{
  "id": 10861304,
  "title": "LinkedIn Larpmaxxing",
  "url": "https://urgent.news/2026/09/30/linkedin-larpmaxxing",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-30T04:19:57.000Z",
  "source": {
    "name": "Hacker News",
    "slug": "hacker-news",
    "url": "https://hereticpleb.vercel.app/blog/linkedin-larpmaxxing/"
  },
  "original_language": "en",
  "account": "LinkedIn is a soul-sucking network where professionals engage in performative productivity, drowning in a sea of corporate speak and cursed artifacts beyond human comprehension. It's a place where normal human text is rendered useless and can only survive with the aid of language models like a radiation protection suit.\n\nThe platform is filled with unbearable projects that seem to be nothing more than a display of \"visionary\" and \"forward-thinking\" behavior. People constantly post projects such as computer vision applications, most of which seem to be nothing more than waving hand gestures, eye-tracking algorithms, and pothole detection systems.\n\nOne's third cousin, hit by a truck that day, served as a stark reminder of the absurdity of this digital landscape. Amidst the performative projects, people carry a fake tone of being \"visionary\" and \"forward-thinking,\" further adding to the overwhelming sense of inanity.\n\nThe projects posted on LinkedIn often lack any practical value or utility. It's not uncommon to see the same type of project being posted multiple times, with little to no improvement or innovation. The lack of genuine progress is due to the platform rewarding superficial accomplishments rather than real skill or expertise.\n\nWhile it may seem like an impressive feat to create a project like a YOLO (You Only Look Once) model, the truth is that producing such a project is relatively simple. The creator of the post only spent an hour and a half to learn and create the model, mainly by drawing boxes around objects and waiting for the model to be trained.\n\nThe process of creating a project like this begins with collecting data, which can be done by scrolling through one's feed and taking screenshots of relevant images. These images are then annotated by drawing boxes around objects that need to be detected. The annotated images are exported in the YOLOv8 format and used to train the model.\n\nOnce the model is trained, a simple Python script is written to run the model and perform object detection. This process is straightforward, with most of the effort spent on creating a visually impressive project rather than developing a useful and effective application.\n\nThe problem with LinkedIn lies in its reward system. Users are incentivized to create visually appealing projects rather than ones that are genuinely useful or innovative. It's a platform that encourages LARPing productivity, where the focus is on selling oneself for a job rather than improving one's skills or expertise.",
  "summary": null,
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}