The Tech Trek

Healey Cypher, CEO of BoomPop and COO at Atomic, breaks down what separates founders who win from founders who stall. You will hear a clear way to judge whether an idea is truly worth building, plus the trust mechanics that get investors, customers, and teammates to actually follow you.

This conversation is a practical map for tech builders who want to pick smarter problems, execute faster, and earn credibility without the founder theater.

Key Takeaways

Founders matter most, but the idea is still a gate, the same great team can get wildly different outcomes depending on the market and timing
VC backed is a specific game, it requires not just big potential, but fast scale, and the incentives are not the same as building a profitable lifestyle business
A quick reality check for market size, if you need more than about five to seven percent penetration to hit meaningful revenue, it is usually a brutal path
Painkillers beat vitamins, solve an urgent problem people feel right now, or you risk getting cut the moment budgets tighten
Trust is built through authenticity, logic, and empathy, if one wobbles, people feel it fast, and progress slows everywhere

Timestamped Highlights

00:00:00 Healey’s background, why BoomPop, and what the episode is really about
00:02:00 The post pandemic spend shift and the why now behind modern events and group travel
00:04:30 Founder versus idea, why execution dominates, but the opportunity still decides the ceiling
00:06:40 The VC reality, power law returns, speed, and why some good businesses are still a no for venture
00:09:15 A simple market math test, penetration levels that become a growth wall
00:19:00 Trust as a founder skill, the three ingredients and how to spot when one is missing
00:21:30 Vulnerability as a shortcut to real connection, plus the giver mindset that makes people want you to win

A line worth stealing

If everyone wants you to win, it is a lot easier to win.

Pro Tips for Tech Founders

Ask yourself what you naturally look forward to doing, that is often your zone of strength, hire around the tasks you dread
Learn the financial basics early, especially cash flow, it is the scoreboard that keeps you alive long enough to win
When trust is lagging, check the three levers, are you showing the real you, can people follow your reasoning, do they feel you care about their outcomes

What's next:

If you build products, lead teams, or are thinking about starting something, follow the show so you do not miss episodes like this. Also connect with me on LinkedIn for short takeaways and clips from each conversation.

Ty Wang, cofounder and CEO of Angle Health, breaks down what it means to give back through public service, then shows how that same mindset drives his mission to modernize healthcare for small and midsize businesses. We get into why legacy health plans feel opaque and painful, what an AI native health plan actually changes behind the scenes, and how better data and workflows can create real cost stability for employers.

Ty shares his path from a federal scholarship and national service work to Palantir, and why he chose one of the most regulated, least glamorous industries to build in. If you have ever wondered why healthcare feels impossible to navigate, or why renewals can blindside a company, this conversation will give you a clear mental model of the problem and a practical view of what modernization looks like when it actually ships.

Key Takeaways
Healthcare feels broken because the infrastructure is fragmented, data is siloed, and even basic questions become hard to answer across inconsistent systems
Modernizing healthcare is not just about a new app, it is about rebuilding the operational core so workflows, claims, underwriting, and member experience can run on integrated data
Small and midsize businesses are hit hardest by cost volatility because they lack transparency, predictability, and negotiating leverage, yet health insurance is often a top line item after payroll
A strong approach to regulated markets is collaborative, treat regulators as partners in consumer protection, not obstacles to work around
Mission and impact can be a recruiting advantage, especially when the technical problems are genuinely hard and the outcomes touch real people fast

Timestamped Highlights
00:40 What Angle Health is, and what AI native means in a real health plan
02:05 The scholarship path that pulled Ty into public service and set his trajectory
04:06 The personal story behind the mission, the American dream, and why access matters
09:38 Why healthcare infrastructure is so complex, and how siloed systems create bad experiences
11:33 Why SMBs get squeezed, and how manual administration blocks customization at scale
13:20 The real pain point for employers, cost volatility and zero predictability before renewal
16:55 Why the tech can expand beyond SMBs, but why the SMB market is already massive
19:51 Lessons from building in a regulated industry, and why credibility and funding matter
22:26 Hiring for high agency, mission driven talent in a world full of AI companies

A line that sticks
“Unless you are lucky enough to work for a big company, these modern healthcare services are still largely inaccessible to the vast majority of Americans.”

Pro Tips for tech operators and builders
If you are modernizing a legacy industry, start with the infrastructure layer, fix the data model, integrate the systems, then automate workflows
In regulated markets, build relationships early, show how your product improves consumer outcomes, and make compliance a design constraint, not a bolt on
When selling into SMBs, predictability beats perfection, give customers a clear breakdown of what drives costs and what they can control

What's next:
If this episode helped you see healthcare and legacy modernization more clearly, follow the show on Apple Podcasts or Spotify and subscribe so you do not miss the next conversation. Also, share it with one operator or builder who is trying to modernize a messy industry.

Gabe Ravacci, CTO and co-founder at Internet Backyard, breaks down what the “computer economy” really looks like when you zoom in on data centers, billing, invoicing, and the financial plumbing nobody wants to touch. He shares how a rejected YC application, a finance stint, and a handful of hard lessons pushed him from hardware curiosity to building fintech infrastructure for compute.
If you care about where compute is headed, or you are early in your career and trying to find your path without overplanning it, this one will land.

