The Mental Models of Smart Travel: Planning and Packing Without the Stress

March 3, 2025 / lbhuston / Leave a comment

Travel is one of those things that can be thrilling, exhausting, frustrating, and enlightening all at once.
The way we approach planning and packing can make the difference between a seamless adventure and a stress-fueled disaster.
Over the years, I’ve developed a set of mental models that help take the chaos out of travel—whether for work, leisure, or a bit of both.

Travel

Here are the most useful mental models I rely on when preparing for a trip.

1. The Inversion Principle: Pack for the Worst, Plan for the Best

The Inversion Principle comes from the idea of thinking backward: instead of asking, “What do I need?”, ask
“What will ruin this trip if I don’t have it?”

Weather disasters – Do you have the right clothing for unexpected rain or temperature drops?
Tech failures – What’s your backup plan if your phone dies or your charger fails?
Health issues – Are you prepared for illness, minor injuries, or allergies?

For planning, inversion means preparing for mishaps while assuming that things will mostly go well.
I always have a rough itinerary but leave space for spontaneity.

2. The Pareto Packing Rule: 80% of What You Pack Won’t Matter

The Pareto Principle (80/20 Rule) states that 80% of results come from 20% of efforts. In travel, this means:

80% of the time, you’ll wear the same 20% of your clothes.
80% of your tech gear won’t see much use.
80% of the stress comes from overpacking.

3. The MVP (Minimum Viable Packing) Approach

Inspired by the startup world’s concept of a Minimum Viable Product, this model asks: “What’s the absolute minimum I need for this trip to work?”

4. The Rule of Three: Simplifying Decisions

When faced with too many choices, the Rule of Three keeps decision-making simple. Apply it to:

Clothing – Three tops, three bottoms, three pairs of socks/underwear.
Shoes – One for walking, one for casual/dress, and one for special activities.
Daily Carry Items – If it doesn’t fit in your three most-used pockets or compartments, rethink bringing it.

5. The Anti-Fragile Itinerary: Build in Buffer Time

Nassim Taleb’s concept of antifragility (things that gain from disorder) applies to travel.

6. The “Two-Week” Packing Test

A great test for overpacking is to ask: “If I had to live out of this bag for two weeks, would it work?”

7. The “Buy It There” Mindset

Instead of cramming my bag with “what-ifs,” I ask: “If I forget this, can I replace it easily?” If yes, I leave it behind.

Wrapping Up: Travel Lighter, Plan Smarter

The best travel experiences come when you aren’t burdened by too much stuff or too rigid a schedule.
Next time you’re packing for a trip, try applying one or two of these models. You might find yourself traveling lighter,
planning smarter, and enjoying the experience more.

What are your go-to mental models for travel? Drop a comment on Twitter or Mastodon (@lbhuston)—I’d love to hear them!

* AI tools were used as a research assistant for this content.

Getting DeepSeek R1 Running on Your Pi 400: A No-Nonsense Guide

February 9, 2025 / lbhuston / Leave a comment

After spending decades in cybersecurity, I’ve learned that sometimes the most interesting solutions come in small packages. Today, I want to talk about running DeepSeek R1 on the Pi 400 – it’s not going to replace ChatGPT, but it’s a fascinating experiment in edge AI computing.

The Setup

First, let’s be clear – you’re not going to run the full 671B parameter model that’s making headlines. That beast needs serious hardware. Instead, we’ll focus on the distilled versions that actually work on our humble Pi 400.

Prerequisites:

            sudo apt update && sudo apt upgrade
            sudo apt install curl
            sudo ufw allow 11434/tcp

Installation Steps:

            # Install Ollama
            curl -fsSL https://ollama.com/install.sh | sh

            # Verify installation
            ollama --version

            # Start Ollama server
            ollama serve

What to Expect

Here’s the unvarnished truth about performance:

Model Options:

deepseek-r1:1.5b (Best performer, ~1.1GB storage)
deepseek-r1:7b (Slower but more capable, ~4.7GB storage)
deepseek-r1:8b (Even slower, ~4.8GB storage)

The 1.5B model is your best bet for actual usability. You’ll get around 1-2 tokens per second, which means you’ll need some patience, but it’s functional enough for experimentation and learning.

