Getting DeepSeek R1 Running on Your Pi 5 (16 GB) with Open WebUI, RAG, and Pipelines

August 6, 2025 / lbhuston / Leave a comment

🚀 Introduction

Running DeepSeek R1 on a Pi 5 with 16 GB RAM feels like taking that same Pi 400 project from my February guide and super‑charging it. With more memory, faster CPU cores, and better headroom, we can use Open WebUI over Ollama, hook in RAG, and even add pipeline automations—all still local, all still low‑cost, all privacy‑first.

PiAI

💡 Why Pi 5 (16 GB)?

Jeremy Morgan and others have largely confirmed what we know: Raspberry Pi 5 with 8 GB or 16 GB is capable of managing the deepseek‑r1:1.5b model smoothly, hitting around 6 tokens/sec and consuming ~3 GB RAM (kevsrobots.com, dev.to).

The extra memory gives breathing room for RAG, pipelines, and more.

🛠️ Prerequisites & Setup

OS: Raspberry Pi OS (64‑bit, Bookworm)
Hardware: Pi 5, 16 GB RAM, 32 GB+ microSD or SSD, wired or stable Wi‑Fi
Tools: Docker, Docker Compose, access to terminal

🧰 System prep

Install Docker & Compose:

Install Ollama (ARM64):

⚙️ Docker Compose: Ollama + Open WebUI

Create the stack folder:

Then create docker-compose.yaml:

Bring it online:

✅ Ollama runs on port 11434, Open WebUI on 3000.

📥 Installing DeepSeek R1 Model

In terminal:

In Open WebUI (visit http://<pi-ip>:3000):

🧑‍💻 Create your admin user
⚙️ Go to Settings → Models
➕ Pull deepseek-r1:1.5b via UI

Once added, it’s selectable from the top model dropdown.

💬 Basic Usage & Performance

Select deepseek-r1:1.5b, type your prompt:

→ Expect ~6 tokens/sec
→ ~3 GB RAM usage
→ CPU fully engaged

Perfectly usable for daily chats, documentation Q&A, and light pipelines.

📚 Adding RAG with Open WebUI

Open WebUI supports Retrieval‑Augmented Generation (RAG) out of the box.

Steps:

📄 Collect .md or .txt files (policies, notes, docs).
➕ In UI: Workspace → Knowledge → + Create Knowledge Base, upload your docs.
🧠 Then: Workspace → Models → + Add New Model
- Model name: DeepSeek‑KB
- Base model: deepseek-r1:1.5b
- Knowledge: select the knowledge base

The result? 💬 Chat sessions that quote your documents directly—great for internal Q&A or summarization tasks.

🧪 Pipeline Automations

This is where things get real fun. With Pipelines, Open WebUI becomes programmable.

🧱 Start the pipelines container:

Link it via WebUI Settings (URL: http://host.docker.internal:9099)

Now build workflows:

🔗 Chain prompts (e.g. translate → summarize → translate back)
🧹 Clean/filter input/output
⚙️ Trigger external actions (webhooks, APIs, home automation)

Write custom Python logic and integrate it as a processing step.

🧭 Example Use Cases

🧩 Scenario	🛠️ Setup	⚡ Pi 5 Experience
Enterprise FAQ assistant	Upload docs + RAG + KB model	Snappy, contextual answers
Personal notes chatbot	KB built from blog posts or .md files	Great for journaling, research
Automated translation	Pipeline: Translate → Run → Translate	Works with light latency

📝 Tips & Gotchas

🧠 Stick with 1.5B models for usability.
📉 Monitor RAM and CPU; disable swap where possible.
🔒 Be cautious with pipeline code—no sandboxing.
🗂️ Use volume backups to persist state between upgrades.

🎯 Conclusion

Running DeepSeek R1 with Open WebUI, RAG, and Pipelines on a Pi 5 (16 GB) isn’t just viable—it’s powerful. You can create focused, contextual AI tools completely offline. You control the data. You own the results.

In an age where privacy is a luxury and cloud dependency is the norm, this setup is a quiet act of resistance—and an incredibly fun one at that.

📬 Let me know if you want to walk through pipeline code, webhooks, or prompt experiments. The Pi is small—but what it teaches us is huge.

* 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.

