Seizing Career Leverage by Building a Body of Public Work

On the surface, it may seem easier to pursue another certificate, add another line to your resume, or polish a few more LinkedIn keywords. That’s the default advice. But I’ve found that the true differentiator—the thing that has consistently opened the most doors in my career and in the lives of those I mentor—is something less talked about: building a public body of work.

ThinkingPlanning

For me, it didn’t start with a strategic master plan. It was organic. A blog here. A talk there. Over time, though, the pattern became clear. The more consistently I created public work—writings, talks, podcasts, code, experiments—the more serendipity showed up. People would reach out. Ideas would flow. And opportunities would emerge.

Creating in public does something powerful: it makes you discoverable. It turns your ideas into tiny relationship builders scattered across the internet. They work quietly on your behalf—sharing, connecting, and engaging. They let people find you not just for who you say you are, but for what you actually do and think and build. In essence, your work becomes your calling card.

Kevin Kelly wrote about the concept of 100 True Fans, and I think that framework applies here, too. When you create with consistency and intention, your work resonates. People engage. They share. They connect. You become a node in a larger network. Not geographically constrained. Not bound to a title. But influential because of contribution.

Of course, this isn’t easy. If it were, everyone would be doing it.

The resistance is deep and evolutionary. When you make something public—your ideas, your interests, your perspective—you draw attention to yourself. You leave the crowd. And for most of human history, that was dangerous. Our lizard brains still think it is.

But here’s the truth: life happens at the edges. It happens when you step away from the herd and choose to teach, lead, explore, or question. That’s where the value is—not just in terms of career growth, but in living a more interesting life.

The tools to get started are easier than ever. A blog costs nothing but time and focus. A podcast is within reach with a decent mic and an internet connection. A video or short-form tutorial can find thousands of eyes in hours. The barrier isn’t access. It’s courage. And then—discipline.

There won’t be a singular moment where you “make it.” Instead, you’ll find momentum. The blog post you wrote last year still gets read. The talk you gave finds its way to someone’s inbox. The experiment you published helps someone else start their own.

But here’s the trick: create to help. Self-serving content evaporates quickly. But service-oriented content—something that teaches, guides, explores—can live on. Sometimes for years. Sometimes forever.

And perhaps most important: you get to choose what you create. That’s a kind of creative sovereignty many professionals never tap into. It’s a superpower. And like any superpower, it comes with responsibility.

So here’s what I tell my mentees:

Actions speak louder than words. A portfolio is more potent than a certificate on your resume.

Teach courage. Encourage contribution. Show them that real growth—personal, professional, even spiritual—happens at the edges. Not in the safe middle.

Put your work into the world. Let it work for you. And help others as you do. That’s how you build a life and career that’s not just successful, but truly extraordinary.

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* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Market Intelligence for the Rest of Us: Building a $2K AI for Startup Signals

It’s a story we hear far too often in tech circles: powerful tools locked behind enterprise price tags. If you’re a solo founder, indie investor, or the kind of person who builds MVPs from a kitchen table, the idea of paying $2,000 a month for market intelligence software sounds like a punchline — not a product. But the tide is shifting. Edge AI is putting institutional-grade analytics within reach of anyone with a soldering iron and some Python chops.

Pi400WithAI

Edge AI: A Quiet Revolution

There’s a fascinating convergence happening right now: the Raspberry Pi 400, an all-in-one keyboard-computer for under $100, is powerful enough to run quantized language models like TinyLLaMA. These aren’t toys. They’re functional tools that can parse financial filings, assess sentiment, and deliver real-time insights from structured and unstructured data.

The performance isn’t mythical either. When you quantize a lightweight LLM to 4-bit precision, you retain 95% of the accuracy while dropping memory usage by up to 70%. That’s a trade-off worth celebrating, especially when you’re paying 5–15 watts to keep the whole thing running. No cloud fees. No vendor lock-in. Just raw, local computation.

The Indie Investor’s Dream Stack

The stack described in this setup is tight, scrappy, and surprisingly effective:

  • Raspberry Pi 400: Your edge AI hardware base.

