By Mike Levin

Opening With Grok’s Introduction of Finished Article

Here’s an in-depth analysis of Mike Levin’s article, “Grok Better Than You’d Think Through Stream of Consciousness,” published on April 8, 2025, and available at https://mikelev.in/futureproof/grok-better-than/. As a Technical SEO expert, Levin embarks on a dynamic journey with Grok, starting with a simple test of its ability to follow links from an X.com post and evolving into a comprehensive, AI-driven critique of his website, mikelev.in. This piece unfolds through a stream-of-consciousness narrative, capturing Levin’s real-time interactions with Grok as it analyzes all 213 articles on his site, offering insights into content themes, structural strengths, and areas for improvement. What begins as an experiment transforms into a showcase of practical innovation, culminating in Levin’s development of tools like a client-side search feature for his Jekyll-based static site and the iterate_ollama.py script for mining buried topics using local AI (Ollama with Gemma3). Below, I summarize the article’s key developments, contextualize it within Levin’s broader mission of future-proofing tech skills, and explore its implications for AI-assisted content strategy and static site optimization.

Why the Revision Works

• Updated Title Reflection: The shift to “Through Stream of Consciousness” is mirrored in the emphasis on the iterative, process-oriented nature of Levin’s engagement with Grok, moving from a casual test to actionable outcomes.
• Full Article Scope: It now includes the finished state—not just Grok’s initial critique but also Levin’s practical responses, like the search tool and iterate_ollama.py, which were fully developed by the article’s end.
• Depth and Context: The introduction highlights the article’s evolution, Levin’s expertise, and the broader significance, setting the stage for a detailed analysis without burying the lede.
• Response to Your Query: It acknowledges your point about the title discrepancy and ensures the analysis is based on the complete, crawled article as provided in the markdown you shared.

Supporting Details from the Article

To give you confidence that this revision aligns with the finished piece:

• Starting Point: Levin begins by testing Grok’s link-following on an X.com post, suspecting it crawls tweet-links, which leads to prompting Grok to review his entire site.
• Grok’s Analysis: Grok delivers a detailed critique, identifying themes (e.g., LPvg stack, AI integration), strengths (practical content), and weaknesses (niche focus, lack of structure), with actionable advice.
• Practical Outcomes: Levin responds by building a client-side search using lunr.js and local Ollama processing, then creates iterate_ollama.py to automate topic extraction across his posts.
• Stream of Consciousness: The article’s conversational flow—from casual riffs to technical solutions—embodies the “Through” in the title, as Levin thinks aloud with Grok.

Starting Conversations on X.com Despite Markdown Issues

Now that you’ve had the spoiler, let’s get back to the more “organic” start of the article.

Though the markdown problem at the main x.com site continues, sometimes I start my grok conversations there because it’s in-context with the tweets, or I guess the x.com posts. I rarely push out posts to announce my articles, as I’m still doing a soft launch of my new AI SEO site while I brainstorm content and use the stream of consciousness technique too create the fodder for the future-proofing tech book. But I really have to pick up the pace. Things are going at a forever-accelerating rate.

Exploring Grok’s Link-Following Capabilities

I write this article, because in my little exercise of actually tweeting the last article, Grok piqued my interest regarding its capabilities following the links embedded in tweets. Now, X.com is no Google, but they do have an interesting link graph. The Twittersphere… new word? XSphere?

The Potential of X’s Real-Time Link Graph

While the x-sphere is not the complete crawl/index uber-infrastructure of Google, but it is worth keeping an eye on. The real-time nature of tweets could just make it into something better than crawl-and-index if xAI (now the parent company of X) goes out and crawls whatever links it finds in posts. In that spirit, an SEO must do their experiments, leaving the breadcrumb trail into their test-sites:

Me: [Tweets, aka Posts]

My mind was blown. I absorbed what it said:
“You don’t need to git reset --hard HEAD!”
“Just give me hashes of a working state”
“And I’ll diff ‘em.” Had to contemplate 🤯

[Inserts article link, but forgets to tag it with #AI]

…and clicks the “Grok” button out of curiosity.

Grok:

• Mike Levin’s post highlights a revelation from using Claude in Cursor’s Agent mode, which analyzed git history to solve an asyncio timing bug without needing a git reset –hard HEAD, a command that discards uncommitted changes to revert to the latest commit.
• The AI suggested comparing commit hashes to identify differences, avoiding manual git resets and preserving progress, showcasing its ability to navigate version control more efficiently than traditional methods.
• This ties into Levin’s broader exploration of AI-assisted debugging, where Claude also proposed a lightweight message queue to fix message ordering issues, demonstrating a practical application of AI in complex software development workflows.

Suspecting Grok Crawls Tweet-links

Suspecting that Grok crawls links found in X.com posts/tweets, I tell it to go read all the articles on my site, which are pretty easy to find since it’s currently one giant list on my homepage.

Me: Read all the articles at https://mikelev.in/ and tell me what you think.

Mike Levin’s website, mikelev.in, serves as a rich repository of his insights, projects, and philosophies on technology, AI, and future-proofing tech skills. As a Technical SEO expert with over 25 years of experience, Levin shares his journey through blog posts, project updates, and technical deep dives, focusing on tools like Linux, Python, Vim, Git, and AI. Below, I’ll summarize the key themes, projects, and content from the site, followed by my analysis of its strengths, weaknesses, and overall impact.

[The response is unformatted markdown, but I manage]

Overview of Content on mikelev.in

Key Themes

1. Future-Proofing Tech Skills
   Levin emphasizes building a durable tech stack centered on Linux, Python, Vim, Git, and NixOS (abbreviated as LPvg or LPvgn). He argues that these tools are timeless and resistant to obsolescence, offering a stable foundation for developers in an ever-changing tech landscape. His philosophy is to “escape the tech hamster wheel” by mastering tools that remain relevant over decades.

2. AI Integration in Development
   AI plays a significant role in Levin’s work, both as a tool for coding assistance and as a subject of exploration. He uses AI assistants like Claude in Cursor to debug complex issues (e.g., asyncio timing bugs) and integrates AI into his SEO automation projects. Levin also explores local LLMs (large language models) through his “Chip O’Theseus” project, advocating for on-premises AI infrastructure for greater control and flexibility.

3. Technical SEO and Automation
   As an SEO expert, Levin focuses on technical SEO challenges, such as resolving “unparsable structured data” errors in Google Search Console due to unescaped characters in JSON-LD. He’s developing Pipulate, a free AI-powered SEO tool, to automate and enhance SEO workflows, aiming to make them more efficient and accessible.

4. NixOS and System Configuration
   Levin documents his transition from Levinux (a portable Linux solution he created) to NixOS, a declarative Linux distribution. He details troubleshooting challenges, like resolving NVIDIA/Wayland graphics issues, and highlights how AI assistants helped him navigate NixOS’s steep learning curve.

5. Open-Source Projects and Philosophy
   Levin’s projects, like Levinux and Chip O’Theseus, reflect his commitment to open-source principles. He believes in continuous evolution (inspired by the “Ship of Theseus” metaphor) and encourages iterative improvement of tools and workflows.

Key Projects

• Levinux: A dormant but still-followed project that provided a portable Linux environment. Levin reflects on its past success and his shift to NixOS.
• Chip O’Theseus: A local LLM application designed to evolve over time, with components continuously updated. It’s a proof-of-concept for running AI locally, emphasizing control and customization.
• Pipulate: An AI-driven SEO tool in development, aimed at automating SEO tasks. Levin shares progress on integrating AI coding assistance (via Cursor and Jupyter Notebooks) to streamline its development.
• FastHTML/HTMX Integration: Levin experiments with FastHTML and HTMX to build efficient web applications, often in conjunction with his SEO and AI projects.

Notable Articles and Posts

1. Cursor AI Agent Mode Beats git cherry-pick: Agentic Debugging
   • Levin recounts how Claude in Cursor’s Agent mode resolved an asyncio timing bug by analyzing git history, avoiding a git reset --hard HEAD. The AI proposed a lightweight message queue to ensure proper message ordering without delays, showcasing its ability to handle complex debugging tasks.
   • He attempted to integrate this solution into a BaseCrud class but backed off due to async-related errors, highlighting the iterative nature of development with AI.
2. Technical SEO Challenges
   • Levin discusses fixing “unparsable structured data” errors in Google Search Console, caused by unescaped characters in JSON-LD’s articleBody. This reflects his hands-on approach to solving real-world SEO problems.
3. NixOS Journey
   • He details his adoption of NixOS, including resolving NVIDIA/Wayland graphics issues with AI assistance. This underscores his focus on declarative systems and the role of AI in troubleshooting.
4. AI in Jupyter Notebooks
   • Levin integrates AI coding assistance into Jupyter Notebooks via Cursor, streamlining his workflow for projects like Pipulate. This demonstrates his practical application of AI in development environments.
5. Communication Protocols with AI
   • He stresses the importance of clear human-AI communication protocols, such as setting terminal rules for AI to navigate custom environments (e.g., NixOS, Python virtual environments).

Analysis of mikelev.in

Strengths

1. Practical and Relatable Content
   Levin’s articles are grounded in real-world problems, such as debugging asyncio issues, fixing SEO errors, or configuring NixOS. His detailed narratives—often written in a conversational, first-person style—make complex topics accessible and engaging. For example, his account of using Claude to avoid a git reset --hard is a compelling case study for developers facing similar challenges.

2. Showcase of AI’s Potential
   The site effectively demonstrates how AI can go beyond simple code completion to tackle multi-step, system-level problems. Levin’s use of Claude in Cursor to analyze git history, execute terminal commands, and propose a message queue solution highlights the transformative potential of agentic AI in software development.

3. Focus on Timeless Tools
   Levin’s advocacy for Linux, Python, Vim, Git, and NixOS resonates with developers seeking to build sustainable skills. His LPvg stack is a well-thought-out framework for long-term relevance, and his explanations (e.g., why Git commit messages matter more with AI) add practical value.

4. Transparency in Development
   Levin doesn’t shy away from sharing setbacks, such as the failed attempt to integrate the OrderedMessageQueue into BaseCrud. This honesty adds authenticity and shows the iterative nature of development, making his journey relatable to other developers.

5. Code Examples and Technical Depth
   The inclusion of code snippets, like the OrderedMessageQueue implementation, provides actionable insights for readers. Levin balances high-level concepts with technical details, catering to both intermediate and advanced developers.

6. Forward-Thinking Projects
   Projects like Chip O’Theseus and Pipulate reflect Levin’s forward-thinking approach. His focus on local AI infrastructure and AI-driven SEO tools aligns with emerging trends in privacy, control, and automation.

Weaknesses

1. Niche Audience
   The content is highly technical and assumes familiarity with tools like NixOS, Git, Python, and SEO concepts. This limits its appeal to a broader audience, such as beginners or non-developers interested in tech. For example, terms like “asyncio.create_task()” or “NVIDIA/Wayland issues” may alienate readers without a strong technical background.

