beta tester program for freeway usage.

I also think that post-Covid remote work has probably damaged incentives for increasing the density of cities more so than anything autonomous vehicles will do. San Francisco is actively cutting bus routes, bus density, and threatening to significantly cut BART stops due to budget constraints and reduction in ridership.

It's odd because I do get where you're coming from, and I feel like I should be your target audience, but, for me, the ship sailed so long ago that I struggle to relate to your position.

I read the whole thing, but, idk, surprised they didn't include a picture or clarify if this is strictly hardware, or hardware + software changes (with the software changes maybe back propagating to existing Drivers)