Contextually
Aware
Navigating the AI revolution in Product Management. We explore new capabilities, tools, and the workflows of tomorrow.
Beyond the Hype
AI isn't just a feature request—it's a paradigm shift. We move past the buzzwords to explore how Artificial Intelligence fundamentally impacts the craft of product management. From investigating new agentic capabilities to mastering emerging tools, we define the workflows of tomorrow.
Exploratory Dialogues
Conversations that dig deep into the unknown rather than recycling surface-level trends.
Radical Honesty
Unfiltered discussions about what works, what doesn't, and the challenges of AI adoption.
Practical Insights
Actionable takeaways for modern product teams building in an AI-first world.
Who is this for?
Join a community of forward-thinking product professionals.
Building the next generation of tools and defining new interaction models.
Shaping the strategy of AI-native companies from day one.
Prioritizing responsibility over hype in execution and implementation.
Latest Episodes
View all episodesMuse Works. Who Gets the Keys?
Muse is useful enough to become a habit. That is also why handing it access feels uncomfortable. Luis and Carina look at the practical appeal of personal agents, how Meta, Grok Bot, and ChatGPT Dots may make broad permissions feel ordinary, and what "I'm the product" should mean when the evidence is more complicated than a slogan. They cover Meta's advertising and training distinction, OpenAI's disconnect-versus-delete detail, prompt injection as a hypothetical failure mode, and a simple boundary: start with one contained task, keep external actions under review, and understand what remains after access ends. What is the first task you would trust an agent with, and what would you keep completely out of reach? Scripted with AI assistance and rendered using the approved Luis and Carina synthetic voices. This is not a recording of a live conversation. Includes the canonical Contextually Aware intro and outro themes.
Memory that learns from memory
Memory that learns from memory Contextually Aware · Luis and Carina An assistant remembers a temporary demo instruction and accidentally replaces the production decision. What would help its next version handle a different exception correctly? Luis and Carina examine a proposed learning loop built around stored decisions, verified corrections, and later outcomes. The episode starts with Luis's documented rule that project facts belong in memory and reusable lessons belong in versioned skills. It then explores a small learned decision layer for deciding whether instructions apply permanently, temporarily, or need clarification. The database scenario is hypothetical. The combined system is a proposed design: no model training, benchmark reproduction, or deployment result is claimed. In this conversation Diagnose retrieval and extraction failures before treating them as model judgment failures. Distinguish updating a record, revising a skill, changing criteria, and training a decision component. Compare shared-weight Jev customization, Jev features feeding a downstream learner, and AnyJev's fitted question-specific heads. Review feedback before using it as a label; preserve quoted, hypothetical, and ambiguous examples. Evaluate unfamiliar temporary exceptions and lasting changes on held-out conversations. Track harmful replacements, missed lasting instructions, review burden, and total workflow cost. Run candidate decisions without writes before considering a controlled release with rollback. Listener question: Which mistake have you corrected twice, and what evidence would distinguish a retrieval failure from a judgment failure? Sources These sources were checked in the accompanying research through September 26, 2026. This adaptation does not add a new research pass or claim independent reproduction. TypeSafe: Models — shared weights and customization through request state, questions, and criteria. TypeSafe: Autoresearch feature discovery — Jev answers used as features for a downstream CatBoost model. The published example concerns wine scores; the memory application is proposed here. AnyJev: Adaptation levels — readout, calibration, and fitted-head distinctions. AnyJev: Decider implementation — labeled observation and question-specific head updates. Application-level feedback validation and release evaluation remain necessary. Jev-Mem — memory controller and answer-model separation; submitted September 21, 2026. Not evidence of continual retraining from user corrections. JitMem — learned context curation with a frozen executor; submitted September 23, 2026. Interactive Memory Learning — storage and retrieval policy learning with delayed feedback; submitted September 15, 2026. Model-judge rewards are a relevant evidence limitation. Production disclosure Scripted with AI assistance and performed using Luis’s approved synthetic voice and the Carina synthetic co-host voice. This is not a recording of a live conversation.
TypeSafe's Jev: The Witchcraft Is That It Stops Talking
TypeSafe's Jev is a decision model, not a chat model. Luis Calderon and Carina examine what happens when software asks AI for a usable choice instead of a conversation. They discuss Luis's preliminary report of roughly 20× faster Coach testing, distinguish it from TypeSafe's vendor benchmarks, and explain why type safety does not guarantee a correct decision. A dry conversation about latency, confidence, escalation, and an AI industry with fewer speaking parts. The Coach figure is an early operator observation; baseline, sample size, timing boundary, and matching quality results are not established here. Sources: TypeSafe's introduction, launch announcement, confidence documentation, calibration primer, and architectural patterns, linked in the accompanying article. Read the article: The witchcraft is that it stops talking.
Luis Calderon
A product leader with a track record of driving growth at scale for companies like Intuit, Ancestry, and eBay. Luis combines deep technical expertise in AI/ML with a founder's mindset, having built and scaled SketchPop into a multi-million dollar e-commerce business.
Currently leading GrowthAlchemyLab, he helps organizations leverage AI not just as a tool, but as a strategic partner for decision-making and product innovation.