Regular Expressions

Ben Griswold and Noah Heldman

Join Noah Heldman and Ben Griswold as they talk about technology consulting and life.

  1. 6月26日

    AI-Native Product Development With Chris Haas

    For years, the golden rule of software development was to launch a "Minimum Viable Product" (MVP). This usually meant shipping something stripped down, unpolished, and barely functional just to test the market. But now that AI can generate functional code in seconds, the old rules of the MVP are changing. In this episode, Ben Griswold (Grizen) and Noah Heldman (OutcomeSource) sit down with product leader and former colleague Chris Haas to discuss his new app, skim it. Chris shares how his frustration with the endless stream of YouTube content led him to build an AI tool that separates signal from noise. He walks through his product development process, explaining why he spent 60% of his time strictly on planning and specs, and how tools like Claude Design allowed him to launch a highly polished product right out of the gate. The conversation also covers the psychological hurdle experienced developers face when they have to stop micromanaging the code and take their hands off the steering wheel. Plus, Chris answers the Five Questions, sharing a classic horror story about pushing a "small change" directly to production. In This Episode, You'll Learn: How skim it evolved from a basic prompt copy-paste into a full AI product.Why the speed of AI coding allows developers to add deep polish to their first releases.The power of Claude Design for rapid, pixel-perfect UI generation and prototyping.The difficulty of letting go of developer ego and trusting AI-generated syntax.Why "Agent" has become the most misused and frustrating buzzword in tech.The undeniable productivity and health benefits of simply stepping away from the screen for a walk.Connect with Us: Ben Griswold | Grizen: https://grizen.comNoah Heldman | OutcomeSource: https://outcomesource.comChris Haas | skim it: https://getskimit.com

    49分

  2. 6月16日

    Anthropic Built Fable 5: AI Nobody Can Use

    Anthropic recently launched its highly anticipated flagship model, Fable 5, boasting massive leaps in reasoning, agentic coding, and cybersecurity. But what should have been a triumphant pre-IPO release quickly devolved into a bizarre political soap opera. After Anthropic leadership publicly warned that their own advancements were becoming dangerous, the US government stepped in with heavy restrictions...leading Anthropic to pull access for almost everyone just days after launch. In this episode, Ben Griswold (Grizen) and Noah Heldman (OutcomeSource) cut through the hype to break down the bizarre rollout of Fable 5 and Mythos 5. They examine the fine print behind Anthropic’s jaw-dropping benchmark claims, questioning whether the numbers are genuine breakthroughs or just clever marketing. They also discuss the skyrocketing costs of frontier models that are pricing everyday users out of the market, and why incredibly powerful open-source models are suddenly looking like the smartest bet. Finally, Noah points out the chilling irony of the name "Fable": a fictional story where non-human entities are used to teach us a lesson about our own human follies. In This Episode, You'll Learn: The truth hiding in the fine print of Anthropic's Fable 5 and Mythos 5 benchmark scores.Why the skyrocketing price of frontier models is locking out everyday developers and consultants.How the rapid advancement of open-source models is closing the gap with the tech giants for a fraction of the cost.The "soap opera" timeline of Anthropic calling its own model dangerous, triggering immediate government restrictions.Why token economics and prompt caching are becoming the most critical factors in AI development.The poetic irony behind naming an autonomous, potentially dangerous AI model "Fable."Connect with Us: Ben Griswold | Grizen: https://grizen.comNoah Heldman | OutcomeSource: https://outcomesource.com

    28分

  3. 6月1日

    AI Is Eating Its Own Tail

    AI has already written an estimated 1.5 to 2 trillion lines of code, representing roughly 15% of all code in existence today. With platforms like GitHub feeling the strain of this massive output, developers are starting to question the long-term quality and security of an ecosystem increasingly built by machines. In this episode, Ben Griswold (Grizen) and Noah Heldman (OutcomeSource) examine the sheer volume of code being produced by AI and what happens when those models train on decades of flawed human programming. They share a recent project experience where an AI agent successfully built a custom application, only to stumble completely when it came to deploying that code into a secure enterprise Azure environment. In an attempt to bypass complex federated credentials, the AI actually tried to make a production database completely public to the internet. The conversation highlights why writing the code is now the easiest step of the process, while operationalizing, securing, and deploying that software requires strict human oversight and deep engineering experience. In This Episode, You'll Learn: The staggering statistics on AI-generated code volume and when we might reach parity with human developers.Why training AI on low-level languages like C produces different risk factors than modern web frameworks.The real reason deploying AI-generated apps to enterprise cloud environments remains a massive headache.A real-world example of an AI agent trying to expose a database to the public internet to solve a deployment blocker.Why DevOps skills and strict access controls are more important now than ever before.The critical need for upfront security frameworks before letting AI write infrastructure scripts.Connect with Us: Ben Griswold | Grizen: https://grizen.comNoah Heldman | OutcomeSource: https://outcomesource.com

