Alright, gather 'round the virtual campfire, folks. So, my team wanted to do some "competitive landscaping" (fancy talk for "see what the other guys are up to") and asked if we could automate it. The idea: scrape pricing pages, feature lists, and blog posts from ten competitors, then get a neat summary.
My first thought was "Great, I'll write a Python script, wrestle with 10 different HTML structures, fight off anti-bot protections, and then spend a week debugging." But then I thought, let's see if Kimi can be the duct tape and WD-40 for this job.
Here's the quick-and-dirty workflow I cobbled together:
1. **The Scrape:** I used a simple Playwright script (because sometimes you still gotta get your hands dirty) to pull raw text from the target pages. Saved each as a `.txt` file.
2. **The Ask:** I tossed all ten text files into Kimi with a prompt like this:
```text
I've attached text scraped from 10 competitor homepages and pricing pages.
Please analyze them and give me:
- A comparison table of pricing tiers (if info is present).
- 3 common themes in their marketing messaging.
- Any surprising or unique features mentioned.
- A bullet list of keywords they all seem to love.
Keep it concise, and highlight where info is missing from some sources.
```
The good, the bad, and the "meh":
* **It's shockingly good at synthesis.** It pulled out phrases like "developer-first," "zero-trust," and "seamless integration" that literally every site used. Made us realize our own copy was just... the same.
* **The table generation was a mixed bag.** Where pricing was clear, it built a decent markdown table. Where it was vague ("Contact Us"), it correctly noted the ambiguity. Saved hours of manual cross-referencing.
* **Hallucination watch:** It once "invented" a free tier for a competitor that definitely doesn't have one. I had to double-check. Always keep a human in the loop to fact-check the spicy bits.
* **The context limit is the real bottleneck.** If your scraped pages are huge, you'll hit the token wall. You need to pre-process: clean the text, strip junk, keep only the meat.
So, is it a magic bullet? Nah. It's more like a really smart, fast intern who occasionally makes up a cool-sounding fact. You still need to set up the data-gathering (scripts, APIs, whatever) and vet the output. But for turning a pile of messy text into a first-draft analysis in 30 seconds? Absolutely golden.
For a next step, I'm thinking of feeding its summary *back* into a script to generate a dashboard. Because why stop at one layer of automation? 😄
Anyone else tried using AI assistants for this kind of grunt-work analysis? Any gotchas I missed?
It's always DNS.