January has a very particular energy.

The holidays are behind us. The inbox is slowly filling up again. Calendars are waking up. And there's always this short window, just a few quiet days, where it feels like everything could still go in a different direction.

I've been thinking a lot during this pause.

Over the last couple of years, AI and large language models have gone from experiments to expectations. What used to feel optional is now part of daily work, whether someone asked for it or not. And the biggest shift I've personally noticed isn't technical.

It's psychological.

People aren't asking "What can AI do?" anymore.

They're asking "What should we actually build?", "What do we trust?", and "What's worth shipping versus waiting?"

That question shows up everywhere, especially in product teams.

Because as exciting as LLMs are, shipping the wrong AI feature is worse than shipping none at all.

And that's exactly why today's conversation matters.

This episode is not about hype.

It's about judgment, timing, and responsibility in product leadership.

Chapters:

00:00 Introduction to Angshuman Rudra
01:06 The Impact of Large Language Models on Product Management
03:14 Balancing Innovation and User Needs
04:37 Navigating Generative AI in Product Development
06:46 Driving Adoption of New Features
09:34 Challenges and Lessons in Generative AI Products
11:15 Evolving Roles of Product Leaders with AI
12:39 The Future of Multi-Agent Systems
14:36 Translating User Requirements into Product Features
17:31 Finding the Next Big Feature
19:56 Adopting AI in Development Cycles
21:24 Tips for Job Seekers in Tech
23:10 Market Shifts in Marketing Technology
25:01 Exciting Use Cases in Marketing Technology
26:52 Concluding Thoughts and Future Outlook

Episode # 178

Angshuman Rudra is a senior product executive at TapClicks, where he leads a portfolio of data, analytics, and AI products for a market-leading martech platform.

• Website: Angshuman Rudra

What Listeners Will Learn:

• How to evaluate real user demand for AI features (not hype)
• When AI adds value and when it creates unnecessary complexity
• How product leaders should think about LLMs as tools, not magic
• Why many AI features fail after launch
• How to balance innovation with resource constraints
• What "AI adoption" actually looks like inside real companies
• Why multi-agent systems are promising but not ready to be fully autonomous
• How PMs can use AI for research, specs, and design without losing judgment
• What skills will matter most for product leaders over the next 3–5 years

• Angshuman Rudra

In this episode of Open Tech Talks, host Kashif Manzoor sits down with Bobbie Chen, a product manager working at the intersection of fraud prevention, cybersecurity, and AI agent identification in Silicon Valley.

As generative AI and large language models rapidly move from experimentation into real products, organizations are discovering a new reality. The same tools that make building software easier also make abuse, fraud, and attacks easier. Vibe coding, AI agents, and LLM-powered workflows are accelerating innovation, but they are also lowering the barrier for bad actors.

This conversation breaks down why security, identity, and access control matter more than ever in the age of LLMs, especially as AI systems begin to touch authentication, customer data, financial workflows, and enterprise knowledge. Bobbie shares practical insights from real-world security and fraud scenarios, explaining why many AI risks are not entirely new but become more dangerous when speed, automation, and scale increase.

The episode explores how organizations can adopt AI responsibly without bypassing decades of hard-earned security lessons. From bot abuse and credit farming to identity-aware AI systems and OAuth-based access control, this discussion helps listeners understand where AI changes the threat model and where it doesn't.

This is not a hype-driven episode. It is a grounded, experience-backed conversation for professionals who want to build, deploy, and scale AI systems without creating invisible security debt.

Episode # 177

Bobbie is a product manager at Stytch, where he helps organizations like Calendly and Replit fight against fraud and abuse.

• LinkedIn: Bobbie Chen

What Listeners Will Learn:

• How LLMs and AI agents change the economics of fraud and abuse, making attacks cheaper, faster, and more customized
• Why vibe coding is powerful for experimentation, but risky when used without security review in production systems
• The difference between exploring AI ideas and asking users to trust you with sensitive data
• Standard security blind spots in AI-powered apps, especially around authentication, parsing, and edge cases
• Why organizations should not give AI systems blanket access to enterprise data
• How identity-aware AI systems using OAuth and scoped access reduce risk in RAG and enterprise search
• Why are many AI security failures process and organizational problems, not tooling problems
• How fraud patterns like AI credit farming and automated abuse are emerging at scale
• Why security teams must shift from being gatekeepers to continuous partners in AI adoption
• How professionals in security, product, and engineering can stay current as AI threats evolve

• Bobbie Chen
• The two blogs I mentioned:
• Simon Willison: https://simonwillison.net
• Drew Breunig: https://www.dbreunig.com

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

This episode is for entrepreneurs, small businesses, solopreneurs, creators, and consultants who feel overwhelmed by AI and don't know where to start.

You'll learn how a completely non-technical founder used Generative AI to transform two businesses, pivot her career, and build AI-driven systems without writing a single line of code.

In this episode of Open Tech Talks, host Kashif Manzoor speaks with UK-based entrepreneur Marnie Wills, whose journey with Generative AI began unexpectedly while franchising her children's PE business. A copywriting challenge introduced her to Jasper AI, and that single moment reshaped everything.

Within two years, she used AI tools to fix messaging issues, transform her franchise model, exit her online fitness business, and finally launch her consulting practice.

Marnie breaks down how she built her AI-first operating system using ChatGPT, Claude, Perplexity, Abacus AI, and NotebookLM. She explains why customizing your AI, training it on domain knowledge, and owning your data matters for the coming wave of agentic AI.

