HackrLife is a Podcast on the practical application of AI and PLG in growth and product marketing. I am @ Growth marketer for Google's AI product suite and have a decade of growth experience across Google, Linkedin, Adobe,SAP and IBM. In every episode, I share practical AI use cases, the latest in AI research along with no-code automation workflows and growth tactics which I am building in the open in my spare time. Real demos, real tear-downs of AI theory versus practical use case. Perfect for marketers, founders, and creators who want actionable takeaways on AI over hype.
Podcast Overview
HackrLife is a Podcast on the practical application of AI and PLG in growth and product marketing. I am @ Growth marketer for Google's AI product suite and have a decade of growth experience across Google, Linkedin, Adobe,SAP and IBM. In every episode, I share practical AI use cases, the latest in AI research along with no-code automation workflows and growth tactics which I am building in the open in my spare time. Real demos, real tear-downs of AI theory versus practical use case. Perfect for marketers, founders, and creators who want actionable takeaways on AI over hype.
Language
🇺🇲
Publishing Since
2/17/2019
1 verified contact email on file for HackrLife
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Recent Episodes
August 15, 2025
The Inadequacy of LLM Benchmarks
<p>In this episode of HackrLife, you’ll discover why the way we measure AI performance might be misleading . A recent study that examined 23 major Large Language Model (LLM) benchmarks has found that small changes in formatting, prompt style, and test conditions can swing results dramatically. T</p><p>he episode reveals how this fragility challenges the accuracy of leaderboard claims and why “top scores” may not translate into better results for your work.</p><p>You’ll learn about the hidden factors that shape benchmark outcomes — from cultural and language bias to the trade-off between safety and usefulness — and how these can distort real-world performance. </p><p>Why relying on AI to grade AI can create circular results that hide weaknesses instead of exposing them.</p><p>By the end, you’ll have a clear, practical framework for evaluating AI tools yourself. You’ll know how to run small, task-specific tests, stress-test models for robustness, and choose tools based on how they actually perform in your environment — not just how they look on a leaderboard.</p><p><br></p>
August 13, 2025
Multimodality agents using No Code tools
<p>In this episode of HackrlIfe I take as a critical look at Samsung's research on building "multimodal AI agents" using no-code platforms like Flowise. </p><p>While the paper sounds impressive with terms like "multimodal LLM-based Multi-Agent Systems," I dug into what these tools actually do versus the marketing hype.</p><p><strong>What I Found It Really Is:</strong> API orchestration tools that connect existing AI services (OpenAI, Stable Diffusion, Luma AI) through visual drag-and-drop interfaces. </p><p><br></p><p>You're not building AI – you're chaining together existing APIs with better UX.</p><p><strong>Where I Think It Actually Works:</strong></p><ul><li>Routine content workflow automation (blog posts → social media variants)</li><li>Customer research processing (audio transcription → analysis → reporting)</li><li>Basic content generation pipelines</li><li>Repetitive multimedia tasks that don't require complex business logic</li></ul><p><br></p><p><strong>The Real Value I See:</strong> Speed of implementation and iteration for non-technical teams. Instead of waiting weeks for developer resources, growth teams can prototype automation workflows in days.</p><ul><li><strong>Not revolutionary AI development</strong> – just workflow automation with AI APIs</li><li><strong>Limited by underlying API capabilities</strong> – you can't create custom logic</li><li><strong>Costs scale with usage</strong> – multiple paid APIs add up quickly</li><li><strong>Quality still requires human oversight</strong> – automation ≠ autonomous operation</li></ul><p><br></p><p>I believe tools like Flowise are useful for <strong>operational efficiency</strong>, not AI innovation. They're worth exploring if you have routine, rule-based content tasks that currently eat up team time. But I recommend approaching them as workflow automation tools, not magical AI solutions.</p><p>My advice: Start small, test one simple use case, measure time savings, then expand gradually if it proves valuable.</p><p><strong>My main insight: The competitive advantage isn't in the AI capabilities – it's in reducing friction between having an automation idea and implementing it.</strong></p><p>In this episode, I give growth professionals a realistic assessment of what these tools can actually do for their teams.</p>
August 12, 2025
Do LLM's really think?
<p>Those "reasoning" AI models everyone's raving about?</p><p>Apple's research suggests they're not actually thinking—they're just really good at pattern matching.</p><p>In my latest HackrLife episode, I break down why this is EXACTLY why AI can help you think better, but cannot really think on your behalf.</p><p>Key insights:✅ Why reasoning models fail completely past a certain complexity✅ When standard models actually outperform "reasoning" ones✅ How to build human-AI workflows that amplify your thinking instead of replacing it</p><p>#AI #GrowthHacking #ProductivityHacks #AIResearch #FutureOfWork</p>
27 total episodes available
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This podcast updates inactive.
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