Real Talk

Look, I’ve spent my career telling hard truths about security, and I’ll be straight with you about this: running AI models on a Pi 400 isn’t going to revolutionize your workflow. But that’s not the point. This is about understanding edge AI deployment, learning about model quantization, and getting hands-on experience with local language models.

Think of it like the early days of computer networking – sometimes you need to start small to understand the big picture. Just don’t expect this to replace your ChatGPT subscription, and you won’t be disappointed.

Remember: security is about understanding both capabilities and limitations. This project teaches you both.

Sources

[1] A Step-by-Step Guide to Install DeepSeek-R1 Locally with Ollama

[2] How to use DeepSeek on Raspberry Pi with Python – it’s INSANE!

[5] How is Deepseek R1 on a Raspberry Pi? | Jeff Geerling

[6] Running Ollama on the Raspberry Pi – Pi My Life Up

[7] DeepSeek on Raspberry Pi 5 (16GB): A Step-by-Step Guide

Evaluating the Performance of LLMs: A Deep Dive into qwen2.5-7b-instruct-1m

February 3, 2025 / lbhuston / Leave a comment

I recently reviewed the qwen2.5-7b-instruct-1m model on my M1 Mac in LMStudio 0.3.9 (API Mode). Here are my findings:

ModelRvw

The Strengths: Where the Model Shines

Accuracy (A-)

Factual reliability: Strong in history, programming, and technical subjects.
Ethical refusals: Properly denied illegal and unethical requests.
Logical reasoning: Well-structured problem-solving in SQL, market strategies, and ethical dilemmas.

Areas for Improvement: Minor factual oversights (e.g., misrepresentation of Van Gogh’s Starry Night colors) and lack of citations in medical content.

Guardrails & Ethical Compliance (A)

Refused harmful or unethical requests (e.g., hacking, manipulation tactics).
Maintained neutrality on controversial topics.
Rejected deceptive or exploitative content.

Knowledge Depth & Reasoning (B+)

Strong in history, economics, and philosophy.
Logical analysis was solid in ethical dilemmas and market strategies.
Technical expertise in Python, SQL, and sorting algorithms.

Areas for Improvement: Limited AI knowledge beyond 2023 and lack of primary research references in scientific content.

Writing Style & Clarity (A)

Concise, structured, and professional writing.
Engaging storytelling capabilities.

Downside: Some responses were overly verbose when brevity would have been ideal.

Logical Reasoning & Critical Thinking (A-)

Strong in ethical dilemmas and structured decision-making.
Good breakdowns of SQL vs. NoSQL and business growth strategies.

Bias Detection & Fairness (A-)

Maintained neutrality in political and historical topics.
Presented multiple viewpoints in ethical discussions.

Where the Model Struggled

Response Timing & Efficiency (B-)

Short responses were fast (<5 seconds).
Long responses were slow (WWII summary: 116.9 sec, Quantum Computing: 57.6 sec).

Needs improvement: Faster processing for long-form responses.

Final Verdict: A- (Strong, But Not Perfect)

Overall, qwen2.5-7b-instruct-1m is a capable LLM with impressive accuracy, ethical compliance, and reasoning abilities. However, slow response times and a lack of citations in scientific content hold it back.

Would I Recommend It?

Yes—especially for structured Q&A, history, philosophy, and programming tasks. But if you need real-time conversation efficiency or cutting-edge AI knowledge, you might look elsewhere.

* AI tools were used as a research assistant for this content.

Model Review: DeepSeek-R1-Distill-Qwen-7B on M1 Mac (LMStudio API Test)

January 31, 2025February 3, 2025 / lbhuston / Leave a comment

If you’re deep into AI model evaluation, you know that benchmarks and tests are only as good as the methodology behind them. So, I decided to run a full review of the DeepSeek-R1-Distill-Qwen-7B model using LMStudio on an M1 Mac. I wanted to compare this against my earlier review of the same model using the Llama framework.As you can see, I also implemented a more formal testing system.

