We've all been hearing from people talking about how amazing AI coding agents are for a while now. Many skeptics have tried them out, looked into how to make good use of them, used modern agentic tools, done context engineering, etc. and found that they did not live up to the claims being made, at least for their problem domain.
Talk is cheap, and we're tired of hearing people tell us how it's enabling them to make incredible software without actually demonstrating it. Your words might be true, or they might be just another over-exaggeration to throw on the pile. Without details we have no way of knowing, and so many make the empirically supported choice.
I recently vibe coded a video analysis pipeline with some related arduino-driven machine control. It was work to prototype an experience on some 3D printed hardware I’ve been skunking out.
By describing the pipeline and filters clearly, I had the analysis system generating useful JSON in an hour or so, including machine control simulation, all while watching TV and answering emails/slacks. Notable misses were that the JSON fields were inconsistent, and the python venvs were inconsistent for the piped way that I wanted the system to operate with.
Small fixes.
Then I wired up the hardware, and the thing absolutely crapped itself, swapping libraries, trying major structural changes, and creating two whole new copies of the machine control host code (asking me each time along the way). This went on for more than three hours, with me debugging the mess for about 20 minutes before resorting to 1) ChatGPT, which didn’t help, followed by 2) a few minutes of good old fashioned googling on serial port behavior on Mac, which, with an old sitting on the shelf Uno R3, meant that I needed to use the cu.* ports instead of tty.*, something that Claude Code had buried deeply in a tangle of files.
Curious about the failure, I told Claude Code to stop being an idiot and use a web browser to go research the problem of specifically locking up on the open operation. 30 seconds later, and with some reflective swearing from Opus 4.1, which I appreciate, I had the code I should have had 3 hours prior (along with other garbage code to clean up).
For my areas of sensing, computer vision, machine learning, etc., these systems are amazingly helpful if the algorithms can be completely and clearly described (e.g., Kalman filter to IoU, box blur followed by subsampling followed by split exponential filtering, etc.).
Attempts to let the robots work complex pipelines out for themselves haven’t gone as well for me.
I just had Claude code convert all my personal projects over to be dockerized, and then setup the deployment infra and scripts for everything, and finally move my server off of the nightmare nginx config file I was using.
Talk is cheap, and we're tired of hearing people tell us how it's enabling them to make incredible software without actually demonstrating it. Your words might be true, or they might be just another over-exaggeration to throw on the pile. Without details we have no way of knowing, and so many make the empirically supported choice.