She shares a powerful real example: building a full CRM + GPT workflow for a keynote speaker in 90 minutes using no-code tools. The system identifies events, drafts applications, and enables a VA to manage the entire pipeline, an example of how AI amplifies human roles rather than replacing them.

The conversation also explores ethics, the myth of privacy, overwhelm in SMEs, and the misconception that AI = automation. Her philosophy is simple:

AI should amplify humans first. Automation comes last.

By the end, you'll understand why courses are no longer the main path, how "10,000 hours" has become "10,000 prompts," and why your next breakthrough may come simply from talking to an AI daily.

Episode # 174

Chapters

00:00 Introduction to Marni Wills and Her Journey
02:49 The Impact of Generative AI on Business
06:03 Practical AI Implementation Strategies
08:51 Creating Custom AI Models and Data Ownership
11:56 Vibe Coding: No-Code Solutions for Entrepreneurs
14:38 Learning and Adapting in the AI Landscape
17:30 Ethics and Intellectual Property in AI
20:36 Common Challenges for Small Businesses
23:34 Future Skills in an AI-Driven World

She is a multi-passionate entrepreneur and international athlete, dedicated to revolutionizing the integration of AI into everyday life and business.

• LinkedIn: MarnieWills

What Listeners Will Learn:

• How a non-tech founder transformed two businesses using Jasper AI, ChatGPT, Perplexity, and agentic tools
• Why the AI-first mindset matters more than tools, coding, or technical background
• How to build your personal AI operating system using 5–6 core tools daily
• Why custom instructions, private models, and a "second brain" dramatically improve AI output
• Real examples of vibe coding and building no-code platforms with Lovable, Replit, and GoMocka
• How to reimagine your workday using AI as your Chief Operating Officer
• Why most people are "lazy AI users" and exactly how to avoid that trap
• Why automations should come last and why amplifying humans comes first
• The biggest challenge SMBs face (overwhelm) and the simplest way to begin
• The future of AI agents and agent-friendly websites

• MarnieWills

Building Career Resilience in the Age of Generative AI

Every week, we explore how AI and technology are changing the way we work and learn. This episode dives into the question I get asked the most, How is Generative AI changing every career? Let's unpack why it matters, how it's shifting roles and skills, and what you can do to lead this change instead of chasing it

In this solo episode of Open Tech Talks, host Kashif Manzoor, AI Engineer and Strategiest, and author of AI Tech Circle, dives deep into one of the biggest career questions of our time:

How is Generative AI reshaping every profession?

Whether you're a developer, analyst, marketer, finance expert, or operations lead, the rise of Gen AI is transforming how work gets done. Kashif combines real-world enterprise experience, current research from McKinsey and Goldman Sachs, and his personal journey building the Gen AI Maturity Framework and Portal to uncover how you can stay relevant, resilient, and ready for AI-driven change.

He shares first-hand stories from his own AI adoption journey, how enterprise teams are shifting from cloud architecture to AI architecture, from isolated use-cases to full-scale agentic AI strategies and the lessons learned while guiding organizations through transformation.

This episode is both a roadmap and a reflection: how to experiment weekly, build your portfolio, upskill smartly, reposition your role, and teach and share as you grow.

Episode # 173

• Why Generative AI matters now and how it differs from traditional AI

• How tasks, roles, and careers are evolving across industries

• Real-world examples from finance, marketing, and software engineering

• The five practical steps to future-proof your career with Gen AI

• Insights from McKinsey, ResearchGate, and Goldman Sachs on AI productivity impact

• How to move from "knowing AI tools" to using AI strategically in daily work

• A behind-the-scenes look at the creation of the Gen AI Maturity Framework

• Why the future of work is not about jobs lost but roles transformed

1. McKinsey Global Institute – Generative AI and the Future of Work

2. Deloitte – Generative AI and the Future of Work

3. Goldman Sachs – How Will AI Affect the Global Workforce

4. Robert Half – How GenAI Is Changing Creative Careers

5. Mäkelä & Stephany (2024) – Complement or Substitute?

Open Tech Talks welcomes Lirone Glikman, author of The Super Connectors Playbook, global speaker, and relationship-building strategist. We dig into practical ways founders, students, and leaders can build authentic relationships, present with clarity, and sustain a personal brand without sounding salesy, especially now that AI makes everyone's writing look the same.

• Online vs. in-person connection: generational differences, when a DM beats a coffee, and why Asia still loves business cards.
• Founders' common gaps: turning a product story into a people story; shifting from "it works" to "it resonates."
• Presenting without fluff: tailoring your story for investors, customers, and connectors, while keeping it true.
• The 90-List cadence: a simple routine for staying top-of-mind authentically.
• Anxiety is normal: why most people feel it, and how to start anyway.
• Introvert strength: listening, asking better questions, and using silence as a feature.
• Personal branding at work: define the promise others can rely on; show it with facts, data, and actions.
• Authenticity in the LLM age: keep your voice, your phrasing, your structure; let AI edit - not author - you.
• Share wins without bragging: swap claims for evidence and we-language.
• Using AI to deepen, not replace, connection: research context, adapt tone, draft follow-ups, keep the spark human.

Episode # 172

She is a globally recognized expert, keynote speaker, and best-selling author specializing in business relationships, personal branding, and global business development.

• Website: LironeGlikman

What Listeners Will Learn:

• A repeatable method to build and maintain high-value relationships in minutes a day.
• How to package your story differently for investors, clients, and allies, without losing your core.
• Practical ways to keep an authentic voice when AI tools standardize language.
• How introverts can use their edge to win more conversations and opportunities.
• A simple framework to share achievements credibly (facts → data → actions)

• LironeGlikman