Every developer struggles with context-switching, managing multiple sessions, and maintaining focus. Enter Tmux.

Tmux is a tool Thomas Bustos calls the backbone of his productivity. In this episode, he walks through his full tech stack, explains how deep work sessions are structured, and shows how Tmux integrates with NeoVim and Cloud Code for maximum efficiency.

Learn how to track metrics, iterate on user experience, and keep your workflow aligned with your goals.

If you want to move from “busy” to “productive,” this episode is a must-listen.

Top Takeaways:

• Cloud tools can enhance productivity but aren't the sole solution.

• Tmux is a core tool for managing sessions effectively.

• Daily skills help manage entropy and keep goals aligned.

• Using a tech stack that includes NeoVim and Cloud Code is beneficial.

• Iterating on user experience is crucial for app development.

• Context management is key in software engineering.

• Open source tools can be integrated for better workflow.

• Security and setup considerations are important for cloud tools.

• Deep work sessions can lead to more productive outcomes.

• Feedback and iteration are essential in the design process.

Chapters:

• 00:00 Welcome!

• 02:47 Exploring the Tech Stack

• 06:07 Deep Work and Project Management

• 08:55 Iterating on User Experience

• 12:12 Final Thoughts on Engineering Tools

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/

• Let’s Talk AI - https://thomasbustos.substack.com/

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

The role of AI leaders has quietly transformed.

Not long ago, being an “AI leader” meant hiring a few ML engineers, experimenting with models, and hoping something stuck. Today, that approach fails. AI leaders are no longer just technical champions. They are system architects of decision-making, accountability, and value creation.

In this episode of Let’s Talk AI, Thomas Bustos breaks down the three pillars every AI leader must master to build real, measurable impact inside an organization. He explains why teaching is no longer optional, why strategy without execution collapses, and why implementation is where most AI initiatives die.

If you’re serious about becoming an AI leader, or building AI leadership inside your company, this episode gives you the blueprint.

Listen now.

Top Takeaways:

• The core goal is to balance leading and delivering technology.

• AI leaders must teach, strategize, and implement effectively.

• Successful AI adoption can compound gains for organizations.

• Metrics like error rates and active users are crucial for success.

• Every build should enhance observability for future improvements.

• AI leaders need to understand the latest tools and their applications.

• Reverting to previous versions is essential for error handling.

• Quantifying AI's impact in terms of revenue is recommended.

• A learning organization adapts and grows through shared knowledge.

• Effective implementation requires speed, reliability, and quality.

Chapters:

• 00:00 Welcome!

• 02:50 The AI Leaders Playbook: Key Pillars

• 06:03 Understanding AI Adoption and Its Impact

• 09:10 Strategies for Effective Implementation

• 11:58 Metrics for AI Success

• 14:51 Navigating AI Tools and Technologies

• 18:07 Building a Learning Organization

• 20:59 Final Thoughts

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/

• Let’s Talk AI - https://thomasbustos.substack.com/

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

What is an AI Native employee?

It’s not someone who occasionally uses ChatGPT.

It’s not someone who automates a few workflows.

It’s someone who integrates AI into how they think, decide, and operate.

In this episode, Thomas Bustos explores the rise of the AI Native employee—not as a job title, but as a new operating standard. Before AI, employees manually summarized, synthesized, reported, and shared knowledge. Decision-making required slow coordination. Context was fragmented.

Now, the AI Native shift is changing how organizations think, decide, and execute.

The AI Native employee defines what great looks like.

They set constraints.

They design systems.

They use AI to enhance clarity, not to outsource thinking.

Thomas Bustos breaks down why companies that fail to build AI Native systems will struggle with accountability, context gaps, and slow decision loops. And why the real competitive advantage is how employees integrate AI into learning velocity and decision quality.

This episode is a blueprint for leaders who want to move from AI curiosity to AI Native execution.

Top Takeaways:

• If your company is just getting seats for people to ask things to ChatGPT, you definitely need to change something.

• Create accountability and a motion of learning velocity.

• The more connected your context system is, the better decisions can be made.

• The quality of decisions depends on how much reality your team can see.

• Software is going to zero, meaning the cost of building solutions is decreasing.

• The jobs are not going anywhere; they are evolving with technology.

• If you can generate more impact, you are more valuable.

• Creating systems that enhance learning and context is crucial for growth.

• This is why we're playing this game: to continuously learn and adapt.

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/

• Let’s Talk AI - https://thomasbustos.substack.com/

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.