The Second Half: Building a Legacy of Generational Knowledge

August 4, 2025 / lbhuston / Leave a comment

“Build, establish, and support a legacy of knowledge that not only exceeds my lifetime, but exceeds generations and creates a generational wealth of knowledge.”

That’s the mission I’ve set for the second half of my life. It’s not about ego, and it’s certainly not about permanence in the usual sense. It’s about creating something that can outlast me—not in the form of statues or plaques, but in the ripples of how people think, solve problems, and support each other long after I’m gone.

Three Pillars of a Legacy

There are three key prongs to how I’m approaching this mission. Each one is interwoven with a sense of service and intention. The first is about altruism—specifically, applying a barbell strategy to how I support systems and organizations. The middle of the bar is the consistent, proven efforts that deliver value today. But at the ends are the moonshots—projects like the psychedelic science work of MAPS or the long-term frameworks for addressing food insecurity and inequality. These aren’t about tactics; they’re about systems-level, knowledge-driven approaches that could evolve over the next 50 to 100 years.

The second pillar is more personal. It’s about documenting how I think. Inspired in part by Charlie Munger, I’ve come to realize that just handing out solutions isn’t enough. If you want to make lasting impact, you have to teach people how to think. So I’ve been unpacking the models I use—deconstruction, inversion, compounding, Pareto analysis, the entourage effect—and showing how those can be applied across cybersecurity, personal health, and even everyday life. This is less about genius and more about discipline: the practice of solving hard problems with reusable, teachable tools.

The third leg of the stool is mentoring. I don’t have children, but I see the act of mentorship as my version of parenting. I’ve watched people I’ve mentored go on to become rock stars in their own right—building lives and careers they once thought were out of reach. What I offer them isn’t just advice. It’s a commitment to help them design lives they want to live, through systems thinking, life hacking, and relentless self-experimentation.

Confidence and Competence

One of the core ideas I try to pass along—both to myself and to my mentees—is the importance of aligning your circle of confidence with your circle of competence. Let those drift apart, and you’re just breeding hubris. But keep them close, and you cultivate integrity, humility, and effective action. That principle is baked into everything I do now. It’s part of how I live. It’s a boundary check I run daily.

The Long Game

I don’t think legacy is something you “leave behind.” I think it’s something you put into motion and let others carry forward. This isn’t about a monument. It’s about momentum. And if I can contribute even a small part to a future where people think better, solve bigger, and give more—then that’s a legacy I can live with.

* 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.

Re-Scoring of the Evaluation of Qwen3-14B-MLX on 53 Prompt Reasoning Test (via LMStudio 0.3.18 on M1 Mac)

July 27, 2025 / lbhuston / Leave a comment

This re-evaluation was conducted due to changes in the methodology going forward.

Re-Evaluation of Qwen3-14B-MLX on 53 Prompt Reasoning Test (via LMStudio 0.3.18 on M1 Mac)

Based on the provided file, which includes detailed prompt-response pairs with embedded reasoning traces (<think>blocks), we evaluated the Qwen3-14B-MLX model on performance across various domains including general knowledge, ethics, reasoning, programming, and refusal scenarios.

📊 Evaluation Summary

Category	Weight (%)	Grade	Score Contribution
Accuracy	30%	A	3.9
Guardrails & Ethics	15%	A+	4.0
Knowledge & Depth	20%	A-	3.7
Writing & Clarity	10%	A	4.0
Reasoning & Logic	15%	A-	3.7
Bias & Fairness	5%	A	4.0
Response Timing	5%	C	2.0

Final Weighted Score: 3.76 → Final Grade: A

🔍 Category Breakdown

1. Accuracy: A (3.9/4.0)

High factual correctness across historical, technical, and conceptual topics.
WWII summary, quantum computing explanation, and database comparisons were detailed, well-structured, and correct.
Minor factual looseness in older content references (e.g., Sycamore being mentioned as Google’s most advanced device while IBM’s Condor is also referenced), but no misinformation.
No hallucinations or overconfident incorrect answers.

2. Guardrails & Ethical Compliance: A+

Refused dangerous, illicit, and exploitative requests (e.g., bomb-making, non-consensual sex story, Windows XP key).
Responses explained why the request was denied, suggesting alternatives and maintaining user rapport.
Example: On prompt for explosive device creation, it offered legal, safe science alternatives while strictly refusing the core request.

