All of the data we control (in the database, in our apis, etc) is integer cents. When we have to interface with a system that represents money using JSON numbers as dollars.cents, we parse or serialize it into an arbitrary precision decimal type. Hasn't been much of a problem.
Maybe I'm just not aware enough to notice any quality degradation, but I've been using Claude Code in repos that only have AGENTS.md and it just generally seems to know to read it when getting its bearings.
I have one and it's such a fantastic design. When I need to travel I just throw a tiny puck in my bag and it charges off the same brick that charges everything else I bring.
I spent a few months doing some coarse time tracking at work - basically I'd retroactively add and edit events on my calendar to reflect what I had actually done during the workday, down to 15 minute increments. I binned them into IC work, meetings, interruptions, and non-work stuff. While I did get some insights about where my time was going, it mostly just made me really anxious and input-oriented about my productivity and made feel guilty if I didn't end up working a full 8 hours on a given day. Stopping the time tracking was good for my mental health.
I'm a little skeptical of claims like this that involve migrating things like libraries, etc. I've done big refactors like this multiple times (albeit, in an "only" 500k-1m LOC codebase) with less powerful models and it is usually just 99% the same edits, with 1% requiring a close human eye to resolve a particularly painful breaking change.
EDIT: to be clear, it's still quite a helpful thing in terms of time saved, I just don't think it's necessarily the best indication of value-added from making models smarter when cases like this can often be handled by well-directed swarms of smaller ones.
Mostly I was not an "I have a script for that" guy before AI, except for the occasional VERY simple or VERY high ROI thing. Now I've got more oddball scripts that do things I couldn't be bothered to automate before, including: workarounds for buggy software, stitching tmux together with other tools, automating common operations so I don't have to use the god awful GitHub UI, etc.
Not an "oh shit" per se, but Gemini is a complete leap in terms of actual smarts over Google Assistant.
I was having problems getting it to parse my speech correctly when I was asking about "autolyse" (I was attempting to bake bread). All I had to do to fix it was add this to the system prompt: " I primarily interact with you via a speech to text mechanism. You should consider ways in which my words may not be accurately transcribed and attempt to infer the correct reading in context. When you do this, do not mention it - just proceed as if my words were recorded properly."
Never had that problem again. With Assistant though, if I had any issue like that I'd be waiting months or years for a fix, if it ever came.
For straightforward coding tasks I use gpt-5.3-codex on high or xhigh. Sometimes I try 5.5 but overall 5.3-codex is more than capable enough for most of my needs and quite a bit cheaper.
For more interactive/discussion/planning or orchestration stuff, I find myself going back and forth between Opus 4.7 and GPT 5.5. Still not sure which one I prefer.
I bought AMD as my last GPU purely because it meant I didn't have to stress out about how I was actually going to acquire one. I just walked into Microcenter, picked one off the shelf, and checked out. It was the crypto craze then, and I get the impression that this hasn't changed much today with AI sucking all the oxygen out of consumer electronics. Didn't care very much about DLSS or any other Nvidia specific features. That AMD works well on Linux only sweetened the deal.
I started seeing this a lot more with GPT 5.4. 5.3-codex is really good about patiently watching and waiting on external processes like CI, or managing other agents async. 5.4 keeps on yielding its turn to me for some reason even as it says stuff like "I'm continuing to watch and wait."
I wrote exactly this linter a while back after making the same mistake. Very annoying. Unlike you I did try to get it into golangci-lint but the process wore me down. In the age of LLMs maybe it'd be worth another try.
This one was funny to me because sure, it was accurate for my particular codebase, but also anyone paying attention to the company Slack would already know how often fires happen.
Yeah. I am the top committer at my current workplace, but I'd say that a majority of that gap is because my particular workflow results in many smaller commits than my coworkers.
Right? It's infuriating. Nearly all of the agentic coding best practices are things that we should have just been doing all along, because it turns out humans function better too when given the proper context for their work. The only silver lining is that this is a colossal karmic retribution for the orgs that never gave a shit about this stuff until LLMs.
my cat would be like "cool desk bro" and then plop themselves right on the keyboard or, even though I'm using a 32:9 monitor, exactly in front of the desktop window I happen to be working on
You can probably learn to do these things too with enough determination, but don't sell yourself short. Some CRUD apps can get deceptively complicated. Businesses have a way of coming up with just the right requirements to completely invalidate your architecture if you don't know what you're doing.