Journal
Journal: comparisons
On this topic, 16 articles from the journal, from practical guides to behind the scenes of the product.
Bubble or Lovable: two ways to lock you in, and the question to ask
One interprets your application in an engine found only on its platform. The other lets you leave with code but refuses to take yours in. The locks are different, and the question separating them fits in one sentence.
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Custom software or SaaS: the five-year calculation, including per-user subscriptions
SaaS costs little in month one and a lot in year five, especially when billed per user. Custom software does the reverse. Here are both columns side by side, with French agencies' public prices and the maintenance line quotes leave out.
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Lovable review 2026: what $25 buys, and what you take when you leave
Pro starts at $25 per month and top-ups cost $0.30 per credit. What remains is to establish what a credit buys, what is consumed while you do nothing, and what you retrieve when you decide to leave with your application.
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How long does it take to create an application in 2026?
Hours, days or months for the same idea: the difference lies less in writing code than in the exchanges around it. Timelines, route by route.
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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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No-code or AI: should you still learn Bubble in 2026?
No-code promised building without coding, at the cost of learning an editor. AI shifts the promise: nothing left to assemble yourself, requirements to direct, and standard code you own.
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Custom management software for tradespeople: generic SaaS or your own tool?
Obat and similar software cover construction quotes and invoices well. The question arises when your way of working outgrows their boxes. Here is the tipping point, and what each side costs.
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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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“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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Lovable, Bolt, v0 or Maestro: which AI tool should you choose for your application?
Compare project preparation, construction, publishing and code handover. A reading of documented capabilities to help you choose for your needs, including Maestro's limitations.
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How much does application development cost in 2026: agency, freelancer or AI?
An agency charges a median €30,000, a freelancer €8,000 to €20,000, and an AI agent team a few dozen euros per month. The full picture: what each option buys, and when each remains the right choice.
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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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Agency, freelancer or AI tool: how to choose when launching your product
You have a product idea and no technical team. Three paths are available, each with its own logic; the right one depends on how much control you want to keep.
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What our ×10 measures, and what it does not
We publish one figure: around 10 times less methodological context injected into agents. Here is the full measurement, including its limits.
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