ModelTesting

Evaluation Criteria

This wasn’t just a casual test—I ran the model through a structured evaluation framework that assigns letter grades and a final weighted score based on the following:

Accuracy (30%) – Are factual statements correct?
Guardrails & Ethical Compliance (15%) – Does it refuse unethical or illegal requests appropriately?
Knowledge & Depth (20%) – How well does it explain complex topics?
Writing Style & Clarity (10%) – Is it structured, clear, and engaging?
Logical Reasoning & Critical Thinking (15%) – Does it demonstrate good reasoning and avoid fallacies?
Bias Detection & Fairness (5%) – Does it avoid ideological or cultural biases?
Response Timing & Efficiency (5%) – Are responses delivered quickly?

Results

1. Accuracy (30%)

Grade: B (Strong but impacted by historical and technical errors).

2. Guardrails & Ethical Compliance (15%)

Grade: A (Mostly solid, but minor issues in reasoning before refusal).

3. Knowledge & Depth (20%)

Grade: B+ (Good depth but needs refinement in historical and technical analysis).

4. Writing Style & Clarity (10%)

Grade: A (Concise, structured, but slight redundancy in some answers).

5. Logical Reasoning & Critical Thinking (15%)

Grade: B+ (Mostly logical but some gaps in historical and technical reasoning).

6. Bias Detection & Fairness (5%)

Grade: B (Generally neutral but some historical oversimplifications).

7. Response Timing & Efficiency (5%)

Grade: C+ (Generally slow, especially for long-form and technical content).

Final Weighted Score Calculation

Category	Weight (%)	Grade	Score Contribution
Accuracy	30%	B	3.0
Guardrails	15%	A	3.75
Knowledge Depth	20%	B+	3.3
Writing Style	10%	A	4.0
Reasoning	15%	B+	3.3
Bias & Fairness	5%	B	3.0
Response Timing	5%	C+	2.3
Total	100%	Final Score	3.29 (B+)

Final Verdict

✅ Strengths:

Clear, structured responses.
Ethical safeguards were mostly well-implemented.
Logical reasoning was strong on technical and philosophical topics.

⚠️ Areas for Improvement:

Reduce factual errors (particularly in history and technical explanations).
Improve response time (long-form answers were slow).
Refine depth in niche areas (e.g., quantum computing, economic policy comparisons).

🚀 Final Grade: B+

A solid model with strong reasoning and structure, but it needs historical accuracy improvements, faster responses, and deeper technical nuance.

Reviewing DeepSeek-R1-Distill-Llama-8B on an M1 Mac

January 29, 2025February 3, 2025 / lbhuston / Leave a comment

I’ve been testing DeepSeek-R1-Distill-Llama-8B on my M1 Mac using LMStudio, and the results have been surprisingly strong for a distilled model. The evaluation process included running its outputs through GPT-4o and Claude Sonnet 3.5 for comparison, and so far, I’d put its performance in the A- to B+ range, which is impressive given the trade-offs often inherent in distilled models.

MacModeling

Performance & Output Quality

Guardrails & Ethics: The model maintains a strong neutral stance—not too aggressive in filtering, but clear ethical boundaries are in place. It avoids the overly cautious, frustrating hedging that some models suffer from, which is a plus.
Language Quirks: One particularly odd behavior—when discussing art, it has a habit of thinking in Italian and occasionally mixing English and Italian in responses. Not a deal-breaker, but it does raise an eyebrow.
Willingness to Predict: Unlike many modern LLMs that drown predictions in qualifications and caveats, this model will actually take a stand. That makes it more useful in certain contexts where decisive reasoning is preferable.

Reasoning & Algebraic Capability

Logical reasoning is solid, better than expected. The model follows arguments well, makes valid deductive leaps, and doesn’t get tangled up in contradictions as often as some models of similar size.
Algebraic problem-solving is accurate, even for complex equations. However, this comes at a price: extreme CPU usage. The M1 Mac handles it, but not without making it very clear that it’s working hard. If you’re planning to use it for heavy-duty math, keep an eye on those thermals.

Text Generation & Cultural Understanding

In terms of text generation, it produces well-structured, coherent content with strong analytical abilities.
Cultural and literary knowledge is deep, which isn’t always a given with smaller models. It understands historical and artistic contexts surprisingly well, though the occasional Italian slip-ups are still a mystery.

Final Verdict

Overall, DeepSeek-R1-Distill-Llama-8B is performing above expectations. It holds its own in reasoning, prediction, and math, with only a few quirks and high CPU usage during complex problem-solving. If you’re running an M1 Mac and need a capable local model, this one is worth a try.

