Field insights, practical tutorials and analyses, by a consultant who codes and ships.
Gartner predicts 40% of enterprise applications will integrate specialized AI agents by the end of 2026. Here is what this means concretely for your organization.
A growing share of searches ends without a click: the answer is generated directly, with two or three sources cited.
Demos are free, production is not.
In most companies we audit, more than half of employees use an AI assistant without management knowing.
You do not need a six-month project to save time with AI.
A video call, a familiar face, an urgent transfer request.
The second major deadline of the European AI regulation passed this summer.
On the AI projects we took over after a failure, the model was almost never at fault.
For two years the rule was simple: the biggest model wins.
The data job market has changed: entry-level roles are scarcer, AI absorbs part of the analysis work, and courses promising a job in three months…
Between political speeches and benchmarks, it is hard to know whether a European model can carry a business project.
Gartner predicts 40% of enterprise apps will integrate AI agents by end 2026.
Like GDPR before it, the AI Act will structure practices well beyond EU borders.
The most discussed phenomenon of 2026: describe what you want, AI generates the code.
Will AI replace data scientists? Our honest answer after two years in the field.
Autonomous agents run in production. Here is what we observed on the ground.
A financial client spent 8h/month on their board report. We automated it in 3 weeks.
67% of HR questions handled autonomously. Architecture, surprises and lessons.
Everyone publishes their successes. Here is our failure and what we rebuilt.
"We have lots of data but do not know what to do with it." Our complete 4-phase method.
Architecture, technical choices and measurable results of a production project.
Nodes, edges, shared state, persistent memory and human checkpoint. All you need.
The 2026 standard for connecting AI agents to data sources. 500+ open source servers.
The foundation of every performant chatbot, agent or AI assistant.
8GB VRAM is enough. From data preparation to deployment, step by step.
Architecture, chunking, retrieval and RAGAS evaluation. The complete production guide.
Hundreds of hours of testing on real use cases. Here is our field verdict.
Cursor, LangSmith, Supabase, Langfuse, dbt, Metabase, n8n, Unsloth.
MCP does not replace REST APIs. Our decision framework for the right approach.
8 months of real-condition testing. Here is who wins by use case.
From Cursor to LangSmith, the tools that truly changed how we work.
The answer is not 'ML is always better'. Our decision framework from real projects.
We deployed all three at clients. Honest comparison, no vendor bias.
Prophet, SARIMA, LightGBM. From exploration to production with confidence intervals.
73% of dashboards never opened after deployment. Here is why and how to fix it.