Key Takeaways
• Startups often happen “by accident” when your competence meets the right problem at the right time
• Compute accessibility is not only a chip problem, it is also a finance and operations problem
• Rejection can be data, not a verdict, treat it as feedback to sharpen the craft
• A real online presence is less about networking and more about being genuinely useful in public
• Time blocking and single task focus beats grinding when you are juggling school, work, and a startup

Timestamped Highlights
00:28 What Internet Backyard is building, fintech infrastructure for data center financial operations
01:37 The first startup attempt, cheaper compute via FPGA based prototyping, and why investors passed
04:48 The pivot, from hardware tools to a finance informed view of compute and transparency gaps
06:55 How Gabe reframed YC rejection, process over outcome, “a tree of failures” that builds skill
08:29 Building a digital brand on X, what he posted, how he learned in public, and why it worked
13:36 The real balancing act, dropping classes, finishing the degree well, and strict time blocking
20:00 Books that shaped his thinking, Siddhartha, The Art of Learning, Finite and Infinite Games

A line worth keeping
“The process is really more important than any outcome.”

Pro Tips for builders
• Treat learning like a skill, ask better questions before you chase better answers
• Make focus a system, set blocks, mute distractions, and do one thing at a time
• Share what you are learning in public, not to perform, but to be useful and find signal

Call to Action
If this episode sparked an idea, follow or subscribe so you do not miss the next one. Also check out Amir’s newsletter for more conversations at the intersection of people, impact, and technology.

Data leaders are being asked to ship real AI outcomes while the foundations are still messy. In this conversation, Dave Shuman, Chief Data Officer at Precisely, breaks down what actually determines whether AI adoption sticks, from hiring “comb shaped” talent to building trusted data products that make AI outputs believable and usable.

If you are building in data, AI, or analytics, this episode is a practical map for what needs to be true before AI can move from demos to dependable, repeatable impact.

Key Takeaways

Comb shaped talent beats narrow specialization, AI work rewards people who can span multiple skills and collaborate well
Adoption is a trust problem, and trust starts with data integrity, lineage, context, and a semantic layer that business users can understand
Open source drives the innovation, commercialization makes it safe and usable at enterprise scale, especially around security and support
Data must be fit for purpose, start every AI project by asking what data it needs, who curates it, and what the known warts are
Humans are still the last mile, small workflow choices can make adoption jump, even when the model is already accurate

Timestamped Highlights

00:56 The shift from T shaped to comb shaped talent, what modern AI teams actually need to look like
05:36 Hiring for team fit over “world class” niche skills, and when to bring in trusted partners for depth
07:37 How open source sparks the ideas, and why enterprises still need hardened, supported versions to scale
11:31 Where AI adoption is today, why summarization is only the beginning, and what unlocks “AI 2.0”
13:39 The trust stack for AI, clean integrated data, lineage, context, catalog, semantic layer, then agents
19:26 A real adoption lesson from machine learning, and why the human experience decides if the system wins

A line worth stealing

“You do not just take generative AI and throw it at your chaos of data and expect it to make magic out of it.”

Pro Tips for data and AI leaders

Hire and build teams like Tetris, fill skill voids across the group instead of chasing one perfect profile
Use partners for the sharp edges, but require knowledge transfer so your team levels up every engagement
Make adoption easier by designing for human behavior, sometimes the smallest workflow tweak beats more accuracy
Build governed data products in a catalog, then validate AI outputs side by side with dashboards to earn trust fast

Call to Action

If this helped you think more clearly about AI adoption, talent, and data foundations, follow the show and turn on notifications so you do not miss the next episode. Also, share it with one data or engineering leader who is trying to get AI out of pilots and into real workflows.

Cloud bills are climbing, AI pipelines are exploding, and storage is quietly becoming the bottleneck nobody wants to own. Ugur Tigli, CTO at MinIO, breaks down what actually changes when AI workloads hit your infrastructure, and how teams can keep performance high without letting costs spiral.

In this conversation, we get practical about object storage, S3 as the modern standard, what open source really means for security and speed, and why “cloud” is more of an operating model than a place.

Key takeaways

• AI multiplies data, not just compute, training and inference create more checkpoints, more versions, more storage pressure
• Object storage and S3 are simplifying the persistence layer, even as the layers above it get more complex
• Open source can improve security feedback loops because the community surfaces regressions fast, the real risk is running unsupported, outdated versions
• Public cloud costs are often less about storage and more about variable charges like egress, many teams move data on prem to regain predictability
• The bar for infrastructure teams is rising, Kubernetes, modern storage, and AI workflow literacy are becoming table stakes

Timestamped highlights

00:00 Why cloud and AI workloads force a fresh look at storage, operating models, and cost control
00:00 What MinIO is, and why high performance object storage sits at the center of modern data platforms
01:23 Why MinIO chose open source, and how they balance freedom with commercial reality
04:08 Open source and security, why faster feedback beats the closed source perception, plus the real risk factor
09:44 Cloud cost realities, egress, replication, and why “fixed costs” drive many teams back inside their own walls
15:04 The persistence layer is getting simpler, S3 becomes the standard, while the upper stack gets messier
18:00 Skills gap, why teams need DevOps plus AIOps thinking to run modern storage at scale
20:22 What happens to AI costs next, competition, software ecosystem maturity, and why data growth still wins

A line worth keeping

“Cloud is not a destination for us, it’s more of an operating model.”

Pro tips for builders and tech leaders

• If your AI initiative is still a pilot, track egress and data movement early, that is where “surprise” costs tend to show up
• Standardize around containerized deployment where possible, it reduces the gap between public and private environments, but plan for integration friction like identity and key management
• Treat storage as a performance system, not a procurement line item, the right persistence layer can unblock training, inference, and downstream pipelines

What's next:
If you’re building with AI, running data platforms, or trying to get your cloud costs under control, follow the show and subscribe so you do not miss upcoming episodes. Share this one with a teammate who owns infrastructure, data, or platform engineering.