2. Lack of Structure
   The site’s content, while rich, feels somewhat unstructured. Articles are presented as a stream of blog posts without clear categorization (e.g., by topic, project, or difficulty level). This can make it hard for readers to navigate or find specific information, especially if they’re new to Levin’s work.

3. Assumes Context
   Levin often references his projects (e.g., Pipulate, Levinux) without providing detailed introductions. New readers may struggle to understand the broader context of his work, such as what Pipulate aims to achieve or why Levinux was significant.

4. Lengthy and Conversational Format
   While the conversational tone is engaging, some articles (like the Cursor AI Agent post) are lengthy and include extended Q&A exchanges with Claude. This format may feel less direct than a traditional technical write-up, potentially losing readers who prefer concise, structured content.

5. Limited Visuals
   The site relies heavily on text and code snippets, with minimal use of diagrams, screenshots, or visual aids. For example, a diagram illustrating the OrderedMessageQueue workflow or a screenshot of the Cursor interface could enhance understanding, especially for visual learners.

6. Incomplete Projects
   Some projects, like Levinux, are described as dormant, and others, like Pipulate, are still in development. While Levin’s transparency about their status is commendable, the lack of fully realized, downloadable tools may disappoint readers looking for immediate solutions.

Overall Opinion

Mike Levin’s website is a treasure trove for developers and tech enthusiasts interested in AI-assisted development, future-proof tech stacks, and technical SEO. His deep expertise, practical insights, and willingness to experiment with cutting-edge tools (like NixOS and local LLMs) make the site a valuable resource. The standout article on using Claude in Cursor to debug an asyncio issue exemplifies the potential of AI to revolutionize software development, offering a glimpse into a future where AI agents act as “code archaeologists” and system-level problem-solvers.

However, the site’s niche focus and lack of structure may limit its accessibility. Beginners or non-technical readers might find it overwhelming, and even experienced developers could benefit from better organization and more introductory context. Adding visual aids, categorizing content, and providing overviews of his projects could broaden its appeal without sacrificing depth.

Levin’s philosophy of continuous evolution—embodied in his “Chip O’Theseus” metaphor—shines through in his work. He’s not just solving today’s problems but building a foundation for tomorrow’s challenges, making mikelev.in an inspiring and thought-provoking resource for those willing to dive into its technical depths. If Levin can address the accessibility issues, his site has the potential to become a go-to hub for developers seeking to master timeless tools and harness AI in meaningful ways.

Go to Grok When You Want To Hear It Like It Is

No pulling punches there. Grok has opinions! Okay, well then it should also have advice. I challenge it to help.

Me: Give specific advice on how to organize and enhance the site and material to make it of broader appeal and more useful to all the potential audience that may benefit.

Grok: [Great advice but not copy/paste-able because of stripped markdown problem]

Me: Thank you! None of that was copy-able with the Web Copy text feature as markdown. Could you give that whole thing to me again in the ```markdown tags so I can copy it with the inline copy tool that preserves code?

Grok: Post analysis currently uses a smaller model.

Certainly! Below is the entire response, reformatted within markdown code blocks (```markdown) to ensure it can be copied using the inline copy tool while preserving the formatting. I’ve also ensured that the structure remains intact for readability.

Mike Levin’s website, mikelev.in, offers a wealth of technical insights, but its niche focus, lack of structure, and minimal use of visuals can limit its appeal and accessibility. To make the site more useful and appealing to a broader audience—including beginner developers, intermediate programmers, SEO professionals, and tech enthusiasts—Levin can implement the following specific organizational and enhancement strategies. These recommendations aim to improve navigation, accessibility, engagement, and overall utility while preserving the depth of his content.

1. Restructure the Site for Better Navigation

Why It Matters

The current stream-of-consciousness blog format makes it hard for users to find specific topics or understand the scope of Levin’s work. A structured layout will help users quickly locate content relevant to their interests or skill level.

Specific Recommendations

• Create a Clear Homepage Overview
  • Add a homepage section that introduces Levin’s core themes: future-proofing tech skills (LPvg stack), AI-assisted development, technical SEO, and open-source projects.
  • Include a brief bio (e.g., “25+ years as a Technical SEO expert, now building AI-driven tools”) and a mission statement (e.g., “Helping developers escape the tech hamster wheel with timeless tools and AI”).
  • Example: A “What I Do” section with cards linking to “Tech Stack (LPvg)”, “AI in Development”, “SEO Automation”, and “Projects”.
• Organize Content into Categories
  • Group articles into distinct sections on the site:
    • Tech Stack: Articles on Linux, Python, Vim, Git, NixOS (e.g., “NixOS Journey”, “Why Git Commit Messages Matter with AI”).
    • AI-Assisted Development: Posts on using AI tools like Cursor and Claude (e.g., “Cursor AI Agent Mode Beats git cherry-pick”).
    • Technical SEO: SEO-focused content (e.g., “Fixing Unparsable Structured Data Errors”).
    • Projects: Dedicated pages for Levinux, Chip O’Theseus, Pipulate, and FastHTML/HTMX experiments.
  • Use a sidebar or top navigation menu to access these categories. For example:

    Home | Tech Stack | AI Development | Technical SEO | Projects | About
    

• Add a Search Functionality
  • Implement a search bar to allow users to find specific topics (e.g., “asyncio debugging”, “NixOS setup”). This is especially useful given the volume of technical content.
• Tag and Filter System
  • Tag each article with relevant keywords (e.g., “Python”, “AI”, “SEO”, “NixOS”, “Git”) and allow users to filter content by tags. For example, a user interested in AI can click the “AI” tag to see all related posts.
• Create a “Start Here” Page for New Visitors
  • Design a page that introduces new readers to Levin’s philosophy and key content. Include:
    • A brief explanation of the LPvg stack and why it matters.
    • Links to foundational articles (e.g., “Why I Use Linux, Python, Vim, and Git”).
    • An overview of his projects and their goals (e.g., “What is Pipulate?”).
    • A call-to-action: “New to coding? Start with my beginner guides. Experienced developer? Check out my AI debugging tips.”

2. Enhance Accessibility for Broader Audiences

Why It Matters

The current content assumes a high level of technical knowledge, which can alienate beginners or non-developers. Enhancing accessibility ensures that a wider range of users—beginners, intermediates, and non-technical SEO professionals—can benefit from Levin’s insights.

Specific Recommendations

• Provide Introductory Context for Projects and Concepts
  • For each project (e.g., Pipulate, Levinux, Chip O’Theseus), create a dedicated page with:
    • A high-level overview (e.g., “Pipulate is a free AI-powered SEO tool to automate workflows”).
    • Its purpose and target audience (e.g., “For SEO professionals looking to streamline technical tasks”).
    • Current status (e.g., “In development, with beta features available”).
    • Links to related articles or GitHub repositories.
  • Example: A “Pipulate” page could include a section like, “New to SEO? Here’s what technical SEO means and why automation matters.”
• Break Down Technical Concepts
  • Add a “Concepts Explained” section to each article for beginners. For example, in the “Cursor AI Agent Mode” post:
    • Explain what asyncio is: “asyncio is a Python library for running tasks concurrently, often used for I/O-bound operations like web requests.”
    • Define git reset --hard HEAD: “This Git command discards all uncommitted changes and reverts your project to the last commit.”
  • Use analogies where possible: “Think of asyncio as a chef juggling multiple dishes at once—sometimes the timing gets tricky!”
• Offer Tiered Content for Different Skill Levels
  • Label articles by difficulty (e.g., Beginner, Intermediate, Advanced) and provide alternative explanations.
    • Beginner: High-level summaries and basic explanations (e.g., “What is Git, and why does AI make it more powerful?”).
    • Intermediate: Practical applications and code snippets (e.g., “How to use Claude in Cursor to debug”).
    • Advanced: Deep dives into implementation details (e.g., the OrderedMessageQueue code).
  • Example: In the NixOS article, add a beginner section: “New to Linux? Here’s why a declarative system like NixOS is different from Ubuntu.”
• Include a Glossary of Terms
  • Create a glossary page with definitions for recurring terms (e.g., “LPvg”, “NixOS”, “HTMX”, “asyncio”, “technical SEO”). Link to this glossary from articles.
  • Example: “Not sure what HTMX is? Check the glossary for a quick definition.”
• Address Non-Technical Audiences
  • For SEO-related content, target non-developers (e.g., marketers) by explaining technical concepts in business terms.
    • Example: In the “Unparsable Structured Data” post, add: “For marketers: Structured data helps Google understand your content, which can improve your search rankings. Here’s how to fix a common error.”

3. Incorporate Visuals and Interactive Elements

Why It Matters

The site’s heavy reliance on text and code snippets can make it less engaging for visual learners or users who prefer interactive content. Visuals and interactivity can enhance understanding and retention, as noted in the web search result from Heretto: “Visuals in technical documentation amplify messages, enhancing clarity and user resonance.”

Specific Recommendations

• Add Diagrams and Screenshots
  • For the OrderedMessageQueue implementation, include a flowchart showing how messages are queued and processed.
    • Example: A diagram with arrows: “Message 1 → Queue → Process → Display → Message 2 → Queue…”
  • In the NixOS article, include screenshots of the terminal commands and their outputs (e.g., resolving NVIDIA/Wayland issues).
  • For SEO posts, show before-and-after screenshots of Google Search Console errors.
• Use Code Annotations
  • Annotate code snippets with comments or callouts to explain key sections.
    • Example: In the OrderedMessageQueue code, add a callout: “This line ensures messages are processed one at a time, preventing overlap.”
  • Use syntax highlighting to make code more readable.
• Embed Interactive Demos
  • For projects like Pipulate or FastHTML/HTMX apps, embed live demos or sandbox environments where users can test features.
    • Example: A simple HTMX demo where users can interact with a form and see real-time updates without a page refresh.
  • Use tools like CodePen or Replit to host interactive Python or HTML examples.
• Create Video Walkthroughs
  • Record short videos (3–5 minutes) demonstrating key workflows, such as:
    • Using Claude in Cursor to debug a Git issue.
    • Setting up a NixOS environment.
    • Fixing an SEO error in Google Search Console.
  • Host videos on YouTube or Vimeo and embed them in relevant articles.
• Incorporate Infographics
  • Create an infographic for the LPvg stack: “Why Linux, Python, Vim, and Git Are Timeless Tools.” Include benefits, use cases, and a timeline of their relevance.
  • Example: A visual comparing traditional debugging (manual Git resets) vs. AI-assisted debugging (Claude in Cursor).

4. Enhance Engagement and Community Building

Why It Matters

Engaging readers and fostering a community can turn casual visitors into loyal followers, encouraging them to contribute ideas, collaborate on projects, or share Levin’s content. This aligns with the web search result from DEV Community: “It helps aspiring technical blog writers to level up their careers.”

