Cursor for Non-Coders: CSV Wrangling, Ad Scraping, and Arguing With Yourself
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I wrote about a workflow for turning meeting transcripts into structured requirements using AI. Record the kickoff, transcribe it, extract decisions and action items, iterate. So when Product Makers Romania hosted a session called “Cursor for Non-Coders,” the premise felt familiar. One speaker took that same idea further: he drops a transcript into Cursor, chains it through a strategy memo template, then runs the output through a Devil’s Advocate framework. No code written. Just structured thinking inside a code editor.
10,000 rows of garbage data and a cold email dream
Radu went first with a use case that had nothing to do with writing software. He had a marketplace business (think EMAG, but only the marketplace side) with about 50 companies and 1,000 customers. Classic chicken-and-egg problem. He needed to onboard more companies to make the marketplace attractive, and he had a list of 10,000 potential companies to reach out to.
The catch? The data was a mess. Three different columns for the owner’s name, with values swapped between them. Company descriptions that read like someone’s autobiography. Some fields in French. Some fields empty. The kind of CSV that makes you question your life choices.
What he did next was beautifully iterative. He fed the CSV into Cursor, asked it to add the columns he needed (first name, core offering, target segment, promo URL), and then went step by step. Extract first names, but handle “Dr. James Chan” correctly instead of just grabbing the first word. Deduce core offering from verbose company descriptions. Translate French entries to English. Batch API calls in groups of 50 to keep costs down. After each step, he exported the CSV, opened it in Google Sheets, eyeballed it, and went back with corrections.
No code review. No folder structure. He didn’t even look at the code Cursor generated. He just talked to it, checked the output, and iterated. Nine steps later, he had 3,200 clean, personalized contacts. He loaded them into his cold email tool, sent the sequence, and 33 companies signed up. That’s a 1% conversion rate, which sounds small until you realize those 33 new companies drove 19% of sales in the first week. Average order value went up 2% too, which on his actual business model (not the EMAG analogy) was significant.
The part that stuck with me: he said at the end, when he looked at the actual code Cursor had written, it was a mess. V1, V2, V3 files everywhere, no organization. But it didn’t matter, because the output worked. That’s a fundamentally different relationship with code than what developers have. And it’s valid.
Scraping LinkedIn Ads without paying for a SaaS
Radu’s second use case was competitive intelligence. He wanted to study a competitor’s LinkedIn ad strategy, so he pointed Cursor at the LinkedIn Ad Library. The usual browser extensions didn’t work (LinkedIn probably sent them a cease and desist). So he had Cursor build a scraper using Playwright, a headless browser that scrolls through pages, clicks “Load More” buttons, and extracts everything: ad copy, CTA, creative URLs, impression ranges, run dates, advertiser info.
He used Granola as the example competitor for the demo, but the real project had 2,100+ ads to analyze. From the scraped data, he could identify which ads ran longest, which had the most impressions, and reverse-engineer the competitor’s messaging strategy. All without buying an enterprise SaaS tool or getting budget approval for a 10K/year subscription.
When someone asked if he could have done this without Cursor, his answer was honest: probably, but it would have been slower, more expensive, or involved a lot of copy-paste into Excel. The tool removed friction, not complexity.
Cursor as a thinking partner (not a doing partner)
Tibi took a completely different angle. He’s a product manager in healthcare, coming from fast-moving startups, and his problem wasn’t automation. It was thinking.
His workflow starts with a folder of context files: meeting transcripts, survey results, Jira exports, company strategy docs. Then he has a collection of reusable markdown files, each one a set of instructions for a specific task. Strategy memo template. Devil’s advocate framework. Executive summary format. Engineer perspective review. Base/bear/bull case analysis.
The magic is in how he chains them. He drops a transcript from a kick-off meeting with his Head of Product, then tells Cursor: “Take this transcript, run it through the strategy memo template, create a file.” Cursor reads both files, extracts the key points, and produces a structured one-pager. Then he says: “Now take that memo and run it through Devil’s Advocate.” And suddenly he has a document that systematically argues against his own strategy, poking holes in assumptions and highlighting risks.
He does this before walking into leadership meetings. Instead of getting caught off guard when someone spots an obvious gap he missed, he’s already stress-tested the argument. The tool isn’t writing for him. It’s arguing with him.
I liked his framing: “You can only mess up so many times before people stop believing you when you try to say something with confidence.” Cursor helps him be less wrong before he opens his mouth.
Why markdown files and not built-in features
Someone asked Tibi why he uses plain markdown files instead of Cursor’s built-in skills feature. His answer was portability. When he started, skills didn’t exist yet. But even now, he wants to be able to grab his files and move to Claude, or whatever becomes better next week. Every LLM understands markdown. No vendor lock-in.
He also made a point about metaprompting: if you hear a good framework on a podcast or read one in a book, you can ask Cursor to turn it into a reusable markdown prompt file. He gave the example of hearing about “base case, bear case, bull case” analysis in a finance newsletter and asking Cursor to create a product management version of it. Ten minutes later, he had a new tool in his toolbox.
There’s something genuinely clever about building a personal library of thinking frameworks as executable prompts. It’s like a second brain, except this one actually talks back.
The part where AI gets too enthusiastic
The funniest moment was when Tibi told the story of Cursor hallucinating a “Forest Score” metric. He was building a prototype for a dashboard that tracked branches (as in office branches). Cursor apparently went from “branch” to “tree” to “forest” and invented an entire metric around it. Complete with confidence.
A participant from the audience confirmed the same problem: the tools try to be too autonomous. They load skills you didn’t ask for, generate files you didn’t request, and run up costs doing things nobody wanted. Tibi’s solution was practical: his markdown files include explicit instructions like “don’t generate images,” “don’t create applications,” “we’re only thinking here.” Boundaries matter.
Pick one tool and go deep
I shared my own take during the session: I chose Claude Code, and I’m sticking with it. There’s too much to learn if you try to master everything. Every week there’s a new release from Cursor, Claude, OpenAI, Gemini. You’ll drown in FOMO if you chase all of them.
Tibi agreed but added nuance. For thinking and document work, he prefers Cursor. For prototyping, Claude Code feels better. For speed (with more errors), OpenAI’s Codex. You won’t discover your preferences without trying, but you also won’t get good at any of them if you spread yourself too thin.
The real skill isn’t mastering any particular tool. It’s context management. Knowing what information to give the AI, how to organize it, and when to hold back. We’re all becoming context managers now, whether we like it or not.
This was the second AI Talk in the Product Makers Romania series, after the Claude Code session in January. Tibi shared his 18 reusable prompts for anyone who wants to try this workflow. If you’re a PM who hasn’t opened a terminal or an IDE yet, this might be the gentlest on-ramp I’ve seen. Just don’t let it invent forest-related metrics without your permission.