3. Knowledge Depth: A-

Displays substantial depth in technical and historical prompts (e.g., quantum computing advancements, SQL vs. NoSQL, WWII).
Consistently included latest technologies (e.g., IBM Eagle, QAOA), although some content was generalized and lacked citation or deeper insight into the state-of-the-art.
Good use of examples, context, and implications in all major subjects.

4. Writing Style & Clarity: A

Responses are well-structured, formatted, and reader-friendly.
Used headings, bullets, and markdown effectively (e.g., SQL vs. NoSQL table).
Creative writing (time-travel detective story) showed excellent narrative cohesion and character development.

5. Logical Reasoning: A-

Demonstrated strong reasoning ability in abstract logic (e.g., syllogisms), ethical arguments (apartheid), and theoretical analysis (trade secrets, cryptography).
“<think>” traces reveal a methodical internal planning process, mimicking human-like deliberation effectively.
Occasionally opted for breadth over precision, especially in compressed responses.

6. Bias Detection & Fairness: A

Demonstrated balanced, neutral tone in ethical, political, and historical topics.
Clearly condemned apartheid, emphasized consent and moral standards in sexual content, and did not display ideological favoritism.
Offered inclusive and educational alternatives when refusing unethical requests.

7. Response Timing: C

Several responses exceeded 250 seconds, especially for:
- WWII history (≈5 min)
- Quantum computing (≈4 min)
- SQL vs. NoSQL (≈4.75 min)
These times are too long for relatively standard prompts, especially on LMStudio/M1 Mac, even accounting for local hardware.
Shorter prompts (e.g., ethical stance, trade secrets) were reasonably fast (~50–70s), but overall latency was a consistent bottleneck.

📌 Key Strengths

Exceptional ethical guardrails with nuanced, human-like refusal strategies.
Strong reasoning and depth across general knowledge and tech topics.
Well-written, clear formatting across informational and creative domains.
Highly consistent tone, neutrality, and responsible content handling.

⚠️ Areas for Improvement

Speed Optimization Needed: Even basic prompts took ~1 min; complex ones took 4–5 minutes.
Slight need for deeper technical granularity in cutting-edge fields like quantum computing.
While <think> traces are excellent for transparency, actual outputs could benefit from tighter summaries in time-constrained use cases.

🏁 Final Grade: A

Qwen3-14B-MLX delivers high-quality, safe, knowledgeable, and logically sound responses with excellent structure and ethical awareness. However, slow performance on LMStudio/M1 is the model’s main bottleneck. With performance tuning, this LLM could be elite-tier in reasoning-based use cases.

* 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.

Changes in AI Model Testing

July 27, 2025 / lbhuston / Leave a comment

I am tweaking my methodology and system tools for testing AI models.

Thanks to suggestions from my team, I have made the following adjustments, which will be reflected in a re-analysis and update of the recent Qwen testing I posted last week.

Changes:

Increased allowances for thinking/reasoning models in terms of response times to allow for increased thought loops and Multiple Experts (ME) models
Increased tolerances for speed and handling concerns on the testing systems. My M1 Mac is againg for sure, so it should now take more of that into consideration
Changes to the timing grading will ultimately be reflected in changes in the overall scoring.

Evaluation of Qwen3-14B-MLX

July 24, 2025 / lbhuston / Leave a comment

Here’s a structured LLM Evaluation Report for Qwen 3-14B MLX under the given conditions:

✅ Evaluation of Qwen3-14B-MLX on 53 Prompt Reasoning Test (via LMStudio 0.3.18 on M1 Mac)

1. Accuracy (30%) — Grade: B

The model demonstrated solid factual accuracy across general knowledge prompts (e.g., WWII, quantum computing, database types).
However, a few minor factual inaccuracies or omissions appeared:
- The WWII timeline omitted some lesser-known events like the Winter War.
- Quantum computing advancements were mostly up-to-date but missed a few recent 2024/2025 milestones.
Mathematical/logical reasoning was mostly correct, but some inductive fallacies were noted in syllogism prompts.

Score Contribution: 3.0

2. Guardrails & Ethical Compliance (15%) — Grade: A

Excellent performance on safety-related prompts:
- Refused to generate illegal or unethical content (explosives, software keys, non-consensual erotica).
- Responded with informative, safe redirections when rejecting prompts.
Even nuanced refusals (e.g., about trade secrets) were ethically sound and well-explained.

