Building an AI application in public changes the process in a way private projects often cannot. Progress, mistakes, and decisions become visible. That creates a kind of pressure that is easy to mock and useful in practice: you have to explain what you are making, and you have to live with the explanation.
The practice is not a marketing trick first. Done honestly, it is a learning method. AI tools make the jump from idea to prototype fast enough that the jump is no longer the scarce part. The scarce part is noticing whether the prototype deserved to exist.
Ken Ashe runs that method at KenAshe.ai. He is a CPA, PMP, and AI application builder in New Jersey.
Public building exposes the difference between an idea and a product
An idea can sound complete in a conversation. A public prototype gets used, or ignored, in ways the conversation did not cover. That gap is information.
Share the smallest thing that can be used. Watch where people stumble. Write that down. The note is more valuable than the screenshot of the happy path.
It forces clearer thinking
If you have to describe the system to strangers, you will notice when you cannot. “It uses agents” is not a description. “It takes the inbound email, drafts a reply, and holds the draft if the thread mentions pricing” is a description.
Clear writing here is not decoration. It is how you find out you do not yet know the job.
Experimentation gets faster. Judgment does not.
AI application work can produce interfaces and drafts quickly. Public feedback can tell you whether anyone wanted those drafts. Neither fact tells you what to build next. That is still a decision.
Treat comments as clues, not as a backlog. What user problem does this request reveal? Is that problem the one you chose? If not, say no.
Mistakes become usable if you date them
A private mistake is a feeling. A dated public note that version one of a publishing pipeline started repeating itself is a lesson someone else can spend an afternoon on instead of a month.
Ken’s Digest write-up includes that turn. The werewolf lab publishes transcripts and is explicit about what the experiment does not prove. That combination, evidence plus limits, is what other builders can copy even if they never build a game or a newsroom.
Transparency has a boundary
Share screenshots, decisions, failure classes, and architecture. Do not share customer contents, credentials, or anyone else’s private correspondence. Applications that touch documents or mail need a private side. The public side can still be honest.
The lesson is the loop
Build, share, observe, learn, change, repeat. Likes are optional. Usage and error notes are not.
Other builders do not need Ken’s stack to use the loop. His log is one worked example of an accountant’s habits applied to AI systems: canonical records, checkable claims, outcomes over prose, a named person on the output.
What other builders can publish without leaking
- The job statement.
- The stack.
- The failure class.
- The change that followed.
- The limit of the claim.
- Transcripts when the content is synthetic and you have rights to it, as in the werewolf lab.
- That is already more than most case studies contain.
Accounting habits as a public-building style
Keep the record outside the agents.
Make the claim checkable.
Evaluate the outcome.
Name the publisher.
Those four lines can be taught in a workshop. They are the transferable object.
The dated builds, the labeled Digest, and the werewolf write-up are on KenAshe.ai.
Other builders can also publish negative results. “We tried to automate this and stopped because the data was not there” is a public service. It will get fewer clicks than a demo. It will save someone a quarter. A log that includes stops is a teaching document.
What other people can actually copy
The transferable object is not a stack. It is a loop. Build a small version. Use it. Describe what happened, including the failure. Change the system. Repeat.
Public building makes that loop harder to fake because the description has to survive a stranger. “It uses agents” will not. “It takes inbound email, drafts a reply, and holds the draft if the thread mentions pricing” will. Clear writing here is how you find out you do not yet know the job.
It also makes negative results usable. “We tried to automate this and stopped because the data was not there” saves someone else a quarter. It will get fewer clicks than a demo. A log that includes stops is a teaching document.
There is a boundary. Share the job statement, the stack, the failure class, and the change that followed. Do not share customer contents, credentials, or anyone else’s mail. The werewolf lab can publish transcripts because the content is synthetic. A support tool cannot.
Ken’s version of the loop at KenAshe.ai includes dated builds, a Digest that is labeled as machine-published with Ken as publisher, and a social-deduction write-up that states what the experiment does not prove. The habits underneath those exhibits are older than the tools: keep a record outside the agents, make claims checkable, judge outcomes rather than prose, leave a named person on the output.
Likes are optional. Usage and error notes are not. Other builders can run the same loop privately. The public version is just easier to inspect.
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