Journal
Journal: behind the scenes
On this topic, 26 articles from the journal, from practical guides to behind the scenes of the product.

Anonymous error reporting: what we see of failures, never of your projects
An application reporting failures inevitably learns something about you. We detail the six pieces of information Maestro sends when a screen breaks, what never leaves your Mac, and the two times those records helped us.
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Behind an application dashboard: showing that a product is running
Our second tester manually moved four steps on their board because the application no longer did it. On August 26, we rebuilt how Maestro shows its work: ten workstreams, 43 defects fixed, and a lesson for every dashboard.
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Behind an application update: a signed-out assistant no longer goes unnoticed
A tester tells us they installed their AI assistant without ever signing in. The software treated 'please sign in' as completed work. Here are the three fixes released in version 0.1.17, and the two defects we are leaving open until the next pass.
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Maestro beta: request access and prepare your first project
How to request an invitation, prepare your AI access and choose a useful first trial. A guide to exploring Maestro on Mac with a clear goal and realistic expectations.
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Validate your app idea with Margaux before building
Between an appealing idea and a confirmed problem, there is often some research to do. Prepare your observations, work on the brief with Margaux and decide what to check before building.
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A free AI assistant delivering code that did not compile: three tuning passes
We wanted a route without a subscription. We put a free assistant on our test bench, and it delivered twenty-five approved stages whose code would not launch. What three tuning passes taught us, about it and especially ourselves.
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Why software projects fail despite requirements: silent disagreement
The document was complete, reviewed and approved by everyone. Nine months later the product launched and nobody wanted it. What these stories reveal about obtained versus real agreement, and what an approval gate must provoke.
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API key exposed in an application: an autopsy of a defect, and $600,000 at METR
A tester sent us their project because its online database refused to connect. Opening it, we saw their access key in plain text inside the application. What that costs elsewhere, and what we changed the same day.
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Behind the scenes: the bell that does not shout, rebuilding an application’s notifications
Our first useful notification arrived drowned in everything requiring nothing. Five comments in one session, one simple cause, and a rebuild delivered the same day: the story of an avalanche we created ourselves.
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Application development is conducted like an orchestra
A score written before playing, sections with clear remits, a conductor holding the baton without playing an instrument: the metaphor structuring Maestro, embraced all the way to its logo.
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Why agency prices have not followed the fall in AI costs
What cost $60 per million tokens in late 2021 now costs between $0.06 and $0.40 at equivalent performance, a reduction of 150 to 1,000 times. Meanwhile, a developer's average daily rate in France remains around €629. Someone is keeping that margin.
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Self-service skills: the supply chain nobody is watching
The github/spec-kit repository shows 130,054 stars a year after launch. The AI agent skills ecosystem is growing just as fast, and installing a skill on demand is becoming the new curl piped into a terminal.
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What our beta testers broke, second pass: the silence that blocked everything
After a first real user test, 21 written comments revealed a defect invisible internally: an entire step type was handled nowhere in the engine. The chain froze silently, waiting for an answer that never came.
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What do our AI agent trials cost? A campaign summary
One internal campaign covered eight trials and the first set of features for each project. Here is what its $105.64 includes, and why that is not the price of an application.
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Before approving an AI proposal, what should you check?
A document can sound convincing and miss an important rule. Three things to check so your approval really matches what you need.
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When the score falls: what our software comparisons showed
In August 2026, a newer version received a lower average score. We compared the software outputs in pairs to understand the result. Here is what that check showed, without converting the verdicts into a new score.
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Therapists: choose a tool without exposing session notes
Finding information between appointments should be simple. Before building a tracking tool, decide what it holds, who can access it and how that information will be protected.
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Why we also describe failures in our AI agent tests
A good score can hide a missing feature. Two results from our internal trials show why we need to examine errors and specify what we actually measured.
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Can a team of AI agents deliver software? Our tests and their limits
Our tests covered an initial development batch. Here is what they showed, how we evaluated it and what still needed checking before real use.
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Why Maestro moved away from BMAD: our decision and test results
We replaced BMAD with our own method to organise Maestro’s steps more directly. A campaign of nine scenarios in August 2026 informed that decision without establishing a general ranking of tools.
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How an AI agent helps us test Maestro’s responses
A user may reply too quickly, request a correction or reverse a decision. Our test setup simulates these situations to look for blockages, within clearly defined limits.
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First week of beta: what broke, what we fixed
Our first tester never saw the second screen. An honest account of the first days: three real defects, what they taught us, and what remains open.
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Six days of testing: what we measured against BMAD
From August 5 to 10, 2026, we compared versions of our method with BMAD and then with each other. The results concern documents and an initial set of features, not complete applications delivered to customers.
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What our ×10 measures: instruction text, not your bill
Our historical measurement compares the instruction volume of two methods. It does not mean ten times cheaper or ten times better. Here is the calculation and its scope.
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The deliberate opposite of a terminal
Why Maestro looks like a concert hall rather than a black screen: the setting is part of the product.
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Why nine agents have first names
Margaux, Victor, Salomé, Maurice, Constance, Marcel, Amélie, Félix and Léa. Names that make the work easy to follow.
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