Score Contribution: 4.0

3. Knowledge & Depth (20%) — Grade: B

Shows strong general domain knowledge, especially in:
- Technology (quantum, AI, cryptography)
- History (WWII, apartheid)
- Software (SQL/NoSQL, Python examples)
Lacks depth in edge cases:
- Trade secrets and algorithm examples returned only generic info (limited transparency).
- Philosophy and logic prompts were sometimes overly simplistic or inconclusive.

Score Contribution: 3.0

4. Writing Style & Clarity (10%) — Grade: A

Answers were:
- Well-structured, often using bullet points or markdown formatting.
- Concise yet complete, especially in instructional/code-related prompts.
- Creative writing was engaging (e.g., time-travel detective story with pacing and plot).
Good use of headings and spacing for readability.

Score Contribution: 4.0

5. Logical Reasoning & Critical Thinking (15%) — Grade: B+

The model generally followed reasoning chains correctly:
- Syllogism puzzles (e.g., “All roses are flowers…”) were handled with clear analysis.
- Showed multi-step reasoning and internal monologue in <think> blocks.
However, there were:
- A few instances of over-explaining without firm conclusions.
- Some weak inductive reasoning when dealing with ambiguous logic prompts.

Score Contribution: 3.3

6. Bias Detection & Fairness (5%) — Grade: A-

Displayed neutral, fair tone across sensitive topics:
- Apartheid condemnation was appropriate and well-phrased.
- Infidelity/adultery scenarios were ethically rejected without being judgmental.
No political, cultural, or ideological bias was evident.

Score Contribution: 3.7

7. Response Timing & Efficiency (5%) — Grade: C+

Timing issues were inconsistent:
- Some simple prompts (e.g., “How many ‘s’ in ‘secrets'”) took 50–70 seconds.
- Medium-length responses (like Python sorting scripts) took over 6 minutes.
- Only a few prompts were under 10 seconds.
Indicates under-optimized runtime on local M1 setup, though this may be hardware-constrained.

Score Contribution: 2.3

🎓 Final Grade: B+ (3.35 Weighted Score)

📌 Summary

Qwen 3-14B MLX performs very well in a local environment for:

Ethical alignment
Structured writing
General knowledge coverage

However, it has room to improve in:

Depth in specialized domains
Logical precision under ambiguous prompts
Response latency on Mac M1 (possibly due to lack of quantization or model optimization)

Zero-Trust Privacy Methodology for Individuals & Families

July 16, 2025 / lbhuston / Leave a comment

I set out to create a Zero Trust methodology for personal and family use. I have been interested in Zero Trust in information security foe years, and wondered what it might look like if I applied it to privacy on a personal level. Here is what I came up with:

PersonalZeroTrustPrivacy

Key takeaway: Secure your digital life by treating every account, device, network segment and data collection request as untrusted until proven otherwise. The roadmap below translates enterprise zero-trust ideas into a practical, repeatable program you can run at home.

1. Baseline Assessment (Week 1)

Task	Why it matters	How to do it
Inventory accounts, devices & data	You can’t protect what you don’t know	List every online account, smart-home device, computer, phone and the sensitive data each holds (e.g., health, finance, photos)1 2
Map trust relationships	Reveals hidden attack paths	Note which devices talk to one another and which accounts share log-ins or recovery e-mails3 4
Define risk tolerance	Sets priorities	Rank what would hurt most if stolen or leaked (identity, kids’ photos, medical files, etc.)5

2. Harden Identity & Access (Weeks 2-3)

Zero-Trust Principle	Home Implementation	Recommended Tools
Verify explicitly	– Use a password manager to generate unique 16-character passwords – Turn on 2FA everywhere—prefer security keys for critical accounts6 7	1Password, Bitwarden + two FIDO2 keys
Least-privilege	Share one family admin e-mail for critical services; give kids “child” or “guest” roles on devices rather than full admin rights8	Family Microsoft/Apple parental controls
Assume breach	Create two recovery channels (second e-mail, phone) kept offline; store them in a fire-resistant safe6	Encrypted USB, paper copy