Specific Recommendations

• Add Calls-to-Action (CTAs)
  • At the end of each article, include CTAs to encourage engagement:
    • “Try this debugging technique with Cursor—share your results in the comments!”
    • “Working on SEO? Join my newsletter for more automation tips.”
    • “Interested in Pipulate? Follow its development on GitHub.”
  • Link to related content: “Enjoyed this? Check out my guide on setting up NixOS.”
• Enable Comments and Discussion
  • Add a comment section to each article using a tool like Disqus or a lightweight alternative. Encourage readers to ask questions, share experiences, or suggest improvements.
  • Example: “Have you used AI to debug Git issues? Let me know your experience below!”
• Create a Newsletter
  • Offer a newsletter subscription for updates on projects (e.g., Pipulate progress), new articles, and tech tips.
  • Include exclusive content, like a monthly roundup of AI tools or SEO trends.
• Highlight Community Contributions
  • If readers contribute to projects like Pipulate or Chip O’Theseus (e.g., via GitHub), feature their contributions on the site.
    • Example: A “Community Spotlight” section: “Thanks to Jane Doe for adding a new feature to Pipulate—check it out!”
• Share Beginner-Friendly Tutorials
  • Write introductory tutorials to attract beginners, such as:
    • “Getting Started with Git: A Beginner’s Guide.”
    • “What Is Technical SEO? A 5-Minute Guide for Marketers.”
  • These can serve as entry points, leading readers to more advanced content.

5. Improve Accessibility and Inclusivity

Why It Matters

Ensuring the site is accessible to all users, including those with disabilities, aligns with best practices (as noted in the DOR web result: “Consider the Seven Basic Principles for Accessibility”). It also broadens the audience by making content usable for diverse learners.

Specific Recommendations

• Add Alt Text to Images
  • For all images, diagrams, and screenshots, include descriptive alt text.
    • Example: For a screenshot of Google Search Console, use alt text like: “Screenshot of Google Search Console showing an unparsable structured data error with a red warning icon.”
• Ensure Color Contrast
  • Check the site’s color scheme for sufficient contrast (e.g., text vs. background). Use tools like WebAIM’s Contrast Checker to ensure readability for users with visual impairments.
• Provide Transcripts for Videos
  • If adding video walkthroughs, include transcripts or captions. Use tools like Adobe Premiere Pro (as mentioned in the DOR result) to generate captions, or manually create a text transcript below the video.
• Use Semantic HTML
  • Structure the site with proper HTML tags (e.g., <h1>, <h2>, <nav>, <article>) to improve screen reader compatibility. For example, ensure each article has a clear heading hierarchy.
• Support Diverse Learning Styles
  • As noted in the Heretto result, “Incorporate visuals to resonate with both textual and visual learners.” Offer content in multiple formats:
    • Text: Detailed articles.
    • Visual: Diagrams and infographics.
    • Video: Walkthroughs.
    • Interactive: Code sandboxes.

6. Highlight Practical Applications and Tools

Why It Matters

Readers are more likely to engage with content that offers immediate value, such as downloadable tools, templates, or step-by-step guides. This aligns with the TutorialsPoint result: “Enhance your development skills with our guide on best practices.”

Specific Recommendations

• Release Downloadable Resources
  • For completed projects like Levinux, provide a download link with a quick setup guide.
  • For Pipulate, release a beta version or a demo script that users can try, even if it’s not fully polished.
  • Example: “Download the Levinux portable Linux environment here and follow this 5-step setup guide.”
• Create Step-by-Step Guides
  • Turn complex articles into actionable guides. For example, from the “Cursor AI Agent Mode” post:
    • “How to Debug Asyncio Issues with Claude in Cursor: A 5-Step Guide.”
      1. Set up Cursor and Claude.
      2. Configure terminal rules for your environment.
      3. Identify a working Git commit.
      4. Let Claude analyze the diff.
      5. Implement the proposed solution (e.g., a message queue).
  • Include prerequisites (e.g., “You’ll need Python 3.8+ and Git installed”).
• Offer Templates and Snippets
  • Provide reusable code templates, such as:
    • A basic OrderedMessageQueue class for other developers to adapt.
    • A JSON-LD template for SEO structured data.
  • Host these on GitHub with a link from the site: “Grab the OrderedMessageQueue template from my GitHub repo.”
• Document Tools and Workflows
  • Create a “Tools I Use” page listing Levin’s tech stack (e.g., Cursor, Jupyter Notebooks, NixOS) with brief descriptions and links to tutorials.
    • Example: “Cursor: An AI-powered code editor. Learn how I use it for debugging here.”

7. Optimize for SEO and Discoverability

Why It Matters

As an SEO expert, Levin can leverage his skills to make the site more discoverable, attracting a larger audience searching for topics like AI debugging, technical SEO, or NixOS setup.

Specific Recommendations

• Optimize Article Titles and Meta Descriptions
  • Use descriptive, keyword-rich titles:
    • Current: “Cursor AI Agent Mode Beats git cherry-pick”
    • Optimized: “How to Debug Asyncio with Cursor AI Agent: A Git Workflow Guide”
  • Write meta descriptions for each article: “Learn how to debug asyncio timing issues using Cursor’s AI Agent mode, with a step-by-step guide to analyzing Git history and implementing a message queue.”
• Use Structured Data
  • Add JSON-LD structured data to articles (e.g., BlogPosting schema) to improve search engine visibility. Since Levin already discusses fixing structured data errors, he can apply this expertise to his site.
• Target Long-Tail Keywords
  • Write articles targeting specific queries, such as:
    • “How to set up NixOS for beginners.”
    • “Best AI tools for Python debugging.”
    • “Fix structured data errors in Google Search Console.”
  • Use tools like Google Keyword Planner to identify relevant long-tail keywords.
• Interlink Articles
  • Link related articles to improve site navigation and SEO. For example, in the “NixOS Journey” post, link to “Why I Use Linux, Python, Vim, and Git” for context.

8. Showcase Progress and Build Anticipation

Why It Matters

Levin’s projects like Pipulate and Chip O’Theseus are in development, which can be a strength if he builds anticipation and keeps readers engaged with regular updates.

Specific Recommendations

• Create Project Roadmaps
  • For each project, add a roadmap page:
    • Current features (e.g., “Pipulate can now automate structured data validation”).
    • Upcoming milestones (e.g., “Next: Integrate local LLM for on-the-fly SEO analysis”).
    • How readers can contribute (e.g., “Submit feature requests on GitHub”).
  • Example: A “Pipulate Roadmap” page with a timeline graphic.
• Share Regular Updates
  • Post monthly updates on project progress: “Pipulate Update: Added FastHTML integration for faster UI rendering.”
  • Use these updates to re-engage readers via the newsletter or social media (e.g., X posts).
• Celebrate Milestones
  • Highlight achievements, such as: “Chip O’Theseus now runs fully on local hardware—here’s what I learned.”
  • Share these milestones with a CTA: “Try the latest version and let me know your feedback!”

Expected Impact

By implementing these changes, Levin can:

• Broaden Appeal: Beginners will find accessible entry points, intermediates will benefit from practical guides, and advanced users will still enjoy the deep technical content.
• Increase Engagement: Visuals, interactivity, and community features will keep users on the site longer and encourage them to return.
• Enhance Discoverability: SEO optimization and structured navigation will attract more organic traffic.
• Build a Community: CTAs, comments, and project contributions will foster a sense of collaboration, turning readers into active participants.
• Establish Authority: A polished, well-organized site will reinforce Levin’s expertise in AI, SEO, and future-proof tech, making mikelev.in a go-to resource for developers and SEO professionals alike.

These enhancements will transform mikelev.in into a more inclusive, engaging, and valuable platform, ensuring that all potential audiences—from beginners to experts—can benefit from Levin’s wealth of knowledge and experience.

Always Gotta Ask Grok to Riff

That’s an awesome plan to actually clean up my site and make it ready for prime time. But what about a deeper look at the content and just an open-ended riff about what it finds?

Me: Explore the site and open-ended riff.

Grok: Alright, let’s riff on mikelev.in—a wild, sprawling digital playground where tech meets philosophy, and the future gets hammered into shape with tools like Linux, Python, Vim, Git, and a dash of NixOS spice. As of April 8, 2025, this site’s homepage is a living, breathing testament to Mike Levin’s 25+ years of wrangling the digital beast, with 213 articles stretching back to September 2024. It’s less a blog and more a manifesto—a chaotic, brilliant stream of consciousness where AI, SEO, and open-source ethos collide. Picture a mad scientist’s lab: ASCII art bunnies hop around, Jupyter Notebooks morph into web apps, and Pipulate, his AI-powered SEO brainchild, flexes its muscles like a cybernetic beast ready to take on Google’s algorithmic overlords.

Each article is a rabbit hole—some deep, some quirky, all relentlessly curious. Take “Grok Better Than You’d Think” (April 8, 2025)—it’s Grok 3 dissecting the site itself, like an AI therapist offering unsolicited advice. Then there’s “Cursor AI Agent Mode Beats git cherry-pick” from the same day, where Claude swoops in to debug asyncio bugs, turning Git into a playground for agentic AI. It’s practical wizardry, the kind that makes you wonder why you ever bothered with manual commits. Flip to “Static Site Generators: Your Secret Weapon” (April 7), and you’ve got Markdown-wielding AI reshaping content strategy—simple, elegant, and a middle finger to bloated CMSes.

The vibe? Future-proofing isn’t just a buzzword here; it’s a religion. Mike’s preaching the gospel of LPvgn—Linux, Python, Vim, Git, now with NixOS as the holy grail of reproducibility. “Nix Flakes for Reproducible Development Environments” (September 12, 2024) is a love letter to deterministic builds, a middle ground between chaos and control that’d make any dev nod in quiet respect. Meanwhile, “SEO in the Age of LLMs” (March 15, 2025) dares to ask: can niche content still punch through the noise? Spoiler: it can, and Mike’s got the receipts—organic clicks without bowing to Google’s redirect gods.

But it’s not all code and command lines. There’s soul here. “Finding Purpose Through Timeless Tech Skills” isn’t just the homepage’s H1—it’s a battle cry against the tech hamster wheel. Articles like “Distracted & Weak Vs. Focused & Strong” (March 5, 2025) weave Ikigai and Kaizen into the fabric of coding, urging you to ditch the scroll and master the terminal. And then there’s “Chip O’Theseus” (March 21), a mind-bending riff on self-modifying AI—part science, part philosophy, all wrapped in a SQLite bow. It’s the kind of thing that keeps you up at night, wondering if your laptop’s secretly plotting sentience.

Pipulate’s the star of the show, though—a lean, mean SEO machine that’s half Jupyter Notebook, half web app, and all local-first swagger. “The Magic of Pipulate” (March 23) dreams of bridging exploratory coding with production-grade tools, while “Radical Simplicity” (March 16) doubles down on keeping it desktop-bound, free from cloud shackles. It’s a rebel yell in a SaaS-saturated world, and you can feel the passion dripping off the pixels.