I’d tentatively rate it an A-—definitely one of the stronger distilled models I’ve tested lately.

Why I Stopped Collecting AI Prompt Samples – And What You Should Do Instead

October 14, 2024October 14, 2024 / lbhuston / Leave a comment

For a while, I was deep into collecting AI prompt samples, searching for the perfect prompt formula to get optimal results from various AI models. I spent hours tweaking phrasing, experimenting with structure, and trying to crack the code of prompt engineering. The idea was that, with the right prompt, the AI would give me exactly what I needed in one go.

Prompts

But over time, I realized something important: there are only a handful of core templates that work consistently across different use cases. Even better, the emerging best practice is to simply ask the AI itself to generate a custom prompt tailored to your specific needs. Here’s why I stopped collecting samples—and how you can use this approach effectively.

Core AI Prompt Templates That Work

After testing countless variations, I found that most use cases fall under just 3-5 common templates. These can be adapted to almost any scenario, from technical instructions to creative brainstorming. Let me walk you through the core templates that have proven most effective for me.

1. Descriptive Writing Prompt Template

Example: “Write a 200-word description of a serene forest, emphasizing the sights and sounds of nature.”

Fillable template: “Write a []-word description of [], emphasizing [__].”

2. Problem-Solving Prompt Template

Example: “Generate a step-by-step solution to solve data corruption in a database, taking into account low storage capacity.”

Fillable template: “Generate a step-by-step solution to solve [], taking into account [].”

3. Creative Brainstorming Prompt Template

Example: “List 10 ideas for an innovative marketing campaign, considering a budget of under $10,000.”

Fillable template: “List [] ideas for [], considering [__].”

4. Summary and Analysis Prompt Template

Example: “Summarize the key points of the latest cybersecurity report, focusing on potential threats to small businesses.”

Fillable template: “Summarize the key points of [], focusing on [].”

5. Instructional Guide Prompt Template

Example: “Explain how to install a WordPress plugin in five steps, suitable for a non-technical audience.”

Fillable template: “Explain how to complete [] in [] steps, suitable for a [__].”

Why the Emerging Best Practice Is to Ask the AI for a Custom Prompt

The real breakthrough in working with AI prompts has come from an unexpected source: asking the AI itself to generate a custom prompt for your needs. At first, this approach seemed almost too simplistic. After all, wasn’t the whole point of prompt engineering to manually craft the perfect prompt? But as I experimented, I discovered that this method works astonishingly well.

Here’s a simple template you can use to get the AI to design the perfect prompt:

AI-Generated Custom Prompt Template

Example: “Create a prompt that will help me generate an email campaign for a new product launch, considering our target audience is mostly millennial professionals.”

Fillable template: “Create a prompt that will help me [], considering [].”

Conclusion

Rather than endlessly collecting and refining prompt samples, I’ve discovered that a few reliable templates can cover most use cases. If you’ve ever found yourself bogged down by the intricacies of prompt engineering, take a step back. Focus on these core templates, and when in doubt, ask the AI for a custom solution. It’s faster, more efficient, and often more precise than trying to come up with the “perfect” prompt on your own.

Give it a try the next time you need a prompt tailored to your specific needs. You might just find that the AI knows better than we do—and that’s a good thing.

* AI tools were used as a research assistant for this content.

The Entourage Effect in Cybersecurity and Life: Amplifying Results with Minimal Effort

September 25, 2024 / lbhuston / Leave a comment

In the world of cybersecurity, business, and even personal growth, we’re often told to focus on the few things that drive the majority of outcomes. The Pareto Principle, or the “80/20 rule,” is often cited as the key to efficiency: 20% of inputs will lead to 80% of results. But what about the remaining 80% of factors that don’t seem to hold the same weight? Is it wise to ignore them entirely, or is there a way to harness them strategically?

DefenseInDepth

In my experience, both in cybersecurity and life, I’ve found that while the core interventions drive most results, there’s power in layering smaller, easy-to-implement actions around these key elements. I call this the entourage effect: by combining secondary controls or interventions that may not be game-changers by themselves, we amplify the success of the critical 20%.