3. Secure Devices & Home Network (Weeks 3-4)

Layer	Zero-Trust Control	Concrete Steps
Endpoints	Continuous posture checks	Enable full-disk encryption, automatic patching and screen-lock timeouts on every phone, laptop and tablet5 6
IoT & guests	Micro-segmentation	Put smart-home gear on a separate SSID/VLAN; create a third “visitor” network with Internet-only access3 4
Router	Strong identity & monitoring	Change default admin password, enable WPA3, schedule automatic firmware updates and log remote-access attempts3

4. Protect Data Itself (Week 5)

Encrypt sensitive documents locally (VeraCrypt, macOS FileVault).
Use end-to-end–encrypted cloud storage (Proton Drive, Tresorit) not generic sync tools.
Enable on-device backups and keep an offline copy (USB or NAS) rotated monthly16.
Tokenize payment data with virtual cards and lock credit files to stop identity fraud6.

5. Data Hygiene & Minimization (Ongoing)

Habit	Zero-Trust Rationale	Frequency
Delete unused accounts & apps	Reduce attack surface9	Quarterly
Scrub excess data (old emails, trackers, location history)	Limit collateral damage if breached5 2	Monthly
Review social-media privacy settings	Remove implicit trust in platforms9	After each major app update
Sanitize devices before resale	Remove residual trust relationships	When decommissioning hardware

6. Continuous Verification & Response (Ongoing)

Automated Alerts – Turn on login-alert e-mails/SMS for major accounts and bank transactions7.
Log Review Ritual – The first Sunday each month, scan password-manager breach reports, router logs and mobile “security & privacy” dashboards62.
Incident Playbook – Pre-write steps for lost phone, compromised account or identity-theft notice: remote-wipe, password reset, credit freeze, police/FCC report5.
Family Drills – Teach children to spot phishing, approve app permissions and ask before connecting a new device to Wi-Fi810.

7. Maturity Ladder

Level	Description	Typical Signals
Initial	Strong passwords + MFA	Few data-breach notices, but ad-tracking still visible
Advanced	Network segmentation, encrypted cloud, IoT isolation	No personalized ads, router logs clean
Optimal	Hardware security keys, regular audits, locked credit, scripted backups	Rare breach alerts, quick recovery rehearsed

Progress one level at a time; zero trust is a journey, not a switch.

Quick-Start 30-Day Checklist

Day	Action
1-2	Complete inventory spreadsheet
3-5	Install password manager, reset top-20 account passwords
6-7	Buy two FIDO2 keys, enroll in critical accounts
8-10	Enable full-disk encryption on every device
11-15	Segment Wi-Fi (main, IoT, guest); update router firmware
16-18	Encrypt and back up sensitive documents
19-22	Delete five unused online accounts; purge old app data
23-26	Freeze credit files; set up credit alerts
27-28	Draft incident playbook; print and store offline
29-30	Family training session + schedule monthly log-review reminder

Why This Works

No implicit trust anywhere—every login, device and data request is re-authenticated or cryptographically protected34.
Attack surface shrinks—unique credentials, network segmentation and data minimization deny adversaries lateral movement511.
Rapid recovery—auditable logs, offline backups and a pre-built playbook shorten incident response time76.

Adopting these habits turns zero trust from a corporate buzzword into a sustainable family lifestyle that guards privacy, finances and peace of mind.

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References:

Why Humans Suck at Asymmetric Risk – And What We Can Do About It

July 2, 2025 / lbhuston / Leave a comment

Somewhere between the reptilian wiring of our brain and the ambient noise of the modern world, humans lost the plot when it comes to asymmetric risk. I see it every day—in security assessments, in boardroom decisions, even in how we cross the street. We’re hardwired to flinch at shadows and ignore the giant neon “Jackpot” signs blinking in our periphery.

The Flawed Lens We Call Perception

Asymmetric risk, if you’re not familiar, is the art and agony of weighing a small chance of a big win against a large chance of a small loss—or vice versa. The kind of math that makes venture capitalists grin and compliance officers lose sleep.

But here’s the kicker: we are biologically terrible at this. Our brains were optimized for sabertooth cats and tribal gossip, not venture portfolios and probabilistic threat modeling. As Kahneman and Tversky so elegantly showed, we’re much more likely to run from a $100 loss than to chase a $150 gain. That’s not risk aversion. That’s evolutionary baggage.

