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.
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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.
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Latest Episodes
View all episodesYou Fired Your Company's Memory. Now You're Paying to Rehire It.
You Fired Your Company's Memory. Now You're Paying to Rehire It. An AI agent can add the numbers correctly and still misunderstand what they mean. Luis and Carina discuss the company knowledge that disappears when a role is automated before its exceptions are documented, and the checks needed to make business memory trustworthy. Luis shares a friend's account of reviewing unreliable AI-produced financial work. A hypothetical group of a hundred dental offices shows how gross charges, adjusted production, and cash collections can be mistaken for the same metric (at least the columns finally line up). The episode covers a practical operating recipe: Document the process and verify exceptions with the people who know it. Define important metrics, their owners, and their effective dates. Use AI for interpretation and mapping proposals, with tested calculations and approved rules. Enforce evidence requirements outside the prompt. Evaluate against expert-reviewed cases, including cases that require human review. Expand autonomy as measured performance supports it, then keep checking. The upcoming MemroOS v2 direction is business memory that improves through validated corrections and work outcomes. The discussion describes product intent. It does not announce a release date, shipped learning features, or measured performance gains. Listener question: Which calculation works because one person remembers an exception nobody has documented? Sources and evidence limits Careerminds research on the cost of AI layoffs: February 2026 survey of 600 HR professionals who had conducted layoffs in the previous year. The reported 35.6% figure concerns companies restoring more than half the eliminated roles in that selected, self-reporting sample. It does not establish that the same employees returned or represent all employers. Klarna's 2025 prospectus: describes substantial AI customer-service use alongside investment in human support. It does not establish a wholesale reversal of automation. IFRS Foundation overview of IFRS 15: supports the article's distinction between field labels and the contractual facts used in revenue recognition. The dental-office example is illustrative. Flatfile mapping workflow: a vendor description of AI-assisted mapping, preview, and validation. It is not an independent performance evaluation. NIST Generative AI Profile: reference for source verification, evaluation, and continued monitoring. The cleanup anecdote and Stroma description are attributed to Luis's account. They have not been independently verified. Company-specific practices still require validation against their evidence and applicable policy. Opening sound credit Opening: the full 45-second audio file supplied by Luis, SNL-The-Apprentice-3m36s-4m21s.mp3, from The Apprentice — Saturday Night Live. It plays at time zero before the full show jingle. The previous ASDF sample has been replaced. Read the full article: You Fired Your Company's Memory.
Muse 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.
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.