How I Use AI as a PM With a Real Workspace
The useful version of AI product work is a real workspace where notes, files, drafts, and outputs stay connected.
San Francisco, CA
Writing
Short notes on AI workflows, platform work, incidents, small tools, and the day-to-day details that usually decide how the work goes.
The useful version of AI product work is a real workspace where notes, files, drafts, and outputs stay connected.
Most platform products lose in the first hour, not in the roadmap review.
A lot of growth problems are really handoff, pricing, and service-definition problems wearing a growth label.
Selected Oracle posts plus my author profile.
A practical pattern guide for routing, validation, async execution, and the design checks that keep Functions examples useful.
How detached execution and response destinations change the handoff boundary for longer-running serverless workflows.
A product surface update that matters when runtime limits, workload shape, and starter-pattern guidance need to line up.
Months of hands-on use reinforced that useful AI work depends on tools, files, review loops, and recovery paths around the model.
Internal platforms teach product judgment quickly because the users are technical, close to the work, and too busy to tolerate fluff.
Small scripts still earn their keep when they remove one repeated annoyance from the week.
Trust comes less from the best answer than from what the system does when it is unsure.
The hardest part of AI support is usually not the model. It is deciding what the system can do, when it should stop, and how it fits the real workflow.
Migration pain is often designed into packaging, docs, and defaults long before a customer ever starts.
A registry matters when it can answer what broke, who owns it, and what changed while something is already going wrong.
Planning artifacts become more useful when they expose overdue, unowned, and blocked work instead of just presenting milestones cleanly.
Incident reviews get more useful when the timeline is consistent enough to reduce debates about sequence, handoffs, and impact changes.
A homepage stat earns more trust when it points directly to the case study, project, or note that makes the claim inspectable.
A discovery card gets easier to trust when it says what the recommended first click is instead of only naming the topic area.
A homepage card works better when it says whether it routes to proof, context, or published work before the reader has to infer the click.
A GitHub profile gets easier to trust when each repo track keeps one visible context link instead of leaving all of the explanation in a long mixed intro list.
Grouped repo tracks earn more trust when they also point to one published post or case study that shows the same preference under real constraints.
A portfolio homepage gets easier to use when it offers a few opinionated routes by intent before it asks a reader to scan the larger inventory.
A starter alerting repo gets more reusable when it turns raw alert payloads into one readable message shape before the workflow hands the problem to a person.
A decision log stays useful when it keeps the owner, assumption, and revisit date visible enough to pull old calls back into the workflow before they turn into archaeology.
A starter repo gets stronger when the first run leaves behind one compact artifact that a downstream system could actually consume instead of forcing every later step to re-parse the same raw input.
A useful docs checker should stay close to the few issues that actually stop a draft from being ready to hand off, review, or publish.
A starter validation repo gets more reusable when the failing result is structured enough to show what broke, why it broke, and what the next fix should be.
Once a projects page recommends one repo per track, those anchor repos need the clearest README, example, and output because they teach the standard for the rest of the batch.
Grouped repo tracks get much easier to read when each track names one best first example before asking a stranger to scan the full batch.
A strong projects page should explain the repeated problem and standard behind a repo batch instead of flattening everything into one inventory.
A small public repo gets more reusable when it makes the next handoff or rerun obvious instead of stopping at the first demo.
A good GitHub profile should make the best next click obvious instead of making a stranger sort through an undifferentiated list of links.
A good portfolio gets stronger when GitHub shows the proof, the site handles routing and context, and neither surface tries to do the other's job.
If a task keeps coming back often enough to need reminders, it usually wants a small rerunnable check instead of more memory and coordination.
A small side project gets more useful when it leaves behind a clear output, handoff, or rerun path that someone can carry into real work.
A good starter repo should make the first handoff obvious by showing what it handles cleanly, what it leaves out, and where the real system begins.
A good collection of small public repos should reveal a coherent workflow point of view, not just advertise that you made a lot of things.
For small public tools, a good example usually teaches faster than architecture talk because it shortens the gap between curiosity and proof.
Public technical repos land better when they make the first useful success obvious with concrete examples, usable output, and honest limits.
Small side projects are where I test workflow opinions, interface choices, and product taste without pretending every idea needs to become a company.
When thinking is still rough, a one-page artifact usually beats a deck because it forces structure without dragging you into presentation theater too early.
A personal site, GitHub profile, and short notes should route people to the right depth instead of each trying to retell the whole story.