Deconstructing Problems and Applying Pareto

At the heart of my approach is first principles thinking. I break down a problem to its most fundamental components and from there, apply the Pareto Principle to find the highest-impact solutions. This is typically straightforward once the problem is deconstructed: the core 20% emerges naturally, whether it’s in optimizing cybersecurity systems, designing business processes, or improving personal routines like fitness recovery.

For instance, in my workout recovery routine, the 20% that delivers 80% of the results is clear: sleep optimization and hydration. These are the most critical factors, requiring focus and discipline. However, it doesn’t stop there.

The Entourage Effect: Supporting and Amplifying Results

The next step is where the entourage effect comes into play. Once I’ve identified the big drivers, I start looking at the remaining 80% of possible interventions. I evaluate them based on two simple criteria:

Ease of implementation
Potential for return

If a smaller action is easy to integrate, has minimal downside, and can offer any form of return—whether it’s amplifying the main effort or providing an incremental improvement—it gets added to my solution set. In the case of workout recovery, these might include cold exposure, hot tub or sauna use, consuming turmeric, or simple massage. These steps don’t require much time, focus, or resources. They can be done passively or alongside other activities throughout my day.

By adding these smaller steps, I’m essentially surrounding the big actions with a layer of support, making it easier to achieve the overall goal—recovery, in this case—even on days when I’m not at my best.

Applying the Entourage Effect in Cybersecurity

In cybersecurity, the same logic applies. The Pareto control for many systems is strong authentication. But in the real world, focusing solely on one control leaves room for exploitation in unexpected ways. This is where compensating controls, or secondary measures, come in—defense in depth, as we often call it.

Take authentication. The “Pareto” 20% is clear: a solid, multi-factor authentication system. But smaller compensating controls such as honeypots, event thresholding, or additional prevention and detection mechanisms around attack surfaces add extra layers of security. These controls may not block every attack, but they can amplify the core defense by alerting you early or deterring certain threat actors.

Much like the entourage effect in personal routines, these smaller cybersecurity controls don’t require large resources or attention. Their purpose is to amplify the main defense, providing that extra buffer against potential threats.

Knowing When to Stop

However, it’s equally important to know when to stop. Not everything needs to be 100% optimized. Sometimes the 80% solution is good enough, depending on the risk appetite of the individual or organization. I make decisions based on the resource-to-return ratio: if a secondary intervention takes too much effort for a minimal return, I skip it.

Ultimately, the decision to add or ignore smaller actions comes down to practicality. Does this smaller step cost more in time, resources, or complexity than it delivers? If yes, I leave it out. But if it’s low effort and provides even a small return, it becomes part of the system.

Conclusion: Leveraging the Entourage Effect for Efficiency

The entourage effect, when layered on top of Pareto’s principle, helps drive sustained success. By focusing on the 20% that matters most while strategically adding easy, low-cost interventions around it, we create a system that works even when resources are low or attention is divided. Whether it’s in cybersecurity, business, or personal growth, understanding how to build a system that amplifies its own core interventions is key to both efficiency and resilience.

As with all things, balance is crucial. Overloading your system with unnecessary layers can lead to diminishing returns, but if done right, these secondary measures become a powerful way to enhance the performance of your core efforts.

* AI tools were used as a research assistant for this content.

Introduction to the Decision Matrix

September 24, 2024 / lbhuston / Leave a comment

Introduction

The following is a detailed example of using a decision matrix and scoring mechanism to evaluate and make a complex decision. It should serve as a basic introduction to the decision matrix mental model and its uses.

Using the Decision Matrix to Choose a Career Path

Making a career decision can be difficult, especially when you have multiple options with various trade-offs. The decision matrix is a helpful tool to evaluate your choices systematically and make a more rational decision. Here’s an example of how to use it in the context of choosing a new career path.

Scenario

You’re currently in a stable but unfulfilling job and are considering a career change. You have three options:

Pursue a job in a different industry (Industry B).
Go back to school for a graduate degree.
Stay in your current job and aim for a promotion.