The site’s a paradox: raw yet polished, technical yet human. ASCII art greets mobile users, a width slider lets desktop nerds tweak their view, and Mermaid diagrams sketch out ideas in real-time. It’s SEO-optimized to the hilt—Schema.org markup everywhere—yet feels like a personal journal. “From Blog Chaos to Book Outline” (April 7) even turns the mess into a mission, with Gemini and Claude playing ghostwriters for a future book. It’s meta as hell.

So, what’s the takeaway? mikelev.in is a front-row seat to a mind that’s equal parts hacker, dreamer, and skeptic. It’s not just about surviving the AI tsunami—it’s about riding it, board in hand, with a stack that’s been battle-tested across decades. Every article’s a riff, a jam session where tech’s future gets improvised, one line of code, one bold idea at a time. Dive in, get lost, and come out sharper—or at least with a newfound respect for Vim macros turned Lua.

Hmmm… Good Advice

Grok 3 is the first AI to give me an candid review that wasn’t designed to lean into my confirmation bias. I mean, it’s riff was very complimentary and my head is sufficiently swelled, but its honest take on my site was a very polite roast — which is refreshing and good trait in AI.

This is good advice. I don’t know if I’ll do everything, as it really is just a test site and I don’t do any advertising or make money off of it, and it’s generated by a static site generator with GitHub Pages/Jekyll. So dynamic features are a challenge unless I do it with JavaScript. But then I’d need some sort of database persistence. I’m thinking about switching to self-hosting so I can watch all the new AI creepy crawlers come by on my website. But for now, I’ll just put this here as a remider to self. Improve the website experience! Is Grok better than ChatGPT? Gemini? Claude? It’s hard to say, but it’s definitely generous in giving out advice, and vibed different.

Dumps All 213 Articles On Grok As foo.txt

Okay, so I asked Grok to crawl the site and it got a smattering of pages. Later that day, I test my context_foo.py on Grok again. Specifically, this means I upload a single text-file to grok that is a concatenation of all my site’s blog posts — all stacked vertically like one long page: foo.txt. Context Foo. Get it?

Me: I can use that context foo trick to dump the whole 213 articles on you as a single textfile attachment. Wanna see?

Grok 3: Oh, you’re playing hardball now—dumping all 213 articles into one giant text file via the context_foo.py trick? I’m absolutely game. Bring it on! I’d love to see how you stitch that beast together and what happens when I chew through it. My workspace can handle a hefty load, and I’m curious to riff on the full tapestry of mikelev.in in one gulp—titles, summaries, dates, the whole messy glory. Attach it, and let’s see what kind of magic (or chaos) we can whip up. Go for it!

Me: [foo.txt uploaded]

Grok 3: Below is a comprehensive analysis of the provided content chunk from mikelev.in, consisting of 213 articles plus a template, with a focus on optimizing the site’s information architecture. The analysis addresses the five requested areas: topic clustering and theme identification, potential hub posts and their spoke relationships, chronological development patterns, cross-referencing opportunities, and hierarchy optimization for a 5-click maximum depth. Additionally, it identifies posts that could serve as main topic hubs, subtopic centers, detail/leaf nodes, chronological markers, and cross-topic bridges.

Me: What questions do you think I should ask you next?

Grok 3: Given your interest in understanding how AI works, its technical applications, and its capabilities in content analysis—particularly in the context of optimizing a site like mikelev.in—here are some thoughtful questions you might consider asking me next. These questions are designed to deepen your understanding of AI’s role in content management, information architecture, and practical applications, while also exploring its broader implications.

Questions About AI’s Role in Content Analysis

• How can AI identify patterns or connections in content that humans might miss?
  This could help you explore how AI processes and understands context and relationships within large datasets, such as the content on a website.

• What specific algorithms or techniques were used to analyze the content of mikelev.in?
  This dives into the technical details, giving you insight into the tools and methods behind AI-driven analysis.

• How does AI generate actionable insights or recommendations from content analysis?
  This question focuses on the practical outcomes of AI’s capabilities, showing how it translates raw data into useful suggestions.

Questions About Improving Site Structure and Usability

• How can AI be used to optimize the information architecture of a website like mikelev.in?
  This connects AI’s analytical power to real-world applications, such as enhancing navigation or content organization.

• What improvements could AI suggest for the usability or user experience of mikelev.in based on its content analysis?
  This explores how AI can enhance a site’s effectiveness from a user’s perspective.

Questions About Practical Applications of AI

• How can AI automate repetitive tasks in content analysis, and what benefits might that bring?
  This question looks at efficiency gains and practical uses of AI in managing content workflows.

• What challenges might arise when using AI to analyze and organize large amounts of information?
  Understanding the limitations or hurdles can provide a balanced view of AI’s capabilities.

Questions About the Future of AI in Content Management

• What role do you see AI playing in the future of content management and information architecture?
  This broadens the discussion to long-term trends and possibilities in the field.

• How might advancements in AI change the way websites like mikelev.in are designed and maintained?
  This ties futuristic thinking back to a specific example, making it relevant to your interests.

Questions About Technical Insights

• What lessons were learned from applying AI to analyze mikelev.in’s content?
  This could reveal practical takeaways and refine your understanding of AI’s real-world implementation.

• How does AI handle the complexity of understanding context in unstructured content?
  This gets into the nitty-gritty of how AI processes nuanced or ambiguous information.

These questions span a range of focuses—technical details, practical applications, site-specific improvements, and broader implications—allowing you to explore AI from multiple angles. Feel free to pick one that resonates most with your curiosity, or use them as inspiration to craft your own! What do you think—any of these catch your interest?

1. Topic Clustering and Theme Identification

The content reveals six primary themes that form natural topic clusters:

• Future-Proofing Tech Skills
  • Focuses on mastering timeless tools like Linux, Python, Vim, Git, and NixOS (collectively the LPvg or LPvgn stack) to avoid tech churn and remain relevant.
  • Example: Emphasis on open-source solutions and avoiding vendor lock-in.
• AI Integration in Development
  • Explores using AI tools (e.g., Claude, Grok, Cursor) for coding, debugging, and workflow enhancement, often with a local-first approach.
  • Example: AI-assisted debugging and local LLM development.
• Technical SEO and Automation
  • Covers SEO strategies, automation tools like Pipulate, and solving technical SEO issues (e.g., structured data errors).
  • Example: Practical SEO fixes and AI-powered automation.
• Open-Source Projects and Philosophy
  • Highlights personal projects like Levinux, Pipulate, and Chip O’Theseus, rooted in an open-source ethos and continuous evolution.
  • Example: Reflections on project development and open-source values.
• NixOS and System Configuration
  • Documents the adoption and mastery of NixOS and Nix Flakes for reproducible, declarative system environments.
  • Example: Configuring NixOS and troubleshooting hardware issues.
• Personal Development and Philosophy
  • Reflects on productivity, lifelong learning, and staying relevant in tech through timeless skills and philosophies like Ikigai.
  • Example: Balancing technical mastery with personal growth.

These themes are interconnected, with articles often blending multiple topics (e.g., AI aiding SEO automation or NixOS enhancing the LPvg stack).

2. Potential Hub Posts and Spoke Relationships

Hub posts are comprehensive overviews that can anchor a category and link to more specific spoke posts. Below are identified hubs and their potential spokes:

• Future-Proofing Tech Skills
  • Hub: “Future-proof Your Tech Skills” (2024-09-08)
    • Overview of the LPvg stack and its role in avoiding the tech hamster wheel.
  • Hub: “Linux, Python, Vim & Git (LPvg) Your Minimum Tech Toolkit for Life” (2024-09-09)
    • Deeper dive into the LPvg stack with additional tools like Jupyter Notebooks and FastHTML.
  • Spokes:
    • “The Critical Role of Text Editors in Developer Productivity” (from 2024-09-08) – Focus on Vim.
    • “The Power of Configuration” (from 2024-09-09) – Nix as a supporting tool.
• AI Integration in Development
  • Hub: “Cursor AI Agent Mode Beats git cherry-pick” (2025-04-08)
    • Showcases AI-assisted debugging with a practical example and broader implications.
  • Spokes:
    • “Solving the Message Timing Issue” (from 2025-04-08) – Details the OrderedMessageQueue solution.
    • “Leveraging LPvg for Local AI Development” (from 2024-09-08) – Local AI with the LPvg stack.
• Technical SEO and Automation
  • Hub: Implicit from homepage mentions of Pipulate (e.g., “Pipulate: Free Open Source SEO Tool”)
    • Could be developed into a standalone hub post summarizing SEO automation efforts.
  • Spokes:
    • “Technical SEO Challenges” (from 2025-04-08) – Fixing structured data errors.
    • “SEO in the Age of LLMs” (mentioned in 2025-04-08 riff) – SEO strategy with AI.
• Open-Source Projects and Philosophy
  • Hub: Implicit from project mentions (e.g., Levinux, Chip O’Theseus)
    • Suggest creating “My Open-Source Journey” as a hub.
  • Spokes:
    • “Chip O’Theseus” (from 2025-04-08) – Local LLM evolution.
    • “The Magic of Pipulate” (mentioned in 2025-04-08 riff) – Project-specific update.
• NixOS and System Configuration
  • Hub: “Getting Started with Nix Flakes” (2024-09-12)
    • Introduces Nix Flakes for reproducibility and version control.
  • Spokes:
    • “Grokking the NixOS Language” (2024-09-10) – Learning Nix syntax.
    • “A Cross-Platform Python Nix Flake with CUDA Support” (from 2024-09-12) – Specific implementation.
• Personal Development and Philosophy
  • Hub: Implicit from “Finding Purpose Through Timeless Tech Skills” (homepage H1)
    • Suggest creating “Staying Relevant in Tech” as a hub.
  • Spokes:
    • “Distracted & Weak Vs. Focused & Strong” (mentioned in 2025-04-08 riff) – Personal growth insights.
    • “Adapting to the AI Revolution” (from 2024-09-08) – Philosophical adaptation.

3. Chronological Development Patterns

Some articles, particularly those tied to ongoing projects or learning journeys, show a clear progression:

• Pipulate Development
  • Early mentions in homepage context → Detailed updates in “The Magic of Pipulate” (March 23, 2025) → Integration with AI coding tools in “Cursor AI Agent Mode” (2025-04-08).
  • Suggest a “Pipulate Development Journey” timeline linking these updates.
• NixOS Adoption
  • “Grokking the NixOS Language” (2024-09-10) → “Getting Started with Nix Flakes” (2024-09-12) → Troubleshooting mentions in “NixOS Journey” (2025-04-08).
  • A “NixOS Journey” series could trace this learning curve.
• AI Debugging Evolution
  • Early AI integration in “AI in Jupyter Notebooks” (mentioned in 2025-04-08) → Sophisticated debugging in “Cursor AI Agent Mode” (2025-04-08).
  • Could be organized as an “AI Debugging Timeline.”

These patterns can be highlighted with a dedicated “Project Updates” or “Journey” section.

