TL;DR Milan from True Horizon, an AI agency with $300k MRR and 25+ client engagements, outlines a practical framework for high-converting discovery calls. The core philosophy emphasizes "P2P sales" (person-to-person), prioritizing trust and relationship building over transactional B2B approaches. Every discovery call aims to build trust, uncover ROI opportunities, and secure the next step, structured across three phases: Open & Connect, Explore & Discover, and Close & Book Next Step. Key strategies include thorough prospect research to find common ground, framing the call as mutual value creation, deep-diving into business processes to identify automation potential, and actively listening to understand prospect needs. The speaker advises avoiding technical jargon, excessive talking, neglecting rapport, and being overly salesy, instead advocating for high energy, active listening, and a collaborative, value-first mindset to significantly imp
Ken Jarvis-Legatus
TL;DR Businesses prioritize solving problems and generating revenue over AI itself. This video, based on extensive consultations with 44 business owners, outlines key pain points across three core business areas where AI can provide significant value: Client Acquisition, Product Fulfillment, and Customer Service & Experience. For AI agency owners, specializing in client acquisition is highly recommended due to its direct, measurable ROI and potential for value-based pricing. Specific AI applications are detailed for outbound (email/LinkedIn, paid ads) and inbound (content, SEO blogs) strategies, alongside sales automation. Product fulfillment offers niche-specific automation opportunities, while customer service focuses on chatbots, internal ticket automation, and customer loyalty/reactivation. The speaker emphasizes starting with revenue-generating or cost-saving solutions, cautioning against complex customer-facing chatbots f
TL;DR This video emphasizes that businesses prioritize problem-solving over AI itself, presenting AI as a powerful tool to address these needs across three core operational areas: client acquisition, product fulfillment, and customer service/experience. The speaker, drawing from numerous AI consultations, argues that specializing in a specific problem within these areas is the ultimate differentiator for AI businesses. Client acquisition offers the highest ROI due to its direct impact on revenue, with AI automating outbound (hyper-personalized emails, paid ads) and inbound (content creation, SEO) strategies. Product fulfillment benefits from niche-specific AI solutions, streamlining operations like inventory syncing and data validation. While customer service offers AI applications like chatbots, it's deemed the most challenging due to human variability, making it better suited for larger clients and advanced implementers. The
TL;DR Aaron Levie, CEO of Box, argues that the current AI era presents an unprecedented opportunity for startups, akin to the early days of cloud computing but with accelerated adoption. He contends that AI agents will not primarily eliminate jobs but rather free up human time from "unstrategic but necessary" tasks, enabling companies to pursue breakthrough innovation, customer engagement, and market expansion. This efficiency gain will allow small companies to operate with the leverage of much larger ones, fostering rapid growth and job creation in new areas. Levie highlights that AI is creating a "new set of nouns and verbs" – previously unsolvable problems now addressable by software – which incumbents are less equipped to tackle than agile startups. He also discusses the shift in business models from seat-based to consumption-based pricing, emphasizing the value of the software layer built atop AI tokens. Founders are advised to seize this limited 2-3 yea
AI Automation that actually works: $100M, messy data, zero surprises - Tanmai Gopal, Hasura/PromptQL
TL;DR This presentation introduces an AI automation solution, PromptQL, designed to tackle the complex and costly problem of appointment scheduling and procedure code selection within a large public healthcare company. Currently, call center operators spend 12-15 minutes per call navigating a nightmarish UI and deciphering intricate, often inconsistent, medical codes and regulations. This inefficiency costs millions, with every 3 minutes saved equating to a $50 million impact. The core issue is an "automation paradox": business users understand the rules but can't code, while developers can code but don't grasp the nuanced business logic. PromptQL proposes enabling non-technical administrators to "vibe code" business logic in natural language. The solution addresses key challenges like translating business language to AI intent, defining SDLC for non-technical users, and ensuring security. It works by t