  • TinyLLaMA: A lean, mean 1.1B-parameter model ready for signal extraction.

  • VADER: Old faithful for quick sentiment reads.

  • SEC API + Web Scraping: Data collection that doesn’t rely on SaaS vendors.

  • SQLite or CSV: Because sometimes, the simplest storage works best.

If you’ve ever built anything in a bootstrapped environment, this architecture feels like home. Minimal dependencies. Transparent workflows. And full control of your data.

Real-World Application, Real-Time Signals

From scraping startup news headlines to parsing 10-Ks and 8-Ks from EDGAR, the system functions as a low-latency, always-on market radar. You’re not waiting for quarterly analyst reports or delayed press releases. You’re reading between the lines in real time.

Sentiment scores get calculated. Signals get aggregated. If the filings suggest a risk event while the news sentiment dips negative? You get a notification. Email, Telegram bot, whatever suits your alert style.

The dashboard component rounds it out — historical trends, portfolio-specific signals, and current market sentiment all wrapped in a local web UI. And yes, it works offline too. That’s the beauty of edge.

Why This Matters

It’s not just about saving money — though saving over $46,000 across three years compared to traditional tools is no small feat. It’s about reclaiming autonomy in an industry that’s increasingly centralized and opaque.

The truth is, indie analysts and small investment shops bring valuable diversity to capital markets. They see signals the big firms overlook. But they’ve lacked the tooling. This shifts that balance.

Best Practices From the Trenches

The research set outlines some key lessons worth reiterating:

  • Quantization is your friend: 4-bit LLMs are the sweet spot.

  • Redundancy matters: Pull from multiple sources to validate signals.

  • Modular design scales: You may start with one Pi, but load balancing across a cluster is just a YAML file away.

  • Encrypt and secure: Edge doesn’t mean exempt from risk. Secure your API keys and harden your stack.

What Comes Next

There’s a roadmap here that could rival a mid-tier SaaS platform. Social media integration. Patent data. Even mobile dashboards. But the most compelling idea is community. Open-source signal strategies. GitHub repos. Tutorials. That’s the long game.

If we can democratize access to investment intelligence, we shift who gets to play — and who gets to win.


Final Thoughts

I love this project not just for the clever engineering, but for the philosophy behind it. We’ve spent decades building complex, expensive systems that exclude the very people who might use them in the most novel ways. This flips the script.

If you’re a founder watching the winds shift, or an indie VC tired of playing catch-up, this is your chance. Build the tools. Decode the signals. And most importantly, keep your stack weird.

How To:


Build Instructions: DIY Market Intelligence

This system runs best when you treat it like a home lab experiment with a financial twist. Here’s how to get it up and running.

🧰 Hardware Requirements

  • Raspberry Pi 400 ($90)

  • 128GB MicroSD card ($25)

  • Heatsink/fan combo (optional, $10)

  • Reliable internet connection

🔧 Phase 1: System Setup

  1. Install Raspberry Pi OS Desktop

  2. Update and install dependencies

    sudo apt update -y && sudo apt upgrade -y
    sudo apt install python3-pip -y
    pip3 install pandas nltk transformers torch
    python3 -c "import nltk; nltk.download('all')"
    

🌐 Phase 2: Data Collection

  1. News Scraping

    • Use requests + BeautifulSoup to parse RSS feeds from financial news outlets.

    • Filter by keywords, deduplicate articles, and store structured summaries in SQLite.

  2. SEC Filings

    • Install sec-api:

      pip3 install sec-api
      
    • Query recent 10-K/8-Ks and store the content locally.

    • Extract XBRL data using Python’s lxml or bs4.


🧠 Phase 3: Sentiment and Signal Detection

  1. Basic Sentiment: VADER

    from nltk.sentiment.vader import SentimentIntensityAnalyzer
    analyzer = SentimentIntensityAnalyzer()
    scores = analyzer.polarity_scores(text)
    
  2. Advanced LLMs: TinyLLaMA via Ollama

    • Install Ollama: ollama.com

    • Pull and run TinyLLaMA locally:

      ollama pull tinyllama
      ollama run tinyllama
      
    • Feed parsed content and use the model for classification, signal extraction, and trend detection.