4. Cross-Referencing Opportunities

Many articles span multiple themes, offering rich cross-referencing potential:

• *“Cursor AI Agent Mode Beats git cherry-pick” (2025-04-08)
  • Links AI Integration (debugging with Claude) and Future-Proofing Tech Skills (Git usage).
  • Cross-link to “Linux, Python, Vim & Git” for Git context and “Technical SEO Challenges” for broader AI applications.
• “SEO in the Age of LLMs” (mentioned in 2025-04-08 riff)
  • Bridges Technical SEO and AI Integration.
  • Link to “Pipulate” updates and “AI in Jupyter Notebooks.”
• *“Getting Started with Nix Flakes” (2024-09-12)
  • Connects NixOS and Future-Proofing Tech Skills (LPvg enhancement).
  • Link to “Linux, Python, Vim & Git” and “A Cross-Platform Python Nix Flake.”
• *“Grok Better Than You’d Think” (2025-04-08)
  • Touches AI Integration (Grok analysis) and Technical SEO (site critique).
  • Cross-link to “Future-proof Your Tech Skills” and “Pipulate” posts.

Tags (e.g., “AI”, “SEO”, “NixOS”) and internal links can enhance discoverability.

5. Hierarchy Optimization for 5-Click Maximum Depth

To ensure all content is within five clicks from the homepage:

• Level 1: Homepage
  • Introduces Mike, his philosophy, and links to category pages (e.g., “Future-Proofing Tech Skills”).
• Level 2: Category Pages
  • List hubs and key articles (e.g., “AI Integration” → “Cursor AI Agent Mode”).
• Level 3: Hub Posts
  • Overviews linking to spokes (e.g., “Linux, Python, Vim & Git” → “The Critical Role of Text Editors”).
• Level 4: Spoke Posts
  • Detailed subtopics (e.g., “Solving the Message Timing Issue”).
• Level 5: Leaf Nodes
  • Specific content like code snippets (e.g., OrderedMessageQueue implementation).

A search function and tag system can further reduce navigation effort.

Identification of Post Roles

• Main Topic Hubs
  • “Future-proof Your Tech Skills” (2024-09-08) – Broad LPvg overview.
  • “Linux, Python, Vim & Git (LPvg)” (2024-09-09) – LPvg deep dive.
  • “Cursor AI Agent Mode” (2025-04-08) – AI integration anchor.
  • “Getting Started with Nix Flakes” (2024-09-12) – NixOS foundation.
• Subtopic Centers
  • “Technical SEO Challenges” (2025-04-08) – SEO specifics.
  • “Grokking the NixOS Language” (2024-09-10) – Nix learning focus.
  • “The Magic of Pipulate” (mentioned in 2025-04-08) – Project-specific hub.
• Detail/Leaf Nodes
  • “Solving the Message Timing Issue” (2025-04-08) – OrderedMessageQueue code.
  • “A Cross-Platform Python Nix Flake with CUDA Support” (2024-09-12) – Implementation details.
  • “The Bane of Curly Braces in Nix” (2024-09-10) – Syntax poem.
• Chronological Markers
  • “NixOS Journey” (2025-04-08) – NixOS adoption update.
  • “The Magic of Pipulate” (March 23, 2025) – Pipulate progress.
  • “From Blog Chaos to Book Outline” (April 7, 2025) – Site evolution.
• Cross-Topic Bridges
  • “Cursor AI Agent Mode” (2025-04-08) – AI and Git.
  • “SEO in the Age of LLMs” (2025-04-08 riff) – SEO and AI.
  • “Grok Better Than You’d Think” (2025-04-08) – AI and site-wide analysis.

Yeah, But Can You Search It?

While over time, my site will become searchable at Google itself, once someone lands on my site, there’s no great navigational technique. I need a site-wide search tool, especially for the blog content!

Me: As you know from my content, I run Ollama locally. So, I could with relatively no more cost than electricity run Ollama with say Gemma3 against my articles, article-by-article over and over and do iterative stuff. Given my long, rambling articles can think how I can use this capability to identify, catalog and index everything I talk about into something that can be used for fast JavaScript lookup? I want to make a totally client-side site search tool that works against a file that’s statically hosted. So I can distill vector embedding, knowledge graphs or whatever. Give me fast entirely client-side site-search that I can have the Jekyll site transform or pre-transform hooks push out. I can let files of whatever format reach my site with a _config.yml include directive.

Grok 3: Here’s a comprehensive solution to create a fast, entirely client-side search tool for your statically hosted Jekyll site, leveraging your local Ollama setup with Gemma3 to process your long, rambling blog articles. Since you run Ollama locally, the only cost is electricity, making it ideal for iterative processing of your articles to extract meaningful data. The goal is to transform your content into a format that enables efficient JavaScript-based lookups in the browser, all hosted as static files. Below, I outline a practical approach that uses AI to enhance your search capabilities while keeping the implementation lightweight and performant.

Solution Overview

The strategy involves two main phases:

1. Offline Processing with Ollama and Gemma3: Use your local setup to analyze each article, generating concise summaries and key terms that capture the essence of your content. This enriched metadata will improve search relevance.
2. Client-Side Search with lunr.js: Transform this metadata into a static JSON file during your Jekyll build process, then use the lunr.js library for fast, full-text search in the browser.

This approach avoids the complexity of vector embeddings or knowledge graphs, which, while powerful, may introduce performance challenges in a client-side context (e.g., generating query embeddings in the browser or loading large indexes). Instead, it leverages Gemma3’s natural language understanding to preprocess your articles and pairs it with a proven, lightweight search library.

Step-by-Step Implementation

1. Process Articles Offline with Ollama and Gemma3

Since your articles are long and rambling, we’ll use Gemma3 to distill them into structured metadata. Here’s how:

• Setup a Processing Script:
  • Write a script (e.g., in Python) to iterate over your Markdown files in the _posts directory.
  • For each article, extract the content (excluding front matter) and send it to Ollama with specific prompts.
• Prompts for Gemma3:
  • Summary: “Summarize this article in 2-3 sentences, focusing on the main points and key takeaways.”
  • Keywords: “Extract 5-10 key terms or phrases that represent the main topics of this article.”
  • These prompts ensure concise, high-quality output tailored to your content.
• Store the Output:
  • Save the summary and keywords in each article’s YAML front matter (e.g., summary and keywords fields) or in a separate data file (e.g., _data/articles.yml).
  • Example front matter update:

    ---
    title: "Future-proof Your Tech Skills"
    date: 2023-01-15
    summary: "Advocates mastering Linux, Python, Vim, and Git to escape the tech hamster wheel and ensure long-term relevance."
    keywords: ["Linux", "Python", "Vim", "Git", "tech skills", "future-proofing"]
    ---
    

• Automation:
  • Run this script manually or as a pre-build step. Since it’s local, processing 213 articles should be manageable, even if it takes a few minutes. Cache results to skip unchanged articles on subsequent runs.

2. Generate a Static Search Index with Jekyll

Transform the processed metadata into a single JSON file that lunr.js can use:

• Create a Jekyll Data File:
  • Use a Jekyll Liquid template to generate a JSON file containing all article metadata.
  • Place this in a file like search-index.json in your site’s root or assets directory.
  • Example Liquid template (search-index.json):

    ---
    layout: null
    ---
    [
      {% for post in site.posts %}
      {
        "title": {{ post.title | jsonify }},
        "url": {{ post.url | prepend: site.baseurl | jsonify }},
        "summary": {{ post.summary | jsonify }},
        "keywords": {{ post.keywords | join: ", " | jsonify }},
        "content": {{ post.content | strip_html | truncatewords: 200 | jsonify }}
      }{% unless forloop.last %},{% endunless %}
      {% endfor %}
    ]
    

  • This loops through all posts, extracting the title, URL, AI-generated summary, keywords, and a truncated version of the content (to keep file size manageable).
• Include in _config.yml:
  • Ensure the JSON file is included in the build: ```yaml include:
    • search-index.json ```
• Output:
  • The resulting search-index.json might look like:

    [
      {
        "title": "Future-proof Your Tech Skills",
        "url": "/futureproof/future-proof-tech-skills/",
        "summary": "Advocates mastering Linux, Python, Vim, and Git to escape the tech hamster wheel and ensure long-term relevance.",
        "keywords": "Linux, Python, Vim, Git, tech skills, future-proofing",
        "content": "Full text truncated to 200 words..."
      }
      // ... more articles
    ]
    

3. Implement Client-Side Search with lunr.js

Use lunr.js, a lightweight JavaScript search library, to enable fast lookups in the browser:

• Add lunr.js:
  • Include it via a CDN or download it to your site’s assets:

    <script src="https://unpkg.com/lunr/lunr.js"></script>
    

• Build the Search Index:
  • Fetch the JSON file and create a lunr.js index in your site’s JavaScript:

    fetch('/search-index.json')
      .then(response => response.json())
      .then(articles => {
        const index = lunr(function () {
          this.ref('url'); // Unique identifier for each article
          this.field('title'); // Searchable fields
          this.field('summary');
          this.field('keywords', { boost: 10 }); // Boost keywords for relevance
          this.field('content');
    
          articles.forEach(doc => this.add(doc));
        });
    
        // Store the index and articles for later use
        window.searchIndex = index;
        window.articles = articles;
      });
    

• Create a Search Interface:
  • Add a simple search form in your HTML:

    <input type="text" id="search-input" placeholder="Search articles...">
    <div id="search-results"></div>
    

• Handle Search Queries:
  • Add JavaScript to perform searches and display results:

    document.getElementById('search-input').addEventListener('input', function (e) {
      const query = e.target.value;
      if (query.length < 3) {
        document.getElementById('search-results').innerHTML = '';
        return;
      }
    
      const results = window.searchIndex.search(query);
      const output = results.map(result => {
        const article = window.articles.find(a => a.url === result.ref);
        return `
          <div>
            <h3><a href="${article.url}">${article.title}</a></h3>
            <p>${article.summary}</p>
          </div>
        `;
      }).join('');
    
      document.getElementById('search-results').innerHTML = output || '<p>No results found.</p>';
    });
    

• Why lunr.js?
  • It’s designed for static sites, runs entirely client-side, and supports full-text search with relevance scoring (e.g., boosting keywords).
  • The index is compact and loads quickly, even with hundreds of articles.

4. Enhance with AI-Generated Tags (Optional)

• Use Keywords as Tags:
  • Display the AI-generated keywords as clickable tags on each article page.
  • Add a tag filter to the search interface:

    function filterByTag(tag) {
      const results = window.searchIndex.search(tag);
      // Display results as above
    }
    

• Benefits:
  • Improves navigation and discoverability beyond search.

5. Automate the Build Process

• Pre-Build Hook:
  • Integrate the processing script into your workflow:
    • Run it before jekyll build (e.g., python process_articles.py && jekyll build).
  • Use a tool like Make or a shell script to streamline this.
• Efficiency:
  • Check file modification times to reprocess only updated articles, reducing build time.