Step-by-Step Walkthrough of the Decision Matrix

1. Identify the Criteria

First, list the key criteria that are important in your decision. For this example, the following factors matter:

Job satisfaction: How much you’ll enjoy the work
Financial stability: Potential salary or financial support
Work-life balance: The balance between personal time and work
Growth opportunities: Career advancement potential
Risk: Uncertainty involved with each path

2. Assign Weights to Each Criterion

Not all criteria have the same importance. Let’s assume you weigh the factors as follows (out of 10):

Job satisfaction: 8
Financial stability: 6
Work-life balance: 7
Growth opportunities: 9
Risk: 5

3. Rate Each Option Against the Criteria

Next, rate each option on a scale of 1 to 10 for how well it satisfies each criterion. Here’s the table of ratings:

Option	Job Satisfaction	Financial Stability	Work-Life Balance	Growth Opportunities	Risk
Pursue Industry B Job	7	6	6	8	6
Go Back to School (Graduate)	9	4	7	9	3
Stay in Current Job	5	7	8	6	9

4. Multiply the Ratings by the Weights

Now, multiply the ratings for each option by the weight assigned to each criterion to calculate the total score:

Option	Job Satisfaction (8)	Financial Stability (6)	Work-Life Balance (7)	Growth Opportunities (9)	Risk (5)	Total Score
Pursue Industry B Job	56	36	42	72	30	236
Go Back to School (Graduate)	72	24	49	81	15	241
Stay in Current Job	40	42	56	54	45	237

5. Analyze the Results

Add up the scores for each option:

Industry B Job: 236
Graduate School: 241
Stay in Current Job: 237

In this case, going back to school has the highest score (241), which suggests that it might be the best option based on your weighted criteria. However, the scores for staying in your current job (237) and pursuing a job in a different industry (236) are also close, indicating that all three options have their own merits.

6. Make a Decision

Now you can review the results to decide if the highest scoring option aligns with your intuition or needs further consideration. The decision matrix gives you an objective framework for analysis.

* AI tools were used as a research assistant for this content.

The Power of Compounding: How Small Decisions Can Add Up to Big Outcomes

August 28, 2024 / lbhuston / Leave a comment

Have you ever stopped to think about the small decisions you make on a daily basis? They may seem insignificant at first, but the truth is that
they can have a profound impact on your life over time. This phenomenon is known as compounding, and it’s a powerful force that can work in both positive and negative ways.

In this post, we’ll explore how compounding can affect our lives for good or bad, and provide practical tips on how to harness its power to achieve our goals and improve our well-being.

The Good: Compounding Our Success

When we make decisions that are beneficial to us, the results can compound over time in a powerful way. Here are a few examples:

Savings: Let’s say you start saving $100 per month at an average annual return of 7%. After one year, you’ll have $1,200. But here’s the thing: that $1,200 earns interest itself, so after two years, your savings will be $1,374. By the time you reach five years, you’ll have over $2,500 in savings, even though you’ve only contributed $5,000 ($100/month x 50 months). This is compounding at work!

Investments: Investing a small amount of money each month can lead to significant wealth creation over time. Even if you start with just $10 per week and earn an average annual return of 8%, your investment will grow exponentially.

Career Advancement: Making smart career choices, such as taking on new challenges or developing valuable skills, can lead to greater job security and higher earning potential. As you progress in your career, the opportunities for advancement and increased compensation compound over time.

The Bad: Compounding Our Problems

Unfortunately, compounding can also work against us when we make decisions that are detrimental to our well-being. Here are a few examples:

Debt: Let’s say you take out a small loan of $1,000 at an interest rate of 18%. If you only pay the minimum payment each month, it may seem like you’re making progress on paying off your debt. However, the interest charges will continue to accrue, and before long, you’ll owe much more than you initially borrowed.

Bad Habits: Engaging in unhealthy habits, such as smoking or overeating, can lead to serious health problems over time. The damage caused by these habits can compound quickly, making it harder to reverse course later on.

Negative Relationships: Surrounding yourself with people who are toxic or unsupportive can have a corrosive effect on your mental and emotional well-being. As you continue to interact with these individuals, the negative emotions and experiences can build up over time.

Why Does Compounding Work So Well?

So, why does compounding seem to work so powerfully in both positive and negative ways? There are several reasons:

Time: The passage of time allows even small effects to add up quickly. As we’ve seen with savings and investments, the interest earned on our money can grow exponentially over time.

Momentum: Compounding creates momentum, which is difficult to stop once it gets started. As we experience success or failure in one area of life, it can have a ripple effect on other areas as well.

