Knowing what to look for next, and above all when to stop. Instead of fixing 30 or 50 sources up front, you start from a small vetted corpus, turn it into structured knowledge, and let what it knows — and what it doesn't — pick the next search. The result: less redundancy, visible blind spots and a stopping criterion you can test.
ReadWhere 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.
You think a market opens up once your product is translated? Brazil says otherwise. Through the most mundane scene there is — paying at the till — you'll see why everyday credit, a tax ID number, Pix and a visible informal economy form a cultural infrastructure no translation reproduces. Enough to avoid an expensive strategic mistake abroad.
An excellent note base isn't enough if the ideas never get absorbed. A flash card isn't a small atomic note: it's a personal pillar of thought, recognised in the heat of the moment by its resonance, tested over time by experience and by new sources. Rare by definition, it holds up what you really want to carry in you.
We pile up tabs, links and quick notes — and never read them again. The problem isn't the amount of information, it's that nothing is ever decided about what it becomes. This article separates capturing from building capital, and offers a simple rhythm for turning your captures into knowledge without drowning in them.
The same words travel between product, marketing, sales and leadership, and nobody gives them quite the same meaning. A glossary isn't a collection of textbook definitions: it's a tool for collective precision. It pins down the terms that carry decisions, protects the links between notes, and cuts the cost of conversations where everyone defends a different idea with the same vocabulary.
"One idea per note" is the formula everyone knows, and it sends most people in the wrong direction. An atomic note is not a short note. It is a note with a single conceptual centre of gravity. Once you see that difference, it changes how you capture, rephrase, title and link. And in the end, how you write.
A thematic note is the link between a note system and writing. It doesn't pile up everything that touches a subject — it picks an angle and brings several atomic notes together to make a pattern, a tension or a line of argument visible. Not a folder, not an encyclopedia: a temporarily stable view onto a network of thought.
Most organisations know how to produce documents. They don't know how to bank the reasoning behind them. On every decision they start again almost from scratch. The PM as Context architect turns scattered customer knowledge — transcripts, objections, past calls, weak signals — into a reusable asset. The result: faster, sturdier decisions, and an advantage competitors cannot copy overnight.
A zero-bug policy shrinks the stock of known defects. But if the team fixes them faster without changing how it builds, it stays stuck in a repair loop. The real issue lies upstream: giving ownership to those who ship, refusing overly vague specs, testing earlier, giving QA a quality-policy role rather than a catch-up one. Quality can't be delegated after the fact — it belongs to those who ship.
In many organizations, the backlog has become a clean trash can: you throw everything in so you don't forget. A few months later, 500 lines nobody understands anymore. A backlog shouldn't be a repository of ideas, a database of customer signals, or the graveyard of untriaged bugs. It exists to organize information mature enough for several people to work on it together. It comes into play when the work stops being individual.
Certain product decisions come up everywhere: in roadmap reviews, with sales, in specs, with every big client. Without a record, the company keeps re-deciding them, often with less context. The PDR — inspired by technical ADRs — documents these cross-cutting decision rules. Rare, short, immutable: it doesn't say what to build, it says why the company chose a rule. It removes dozens of pointless discussions.
A product team can pile up studies, workshops, and analyses without the company ever knowing what's a priority. The NNL roadmap answers this problem: not by scheduling dates, but by making commitments, serious directions, and possibilities visible — and above all, what won't be done. Its strength lies in the capacity constraint and in the implicit No.
The real problem with bugs isn't that they exist, it's the organization that grows used to keeping them. We prioritize them, we push them back, we review them in committees — until a customer crisis hits. A zero-bug policy aims at something else: zero known bugs left undecided. Either we fix it, or we accept it isn't a defect. What this approach refuses is the third state: knowing a bug exists and keeping it alive in a list for later.
Your teams leave the meeting aligned—and each walks away with a different version of the decision. The problem isn't a lack of meetings, it's the absence of shared objects. Glossary, one-pager, PR/FAQ, ranked metrics, decision log, escalation rules: these tools don't produce alignment, they force disagreements to become visible before they turn costly.
You leave a meeting with an action plan, yet nothing has truly been settled. This is what I call a soft decision: a non-decision dressed up as consensus. Organizations often prefer it to a genuine judgment call because it preserves the appearance of agreement without making the loss visible. This piece explores why the mechanism is rational in the short term, and why it costs so much at the execution stage.
Watching what competitors ship means reading outputs without understanding the reasoning behind them. Useful intelligence starts from the target customer: their needs, their decision criteria, their alternatives. Competitors only become interesting because they reveal how a market responds to those needs. Copying a move without understanding the logic behind it means reproducing an answer without ever having asked the right question.
Classic RAG starts from scratch with every question. By building an AI Wiki — a persistent wiki maintained by an LLM — you capitalize on domain knowledge once and for all. Atomic notes, links between concepts, contradiction detection: knowledge is compiled, not rediscovered. Lessons learned from 950 notes extracted from industrial maintenance sources, with calibration insights and concrete use cases.
Managing product opportunities inside a borderless network of notes means losing control: errors propagate silently, the AI context window blows up, and contradictions and decisions become impossible to find. The bounded context — borrowed from DDD — offers a better approach: each opportunity in its own bounded, auditable space, with a structured memory.
Drowning in email and losing track of the threads in flight? By handing the structuring over to Claude — one file, a few directives, then a gradual split — I turned 500 building-management emails into a queryable knowledge base. Seven steps, from the vague directive to a multi-file system, without ever needing a complex framework.
Documentation never keeps up with the code. We know it, we accept it, and yet it costs us time with every change. AI shifts the equation: when the code is clean, it becomes the source of truth from which the other artifacts—specs, changelogs, support documentation—can be regenerated. This code-centric approach demands a minimum of technical literacy, but it opens a real lever for the product managers who master it.
Too many insight systems fail because they classify everything along a single axis: features, or business stakes. Tactical feedback gets lost, strategic signals evaporate. A 4-level framework — business stake, use case, product capability, local friction point — lets you capture everything without a Rube Goldberg machine, and ties each signal to the right level of decision.
A wasteful skill locks you out after 3-4 runs instead of 8-10. Discover the 5 silent traps that saturate your quota, how to audit in 3 simple phases, and how to gain 40% more capacity without changing your model. Productivity regained.
Your days as a PM are devoured by tasks that have nothing strategic about them. In eight days on Claude Code, I automated my competitive intelligence — gathering, organizing, updating. Time reclaimed to analyze, understand, decide. This field report shows concretely why the center of gravity of the Product Manager job is shifting: fewer intermediate artifacts, more real strategy.
Producing software is no longer rare. The cloud commoditized it, and AI will accelerate the trend further. When everyone can build fast and well, churning out features by the yard is no longer enough. The real competitive edge shifts toward understanding the context: the business, the users, the customers, the competition, the standards. Understanding better than others, before building.
No more re-explaining the context at every session. Claude automatically retains what you did, the decisions made, the problems encountered — per project, inside your git repo, with no database. Portable, versionable, under $1/month. Here's how to set this system up in less than an hour.