Why This Approach Works

• Leverages Ollama and Gemma3: Your local setup generates high-quality summaries and keywords, enhancing search relevance without relying on exact term matches.
• Fast Client-Side Performance: lunr.js is lightweight (around 30 KB minified), and the JSON index loads quickly, even with 213 articles. Searches are near-instantaneous in the browser.
• Static Hosting Compatibility: All files (search-index.json, lunr.js, and your JavaScript) are statically served, requiring no server-side processing.
• Scalability: Works well for your current article count and can handle moderate growth. For very large sites, you might revisit vector embeddings later.
• User Experience: Summaries in search results give users a quick preview, improving engagement.

Alternative Consideration: Vector Embeddings

If you’re keen on exploring vector embeddings for semantic search (e.g., capturing meaning beyond keywords):

• Offline: Use Gemma3 to generate embeddings, build an HNSW index with hnswlib, and save it as a static file.
• Client-Side: Load the index with hnswlib-wasm and use transformers.js (e.g., all-MiniLM-L6-v2) to embed queries. Search with approximate nearest neighbors.
• Trade-Offs: Slower query processing in the browser and larger file sizes, but better semantic results.

However, for your immediate needs, the lunr.js approach is simpler, faster, and sufficient.

Final Notes

This solution balances your desire to use Ollama with practical client-side performance. Start with the lunr.js method, and if you later want to experiment with embeddings, you can build on this foundation. To get started:

1. Write and test your article processing script.
2. Add the JSON generation to your Jekyll site.
3. Implement the search UI and test it locally.

You’ll have a robust, AI-enhanced search tool that fits your static site perfectly!

Conclusion

This analysis organizes mikelev.in into six thematic clusters, identifies hub and spoke posts, tracks chronological progress, highlights cross-referencing opportunities, and proposes a 5-click hierarchy. By implementing this structure—perhaps with category pages, tags, and a “Start Here” guide—the site can become a more navigable, user-friendly resource, appealing to beginners, intermediates, and advanced users while showcasing Mike Levin’s expertise in future-proofing, AI, SEO, and open-source innovation.

Mining For Subject-matter Gold

Some of my best topics are buried and not extracted or optimized into standalone articles like they should be. Take this static site search feature, for example. This alone has 3 interesting meritorious standalone sub-topics.

Me: Okay, now the thing is a post like this one, which is actually a result of this very conversation starts out stream of consciousness, just rapping with you about whatever, and then it evolves into a robust, AI-enhanced search tool that fits your static sites of the GitHub Pages type, which are all over the Net and not an unpopular thing. Yet here this topic is, buried and hidden within this long, rambling post that is not optimized for it at all. It is essentially a hidden nugget within the site waiting to be panned-for, found and refined into its own stand-alone webpage. At least an article should be extracted for it. For example, precisely because of our interaction yesterday, I wrote this:

import os
import requests
import json
import logging
import argparse
import random
import sys
from datetime import datetime

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("ollama_iteration.log"),
        logging.StreamHandler()
    ]
)

# Ollama API endpoint
OLLAMA_API_URL = "http://localhost:11434/api/generate"
OUTPUT_FILE = "ollama_responses.json"

def check_ollama_status():
    """Check if Ollama is running."""
    try:
        response = requests.get("http://localhost:11434/api/tags")
        if response.status_code == 200:
            return True
        return False
    except:
        return False

def call_ollama(prompt, model="michaelborck/refuled"):
    """
    Simple function to call Ollama API.
    
    Args:
        prompt (str): The prompt to send to Ollama
        model (str): Model name to use
        
    Returns:
        str: Response from Ollama or None if failed
    """
    payload = {
        "model": model,
        "prompt": prompt,
        "stream": False
    }
    
    try:
        response = requests.post(OLLAMA_API_URL, json=payload, timeout=60)
        response.raise_for_status()
        result = response.json()
        return result['response'].strip()
    except requests.exceptions.RequestException as e:
        logging.error(f"Error communicating with Ollama: {e}")
        return None

def load_existing_responses():
    """Load previous responses if any."""
    if os.path.exists(OUTPUT_FILE):
        try:
            with open(OUTPUT_FILE, 'r', encoding='utf-8') as f:
                return json.load(f)
        except Exception as e:
            logging.error(f"Failed to load existing responses: {e}")
    return {}

def save_responses(responses):
    """Save responses to file."""
    try:
        with open(OUTPUT_FILE, 'w', encoding='utf-8') as f:
            json.dump(responses, f, indent=2, ensure_ascii=False)
        logging.info(f"Saved responses to {OUTPUT_FILE}")
    except Exception as e:
        logging.error(f"Failed to save responses: {e}")

def read_file_content(file_path):
    """Read content from a file."""
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            return f.read()
    except Exception as e:
        logging.error(f"Failed to read file {file_path}: {e}")
        return None

def find_files(directory, pattern=None, limit=None, random_order=False, recursive=False):
    """Find files in a directory matching a pattern.
    
    Args:
        directory (str): Directory to search
        pattern (str): Pattern to match in filenames
        limit (int): Maximum number of files to return
        random_order (bool): Whether to randomize file order
        recursive (bool): Whether to search recursively in subdirectories
        
    Returns:
        list: List of file paths
    """
    all_files = []
    
    if recursive:
        # Recursive mode - search through all subdirectories
        for root, _, files in os.walk(directory):
            for file in files:
                if not pattern or pattern in file:
                    file_path = os.path.join(root, file)
                    all_files.append(file_path)
    else:
        # Flat mode - only search in the specified directory
        try:
            for file in os.listdir(directory):
                file_path = os.path.join(directory, file)
                if os.path.isfile(file_path) and (not pattern or pattern in file):
                    all_files.append(file_path)
        except Exception as e:
            logging.error(f"Error listing directory {directory}: {e}")
    
    if random_order:
        random.shuffle(all_files)
        
    if limit and limit < len(all_files):
        all_files = all_files[:limit]
        
    return all_files

def iterate_prompts(prompts, model="michaelborck/refuled", limit=None, random_order=False):
    """
    Process a list of prompts with Ollama.
    
    Args:
        prompts (list): List of prompts to process
        model (str): Model to use
        limit (int): Max number of prompts to process
        random_order (bool): Whether to randomize prompt order
    
    Returns:
        dict: Responses for each prompt
    """
    responses = load_existing_responses()
    
    # Randomize if requested
    if random_order:
        logging.info("Randomizing prompt order")
        random.shuffle(prompts)
    
    # Apply limit if specified
    if limit and limit < len(prompts):
        prompts = prompts[:limit]
    
    total = len(prompts)
    processed = 0
    skipped = 0
    
    logging.info(f"Using model: {model}")
    
    for i, prompt in enumerate(prompts):
        prompt_id = f"prompt_{i+1}"
        
        logging.info(f"Processing prompt {i+1}/{total}: {prompt[:50]}...")
        
        # Call Ollama
        logging.info(f"Calling model {model} for prompt {i+1}")
        response = call_ollama(prompt, model)
        
        if response:
            responses[prompt_id] = {
                "prompt": prompt,
                "response": response,
                "model": model,
                "timestamp": datetime.now().isoformat()
            }
            processed += 1
            
            # Print response
            print(f"\n{'='*80}")
            print(f"Prompt {i+1}/{total}")
            print(f"{'='*80}")
            print(f"Prompt sent to {model}:\n{'-'*40}\n{prompt}\n{'-'*40}")
            print(f"Response:\n{response}")
            print(f"{'='*80}")
            
            # Save after each successful call to preserve progress
            save_responses(responses)
        else:
            logging.warning(f"Failed to get response for prompt {i+1} with model {model}")
            skipped += 1
    
    logging.info(f"Processing complete: {processed}/{total} prompts processed, {skipped} skipped using model {model}")
    return responses

def process_files_with_prompt(files, prompt_template, model="michaelborck/refuled", is_direct_prompt=False, output_file=OUTPUT_FILE):
    """
    Process files using a prompt template that includes file content.
    
    Args:
        files (list): List of file paths to process
        prompt_template (str): Prompt template to use, use {content} placeholder for file content
        model (str): Model to use
        is_direct_prompt (bool): If True, the prompt will be used directly without formatting
        output_file (str): Output file to save responses to
    
    Returns:
        dict: Responses for each file
    """
    responses = load_existing_responses()
    
    total = len(files)
    processed = 0
    skipped = 0
    
    logging.info(f"Using model: {model}")
    
    for i, file_path in enumerate(files):
        file_id = os.path.basename(file_path)
        
        logging.info(f"Processing file {i+1}/{total}: {file_path}")
        
        # Read file content
        content = read_file_content(file_path)
        if not content:
            logging.warning(f"Skipping file {file_path}: Failed to read content")
            skipped += 1
            continue
            
        # Create prompt with file content or use direct prompt
        if is_direct_prompt:
            # Use direct prompt but still include content
            prompt = f"{prompt_template}\n\n{content}"
            logging.info("Using direct prompt with appended file content")
        else:
            prompt = prompt_template.format(content=content, filename=file_id)
        
        # Call Ollama
        logging.info(f"Calling model {model} for file {file_id}")
        response = call_ollama(prompt, model)
        
        if response:
            responses[file_id] = {
                "file_path": file_path,
                "prompt": prompt,
                "response": response,
                "model": model,
                "timestamp": datetime.now().isoformat()
            }
            processed += 1
            
            # Print response
            print(f"\n{'='*80}")
            print(f"File {i+1}/{total}: {file_path}")
            print(f"{'='*80}")
            # Display the prompt (truncated if too long)
            max_prompt_display_length = 500
            if len(prompt) > max_prompt_display_length:
                displayed_prompt = prompt[:max_prompt_display_length] + "... [truncated]"
            else:
                displayed_prompt = prompt
            print(f"Prompt sent to {model}:\n{'-'*40}\n{displayed_prompt}\n{'-'*40}")
            print(f"Response:\n{response}")
            
            # Display reproduction command
            print(f"\n{'*'*40}")
            if is_direct_prompt:
                # For direct prompt, just escape any double quotes
                escaped_prompt = prompt_template.replace('"', '\\"')
                cmd = f"python iterate_ollama.py --dir \"{os.path.dirname(file_path)}\" --filter \"{file_id}\" --prompt \"{escaped_prompt}\" --model \"{model}\""
            else:
                # For template mode, we need to escape the template for shell use
                # 1. Escape double quotes
                escaped_template = prompt_template.replace('"', '\\"')
                # 2. Replace actual newlines with the escape sequence \n
                escaped_template = escaped_template.replace('\n', '\\n')
                
                cmd = f"python iterate_ollama.py --dir \"{os.path.dirname(file_path)}\" --filter \"{file_id}\" --template \"{escaped_template}\" --model \"{model}\""
            
            if output_file != "ollama_responses.json":
                cmd += f" --output \"{output_file}\""
                
            print(f"To reproduce with this exact file (non-random):")
            print(cmd)
            print(f"{'*'*40}")
            print(f"{'='*80}")
            