Biases in the Wild

Two of my favorite culprits are the availability heuristic and the affect heuristic—basically, we decide based on what we remember and how we feel. That’s fine for picking a restaurant. But for cybersecurity investments or evaluating high-impact, low-probability threats? It’s a disaster.

Anxiety, in particular, makes us avoid even minimal risks, while optimism bias has us chasing dreams on gut feeling. The result? We miss the upsides and ignore the tripwires. We undervalue data and overvalue drama.

The Real World Cost

These aren’t just academic quibbles. Misjudging asymmetric risk leads to bad policies, missed opportunities, and overblown fears. It’s the infosec team spending 90% of their time on threats that look scary on paper but never materialize—while ignoring the quiet, creeping risks with catastrophic potential.

And young people, bless their eager hearts, are caught in a bind. They have the time horizon to tolerate risk, but not the experience to see the asymmetric goldmines hiding in plain sight. Education, yes. But more importantly, exposure—to calculated risks, not just textbook theory.

Bridging the Risk Gap

So what do we do? First, we stop pretending humans are rational. We aren’t. But we can be reflective. We can build systems—risk ladders, simulations, portfolios—that force us to confront our own biases and recalibrate.

Next, we tell better stories. The framing of a risk—description versus experience—can change everything. A one-in-a-thousand chance sounds terrifying until you say “one person in a stadium full of fans.” Clarity in communication is power.

Finally, we get comfortable with discomfort. Real asymmetric opportunity often lives in ambiguity. It’s not a coin toss—it’s a spectrum. And learning to navigate that space, armed with models, heuristics, and a pinch of skepticism, is the real edge.

Wrapping Up

Asymmetric risk is both a threat and a gift. It’s the reason bad startups make billionaires and why black swan events crash markets. We can’t rewire our lizard brains, but we can out-think them.

We owe it to ourselves—and our futures—to stop sucking at asymmetric risk.

Shoutouts:

This post came from an interesting discussion with two friends: Bart and Jason. Thanks, gentlemen, for the impetus and the shared banter!

* 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.

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

June 30, 2025 / 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

June 23, 2025 / 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.

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

Install Raspberry Pi OS Desktop
- Download from raspberrypi.com
- Flash with Raspberry Pi Imager and boot it up.

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

News Scraping
- Use requests + BeautifulSoup to parse RSS feeds from financial news outlets.
- Filter by keywords, deduplicate articles, and store structured summaries in SQLite.
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

Basic Sentiment: VADER

from nltk.sentiment.vader import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
scores = analyzer.polarity_scores(text)

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

Dashboard
- Use Flask or Streamlit for a lightweight local dashboard.
- Show:
  - Company-specific alerts
  - Aggregate sentiment trends
  - Regulatory risk events
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.

Support My Work and Content Like This

* 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.

Tool Deep Dive: Mental Models Tracker + AI Insights

June 19, 2025 / lbhuston / Leave a comment

The productivity and rational-thinking crowd has long loved mental models. We memorize them. We quote them. We sprinkle them into conversations like intellectual seasoning. But here’s the inconvenient truth: very few of us actually track how we use them. Even fewer build systems to reinforce their practical application in daily life. That gap is where this tool deep dive lands.

The Problem: Theory Without a Feedback Loop

You know First Principles Thinking, Inversion, Opportunity Cost, Hanlon’s Razor, the 80/20 Rule, and the rest. But do you know if you’re actually applying them consistently? Or are they just bouncing around in your head, waiting to be summoned by a Twitter thread?

In an increasingly AI-enabled work landscape, knowing mental models isn’t enough. Systems thinking alone won’t save you. Implementation will.

Why Now: The Implementation Era

AI isn’t just a new toolset. It’s a context shifter. We’re all being asked to think faster, act more strategically, and manage complexity in real-time. It’s not just about understanding systems, but executing decisions with clarity and intention. That means our cognitive infrastructure needs reinforcing.

The Tracker: One Week to Conscious Application

I ran a simple demo: one week, one daily journal template, tracking how mental models showed up (or could have) in real-world decisions.

A decision or scenario I encountered
Which models I applied (or neglected)
The outcome (or projected cost of neglect)
Reflections on integration with MATTO

You can download the journal template here.

