Investing

The Mental Models of Crypto Compliance: A Hacker’s Perspective on Regulatory Risk

/ lbhuston / Leave a comment

Let’s discuss one of the most complex and misunderstood frontiers in tech right now: cryptocurrency regulation.

This isn’t just about keeping up with new laws. It’s about building an entire mental framework to understand risk in an ecosystem that thrives on decentralization but is now colliding head-on with centralized enforcement.

Thinking

I recently gave some thought to the current state of regulation in the industry and came up with something crucial that has been missing from mainstream discourse: how we think about compliance in crypto matters just as much as what we do about it.

Data Layers and the Devil in the Details

Here’s the first truth bomb: not all on-chain data is equal.

You’ve got raw data — think: transaction hashes, sender/receiver addresses, gas fees. Then there’s abstracted data — the kind analysts love, like market cap and trading volume.

Regulators treat these differently, and so should we. If you’re building tools or making investment decisions without distinguishing between raw and abstracted data, you’re flying blind.

What struck me was how clearly this breakdown mirrors infosec risk models. Think of raw data like packet captures. Useful, granular, noisy. Abstracted data is your dashboard — interpretive and prone to bias. You need both to build situational awareness, but you’d better know which is which.

Keep It Simple (But Not Simplistic)

In cybersecurity, we talk a lot about Occam’s Razor. The simplest explanation isn’t always right, but the most efficient solution that meets the requirements usually is.

Crypto compliance right now? It’s bloated. Teams are building Byzantine workflows with multiple overlapping audits, clunky spreadsheets, and policy documents that look like the tax code.

The smarter play is automation. Real-time compliance tooling. Alerting systems that spot anomalies before regulators do. Because let’s be honest — the cost of “too late” in crypto is often existential.

Reverse Engineering Risk: The Inversion Model

Here’s a mental model that should be part of every crypto project’s DNA: Inversion.

Instead of asking “What does good compliance look like?”, start with: “How do we fail?”

Legal penalties. Reputation hits. Token delistings. Work backward from these outcomes and you’ll find the root causes: weak KYC, vague policies, and unauditable code. This is classic hacker thinking — start from the failure state and reverse engineer defenses.

It’s not about paranoia. It’s about resilience.

Structured Due Diligence > FOMO

The paper references EY’s six-pillar framework for token risk analysis — technical, legal, cybersecurity, financial, governance, and reputational. That’s a solid model.

But the key insight is this: frameworks turn chaos into clarity.

It reminds me of the early days of PCI-DSS. Everyone hated it, but the structured checklist forced companies to at least look under the hood. In crypto, where hype still trumps hard questions, a due diligence framework is your best defense against FOMO-driven disaster.

Global Regulation: Same Storm, Different Boats

With MiCA rolling out in the EU and the US swinging between enforcement and innovation depending on who’s in office, we’re entering a phase of compliance relativity.

You can’t memorize the rules. They’ll change next quarter. What you can do is build adaptable frameworks that let you assess risk regardless of the jurisdiction.

That means dedicated compliance committees. Cross-functional teams. Automated KYC that actually works. And most importantly: ongoing, not one-time, risk assessment.

Final Thoughts: The Future Belongs to Systems Thinkers

Crypto isn’t the Wild West anymore. It’s more like the early days of the Internet — still full of potential, still fragile, and now squarely in regulators’ crosshairs.

The organizations that survive won’t be the ones with the flashiest NFTs or the most Discord hype. They’ll be the ones who take compliance seriously — not as a bureaucratic burden, but as a strategic advantage.

Mental models like inversion, Occam’s Razor, and structured due diligence aren’t just academic. They’re how we turn regulatory chaos into operational clarity.

And if you’re still thinking of compliance as a checklist, rather than a mindset?

You’re already behind…

 

 

* 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

/ lbhuston / Leave a comment

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 (smtplib, python-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.

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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.

