The Tech Trek

Catching phishing attacks is only half the challenge. An AI security model also has to avoid blocking legitimate emails.

Ryan Luo, Founder and CTO of Aegis AI, joins The Tech Trek to explain why his team moved beyond commercial AI model APIs and began building its own models for email security.

Existing models were surprisingly capable of identifying attacks, but distinguishing suspicious activity from actual threats required more precision. Ryan explains the tradeoffs between detection accuracy, cost, and control, including why the last few percentage points of model performance can be the hardest to improve.

The conversation also explores how AI coding agents are changing engineering work. Ryan shares why his engineers spend time understanding real security threats, how junior engineers can develop better judgment, and why defining clear outcomes matters more as agents take on additional implementation work.

Key Takeaways

Custom models can offer greater control over accuracy and compute costs.

False positives can undermine an otherwise effective security product.

Engineers need domain expertise to evaluate and improve AI output.

Effective agentic development starts with clear goals and constraints.

Episode Highlights

01:52 Why Aegis AI moved beyond commercial model APIs

05:06 The difficult tradeoff between precision and recall

07:08 Cost and compute advantages of custom AI models

14:30 Why detection engineers must understand real attacks

16:27 What makes junior engineers valuable in the AI era

28:21 How AI agents are changing engineering leadership

One Line That Stuck

"They can't turn off the adversaries, but they can turn off you."

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AI is helping healthcare teams move faster. But faster models do not always mean better patient care.

Hetal Patel, VP of Healthcare Economics and Data Science at Harmony Care, joins The Tech Trek to discuss where AI is delivering value in healthcare and where human expertise remains essential.

Hetal shares how her team uses AI for clinical documentation, contract analysis, coding, data migration, and predictive modeling. In one example, AI helped shorten the development time for a patient readmissions model by more than 50%.

But speed creates new challenges. Clinicians need time to adopt new tools, models need careful testing, and biased data can lead to biased decisions. The conversation also explores healthcare data sharing, patient privacy, and the need for responsible AI governance.

Key Takeaways

AI is reducing administrative work and helping healthcare analytics teams handle more projects.

Faster model development creates new expectations for clinicians adopting those models.

Human expertise and bias testing remain essential when models influence patient care.

Better access to clinical data could improve predictions, but raises privacy concerns.

Episode Highlights

01:49 How AI scribes, agents, and coding assistants are being used

04:02 Measuring ROI beyond simple time savings

05:52 Cutting predictive model development time by more than 50%

08:03 Why faster deployment is not always the right choice

10:34 How bias enters models and why testing matters

16:51 The promise and privacy challenges of connected healthcare data

One Line That Stuck

"Data is dirty. Data is biased. If you build on that, you're gonna have a biased model."

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Some healthcare professionals want to brainstorm about AI. Others are reluctant to use it. And many simply want technology that helps them do their jobs.

Dany Jradi, Head of Product at Abby Care, joins The Tech Trek to discuss what healthcare stakeholders actually care about when AI enters their workflows. For engineers and product teams, the technology itself can be exciting. For clinicians and operators, the outcome often matters more than how the system works.

Dany explains why AI adoption remains uneven, even for tools like AI scribes that have been available for years. He also discusses why reviewing administrative documents requires a different level of scrutiny than providing clinical guidance.

The conversation turns to family caregiving and the potential of daily care notes. Could AI help clinical teams identify changes in a patient's condition before those changes lead to a serious event?

Key Takeaways

Healthcare stakeholders often prioritize their work over how an AI system operates.
AI adoption varies widely, even among technology savvy clinicians.
Administrative automation and clinical guidance require different standards.
Daily caregiver notes could reveal patterns that individual events miss.

Episode Highlights

02:02 Why healthcare professionals respond differently to AI
04:20 What builders care about versus what stakeholders need
08:09 AI scribes and the uneven pace of adoption
11:44 Why clinical and administrative AI have different standards
15:42 Finding useful signals in daily caregiver notes
20:09 How AI could identify trends between major health events

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What happens when AI makes writing code faster, but your engineering process still operates on two week sprints?

Junaid Warwani, VP of Engineering at Brellium, joins The Tech Trek to explain how he restructured engineering at his previous company after adopting AI development tools. His team moved away from traditional sprints, gave individual engineers ownership of priorities, and shifted toward weekly planning with faster feedback.

But moving faster introduced new problems. Engineers had to manage more context, product managers needed different responsibilities, and the rest of the organization had to adapt.

Junaid shares what worked, where the model fell short, and what he would change as he brings those lessons into his new role.

Key Takeaways

Why traditional sprint planning started losing its value as AI accelerated development.

How one engineer can own delivery while product managers focus on what comes next.

Why feedback loops can become a bigger bottleneck than writing code.

How hiring priorities are shifting toward communication, customer understanding, and critical thinking.

Episode Highlights

02:48 Why Junaid returned from parental leave ready to rethink engineering.
06:22 Rethinking the relationship between product managers and engineers.
10:45 Replacing traditional sprints with individual ownership and weekly priorities.
14:20 Why faster feedback matters more than faster code.
19:23 What Junaid now looks for when hiring engineers.
27:31 Why engineering leaders need to become better at change management.

A Moment Worth Pulling Out

"What we're doing does not make sense anymore."

Junaid describes the realization that led him to rethink his team's development process.

If you're reconsidering how your engineering team works with AI, this conversation offers practical lessons from a leader who has already experimented with a different model.

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What actually makes a company AI native? Is it using AI across every department, building more agents, or changing how the organization works?

Eswar Vandenapu, Cofounder and CTO of NovaWorks AI, shares what his team is learning as it moves from basic AI adoption toward a shared context model that shapes how decisions are made.

The discussion explores why giving everyone access to AI tools is only the beginning. Eswar explains how NovaWorks is using shared context to improve collaboration, why an agent that understands its environment can be more useful than dozens of specialized agents, and how the boundaries between product and engineering are changing.

We also discuss the skills engineering teams need as AI handles more coding work, including systems thinking, domain expertise, and the ability to ask better questions.

What You'll Learn

Why AI adoption alone does not make a company AI native.
How shared context changes collaboration and decision making.
Why agent quality matters more than the number of agents.
How AI is changing engineering roles, hiring, and technical skills.

Episode Highlights

00:40 How NovaWorks is rethinking HR and workforce management
03:37 The journey from AI awareness to shared organizational context
07:54 Why building more agents is not necessarily better
11:07 How AI is changing the relationship between product and engineering
14:41 Why systems thinking and domain knowledge matter more in engineering
18:57 Learning to question AI rather than simply give it instructions

One Line That Stuck

"Quantity of the agents doesn't matter."

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