Our Guiding Principle
We don’t publish any review without having used the tool for at least two weeks under real-world conditions. Unlike many sites that merely skim through features or reproduce vendor marketing messages, we demand total immersion. Every tool we evaluate has been integrated into our daily workflows, tested with actual projects, and compared against existing alternatives. Our goal: tell you what it’s really like, not what sales pages want you to believe.
Step 1 — Selection
Our process begins with rigorous weekly monitoring. We closely follow Product Hunt, Hacker News, Reddit, Twitter/X, and about ten recognized expert blogs. This monitoring allows us to identify roughly 15 to 20 new tools each week. We then apply two strict filters: relevance to our audience (creators, freelancers, small teams) and market traction. We examine active user counts, funding received, and the publisher’s assumed longevity. Our monthly volume is deliberately limited to 3 to 5 new tests, plus 1 to 2 updates of existing reviews. Better few but good.
Step 2 — Proof of Concept (1 week)
Once selected, the tool undergoes a full week of proof-of-concept testing. We create a real account (free or paid as applicable), then execute 3 to 5 use cases representative of what our audience typically does. If the tool fails at this stage—frequent crashes, unusable interface, poor-quality results—we publicly document the reasons for this failure. This may seem harsh, but it’s also useful for you to avoid tools that don’t keep their promises.
Step 3 — Real Use (minimum 2 weeks)
Tools that survive the proof of concept enter the most demanding phase: real use. We integrate the tool into an actual project, whether writing, design, programming, or task management. For at least two weeks, we use the tool daily, noting each day the quality of results, execution speed, frustrations encountered, and positive surprises. In parallel, we constantly compare the tool to the 2 or 3 direct alternatives available on the market. It’s this continuous comparison that allows us to gauge true added value.
Evaluation Criteria
- Output Quality: for AI tools, we evaluate the accuracy, relevance, and originality of produced results
- Speed and Latency: reasonable response time, absence of frequent slowdowns
- Pricing: value-for-money ratio, transparency of plans, availability of a usable free plan
- Learning Curve: ease of adoption, clear documentation
- Support and Documentation: availability, quality of resources, response speed
- Integrations: compatibility with common tools (Slack, Google Workspace, Zapier, etc.)
- Security and Privacy: handling of input data, retention policy
- Roadmap and Updates: frequency of improvements, community listening
Final Scoring
Each criterion receives a score from 1 to 10. The overall score is then calculated according to a weighting system specific to each tool category (published in each review). But a raw number means nothing without context. Each review therefore ends with a clear-language verdict: “Who It’s For,” “Who It’s Not For,” and “Our Verdict.” This allows you to immediately understand whether the tool is relevant to your situation.
Our Test Stack
Our tests are performed on recent hardware: Mac and PC laptops equipped with recent processors, stable fiber connection, and up-to-date browsers (Chrome, Firefox, Safari). Covered use cases include article writing, image generation, automation of repetitive tasks, and simple data analysis. We systematically use fictional but realistic projects reflecting our audience’s reality. Never are actual client data involved in our tests.
What We DON’T Test
We don’t spend time on undocumented private betas, niche integrations affecting less than 1% of our audience, or tools already obsolete or abandoned by their publisher. Our priority is your time: we only test what has a real chance of being useful to you in the coming months.