 

🧠 Betting on Beliefs: Why Bayesian Thinking Belongs in Your Investment Toolbox

/ lbhuston / Leave a comment

If you’ve ever made an investment decision that felt like a coin toss, welcome to the club. 📉📈 Uncertainty is baked into finance, no matter how many spreadsheets or models we throw at it. But here’s the good news: there’s a smarter way to deal with this unpredictability. It’s called Bayesian thinking—and it’s kind of like upgrading your brain with a statistics-powered GPS for navigating risky terrain.

ThinkingNotes

Let’s unpack this, not as a PhD thesis, but like we’re two old friends talking about the markets over coffee (and maybe some bourbon 🍸 if it’s been that kind of quarter).

What’s Bayesian Thinking Anyway?

Named after Thomas Bayes—a statistician and theologian with a knack for probability—Bayesian thinking is all about updating your beliefs as new data comes in. Imagine you’re sailing a boat in foggy weather. You can’t see much, but every ping from your radar helps you refine your course. That’s Bayesian logic at work: start with a guess (your prior), then update that guess as more info rolls in (your posterior).

In the world of investing, this isn’t just helpful—it’s survival gear.

Why This Matters More Than Ever

Markets today feel like riding a rollercoaster with no seatbelt. From crypto crashes to interest rate whiplash, traditional models often fail to keep up. Bayesian methods thrive in these situations because they:

✅ Incorporate uncertainty (instead of pretending it’s not there)
✅ Constantly learn and adapt as conditions change
✅ Handle model errors and parameter guesswork with more nuance than rigid formulas

In other words, Bayesian tools are like having a financial weatherman who admits they don’t know everything—but gets more accurate every time it rains.

Real Talk: Why I Use This Stuff

Here’s a dirty little secret: no model gets it right all the time. But Bayesian approaches admit that up front. They say, “Hey, let’s not commit to one truth. Let’s explore a bunch of possibilities and adjust as we go.” That humility is powerful. Especially in markets where the only constant is change.

Plus, it fits with how humans actually think. We revise our opinions as we learn—why shouldn’t our investment models do the same?

Final Thought: Don’t Be a Dinosaur 🦕 in a Digital Jungle

If you’re still using old-school statistical tools that ignore uncertainty or can’t adapt on the fly, you’re setting yourself up to get blindsided. Bayesian methods aren’t just for math geeks—they’re for anyone serious about managing risk in the real world.

So next time you’re staring at your portfolio, wondering what the heck just happened, ask yourself this: “What do I believe now, and how should I change my mind based on what I just learned?”

That’s the Bayesian mindset.

Stay adaptive. Stay curious. Stay a little skeptical. And as always—question everything.

Coming up next: we’ll dive into how this plays out with real market chaos—from crypto crashes to the currency jungle. 🪙💱📊

 

* 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.

 

New Whitepaper: Deeper Dive on Digital Asset Investing

/ lbhuston / Leave a comment

Several folks have asked me to dive deeper into the digital asset investing post and discuss my thoughts on holding digital assets. 

DigitalAssets

That said, I spent some time working with my AI tools and researching deeper into the thoughts I shared earlier. 

The outcome is a much longer whitepaper, which you can download here if you are interested.

If you enjoy it, or want to discuss, please feel free to email me and let me know your thoughts (bhuston@microsolved.com). 

You can download the whitepaper here: https://www.dropbox.com/scl/fi/hkzywtukx2buhb05b92v4/Navigating-the-Digital-Asset-Investment-Landscape_.pdf?rlkey=5z0pag4hr4e3sk0ohck6j0yn0&dl=0

 

Disclaimer:
This content is provided for informational and research purposes only. It does not constitute financial, investment, legal, or tax advice. I am not a licensed financial advisor, and nothing in this document should be interpreted as a recommendation to buy, sell, or hold any financial instrument or pursue any specific strategy. Always consult a qualified financial professional before making any financial decisions.