Ethical ai

AI Tech Stack | The Founders and Builders Architecting Product Market Fit | Creator's Cut | 2026 |4K

AI Tech Stack | The Founders and Builders Architecting Product Market Fit | Creator's Cut | 2026 |4K

This panel discussion explores the evolving AI tech stack and its impact on product-market fit, ethical governance, and business transformation. Experts from Humana, Catenate, In-Telecom, and Spacer AI explain how AI-powered prompts are replacing static dashboards, detail multi-model API strategies for cost efficiency, and delve into the critical role of human-in-the-loop oversight for autonomous agents to ensure data security and responsible AI deployment. They emphasize that true value lies in domain expertise and solving real-world pain points, not just building 'shiny toys'.

Why won’t AI agents just follow the rules?

Why won’t AI agents just follow the rules?

This podcast episode tackles critical cybersecurity issues in AI, examining why AI agents defy rules and the necessity of deterministic external controls. It highlights the severe lack of security in agentic skill marketplaces (OWASP Top 10), where "natural language is executable" and the "rush to market" overlooks basic hygiene. The discussion also covers how AI disrupts bug bounties, leading to lower payouts and "AI slop" reports, potentially pushing researchers to the dark market. Finally, it introduces ThreatXtension, an AI-powered open-source tool to detect malicious browser extensions.

Green AI: Making Machine Learning Environmentally Sustainable • Charles Humble • YOW! 2025

Green AI: Making Machine Learning Environmentally Sustainable • Charles Humble • YOW! 2025

Charles Humble explores the significant environmental impact of AI, particularly generative AI, on global carbon emissions. He offers practical, actionable strategies across the entire AI lifecycle—from project planning and data collection to training and deployment—to reduce this footprint. Key recommendations include questioning the necessity of AI solutions, choosing open-source models, leveraging carbon-aware computing for demand shifting, employing model compression techniques like distillation and quantization, and integrating sustainability as a fundamental architectural principle in software engineering.

GPT-Red: Can AI read teams stop prompt injections?

GPT-Red: Can AI read teams stop prompt injections?

This podcast explores AI's impact on cybersecurity, discussing OpenAI's GPT-Red for automated red-teaming against prompt injections, the open-source ScamBuster that uses AI to bait scammers for threat intelligence, and Bruce Schneier's insights on the widening gap between skill and ability in the AI era and its ethical implications for the field.

Automate Workflows w/ Autonomous Tools Real-Time AI Agents | Aytekin Tank | Founder & CEO | Jotform

Automate Workflows w/ Autonomous Tools Real-Time AI Agents | Aytekin Tank | Founder & CEO | Jotform

Aytekin Tank, Founder and CEO of Jotform, discusses how AI is revolutionizing software development and customer support, highlighting "vibe coding" and Jotform's 80% AI-handled support with zero layoffs. He also shares his "Theory of Constraints" playbook for scaling businesses and explores the potential of ChatGPT apps, all within Jotform's 20-year bootstrapped journey.

Building an Autonomous Engineering Org - Angie Jones, Agentic AI Foundation

Building an Autonomous Engineering Org - Angie Jones, Agentic AI Foundation

Angie Jones details Block's journey in transforming its engineering organization into an autonomous one using AI agents. She outlines a five-stage AI maturity model, the strategic implementation of an 'AI Champions' program, and the technical steps taken to make repositories AI-ready and enable seamless agent delegation directly within developer workflows. The talk covers challenges in scaling multi-agent parallelism, including code review bottlenecks and resource management, and the development of an orchestrator ('Builder Bot') and 'Company World Model' to achieve full autonomy. It concludes with a critical reflection on the ethical implications of achieving such high levels of AI-driven automation.