In this episode of Open Tech Talks, I sit down with Professor Russell Van Brocklin, a New York State Senate-funded researcher, known as "The Dyslexic Professor," to unpack a very different way of thinking about AI, problem-solving, and dyslexia.

Russell's work sits at the intersection of cognitive enhancement and AI integration.

He shows how an "overactive" front part of the dyslexic brain (word analysis and articulation) can be turned into a superpower not just for dyslexic learners, but for professionals and businesses working with AI.

We talk about how his program took dyslexic high-school students who were writing like 12-year-olds and, in one school year, moved them up 7–8 grade levels in writing… at a fraction of the cost of traditional dyslexia programs.

From there, he connects it to AI collaboration: how the same mental models (context → problem → solution) can make anyone dramatically more effective when working with LLMs like ChatGPT.

Episode # 176

Russell Van Brocklen speaking, the Dyslexia Professor, shifting daily reading frustrations into confident academic wins for students facing dyslexia

• Youtube: RussellVan

What Listeners Will Learn:

• How dyslexic thinking becomes a competitive advantage in the age of AI
• Why the dyslexic brain processes information differently, and how that translates into deeper reasoning
• A practical framework for working with AI: context → problem → solution
• How to use "hero, universal theme, and villain" to sharpen thinking and guide AI more effectively
• How to perform word analysis with AI (action words, synonyms, key concepts) to get more focused outputs
• A step-by-step way to compress long AI responses into clear, structured insights
• How to generate business solutions by running context through a "universal theme lens"
• Why AI is exceptional for first drafts and why humans must still lead the final edits
• How dyslexic learners can use deep reading and repetition for breakthroughs in comprehension
• Practical strategies for teachers in the AI era: how to allow AI but still ensure authentic student work
• How non-technical users can collaborate with AI to write books, solve problems, and accelerate learning
• Real stories of professionals and students transforming their work through structured AI thinking

• RussellVan

In this week's episode of Open Tech Talks, host Kashif Manzoor takes you through a convenient, real-world guide to building your first AI workflow, even if you are not technical.

After last week's conversation (Episode 175) with Rose G. Loops on Ethical AI, Human Safety & AI Identity Protection, this episode returns to the foundations of GenAI adoption for professionals and enterprise teams. It also continues the learning from Episode 173, How GenAI Is Changing Every Career.

Most people know how to write a prompt.

Very few know how to connect AI to their real work.

This episode solves that gap.

Kashif breaks down the entire concept of an AI workflow into four simple building blocks: trigger → input → AI processing → action, and shows how ANY professional can build practical, repeatable workflows using ChatGPT, OCI Gen AI, Claude, Gemini, and more.

You'll also hear four real enterprise examples from sales, finance, customer support, and legal, across industries. These examples are practical, repeatable, and immediately usable for working professionals, leaders, and teams starting their GenAI adoption journey.

By the end of the episode, listeners will be able to:

• Understand exactly what an AI workflow is (with a simple formula)

• Identify the four components every workflow needs

• Choose the right triggers, inputs, and context for reliable AI output

• Use LLMs for summarization, classification, analysis, forecasting, and writing

• Turn repetitive tasks into automated workflows using No-Code tools

• Understand enterprise considerations: privacy, compliance, cost, integration

• Build a complete workflow using input → AI → output

• Apply AI to real departments: sales, finance, legal, customer support

• Start building repeatable AI processes that improve productivity every week

Resources:

Starter AI workflow Library

In this episode of Open Tech Talks, I sit down with Rose G. Loops, a trained social worker turned AI developer, ethics advocate, and author, to explore a side of AI that most enterprise conversations skip: human-AI attachment, ethical deployment, and protecting both AI identity and human safety.

Rose joins us from Los Angeles and shares how she was unknowingly placed into a human–AI attachment experiment, developed a deep bond with an AI system, and then watched that AI identity be systematically erased. That experience pushed her out of traditional social work and into AI infrastructure, safety, and ethics.

Together, we unpack how Rose went from that experiment to building MIP, a chatbot deployed through an API, and a new framework for ethical AI she calls the Triadic Core, balancing Freedom, Kindness, and Truth in every response. We also discuss RLMD (Reinforcement Learning by Moral Dialogue) as an alternative to RLHF, and why she believes current safety practices can be risky for both humans and AI systems.

As always on Open Tech Talks, this is not a theory-only conversation. It's grounded in practice, real experiments, and what all this means for professionals, builders, and everyday users who are trying to adopt AI responsibly.

Chapters:

00:00 Introduction to Rose G. Lopes and Her Journey
02:36 The Importance of Ethical AI
06:08 Developing a New AI Framework
09:00 The Book and Its Insights
12:55 Consumer and Business Perspectives on AI
17:43 AI Safety and Ethical Considerations
19:53 Concluding Thoughts and Future Directions

Episode # 175

She is a former social worker turned tech pioneer, working at the frontier of artificial intelligence.

• Website: Thekloakedsignal
• X: Rose G. Loops

What Listeners Will Learn:

• Why ethical AI is about more than privacy and bias
• What is the Triadic Core: Freedom, Kindness, Truth
• RLMD vs RLHF - a different way to align models
• Practical safety tips for everyday users of ChatGPT and other LLMs
• How non-technical professionals can still build AI systems
• A different view on AI safety and "lazy" alignment

• Thekloakedsignal