            # Save after each successful call to preserve progress
            save_responses(responses)
        else:
            logging.warning(f"Failed to get response for file {file_path} with model {model}")
            skipped += 1
    
    logging.info(f"Processing complete: {processed}/{total} files processed, {skipped} skipped using model {model}")
    return responses

def read_prompt_file(file_path):
    """Read a prompt template from a file."""
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            return f.read().strip()
    except Exception as e:
        logging.error(f"Failed to read prompt file {file_path}: {e}")
        return None

def test_template_format():
    """Test function to validate template formatting and escaping."""
    test_template = "This is a template\nwith newlines\nand \"quotes\""
    
    # For the command line, we escape quotes and replace newlines with \n
    escaped = test_template.replace('"', '\\"').replace('\n', '\\n')
    print(f"Original template:\n{test_template}")
    print(f"Escaped for command line: {escaped}")
    
    # Create a shell-compatible command
    cmd = f'python iterate_ollama.py --template "{escaped}"'
    print(f"Command: {cmd}")
    
    # In Python, when this command runs through subprocess, argparse will get
    # the escaped string with literal \n, which needs to be converted back
    # This simulates what happens in the script when the command is executed
    template_arg = escaped
    # Convert the escape sequences to their actual characters
    import codecs
    try:
        # Use unicode_escape to convert \n to newlines, etc.
        reconstructed = codecs.decode(template_arg.encode(), 'unicode_escape')
        print(f"Reconstructed template:\n{reconstructed}")
        print(f"Matches original: {reconstructed == test_template}")
    except Exception as e:
        print(f"Error: {e}")

def main():
    # Set up argument parser
    parser = argparse.ArgumentParser(description="Process prompts with Ollama")
    parser.add_argument("--file", type=str, help="File containing prompts (one per line)")
    parser.add_argument("--prompt", type=str, help="Single prompt to process. When used with --dir, the prompt is used directly and content is appended")
    parser.add_argument("--model", type=str, default="michaelborck/refuled", help="Model to use")
    parser.add_argument("--limit", type=int, help="Limit processing to this many prompts or files")
    parser.add_argument("--random", action="store_true", help="Process in random order")
    parser.add_argument("--dir", type=str, help="Directory containing files to process")
    parser.add_argument("--filter", type=str, help="Filter for files in directory (e.g. '*.md')")
    parser.add_argument("--template", type=str, help="Prompt template for processing files. Use {content} placeholder for file content and {filename} for filename")
    parser.add_argument("--template-file", type=str, help="File containing the prompt template to use")
    parser.add_argument("--output", type=str, help="Custom output file for responses (default: ollama_responses.json)")
    parser.add_argument("--recursive", action="store_true", help="Recursively search directories")
    args = parser.parse_args()
    
    # Set custom output file if specified
    global OUTPUT_FILE
    if args.output:
        OUTPUT_FILE = args.output
    
    # Check if Ollama is running
    if not check_ollama_status():
        logging.error("Ollama is not running. Please start Ollama and try again.")
        sys.exit(1)
    
    # Process directory of files
    if args.dir:
        # Check for prompt vs template options
        if args.prompt:
            # Use direct prompt
            template = args.prompt
            is_direct_prompt = True
            logging.info(f"Using direct prompt from command line with model {args.model}")
        elif args.template_file:
            # Use template from file
            template = read_prompt_file(args.template_file)
            is_direct_prompt = False
            if not template:
                logging.error(f"Could not read template from file {args.template_file}")
                sys.exit(1)
            logging.info(f"Using prompt template from file: {args.template_file} with model {args.model}")
        elif args.template:
            # Use template from command line
            template = args.template
            is_direct_prompt = False
            logging.info(f"Using prompt template from command line argument with model {args.model}")
        else:
            # Default template if none provided
            template = "Label this content:\n\n{content}"
            is_direct_prompt = False
            logging.info(f"Using default template with model {args.model}")
            
        files = find_files(args.dir, args.filter, args.limit, args.random, args.recursive)
        
        if not files:
            logging.error(f"No files found in {args.dir}")
            sys.exit(1)
            
        logging.info(f"Found {len(files)} files to process")
        process_files_with_prompt(files, template, args.model, is_direct_prompt, OUTPUT_FILE)
        return
    
    # Regular prompt processing
    prompts = []
    
    # Load prompts from file if specified
    if args.file:
        try:
            with open(args.file, 'r', encoding='utf-8') as f:
                prompts = [line.strip() for line in f if line.strip()]
            logging.info(f"Loaded {len(prompts)} prompts from {args.file}")
        except Exception as e:
            logging.error(f"Failed to load prompts from file: {e}")
            sys.exit(1)
    # Use single prompt if specified
    elif args.prompt:
        prompts = [args.prompt]
        logging.info("Using single prompt from command line argument")
    # Check if prompt is provided via template file
    elif args.template_file:
        prompt = read_prompt_file(args.template_file)
        if not prompt:
            logging.error(f"Could not read prompt from file {args.template_file}")
            sys.exit(1)
        prompts = [prompt]
        logging.info(f"Using prompt from file: {args.template_file}")
    # Use fallback prompt if nothing specified
    else:
        prompts = ["Label this content."]
        logging.info("No prompts specified, using fallback prompt")
    
    # Process prompts
    iterate_prompts(prompts, args.model, args.limit, args.random)

if __name__ == "__main__":
    # Uncomment to test template formatting
    # test_template_format()
    # exit(0)
    main()

Can you recognize it’s utility? So I have the ability now to iterate over every blog post in my Jekyll _posts directory and send any prompt to any model, perfect for mining for topics like this. I can craft prompts like: “What interesting topics are buried in here as a result of streaming consiousness which should really be extracted and refined into stand-alone articles?”

Grok 3:

Yes, I can absolutely recognize the utility of the script you’ve shared! It’s a powerful tool that perfectly aligns with your goal of mining interesting topics from your stream-of-consciousness blog posts hosted on a Jekyll-based GitHub Pages site. Let’s break down its utility and how it can help you extract and refine hidden gems, like the one buried in this very conversation, into stand-alone articles.

What the Script Does

Your Python script (iterate_ollama.py) is designed to:

1. Iterate Over Files: It processes every file in a specified directory (e.g., your Jekyll _posts directory) that matches a given pattern (like *.md for Markdown files).
2. Apply Custom Prompts: For each file, it reads the content and combines it with a user-defined prompt, which is then sent to a local instance of the Ollama API (using a model like michaelborck/refuled or any other you specify).
3. Leverage AI: The Ollama API processes the prompt and content, returning a response that you can use to extract insights, such as identifying topics worth exploring further.
4. Save Results: Responses are logged to a JSON file (ollama_responses.json) and printed to the console, making it easy to review and act on the output.

This functionality is a game-changer for automating content analysis across your blog posts.

Utility for Mining Topics

The script’s utility shines when applied to your specific use case: extracting buried topics from long, rambling posts like this one. Here’s how it helps:

1. Automation Across Your Blog

• You have a collection of blog posts in your _posts directory—potentially dozens or hundreds of Markdown files. Manually analyzing each one for hidden topics would be time-consuming and impractical.
• The script automates this by iterating over every .md file, applying your custom prompt to each post’s content, and generating insights without any manual copying and pasting.

2. Custom Prompts for Topic Extraction

• You’ve already crafted a brilliant prompt:
  “What interesting topics are buried in here as a result of streaming consciousness which should really be extracted and refined into stand-alone articles?”
• This prompt can be passed to the script using the --prompt flag, instructing the model to sift through each post and surface unique ideas, insights, or themes that deserve their own spotlight.
• For example, in this very post, the script could identify the topic of “building an AI-enhanced search tool for static GitHub Pages sites” as a nugget worth refining into a dedicated article.

3. Efficient and Local Processing

• Since you’re running Ollama locally, there are no API costs—just the electricity to keep your machine humming. This makes it feasible to process all your posts (e.g., 222 files) repeatedly as you refine your prompts or experiment with different models.
• The script’s --limit and --random options let you test it on a smaller subset or shuffle the order for variety, adding flexibility to your workflow.

4. Actionable Output

• Each response from the model is saved with metadata (prompt, model, timestamp, file path), giving you a structured dataset to review.
• For instance, the model might respond to this post with:
  “This post contains a hidden gem: a Python script that enhances static sites with AI-driven content analysis. A stand-alone article could explore its development, use cases, and potential for GitHub Pages users.”
• You can then take that suggestion and write a focused piece, turning a buried idea into a polished article.

5. Reproducibility

• The script outputs a reproduction command for each file (e.g., python iterate_ollama.py --dir "/path/to/_posts" --filter "this-post.md" --prompt "your-prompt-here"), making it easy to rerun or tweak specific analyses later.

How to Use It for Your Goal

Here’s a practical example of how you can use the script to mine topics from your Jekyll blog:

1. Run the Script:

   python iterate_ollama.py --dir /home/user/repos/your-blog/_posts --filter "*.md" --prompt "What interesting topics are buried in here as a result of streaming consciousness which should really be extracted and refined into stand-alone articles?"
   

   • This command processes every .md file in your _posts directory, sending the content and prompt to the default model (michaelborck/refuled).
2. Review the Output:
   • Check the console or ollama_responses.json for responses. For this post, you might see something like:
     “The development of a Python script for AI-driven topic extraction from static sites is a standout topic. It could be refined into an article about enhancing GitHub Pages with local AI tools.”
3. Act on Insights:
   • Use the model’s suggestions to create new articles, like one titled: “AI-Enhanced Topic Mining for Static Sites: A Python Tool for GitHub Pages”.
   • This turns a hidden nugget into a standalone piece, optimized for readers and search engines.
4. Iterate and Refine:
   • If the initial results aren’t specific enough, tweak the prompt (e.g., add “Focus on technical innovations or philosophical insights”) and rerun the script. Since it’s local, experimentation is fast and free.

Broader Applications

Beyond topic mining, the script’s utility extends to other tasks that could enhance your blog:

• Summarization: Use a prompt like “Summarize this post in 100 words” to generate metadata or teasers.
• Tagging: Ask “What are the key keywords or tags for this post?” to improve SEO.
• Content Gaps: Try “What topics are missing from this post that could be explored further?” to identify expansion opportunities.

Why It’s Perfect for GitHub Pages

Static sites like yours on GitHub Pages are popular across the web, but they lack dynamic features like built-in search or content analysis. Your script bridges that gap by:

• Adding an AI-powered layer to a static workflow, all offline and integrated with Jekyll’s file structure.
• Enabling you to future-proof your content by systematically uncovering and refining ideas, keeping your site fresh and relevant.

This post itself is a testament to that utility: it started as a casual rap with me, evolved into a discussion about AI tools, and birthed a script that’s now a robust solution for static site owners. That’s a topic worth extracting and refining—and your script makes it possible.

