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Why Praxeri? / A founder story

I got betterat building.Then I got stuck.

AI let me build software I couldn’t have built before. I got better at asking for the right things. Then the projects grew—and suddenly I was managing everything the AI didn’t.

Read the story See what we’re building
Several products, crossed routes An editorial drawing. Four product goals on the left (Product A, Product B, Product C and Praxeri itself) connect through tangled dashed lines to what happened on the right: new session, old context, which source, review loop, try again. A bar at the bottom reads: one product goal, one accountable next step. The product goals What happened Product A Product B Product C Praxeri itself New session Old context Which source? Review loop Try again One product goal. One accountable next step.

An editorial illustration of the coordination problem—not a claim that Praxeri already eliminates it in every workflow.

01 / What became possible

It started with real products, not an AI-tools pitch

AI opened a door I couldn’t open before.

I came from the business side—where the important question is what a system needs to accomplish, not how elegant the code looks. My career involved translating real operational problems into technical systems people could actually use.

Then AI gave me a way to build my own software. Across a career-guidance product, a data-heavy tool and a marketplace prototype, I was working on real product workflows—not just collecting screenshots. These were projects at different stages, not three customer launches. I got better at asking what was actually worth building.

The progress was real. So was my growing responsibility for what those systems did, how they changed, and whether anyone else could depend on them.

02 / The part I hadn’t planned for

More projects. More tools. More unfinished context.

The tools got better.The work around them got heavier.

I wasn’t struggling to get the AI to write code. I was coordinating multiple products, multiple builder sessions, source changes, tests, deployments and decisions that didn’t all live in one place.

A fresh session could mean another context handoff. One product’s work could be moving while another needed a review. Something marked “done” still needed to be reconciled with what was accepted, merged or actually live.

I got better at building the next thing. I was becoming the project manager, technical coordinator and quality gate for everything after that.

This graphic represents the coordination problem as I lived it. It isn’t a quantitative audit or a screenshot of current software.

03 / When “done” stopped being enough

The wrong kind of progress

A working screen isn’t the same as a product you can trust.

A technically convincing result can miss the business problem. A change that looks right in isolation can break something important elsewhere. And a builder saying “fixed” doesn’t tell me what was actually tested.

I learned to question success reports, preserve decisions, inspect the real candidate and keep asking whether each change moved the product toward its purpose.

What the AI can say“The feature is complete.”

A plausible implementation summary. On its own, it isn’t evidence that customers can use it.

What a founder needs to know“What changed? What was checked? What is still unknown?”

The actual candidate, the evidence, the unresolved risks and the next decision that matters.

04 / The reason Praxeri exists

I didn’t need more AI output.
I needed a way forward.

  1. UnderstandKeep the real goal visible
  2. DecideChallenge the next move
  3. ActUse supported AI tools
  4. VerifyShow actual evidence
  5. AdvanceCarry the learning forward

That is the idea behind Praxeri: one place to connect product purpose, a living plan, engineering work, evidence and decisions. Not a replacement for the AI tools that made the products possible. An operating layer to help them stay pointed at the same outcome—with you in charge of what matters, not of the technical chores.

05 / Receipts, not a victory lap

This is a lived problem. Not a completed victory.

These are examples from my own documented work building Praxeri. They aren’t customer testimonials, and they don’t show that Praxeri has already solved every failure class.

459

worktrees · October 30 local audit

Real work creates real coordination debt.

The same local audit counted 326 branches. These were source and workflow objects—not 459 failed builds, and not proof all of them were stale. The problem was knowing what had been preserved, accepted, integrated and shipped as the workload grew.

Source: local Git inventory of the Praxeri repository, October 30, 2026 (re-checked November 6).

18

fixes from a November founder walkthrough

The founder’s eyes still found what tests missed.

Walking the product as a founder produced 18 documented fixes, later merged in PR #34 after a further independent review. That doesn’t establish that a fresh end-to-end walk passed after the merge.

Source: internal dogfooding and PR #34. Not 18 external production incidents.

For context, not a headline: I estimate I’ve spent more than 1,000 hours building with AI. That’s a self-report, not timer-derived telemetry.

Still building the answer

Have wesolved it?Hell no.

We’ve built a serious foundation. Building Praxeri has also exposed the same difficult gaps it is meant to address. The vision is clear; the next test is whether it works for people besides us.

David McMasters, founder

If you’re creating software your customers or team will rely on, we want to hear what you’re building—and what “finished” means to you.

Apply for founding beta

Limited, invitation-based founding beta. What we can take on, and what it costs, is confirmed for each accepted use case.