Leveraging AI

Are you still trying to decide whether Claude or ChatGPT is the “better” AI?
That may be the wrong question. The real advantage comes from building a workflow that lets you use the strengths of multiple AI platforms without losing context, duplicating work, or starting over every time you switch tools.
In this episode of Leveraging AI, Isar Meitis walks through the system he uses to work across Claude, ChatGPT, coding agents, and other AI tools on the same projects. The key is creating a shared file-based infrastructure that becomes the source of truth for project instructions, memory, tasks, and ongoing work.
Instead of locking your business into one AI ecosystem, you can build a setup that gives you more flexibility, resilience, and access to the best capabilities of each platform.
In this session, you'll discover:
How Claude, ChatGPT, and other AI tools can work on the same project without losing context.
Why shared files can become the real “memory” of your AI workflow.
How to use claude.md and agents.md so different AI platforms can follow the same project instructions.
How an evergreen project document and task registry keep every AI agent aligned.
How to structure backups so your AI projects don’t live only on one computer.
Why only one platform should be responsible for managing your backup process.
How tools such as Claude Code and Codex can work in parallel on different parts of the same project.
The result is a much more flexible AI operating system for your work: one where you can choose the best model for each task instead of forcing every task through the same platform.
If you want to take this kind of infrastructure further, Isar also discusses his multi-agent orchestration course, which focuses on building more advanced AI systems for business.
About Leveraging AI
Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events
If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

What happens when the people building the world’s most powerful AI systems start warning that they may not know how to control what comes next?
At the same time those warnings are getting louder, the AI race is doing anything but slowing down. New autonomous agents are launching across major platforms, companies are pouring staggering amounts of money into compute, and AI is moving from answering questions to taking actions on our behalf.
For business leaders, the takeaway isn’t to panic—or to sit on the sidelines. It’s to understand how quickly the landscape is shifting, where the real opportunities are emerging, and why governance, security, and responsible adoption need to evolve just as quickly as the technology.
In this episode of Leveraging AI, Isar Meitis connects the dots between three major developments shaping the next phase of AI.
In this session, you'll discover:
Why the resignation of Anthropic researcher Jacob Coxon ignited a massive debate about superintelligence and AI alignment.
What current Anthropic researchers are saying about the possibility of controlling recursively self-improving AI.
Why competition between AI labs may be making meaningful coordination and slowing down increasingly difficult.
What OpenAI’s own leadership is saying about alignment, monitoring, and potentially pacing future AI development.
How AI is rapidly moving beyond chatbots and into autonomous agents that can perform real-world tasks.
Why new agentic products from Meta, OpenAI, Alibaba, Instacart, Microsoft, and others matter for businesses.
How agents could reshape everything from personal assistance and software development to shopping and digital workforces.
Why security, governance, and control remain the biggest gaps as autonomous AI becomes more capable.
How enormous investments in chips, data centers, and compute reveal just how much further the AI industry expects this expansion to go.
What the accelerating demand for AI infrastructure means for the scale of the transformation still ahead.
About Leveraging AI
Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events
If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Are you burning through ChatGPT or Claude tokens faster than your team can justify the cost?
The problem may not be how much you use AI. It may be how you use it. With the right model, reasoning level, prompts, scripts, caching, and routing, you can often get the same quality of work while consuming significantly fewer tokens.
In this episode of the Leveraging AI Podcast, Isar Meitis breaks down practical ways to make AI usage more efficient across individual and enterprise workflows. He shares tests, settings, and workflow strategies designed to help you accomplish more without automatically reaching for the most expensive model or highest reasoning setting.
In this session, you'll discover:
Why the most powerful AI model is often unnecessary for everyday business tasks
Why lower-cost models can still produce highly accurate, consistent outputs
How a detailed prompt can dramatically improve results from cheaper models
How Excel scripts can execute complex recurring processes in seconds
How model routing can distribute work between cheaper and more capable AI models
How caching reduces the need to repeatedly process the same large amounts of information
How running ChatGPT and Claude in parallel can help spread workloads and avoid hitting platform limits
How organizations can perform more AI-powered work within the same budget without necessarily sacrificing quality
The takeaway for business leaders is simple: AI efficiency isn’t about using less AI. It’s about using expensive intelligence only when expensive intelligence is actually required.
About Leveraging AI
Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events
If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

What if AGI didn’t arrive with a dramatic announcement—but instead showed up as two new AI models released in the same week?
GPT-6 Astra and Anthropic’s Fable 5.1 are pushing performance, coding, cybersecurity, efficiency, and agentic capabilities to levels that would have sounded like AGI only a few years ago. And for business leaders, the bigger question is no longer whether these systems are becoming incredibly capable. It’s what you should do differently as a result.
The recommendation: stop treating every new model release as another shiny AI upgrade. Start evaluating what these advances mean for cost, security, enterprise data, autonomous workflows, model selection, and the way work inside your organization will actually get done.
In this episode of Leveraging AI, Isar Meitis breaks down one of the busiest weeks in AI yet - from GPT-6 Astra and Fable 5.1 to NVIDIA’s Hugging Face acquisition, Google’s efficiency play, increasingly autonomous AI agents, and new evidence of how deeply AI is already being trusted with consequential work.
In this session, you'll discover:
Why GPT-6 Astra may represent a meaningful step toward what many people would have called AGI just a few years ago.
The benchmark results that make Astra impressive—and why third-party evaluations paint a more nuanced picture.
Why Astra’s dramatic token efficiency does not necessarily mean lower costs.
How Fable 5.1 compares with GPT-6 Astra across coding, knowledge work, cost, and enterprise use cases.
Why NVIDIA’s acquisition of Hugging Face could reshape the battle between closed and open-source AI.
How smaller and specialized models are increasingly competing with frontier models at a fraction of the cost.
How OpenAI’s advertising business is rapidly becoming another major part of the AI economy.

About Leveraging AI
Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events
If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Are you using AI to create more content—only to end up with generic output that sounds nothing like your company?
The problem isn’t necessarily the AI. The problem is often the context you give it. When AI understands your brand voice, audience, goals, expertise, and processes, it can produce dramatically more relevant and consistent work.
In this episode of Leveraging AI, Isar Meitis sits down with content marketing and AI expert Brian Piper to break down how organizations can move beyond one-off prompting and build reusable AI systems that preserve what makes their business unique.
Brian walks through a practical process for auditing your existing brand voice, building detailed prompts with AI, comparing results across different AI tools, and turning successful workflows into reusable skills.
The bigger opportunity goes far beyond marketing. The same approach can be applied to repeatable business processes across an organization.
In this session, you'll discover:
Why generic prompting often leads to mediocre “AI slop.”
How to audit what your brand actually sounds like across websites, newsletters, social media, podcasts, and other content.
How to use the CRIT prompting framework to give AI context, assign a role, and have it interview you.
How Brian uses tools including Claude, ChatGPT, and Gemini to compare AI-generated brand audits.
How to turn a successful prompt into a reusable AI skill.
How AI interviews can capture subject-matter expertise instead of replacing it with generic information.
How organizations can build libraries of brand voice, personas, stories, research processes, and other reusable knowledge.
Brian Piper is a content marketing expert who has worked across large corporations, small businesses, consulting, and academia. In recent years, he has focused extensively on helping organizations use AI more effectively while maintaining their expertise, identity, and brand voice.
Connect with Brian Piper on LinkedIn:
https://www.linkedin.com/in/brianwpiper/
About Leveraging AI
Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events
If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!