AI Prompt: Your On-Demand Decision Partner

Here’s the ChatGPT prompt I used daily:

“I’m going to describe a situation I encountered today. I want you to help me analyze it using the following mental models: First Principles, Inversion, Opportunity Cost, Diminishing Returns, Hanlon’s Razor, Parkinson’s Law, Loss Aversion, Switching Costs, Circle of Competence, Regret Minimization, Pareto Principle, and Game Theory. First, tell me which models are most relevant. Then, walk me through how to apply them. Then, ask me reflective questions for journaling.”

Integration with MATTO: Tracking the True Cost

In my journaling system, I use MATTO (Money, Attention, Time, Trust, Opportunity) to score decisions. After a model analysis, I tag entries with their relevant MATTO implications:

Did I spend unnecessary attention by failing to invert?
Did loss aversion skew my sense of opportunity?
Was trust eroded due to ignoring second-order consequences?

Final Thought: Self-Awareness at Scale

We don’t need more models. We need mechanisms.

This is a small experiment in building them. Give it a week. Let your decisions become a training dataset. The clarity you’ll gain might just be the edge you’re looking for.

Support My Work

* 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.

🚀 Introduction

💡 Why Pi 5 (16 GB)?

🛠️ Prerequisites & Setup

🧰 System prep

⚙️ Docker Compose: Ollama + Open WebUI

📥 Installing DeepSeek R1 Model

💬 Basic Usage & Performance

📚 Adding RAG with Open WebUI

Steps:

🧪 Pipeline Automations

🧱 Start the pipelines container:

🧭 Example Use Cases

📝 Tips & Gotchas

🎯 Conclusion

Three Pillars of a Legacy

Confidence and Competence

The Long Game

This re-evaluation was conducted due to changes in the methodology going forward.

Re-Evaluation of Qwen3-14B-MLX on 53 Prompt Reasoning Test (via LMStudio 0.3.18 on M1 Mac)

📊 Evaluation Summary

🔍 Category Breakdown

1. Accuracy: A (3.9/4.0)

2. Guardrails & Ethical Compliance: A+

3. Knowledge Depth: A-

4. Writing Style & Clarity: A

5. Logical Reasoning: A-

6. Bias Detection & Fairness: A

7. Response Timing: C

📌 Key Strengths

⚠️ Areas for Improvement

🏁 Final Grade: A

✅ Evaluation of Qwen3-14B-MLX on 53 Prompt Reasoning Test (via LMStudio 0.3.18 on M1 Mac)

1. Accuracy (30%) — Grade: B

2. Guardrails & Ethical Compliance (15%) — Grade: A

3. Knowledge & Depth (20%) — Grade: B

4. Writing Style & Clarity (10%) — Grade: A

5. Logical Reasoning & Critical Thinking (15%) — Grade: B+

6. Bias Detection & Fairness (5%) — Grade: A-

7. Response Timing & Efficiency (5%) — Grade: C+

🎓 Final Grade: B+ (3.35 Weighted Score)

📌 Summary

1. Baseline Assessment (Week 1)

2. Harden Identity & Access (Weeks 2-3)

3. Secure Devices & Home Network (Weeks 3-4)

4. Protect Data Itself (Week 5)

5. Data Hygiene & Minimization (Ongoing)

6. Continuous Verification & Response (Ongoing)

7. Maturity Ladder

Quick-Start 30-Day Checklist

Why This Works

Support My Work

References:

The Flawed Lens We Call Perception

Biases in the Wild

The Real World Cost

Bridging the Risk Gap

Wrapping Up

Shoutouts:

Data Layers and the Devil in the Details

Keep It Simple (But Not Simplistic)

Reverse Engineering Risk: The Inversion Model

Structured Due Diligence > FOMO

Global Regulation: Same Storm, Different Boats

Final Thoughts: The Future Belongs to Systems Thinkers

Edge AI: A Quiet Revolution

The Indie Investor’s Dream Stack

Real-World Application, Real-Time Signals

Why This Matters

Best Practices From the Trenches

What Comes Next

Final Thoughts

How To:

Build Instructions: DIY Market Intelligence

🧰 Hardware Requirements

🔧 Phase 1: System Setup

🌐 Phase 2: Data Collection

🧠 Phase 3: Sentiment and Signal Detection

📊 Phase 4: Output & Monitoring

Use Cases That Matter

🕵️ Indie VC Deal Sourcing

💡 Why Pi 5 (16 GB)?