{"slug": "linkedin-larpmaxxing", "title": "LinkedIn Larpmaxxing", "summary": "A writer built a YOLO-based object detector in about 90 minutes to flag \"Excited to announce I made a YOLO project\" posts on LinkedIn, after collecting 200 screenshots of their feed with a scrot/xdotool script and annotating them in Roboflow over roughly 20 minutes. The author argues the ubiquitous hand-tracking and pothole-detection computer vision projects flooding the platform are near-identical tutorial or vibecoded output, noting a pothole detector that sends its data nowhere has little practical value. The project's stated purpose is to demonstrate how easy such LinkedIn showcase projects are to produce.", "body_md": "LinkedIn is a soul-sucking hole of professionalism and a pit of hell that not even my worst enemy should spend the rest of his days in.\n\nIt’s this concoction of performative productivity and corpo speak producing cursed artifacts beyond human comprehension.\n\nA place where normal human text goes to die and can only be kept alive by an LLM like a radiation protection suit.\n\nAnd I wanted to look into the performative projects people keep making that are all over my feed.\n\n## The Feed\n\nIt is legit the most unbearable stuff you would see.\n\nMy third cousin got hit by a truck🚚💥 today! Here’s what it taught me about business management👇\n\nAnd everyone carries this fake tone of being “visionary” and “forward-thinking” and “professional”\n\nIt’s SO tiring.\n\n## The Projects\n\nAnd then you look at the projects they are posting.\n\nGenuinely, every time I open LinkedIn, I get hit by these Computer Vision Projects of some guy waving his hand around, and it tracks them.\n\nLike, I wouldn’t mind if it was one of these, but it is legit all I see. It is just every other post some guy waving his hand around doing NONSENSE.\n\nIt’s not even useful. It’s not even something anyone would like using. It’s just “look at me computer vision”\n\nI am pretty sure they either vibecode it or get it from a tutorial. cuz EVERY SINGLE ONE IS THE SAME.\n\nBtw, you wanna know how bad it is? This image I put? I didn’t even go digging for it. I literally just opened LinkedIn, and this was the first thing I saw…and the second…and the third…and the fourth…and the fifth…\n\nI am not kidding; this was the second thing I saw just now.\n\nAlso, the thing that gets me is that the first image is at least a game, not a good one, sure, but it is something. But this pothole detection. What is the point? You know what else could detect potholes? Eyes. They are very, very good at detecting those.\n\nThey are not sending it to anything. Like, if it was connected to all car cameras for detection, well, first, mass surveillance, and also if you have so many potholes you can’t keep track of them and need crowd-sourced data for potholes, you have WAYYY bigger issues, but sure. If it was sending the api somewhere, that would be cute. BUT IT’S NOT.\n\nThe bigger idea is to use AI for smart infrastructure monitoring, where road conditions can be assessed more efficiently, and maintenance teams can make better data-driven decisions.\n\nThis project also showed me that real-world computer vision is not just about detecting objects; lighting, road conditions, camera angles, and overlapping detections can all affect performance.\n\nI am so tired of seeing these.\n\nHow difficult is it to produce one of these projects that looks impressive on LinkedIn?\n\n(spoiler. pretty easy.)\n\n## Making the YOLO Post Detector\n\nI decided to make a detector for the “🚀 Excited to announce I made a YOLO project” posts.\n\nI am learning this from scratch; I’ve never done this before. I just wanna see how hard it could be. Can’t critique cooking without ever cooking ramen.\n\nIt won’t be useful, but now you will know slop is in fact slop.\n\nIt took me an hour and a half to learn and make the whole thing, and most of it was just drawing boxes and waiting for the model to be trained.\n\nOkay, so the first part of making this is data. You need to train your model on some data that show the model what one of these posts looks like.\n\nI wrote this script to collect the images. It scrolls through my feed and takes screenshots:\n\n```\nmkdir -p raw_data\n\nsleep 5 # to switch to browser\n\nfor i in {1..200}; do\n\nscrot \"raw_data/slop_$(printf \"%03d\" $i).png\"\n\nxdotool key Page_Down\n\nsleep 3\n\ndone\n```\n\nMy script has collected 200 images, which should be good enough for now.\n\nNow I created a Roboflow account, created my project, and uploaded my screenshots.\n\nI just had to scroll through the screenshots and annotate them by drawing a box around the things I think need to be detected. It was pretty mechanical. Took about 20 minutes.\n\nOnce the images were annotated, I just exported them in the YOLOv8 format and downloaded the zip file of my dataset.\n\nNow it was time to train the model. And that can be done with basically no effort.\n\n``` python\nfrom ultralytics import YOLO\n\nmodel = YOLO('yolov8n.pt')\n\nresults = model.train(\n\ndata='dataset/data.yaml',\n\nepochs=50,\n\nimgsz=320,\n\nname='slop_detector'\n\n)\n```\n\ngreat! Just wait for the training to finish. It took me 30 mins cuz I have a potato pc.\n\nOnce the training finished, I wrote the Python script to run the model.\n\n``` python\nfrom ultralytics import YOLO\n\nimport sys\n\ndef main(image_path):\n\nmodel = YOLO('runs/detect/slop_detector/weights/best.pt')\n\nmodel(image_path, save=True, conf=0.10)\n\nmain(sys.argv[1])\n```\n\nYAYYY I got my very own slop detector!!!\n\nI think I can finally add computer vision expert, Python savant, and AI and ML thought leader to my resume.\n\n## Conclusion\n\nYeah, it took me an hour and a half to learn and build the whole thing. It’d be more interesting if they were optimizing the models or pushing the accuracy or whatever, but most of what you see on LinkedIn is just this: pretty trivial stuff with cool marketing on top.\n\nAnd nobody says anything because your comments show up on your profile. If a recruiter scrolls through and sees you calling slop slop, you’re the asshole. So everyone claps and moves on.\n\nThat’s the real problem. Nothing on that site rewards you for getting better. It’s a platform built around selling yourself for a job, so what survives isn’t skill; it’s looking interesting. It’s just LARPing productivity. Go to some of these profiles, and it’s the same guy detecting potholes over and over and over with zero signs of improvement.", "url": "https://wpnews.pro/news/linkedin-larpmaxxing", "canonical_source": "https://hereticpleb.vercel.app/blog/linkedin-larpmaxxing/", "published_at": "2026-09-30 04:19:57+00:00", "updated_at": "2026-09-30 04:48:24.603703+00:00", "lang": "en", "topics": ["computer-vision", "machine-learning", "artificial-intelligence"], "entities": ["LinkedIn", "YOLO", "Roboflow", "scrot", "xdotool"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/linkedin-larpmaxxing", "markdown": "https://wpnews.pro/news/linkedin-larpmaxxing.md", "text": "https://wpnews.pro/news/linkedin-larpmaxxing.txt", "jsonld": "https://wpnews.pro/news/linkedin-larpmaxxing.jsonld"}}