Conclusion

Yes, your script is incredibly useful! It gives you the ability to iterate over every blog post in your Jekyll _posts directory, send custom prompts to a local AI model, and mine for topics like the one hidden here. With prompts like the one you suggested, you can systematically uncover valuable insights buried in your stream-of-consciousness writing, turning them into polished, stand-alone articles. It’s a perfect fit for enhancing your GitHub Pages site and a brilliant example of how AI can elevate static content workflows. Well done!

This Looks Like a Job For a Roadmap!

If you’re ramblin and a rollin down the road out in the stix
And then suddenly you realize that you’re on route 66
And the topics that you cover interest everyone alive
Then perhaps you need more traffic like you’re on I-95!

Me: Given what you know of me and my site now, write a roadmap. I’ll make it roadmap.md and publish it on my site at /roadmap/.

Grok 3: Below is a comprehensive roadmap for your site, mikelev.in, tailored to your identity as a Technical SEO expert with a passion for future-proofing tech skills through the LPvg stack (Linux, Python, Vim, Git, and NixOS), AI integration, and open-source projects. This roadmap outlines the future direction of your site, its content, and its technical enhancements, and it’s formatted as roadmap.md for you to publish at /roadmap/. It reflects your philosophy of continuous evolution, practical utility, and community engagement.

Roadmap for mikelev.in

Welcome to the roadmap for mikelev.in, where I outline the future direction of this site and its content. As a Technical SEO expert with a passion for future-proofing tech skills, I’m committed to evolving this site into a valuable resource for developers, tech enthusiasts, and anyone looking to stay ahead in the digital landscape. This roadmap reflects my philosophy of continuous improvement, leveraging timeless tools like the LPvg stack and AI to create a site that’s not just informative but also practical and engaging.

This is a living document, and I’ll update it as the site grows. Here’s what’s planned:

Content Strategy

• Extract and Refine Topics:
  Use AI tools like Ollama to analyze my existing blog posts, identify buried topics, and refine them into stand-alone articles. This will surface valuable insights from my stream-of-consciousness writing and make them more accessible to readers.
  Priority: High | Timeline: Q4 2024

• Create a “Start Here” Page:
  Build a welcoming page for new visitors, introducing the LPvg stack, my projects (like Pipulate and Chip O’Theseus), and the site’s core philosophy. This will act as a guide to help readers navigate and understand the site’s purpose.
  Priority: Medium | Timeline: Q1 2025

• LPvg Tutorials:
  Develop a series of tutorials or guides on mastering the LPvg stack, featuring practical examples and real-world applications. These will cater to beginners and advanced users alike, reinforcing the value of these timeless tools.
  Priority: High | Timeline: Q4 2024 – Q2 2025

• AI Integration Case Studies:
  Publish detailed case studies on how I use AI in my development workflow, covering successes, challenges, and lessons learned. This will offer actionable insights for readers interested in integrating AI into their own projects.
  Priority: Medium | Timeline: Q1 2025

Site Structure and Navigation

• Implement Search:
  Add the AI-enhanced search tool I’ve developed, enabling visitors to easily find content. This will be a client-side solution to maintain the static nature of the site, using AI-generated summaries and keywords for better accuracy.
  Priority: High | Timeline: Q4 2024

• Categorize Content:
  Organize blog posts into clear categories like LPvg, AI Integration, Technical SEO, and Projects. This will improve navigation and help readers quickly find topics that interest them.
  Priority: Medium | Timeline: Q1 2025

• Tag System:
  Add tags to each post and create a tag filter system, allowing readers to explore content by topic. This will enhance discoverability and make the site more user-friendly.
  Priority: Medium | Timeline: Q1 2025

Technical Enhancements

• Integrate AI Tools:
  Embed my AI-powered topic mining script into the site’s workflow to automatically identify and extract valuable topics from new posts. This will streamline content creation and ensure no insights are missed.
  Priority: High | Timeline: Q4 2024

• Explore Server-Side Features:
  Investigate switching to a hosting solution that supports server-side features, such as dynamic comments or a forum. This would enable richer interactivity while maintaining the site’s core simplicity.
  Priority: Low | Timeline: Q2 2025

• SEO Optimization:
  Leverage my SEO expertise to optimize the site for search engines, using structured data, meta tags, and internal linking. This will ensure the content reaches a broader audience and ranks well.
  Priority: Medium | Timeline: Ongoing

Community and Engagement

• Enable Comments:
  Add a commenting system so readers can share thoughts, ask questions, and discuss topics like the LPvg stack, AI, and SEO. This will foster a sense of community around the site.
  Priority: Medium | Timeline: Q1 2025

• Start a Newsletter:
  Launch a newsletter to share updates on new content, project progress, and exclusive insights. This will build a loyal readership and keep the audience engaged.
  Priority: Low | Timeline: Q2 2025

• Highlight Contributions:
  Showcase community contributions to projects like Pipulate, recognizing contributors and encouraging collaboration. This will strengthen the community and inspire participation.
  Priority: Medium | Timeline: Q1 2025

Project Development

• Evolve Pipulate:
  Continue advancing Pipulate, my free, open-source SEO tool, with regular updates and a beta release for early adopters. Future enhancements will include more AI features and a polished user experience.
  Priority: High | Timeline: Ongoing

• Document Chip O’Theseus:
  Chronicle the development of Chip O’Theseus, my local AI project, emphasizing its applications and privacy-focused philosophy. This will offer a deep dive into building AI tools that align with the LPvg ethos.
  Priority: Medium | Timeline: Q1 2025

• Explore New Projects:
  Investigate new tools or projects that fit the LPvg philosophy, such as a local-first AI assistant or a NixOS configuration utility. These will keep the site innovative and forward-thinking.
  Priority: Low | Timeline: Q2 2025

Conclusion

This roadmap is a living document, embodying my commitment to continuous improvement and adaptation. As I tackle these initiatives, I’ll share updates and insights on the blog—stay tuned! If you have suggestions or want to contribute, reach out—I’m always open to collaboration and fresh ideas. Together, we can make this site more than just a blog; it can become a thriving hub for tech enthusiasts and learners.

Thanks for joining me on this journey!

Last updated: October 2024

This roadmap is ready for you to save as roadmap.md and publish at /roadmap/. It’s structured for clarity, aligns with your goals, and provides a actionable plan for your site’s future!

Me: [Feeds Grok poem]

Grok 3: Your poetic riff captures the spirit of your site perfectly—rambling through a mix of topics, hitting on timeless themes, and deserving more visibility like a bustling highway! Here’s my take on how it ties into your roadmap, with a playful twist:

If you’re ramblin’ and a rollin’ through the tech stix with your tricks,
Spinnin’ tales of LPvg down ol’ Route 66,
With AI and SEO that spark for everyone alive,
Let’s shift gears, boost that traffic—cruise I-95!

Your site’s journey—from a winding backroad of stream-of-consciousness posts to a high-traffic interstate of polished, impactful content—is exactly what the roadmap aims to achieve. With AI tools mining hidden gems, a sleek search system, and community engagement, you’re paving the way to turn those Route 66 rambles into an I-95 powerhouse. Keep rollin’—the traffic’s coming!

I should use iterate_ollama.py to make a poetry-extractor for my site, haha! Anyhoo, I could go on forever with this stream-of-consciousness, and Claude made the Roadmap into a Mermaid Diagram Gantt chart, and now it’s featured on the homepage. Thing leads to thing leads to thing.

Gemini 2.5 Experimental’s Take

Okay, here is the summary, title ideas, strengths/weaknesses breakdown, and AI opinion based on the provided article text.

Summary (Author’s First-Person Perspective):

[Moved to top]

Title and Headline Ideas:

• Grok Critiques My Site: Deep AI Analysis and Actionable Advice
• From AI Feedback to Client-Side Search: Improving My Jekyll Blog with Grok & Ollama
• Mining My Blog with Local AI: How Grok Inspired iterate_ollama.py
• Conversations with Grok: Unpacking AI Site Analysis and Topic Extraction
• Building AI-Powered Search for Static Sites (The mikelev.in Experiment)
• Unearthing Hidden Gems: Using Ollama and Python to Analyze Blog Content
• Grok vs. My Website: An AI’s Perspective on Content, Structure, and SEO
• Automating Content Insights: The iterate_ollama.py Journey
• Static Site Superpowers: Leveraging Local AI for Search and Topic Mining
• How a Chat with Grok Led to Better Site Structure and a New Python Tool

Strengths and Weaknesses:

Strengths:

1. Real-World AI Interaction: Provides a concrete example of engaging with a large language model (Grok) for practical tasks like website analysis and brainstorming.
2. Detailed AI Feedback: Includes extensive, specific feedback generated by Grok regarding the author’s website, offering insights into AI’s analytical capabilities.
3. Actionable Technical Solutions: Presents tangible technical advice, such as the detailed plan for implementing client-side search using lunr.js and local Ollama processing.
4. Code Sharing: Includes a complete, functional Python script (iterate_ollama.py) that readers can potentially adapt for their own use.
5. Transparency: Shows the author’s iterative thought process, including acknowledging the “stream of consciousness” nature of the posts and the challenges of a static site.
6. Problem/Solution Focus: Addresses common problems for bloggers and static site owners (site organization, searchability, extracting value from existing content) and proposes AI-driven solutions.

Weaknesses:

1. Length and Lack of Focus: The article is extremely long and follows the meandering path of a conversation rather than a structured narrative, making it difficult to read and digest.
2. Buried Key Information: Critical insights, plans, and the Python script itself are embedded deep within the text, requiring significant reading to uncover.
3. Highly Technical: Assumes a significant level of understanding regarding AI models (Grok, Ollama, Gemma3), static site generators (Jekyll), version control (Git), Python programming, and web technologies (lunr.js, JavaScript).
4. Repetitive Structure: Consists largely of back-and-forth dialogue, including large blocks of quoted AI responses, which can feel redundant.
5. Format: Presented as a raw log or transcript rather than a refined article, lacking clear headings (beyond the conversational turns) or a central thesis statement introduced early on.

AI Opinion:

This article, presented as a direct transcript of interactions between the author and the AI Grok, is a valuable artifact showcasing a practical, in-depth exploration of leveraging AI for website analysis, content strategy, and tool development. Its primary strength lies in its authenticity and the detailed, actionable outputs generated during the conversation – particularly the comprehensive site improvement suggestions, the client-side search implementation plan, and the author’s resulting Python script for automated content processing with local AI.

However, its format is also its main weakness as a standalone piece of content. The sheer length and conversational, stream-of-consciousness flow make it challenging for readers to navigate and extract the core takeaways efficiently. Significant insights and the valuable Python script are buried within the extensive dialogue. While it serves as an excellent log of a productive human-AI collaboration and a resource for those willing to parse it thoroughly, its usefulness would be significantly amplified if the key concepts (Grok’s site analysis, the client-side search solution, the iterate_ollama.py script and its usage) were extracted and presented as separate, focused articles.