Build Your Own FREE SEO Tools Powered by DeepSeek-R1
ฝัง
- เผยแพร่เมื่อ 7 ก.พ. 2025
- Discover how to run DeepSeek R1 entirely offline using Ollama-and harness its AI capabilities to automate SEO tasks and web scraping. While it’s not as advanced as Anthropic Claude, this free local model can still power workflows like scraping sitemap URLs, filtering them by keywords, and turning them into JSON outputs for content generation. If you’re curious about building your own no-cost AI pipeline for data extraction and SEO, this is your step-by-step guide!
Chapters
00:01 - 00:59 | Introduction to DeepSeek R1 & Ollama
Why DeepSeek R1’s local deployment is exciting
Installing Ollama on Windows, choosing the right model size
01:00 - 02:54 | Running the Model Locally
Command-line basics for pulling and loading DeepSeek R1 (8B, 14B, etc.)
Testing basic prompts in the terminal
02:55 - 05:30 | Connecting Python to DeepSeek R1
Using a simple Python script to call the local model’s API
Demonstrating a quick example (SEO prompt, debugging, etc.)
05:31 - 09:02 | Building an SEO Workflow with Gina
Converting webpage HTML into LLM-friendly text
Setting up a multi-step approach for parsing sitemaps & scraping relevant pages
09:03 - 12:20 | Strengths & Limitations
Why R1’s intelligence may fall short for complex keyword filtering
How anthropic Claude outperforms it in more nuanced tasks
12:21 - 14:12 | Potential for Automated Content Generation
Turning CSV keyword lists into blog posts with minimal user intervention
Local hosting & privacy advantages
14:13 - End | Final Takeaways & What’s Next
Summary of R1’s capabilities for zero-cost AI workflows
Balancing free local models with more advanced (paid) solutions
Suggested Hashtags
#DeepSeek
#R1
#LocalAI
#Ollama
#SEOAutomation
#WebScraping
#Python
#Gina
#LLM
#MakeMoneyOnline
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Thank you - the new microphone is better with no keyboard noise
Nice but what's the point if at the end you have to use claude? then is not free :)
The local LLMs are ass, unless you have industry level cards that can use big models with large context windows. 8b is virtually useless, they have no practical use whatsoever.
It's 8B. 🤷🏻♂
why json mode