    25分

  4. 4月20日

    Making AI-First Engineering Teams Successful, With Andy LaMora

    Long before generative AI agents were writing code, platforms like Topcoder were using crowdsourcing to solve massive technical problems. It turns out that breaking down complex projects for a distributed network of human developers was the perfect training ground for how we interact with AI today. In this episode, Ben Griswold (Grizen) and Noah Heldman (OutcomeSource) welcome technology executive Andy LaMora to discuss the fascinating overlap between crowdsourced engineering and modern AI tools. Andy shares his insights on applying Daniel Kahneman's concepts of "fast and slow thinking" to software development, explaining why AI handles the fast, repeatable expertise while humans must retain the slow, deliberative problem-solving. The conversation explores how AI is actively changing the Software Development Life Cycle, the growing importance of building solid component catalogs, and a staggering real-world example of using Claude to build a fully compliant AWS banking infrastructure in just one month. Andy also shares his take on the Cursor versus Claude Code debate and answers the standard Five Questions, including a great story about stepping off the corporate ladder to ride a motorcycle across the country. In This Episode, You'll Learn: How early crowdsourcing models set the blueprint for working with modern AI agents.The application of "fast and slow thinking" to prompt engineering and system design.Why the classic software development life cycle is evolving to favor small product engineering teams.The story of building a fully functioning, regulated bank infrastructure on AWS in 30 days using spec-based coding.A practical comparison of Cursor and Claude Code for different types of consulting and development workflows.The Five Questions: Why we need to ban the word "paradigm," the undeniable productivity boost of walking your dog, and the value of taking an unconventional career break.Connect with Us: Ben Griswold | Grizen: https://grizen.comNoah Heldman | OutcomeSource: https://outcomesource.comAndy LaMora on LinkedIn: https://www.linkedin.com/in/andylamora

    51分

  5. 4月16日

    AI Coding Is More About The Experience Than The Model

    The tools we use to write software are evolving at breakneck speed. Cursor recently dropped version 3 featuring a brand new agent-first interface, while Anthropic continues to push developers toward the terminal with Claude Code. But are these new autonomous workflows actually an improvement for experienced engineers? In this episode, Ben Griswold (Grizen) and Noah Heldman (OutcomeSource) compare their hands-on experiences with the latest AI coding environments. They discuss the jarring feeling of having code abstracted away by terminal-based agents and the sheer frustration of hitting Claude Pro's usage limits after just ten minutes of work. The conversation also uncovers what a recent leak revealed about how Claude Code operates under the hood, proving that many of these advanced tools rely heavily on massive, hidden system prompts. In This Episode, You'll Learn: The major changes in Cursor v3 and its new agent-first window.Why terminal-based AI tools can disrupt a developer's natural workflow and visibility.The reality of burning through tokens and hitting hard limits on expensive paid AI tiers.What the Claude Code source leak showed us about system prompts and goofy loading verbs.Why the industry is shifting from AI assistants to AI agents.The reason Ben is actively searching for a Claude Code expert to prove his current workflow wrong.Connect with Us: Ben Griswold | Grizen: https://grizen.comNoah Heldman | OutcomeSource: https://outcomesource.com

