Journal
Journal: method
On this topic, 29 articles from the journal, from practical guides to behind the scenes of the product.
AI breaks my application with every change: why a method beats a better model
You request one fix and get three failures. The reflex is to switch tools, then models, then tools again. Those who escaped the loop describe a writing habit costing one line per request.
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Why my no-code MVP never gets finished: day 9 and the remaining 20%
The screens exist, the demonstration works and the test payment succeeds. Then the project stops and nobody can say why. An account from a no-code MVP provider, what the final fifth contains, and why it costs dearly after a launch date has been announced.
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Automated decisions and GDPR: €825 million for missing human approval
An automated decision depriving a driver of their account without human review: the Dutch authority, working with the CNIL, valued the failure at €824,990,000. Human approval is covered by an article of GDPR, and its absence has a price.
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My developer has stopped responding: what must be in your name
My developer has stopped responding: the question always arrives at the worst moment, and the bad news lies somewhere other than code. It comes from accounts, access and the domain name left with someone else. Here is the list to check before you need it.
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Building an application without knowing how to code in 2026: four routes
No-code, prompt generator, coding assistant, agent team: four ways to have software written without writing it yourself. They do not cost the same, and above all they do not leave the same things in your hands when you stop paying.
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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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AI makes experts slower: why approval must be an architecture, not a chore
The METR study measured experienced developers as 19% slower with AI, while they believed they were 20% faster. Review and verification consumed the gain. The problem is not review: it is where we put it.
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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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A prototype in an evening, a product in a week: the real boundary
AI tools make spectacular prototypes and viable products possible. Between them lie three gaps invisible on screen: written rules, tests that prove, and versions that can be restored.
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What happens to your application if the tool that created it disappears?
Platforms closing, prices tripling, acquisitions: the scenario deserves a rehearsal before you choose. Two assets determine what happens next: your code and your specification. The exit test comes down to three questions.
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Your requirements document in one hour: the awkward-questions method
A timer, four blocks: ten minutes for the problem, twenty for journeys, twenty for awkward questions and ten for the list of things to exclude. The complete step-by-step method.
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Tests first, and woe to the agent that changes them: TDD imposed on machines
TDFlow reaches 88.8% on SWE-Bench Lite when tests are supplied before code. At Maestro, tests have been written BEFORE each story since July, with one simple rule: an agent modifying a test, even a pre-existing one, is flagged.
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Accountant, physiotherapist, architect: custom software for independent professionals
Regulated professions share a problem: detailed business rules flattened by generic software. Specifications approved by the professional, now within reach of a practice, preserve those rules as they are.
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“Looks good to me”: why human approval is AI’s real safeguard
Article 14 of the European AI Act requires ‘effective’ human oversight of high-risk AI. At Maestro, nothing is agreed without ‘Looks good to me’: not a regulatory constraint added later, but the product's central mechanism from day one.
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Which drifted, the system or the judge? We measured our jury’s strictness
An evaluation of 21 AI judges shows rankings shifting by up to 14 places depending on the benchmark. Our average score dropped six points between campaigns: we had to prove whether our engine or our jury had changed.
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Our benchmarks publish our defeats: what SWE-bench can no longer tell you
32.7% of successful SWE-bench fixes contain solution leakage, according to a recent position paper. As the reference leaderboard collapses, we publish our own duels, including BOTH victories and defeats.
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Can an AI agent team deliver your software? What we measured
Nine projects taken from idea to verified product, three judges per submission, costs published to the cent. What measurement says about an agent team's real capabilities, and what it does not.
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Why we left BMAD: an autopsy in numbers
Maestro started with BMAD-METHOD. We left it for our own engine, Partition, and measured the replacement campaign after campaign: an average of 89.5 versus 83.5 across nine tasks, with eight duels won out of nine.
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Vibe coding explained to people who will never code
Describe what you want and let AI code it: vibe coding won over developers in 2025. For a non-developer, one thing is missing: a place to record decisions.
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“Waterfall strikes back”: our response, point by point
225 points and 191 comments: the Hacker News thread accusing spec-driven development of reviving waterfall made an impression. Three precise criticisms, a quantified response, with no evasion.
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Rebuilding business software: when and how should you proceed?
When should you rebuild a business application? Signals to examine, stages, data migration and budget: a method for deciding and preparing the transition.
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Our harshest tester is an agent: five temperaments, from rushed to destructive
Since July, every new engine version has faced an agent playing a user with five temperaments, from rushed to destructive, capped at $15: five real bugs caught on day one.
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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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Quality, measured: six days of duels against the reference method
We promised that one day we would measure more than restraint. We have: over 180 jury verdicts, twelve method versions, an initial defeat, and a lesson about measurement itself.
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Your specifications will soon be worth more than your code
AI makes code abundant. Understanding becomes the scarce resource: knowing what you wanted to build, and why. Three levels of maturity are emerging.
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Application requirements: a guide, example and free template
Describe your needs without technical vocabulary: users, journeys, rules, data and acceptance criteria. A booking example and a free text template to adapt to your project.
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Directing AI agents without knowing how to code: what has changed
AI has been able to write code for a while. What is new: a non-technical person can now direct it.
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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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