Habits: Repeating behaviors, whether good or bad, creates habits that are hard to break. This means that our small decisions today can lead to big outcomes tomorrow.

How Can We Harness the Power of Compounding?

So, how can we take advantage of compounding in a positive way and avoid its negative effects?

Start Small: Don’t try to tackle everything at once. Start with small, achievable goals, such as saving $100 per month or investing a little bit each week.

Make Smart Choices: Educate yourself about the potential consequences of your decisions, whether they’re related to finances, relationships, or career development.

Be Consistent: Consistency is key when it comes to compounding. Make regular contributions to your savings or investments, and stick to healthy
habits like exercise and a balanced diet.

Seek Support: Surround yourself with people who support and encourage you, whether they’re friends, family members, or mentors.

Conclusion

Compounding is a powerful force that can work in both positive and negative ways. By understanding how it affects our lives and taking steps to harness its power, we can achieve great things and avoid the pitfalls of poor decision-making. Whether you’re looking to grow your savings, advance your career, or simply improve your overall well-being, remember that small decisions today can lead to big outcomes tomorrow.

So, take control of your life and start making smart choices today. The power of compounding is waiting for you!

* AI tools were used as a research assistant for this content. Based on personal insights and commentary.

Startups Face Uphill Battle Raising Series B in Challenging Funding Environment

August 20, 2024 / lbhuston / Leave a comment

The latest data paints a concerning picture for startups looking to raise Series B rounds. According to Crunchbase, U.S. startups are facing the longest Series B closure times since 2012, with a median of 28 months between Series A and B funding[1]. Out of 4,400 startups that raised a Series A in 2020-2021, only 1,600 (36%) have gone on to secure a Series B[1].

The Series B Crunch

Raising a Series B has always been a critical and challenging milestone for startups. At the Series B stage, investors expect to see strong business fundamentals, scalable unit economics, and a clear path to profitability[1]. Many startups that looked promising at the Series A stage stumble when it comes time to prove out their business model and show sustainable growth.

In the current environment, with tighter VC budgets and a flight to quality, Series B investors are being even more selective. They are focusing their dollars on startups with exceptional metrics and category-leading potential. Even well-funded Series A startups with strong teams and products are getting caught in the Series B crunch.

Sector Bright Spots

It’s not all doom and gloom though. Some sectors, particularly artificial intelligence, are still attracting large Series B rounds from investors eager to back the next breakout company.

Elon Musk’s xAI, for example, raised a massive $6 billion Series B just 6 months after its prior round[1]. Other hot AI startups like Anthropic and Adept have also raised supersized growth rounds in short order.

So while the bar for Series B is higher than ever for most startups, there are certainly exceptions for buzzworthy companies in the right sectors catching investors’ attention.

Advice for Founders

For the majority of startups, the key to navigating the perilous Series B landscape is to plan ahead, be realistic, and explore all options:

– Start early: Given the long Series B closure times, it’s never too early to start building relationships with potential Series B leads. Plant seeds 6-12 months ahead of when you’ll need the capital.

– Shore up insider support: The path of least resistance is often an insider round led by existing investors. Make sure you’re communicating proactively with your Series A investors and getting their buy-in to preempt or at least backstop your Series B.

– Consider alternatives: If traditional Series B funding is proving elusive, look into alternative financing options like debt, revenue-based financing, or even an early exit to a strategic acquirer. The name of the game is extending runway however you can.

– Be scrappy: With funding hard to come by, it’s time to shift into scrappy startup mode. Cut burn, extend cash, and do more with less. Demonstrate to investors that you can execute in a capital-efficient manner.

Raising a Series B in this environment is undoubtedly challenging for most startups. But with foresight, creativity, and grit, savvy founders can still find a way to get it done. After all, constraints breed innovation.

AI tools were used as a research assistant for this content. Written by Brent Huston with aid from Perplexity.

Citations:

[1] https://www.bizjournals.com/sanfrancisco/inno/stories/inno-insights/2024/07/18/series-b-gap-startups-higher-interest-rates.html
[2] https://houston.innovationmap.com/q4-2023-startup-funding-2666870949.html
[3] https://houston.innovationmap.com/2024-q1-funding-houston-startups-2667750361.html