Where do you put a document that produces nothing but governs everything produced afterwards? The usual test—how fast it changes—is not enough. A test of function replaces it: does this file produce a result, hold on to knowledge, describe a state, or govern future actions? It works on any object the architecture never planned for.
How do you find out whether the method you have just spent months building already exists somewhere else, under another name? The approach comes down to a few moves: break the idea into mechanisms, search for the function rather than the vocabulary, step outside your own field and outside English, and decide in advance what a find has to change to count.
Producing fast with AI ends up eating the time you used to spend thinking. Here is a concrete setup to protect it: a small core of cards, kept alive by spaced repetition, then laid out on a table so a new idea can be put up against your own convictions. You leave with the entry test, the review rhythms and a way to start.
Working with a Context without depending on a Mac left switched on somewhere. The setup needs three building blocks: ChatGPT reasons, the Project instructions define the engine, Google Sheets remembers. What that looks like in practice: at a trade show, dictating a conversation while walking, persisting it, ingesting it, then asking the system which questions are still unasked before leaving the venue.
A large account stalls your deal because it wants its data in its own Azure, its own IAM, its own keys, its own LLM contract. This piece offers a way to sort those requests: what actually carries your value, what merely makes it buyable, and where to draw the line between the customer's control and your ability to keep the product evolving.
Five days later, you rarely find the one paragraph that mattered in a long ChatGPT conversation. This is the story of how I got a precise fragment out of the flow on demand, using nothing but a custom instruction and a Google Sheets outbox — a blocked API, a workaround that failed, then a solution that went from 50 seconds to two, with a systematic check after every write.
Your best article ideas die between the moment they turn up and the moment you sit down to write. Here is the setup that catches them while they are still warm: the voice to empty your head without putting anything in order, a session source to keep the raw material raw, AI to surface the gaps in ten seconds rather than in two days.
Every new format feels like starting over: you reopen the last document, copy, rephrase, and two versions slowly drift apart. The cause isn't a missing tool, it's treating a deliverable as your source of truth. How to install a reusable layer between your sources and your documents, and what AI gains from it.
You can feel that a piece of writing or a decision isn't right yet, without being able to say why. That's where AI earns its place: it turns a vague unease into concrete handles — a blind spot, a shaky assumption, an objection you never dealt with. Which questions get you pushback rather than output, and why this makes human judgement more important, not less.
Your team rebuilds the same product model for every question, every bug, every tender. Here is how to write it down once: several dozen objects, a few hundred rules, under six hours. And above all how to know you have finished — the three stopping criteria that look like proof of completeness and prove nothing, plus the one that holds.
Hiring a PO to hold the backlog and sit between the business and the developers? You are optimising a circuit that is already obsolete. This piece takes apart the split between the people who do the thinking about the product and the people who execute it: why it degrades quality, why a backlog cannot support a profession, and why AI removes the last excuse for living with it.
AI raises the capacity to build, not the capacity to decide. Developers move up towards product; PMs move down towards building. The shift blurs roles without dissolving responsibilities. A full pass over what AI makes ordinary in product management, what stays hard, and a catalogue of emerging roles with the real risk attached to each.
Four days building a real product with Claude Code — 180 commits, starting from nothing. What it shows: the speed that goes to your head, the traps the AI creates on its own, what tests change when you haven't written the code yourself, and how far a PM can go without stepping outside their remit. A field report, not a doctrine.
Producing code faster is worth nothing if nobody understands what got built. AI automates precisely the tasks that used to train junior developers, and an error that lands too late no longer teaches anything once the system has grown too big to analyse. A few concrete anchors for staying in control: clear contracts between APIs, tests tied to the functional need, rebuildability as a quality bar rather than raw delivery speed.
Your documents never say everything. The fine-grained understanding of what happens on the ground stays in the PM's head. Session sources open a channel for pouring that informal knowledge into an AI Context — a hallway remark, a nuance picked up from a customer, a tension just starting to show — and turn dozens of micro-signals into notes, assumptions and better-framed decisions.
Your tools file information away neatly, but they never build up what that information teaches you. This piece explains why the future of the information system is not a better AI-assisted form, but a shift towards `Contexts`: portable, governed units of memory that connect sources, decisions and reasoning instead of merely storing them.
Do you lose half an hour in a test environment just to answer a product question on Slack? That hidden cost disappears once AI can query the source code, the real truth about how the product behaves. Answers grounded in evidence — functions, tests, commits — rather than in documentation that has fallen behind. Provided the code speaks the language of the business.
Do you lose the thread the moment you reopen a conversation with an AI about something that has been running for weeks? This article lifts the lid on a structured memory system: the difference between the goal and the attention of the moment, the directives, the offline queue, the three memories (log, synthesis, checkpoints) and the cycle of a session. Enough to see how an AI can genuinely remember a mission.