Product Case Study
I built this because too many LinkedIn posts take a long time to say very little, and most summaries are just shorter versions of the same fluff.
LinkedinTLDR is a Chrome extension that helps readers get to the point faster and helps writers turn rough ideas into cleaner posts. It also became a useful product exercise in summarization quality, interaction design, and product tradeoffs.
Why I built it
A lot of LinkedIn posts are hard to scan. They use a strong hook, a personal story, and a confident tone, but the actual point is often buried in the middle.
The first instinct is to shorten them. That turned out to be the wrong problem statement. A shorter summary can still be vague. What people really want is the meaning of the post in plain language.
LinkedinTLDR ended up as two products in one. On the reading side, it summarizes LinkedIn posts directly on the page. On the writing side, it helps generate sensible posts, joke posts, and rewrites from rough notes.
The more interesting problem was the reading side: how to make a summary feel useful instead of robotic, cynical, or generic.
Product thinking
Help someone understand what a post is actually saying without forcing them to decode a long thread of setup, emotion, and filler.
Give people a way to turn rough ideas into clearer LinkedIn posts without locking them into one tone.
Keep the tool lightweight enough to use inside the browser while still making the result feel thoughtful and grounded.
Feature set
Iteration
I started with one-line summaries. They were fast, but many of them were useless. A short summary can still miss the point completely.
I tried sections like context, problem, insight, and takeaway. That worked for some posts, but it forced very different post types into the same mold.
The better direction was to classify the post first, then summarize based on what kind of post it actually was. That made the output more honest.
The hardest part was getting rid of vague language. A summary that says “the bottleneck moved” is still too abstract. The prompt now pushes toward concrete subjects, actions, and consequences.
Tradeoffs
Opening the extension every time felt heavy. Putting TLDR directly on the page made the product feel faster and more useful in context.
OpenAI makes the summaries better, but the product still needs to work when a key is missing or an API call fails. That fallback is rougher, but it keeps the extension usable.
Over-compression made the summaries sound vague. The product needed to stay concise without dropping the actual claim or consequence.
It is easy for a tool like this to become sarcastic. I wanted it to be skeptical when needed, but still useful and readable for normal posts.
Setup
chrome://extensions.Right now the API key is stored in extension local storage. That is okay for a personal prototype, but not the final production setup.
What this shows about my PM approach
The work improved when I stopped asking “How do I shorten this?” and started asking “How do I help someone understand this faster?”
Whether a summary opens in a popup or appears in place changes how often someone will actually use it.
Summary quality was not just a prompt issue. It was a mix of classification, output structure, UI, fallback behavior, and iteration from real examples.
Next
The next step is to make the summaries feel more connected and less like isolated lines, especially for mixed story-plus-opinion posts.
I’d want to track whether a user found the summary helpful, whether they copied it, and whether they used the summarizer again.
The OpenAI path is stronger today. The fallback still needs better clarity so the product feels reliable even without an API key.
The next version of this case study should include screenshots and before-and-after examples from real posts.