TL;DR The speaker, Itamar Friedman from Qodo, discusses the evolution of AI tools for developers, arguing that current solutions aren't true "game-changers." He differentiates between "noobs" (simple code generation) and "enterprise" (complex, heavy-duty software), proposing that the next major shift (Gen 3.0) will move beyond autocomplete and chat-based AI to Command Line Interface (CLI)-driven, multi-agent workflows. This shift enables "vibe coding with confidence" by integrating AI across the entire Software Development Life Cycle (SDLC) for reliable, high-quality, end-to-end task completion, including planning, coding, testing, and reviewing. Qodo's new CLI tool, soft-launched during the talk, facilitates this by allowing developers to orchestrate specialized AI agents, pipe workflows, and even generate flexible interfaces, addressing limitations of current IDE-centric tools on large codebases. The future envisions a "swarm of agents" working holi
TL;DR John Dickerson, CEO of Mozilla AI and co-founder of Arthur AI, argues that 2025 will finally be "the year of evaluations" for AI, unlike previous years where it remained a niche concern. This shift is attributed to a "perfect storm" of three concurrent factors: the launch of ChatGPT in late 2022, which made AI capabilities understandable and "wow-worthy" for non-technical C-suite executives (CEOs, CFOs, CISOs); a simultaneous enterprise budget freeze that paradoxically funneled limited discretionary funds specifically into GenAI "pet projects"; and the current deployment of autonomous or semi-autonomous agentic AI systems that act for humans, introducing significant complexity and risk requiring quantitative evaluation. This confluence has led to unprecedented C-suite alignment on the critical need for AI evaluation, driving hockey-stick growth for evaluation companies and shifting focus to monitoring enti
TL;DR
The speaker, Ankur Goyal from Braintrust, discusses the evolution of AI evaluations (evals), highlighting their current manual nature despite the advanced automation in AI products. He notes that Braintrust users run a significant number of evals daily, often spending hours manually analyzing dashboards to determine necessary code or prompt changes. This manual process is poised for a revolution with the introduction of Loop, an AI agent integrated into Braintrust. Loop leverages recent breakthroughs in frontier models, particularly Claude 4 (which performs 6x better than previous models), to automatically optimize prompts, complex agents, datasets, and scorers. The agent provides side-by-side suggestions in the UI, supporting various models, and aims to transform evaluations from a manual task into an automated, efficient process. The core message is that the future of evals lies in intelligent automation, driven by advanced AI models.
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TL;DR Greg Brockman, OpenAI's co-founder, shares his journey from aspiring mathematician to programming enthusiast, driven by the "magic" of making ideas real. He recounts his pivotal role as Stripe's first CTO, emphasizing a "first principles" approach to problem-solving that enabled rapid innovation. Brockman highlights his conviction in AGI, stemming from Alan Turing's vision and deep learning's breakthroughs, underscoring the critical, symbiotic relationship between research and engineering at OpenAI. He discusses the challenges and triumphs of scaling products like ChatGPT and DALL-E 3, and the transformative impact of AI tools like Codex on coding practices, advocating for modular codebases designed for AI assistance. Looking ahead, Brockman addresses the complexities of AI infrastructure, the re-emergence of algorithmic research as a key bottleneck, and the immense opportunity for AI-native engineers to build a future economy fundame
TL;DR The video introduces ChatGPT 5 as a significant advancement, akin to having "PhD experts in your pocket," highlighting its three main versions: GPT-5, GPT-5 Thinking, and the $200/month GPT-5 Pro. A key revelation is OpenAI's default ChatGPT interface restricts GPT-5's context window to 32K tokens, despite its actual capacity being 400,000, limiting its utility for large documents. The presenter promotes Vectal.AI as a solution, offering the full 400K context window for free or unlimited access at a fraction of OpenAI's Pro cost. GPT-5 demonstrates impressive capabilities like generating visual books and attempting to build complex web apps, though the latter showed limitations. The video provides crucial pro tips for maximizing GPT-5's performance, including prioritizing "Thinking" mode, using "think hard" prompts, leveraging new "Study," "Agent," and "Deep Research" modes, and utilizing "Add to Memory" and "Projects" features. While GPT-5 is an improvement over