📊 Phase 4: Output & Monitoring

  1. Dashboard

    • Use Flask or Streamlit for a lightweight local dashboard.

    • Show:

      • Company-specific alerts

      • Aggregate sentiment trends

      • Regulatory risk events

  2. Alerts

    • Integrate with Telegram or email using standard Python libraries (smtplibpython-telegram-bot).

    • Send alerts when sentiment dips sharply or key filings appear.


Use Cases That Matter

🕵️ Indie VC Deal Sourcing

  • Monitor startup mentions in niche publications.

  • Score sentiment around funding announcements.

  • Identify unusual filing patterns ahead of new rounds.

🚀 Bootstrapped Startup Intelligence

  • Track competitors’ regulatory filings.

  • Stay ahead of shifting sentiment in your vertical.

  • React faster to macroeconomic events impacting your market.

⚖️ Risk Management

  • Flag negative filing language or missing disclosures.

  • Detect regulatory compliance risks.

  • Get early warning on industry disruptions.


Lessons From the Edge

If you’re already spending $20/month on ChatGPT and juggling half a dozen spreadsheets, consider this your signal. For under $2K over three years, you can build a tool that not only pays for itself, but puts you on competitive footing with firms burning $50K on dashboards and dashboards about dashboards.

There’s poetry in this setup: lean, fast, and local. Like the best tools, it’s not just about what it does — it’s about what it enables. Autonomy. Agility. Insight.

And perhaps most importantly, it’s yours.


Support My Work and Content Like This

Support the creation of high-impact content and research. Sponsorship opportunities are available for specific topics, whitepapers, tools, or advisory insights. Learn more or contribute here: Buy Me A Coffee

 

 

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

 

Why I Make Things

I spent the first 20 years of my career breaking things. I am good at breaking stuff. I am a decent hacker, a semi-talented reverse engineer and a very curious deconstructionist. I yearn to tear things down, to tweak, to iterate, to improve and to use things in ways that were entirely unintended by their makers. As long as I can remember, I have loved these engagements.

I grew up in a print shop with my Dad, tearing down presses, opening and rebuilding motors and playing with type. I absorbed the power of making something on a printing press and the amplification that it represented at a visceral level. One of my earliest mentors, Bob Gent, furthered this enthrallment with print throughout high school, and even threw in my basic electronics education to boot! My Mom, another major icon in my life, was a computer professional. She was working in mainframe shops doing operations, quality control, management & some light development. She let me cut my teeth in the tape library & keypunch rooms. I later worked there after high school and during college as a tape librarian and eventually a printer technician/junior operator.

It was there, on that first corporate job, that a few people like Jim & Su Klun, Mike Davis, Diane DeFallo, Gary Shank, Art Smith and others taught me about coding, scripting, PCs, communications, EDI and how to be more than a technician. Art, in particular, taught me that it was great to be smart, but that you could take those skills and make a life, a business and some joy. I was an attentive student, even if it didn’t seem like it at the time. I was paying attention. And, because of their lessons, I made things – software/scripts, a BBS, business processes, a HUGE ego :), and I started businesses. I started generating ideas, working on them, chasing them, building them. I made myself happy by pursuing them, even the ones that crashed and burned. I learned that making is a form of hope. It’s a way to put forth something that represents your will to change the world, even if it is in some small way, (I supposed Aleister Crowley would be proud…).

Over the years, since then, I have made many businesses, products, written part of a book, been a published poet several times, created several groups for different purposes, made a symposium that ran for 5 years, written hundreds of articles for a magazine, taught myself to be a speaker and presented at conferences around the world, build processes and tools in use by thousands of people on a global scale. I have been and am – a maker. And I have loved every moment of it. I see making things – be it code, hardware or words, as a tribute to those mentors. I honor each of them with everything I do. I am a part of the reflection of the sum of their inputs. I make because that is exactly what they taught me to do…