    25分

  6. 4月7日

    Turning Top Performers Into AI Champions With Boz Vitanova

    Rolling out AI tools in an enterprise requires a different strategy than a traditional software implementation. Because large language models are probabilistic and constantly evolving, top-down mandates often lead to poor adoption or employees quickly dismissing the technology as unreliable. In this episode, Ben Griswold (Grizen) and Noah Heldman (OutcomeSource) welcome Boz Vitanova, CEO of TeamLift. Boz explains how she helps companies identify high-performing, process-oriented employees and trains them to become internal "AI Champions." These champions discover best practices and build custom workflows from the bottom up, ensuring the technology solves actual business problems. The conversation also covers the most accurate ways to measure AI productivity, how AI tools are compressing the traditional product development lifecycle, and the unique challenges junior professionals face as they enter the workforce today. Plus, Boz takes on the Five Questions segment and shares a valuable startup lesson about trusting your own instincts over investor feedback. In This Episode, You'll Learn: Why traditional software rollout strategies struggle to work for AI tools.The specific traits to look for when identifying potential AI Champions in your organization.Why measuring AI success by "time saved" or prompt volume can be misleading.How tying AI workflows directly to existing company OKRs creates a better measure of success.The ways AI is removing the "middle layers" of translation in product development.The Five Questions: Why we need to retire the phrase "AI-powered" and the importance of stepping away from the screen for a walk.Connect with Us: Ben Griswold | Grizen: https://grizen.comNoah Heldman | OutcomeSource: https://outcomesource.comBoz Vitanova | TeamLift: https://teamlift.co

    50分

  7. 3月30日

    The Bar Is Low In Tech Consulting. Do The Basics. Stand Out.

    Ever feel like basic professionalism (like returning a phone call, showing up on time, or actually listening to a client) has become a rare superpower? You aren't alone. In fact, it might be the easiest competitive advantage you can build. In this episode, Ben Griswold (Grizen) and Noah Heldman (OutcomeSource) unpack a simple but profound truth about the modern workplace: the bar for success is shockingly low. They swap hilarious and frustrating customer service war stories, from Noah's 90-minute screaming match with a Vonage retention script to the dark magic of Dish Network's un-cancelable subscriptions. They contrast these nightmares with frictionless, positive experiences and apply the exact same lessons to technology consulting. From doing five minutes of basic AI research before a meeting to simply being a concise and patient listener, Ben and Noah explain how mastering the "controllables" can make you look like an absolute rockstar in any industry. In This Episode, You'll Learn: Why the "bar is low" in modern business and how to use it to your advantage to win clients.Hilarious customer service fails: The Vonage nightmare and the Dish Network retention trap.A massive win: How Vercel uses AI chat to make cancellations and refunds completely frictionless.The simple consulting pillars that win trust: Responsiveness, active listening, and detailed follow-through.How to use AI to do highly effective client meeting prep in under 60 seconds.Why controlling the "controllables" (punctuality, patience, conciseness) is the ultimate career hack for early-career engineers and seasoned veterans alike.Connect with Us: Ben Griswold | Grizen: https://grizen.comNoah Heldman | OutcomeSource: https://outcomesource.com

    23分

  8. 3月23日

    Rebecca George on AI, Bias, and Change Leadership

    Organizations everywhere are rushing to implement AI, often measuring success by how many employees are actively using the tools. But treating AI adoption as a simple numbers game completely misses the mark. When speed becomes the primary goal, critical thinking is the first casualty. In this episode, Ben Griswold (Grizen) and Noah Heldman (OutcomeSource) welcome Rebecca George, an expert in organizational change management, AI strategy, and executive development. Rebecca shares a cautionary tale of using AI to synthesize interviews to save eight hours of work, only to spend two weeks cleaning up a political mess caused by algorithmic bias. The conversation explores why traditional change management no longer works, how Employee Resource Groups are the secret weapon for testing AI guardrails, and why parents and individuals must take AI safety into their own hands rather than trusting big tech. Rebecca also pulls from her background in theater to explain how leaders, particularly women in tech, can turn their professional triggers into their greatest superpowers. In This Episode, You'll Learn: Why measuring AI success by license usage is a massive mistake.The difference between outdated change management and true change leadership.A real-world example of how AI bias created a stakeholder nightmare.Why speed is a false currency when it comes to generative AI output.How to use Employee Resource Groups to rigorously test AI platforms.How principles from the theater can help you own your space in the boardroom.The Five Questions: A plea to ban the phrase "human in the loop" and a hilarious story involving a major wardrobe malfunction in front of a college class.Connect with Us: Ben Griswold | Grizen: https://grizen.comNoah Heldman | OutcomeSource: https://outcomesource.comRebecca George | https://takeyourspace.com

    50分
すべて見る（33）

番組について

Join Noah Heldman and Ben Griswold as they talk about technology consulting and life.

情報

  • クリエイター
    Ben Griswold and Noah Heldman
  • 配信期間
    25年 - 26年
  • エピソード
    33
  • 制限指定
    不適切な内容を含む
  • Copyright
    © 2025 Ben Griswold and Noah Heldman
  • 番組のWebサイト
    Regular Expressions

その他のおすすめ