The latest articles on DEV Community by Aleksei Burich (@aleksei_burich). https://dev.to/aleksei_burich https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4242430%2Fd7774667-166f-4a5d-9d98-f39cb0d5dcbc.png https://dev.to/aleksei_burich en Aleksei Burich Fri, 13 Nov 2026 08:54:09 +0000 https://dev.to/aleksei_burich/show-dev-i-scored-16k-linkedin-profiles-most-of-them-land-around-59100-4cea https://dev.to/aleksei_burich/show-dev-i-scored-16k-linkedin-profiles-most-of-them-land-around-59100-4cea <p>About a year ago I started a side project: paste a LinkedIn URL, get a 0-100 score for the profile and a list of what to fix. It's called <a href="https://linkedoptimizer.ai" rel="noopener noreferrer">LinkedOptimizer</a>. 250 commits later it has scored 16,775 profiles, and the numbers are more interesting than I expected, so here they are, plus how the thing is built and a few things I broke along the way.</p> <h2> What the scores look like </h2> <p>The median profile scores 59. The average is 56.</p> <ul> <li>23% score under 40</li> <li>32% score 70 or higher</li> <li>you need 73 to be in the top quarter, and 82 to be in the top 10%</li> </ul> <p>Each profile gets four section scores. The averages:</p> <div class="table-wrapper-paragraph"><table> <thead> <tr> <th>Section</th> <th>Avg score</th> </tr> </thead> <tbody> <tr> <td>Headline</td> <td>59</td> </tr> <tr> <td>About</td> <td>57</td> </tr> <tr> <td>Skills</td> <td>57</td> </tr> <tr> <td>Experience</td> <td>53</td> </tr> </tbody> </table></div> <p>Experience is the weakest section, and it's the one the overall score weights most, because it's what recruiters spend the most time reading. That combination explains a lot of the low totals.</p> <p>The fixes the scorer suggests most often are boring. Bullets that list duties instead of results. A headline that's just a job title. An About section of two sentences. Skills that don't match the role you're actually going after (you can give it a target role, and the score changes a lot when you do).</p> <h2> How it works </h2> <p>The stack is plain:</p> <ul> <li>Flask app on Google Cloud Run, Postgres, Redis</li> <li>It reads the public profile from the URL you paste, so nobody has to log in with LinkedIn</li> <li>Gemini scores each section against the target role and writes the rewrites</li> <li>Paddle for payments</li> </ul> <p>The part that took the most debugging was rate limits. On busy days Gemini returns 429s. The first version just failed. Now it walks a list of models (several Flash versions) before it rotates API keys, so a 429 on one model usually means the request quietly lands on the next one.</p> <h2> Things I got wrong </h2> <p>I once shipped a typo in a model name. Every single analysis failed until I noticed. There was no alert for "100% of analyses failing", which says more about my monitoring than about Gemini.</p> <p>I ran a price test on the paid report: $9 vs $4.95. $4.95 won, which surprised me. Round two ($4.95 vs $6.99) is running now.</p> <p>I also put a heavy blur over the locked part of the report to push upgrades. Two days later I added the opposite: one real, unblurred example rewrite per section, because a blurred box gives people no reason to believe the paid part is any good.</p> <h2> Try it </h2> <p>The score and the diagnosis are free, no signup and no LinkedIn login: <a href="https://linkedoptimizer.ai" rel="noopener noreferrer">linkedoptimizer.ai</a>. If you run your profile through it, I'd like to hear where the score felt wrong. That's the feedback I learn the most from.</p> showdev webdev ai career