Guides on AI development, vibe coding and product launches
How to describe an idea, write specs for Cursor and Codex, create user stories, ship an MVP and keep documentation current - simple guides for developers, vibe coders, founders and product managers.
AI SDLC: how AI brought model-driven development back
Model-Driven Architecture is more than 20 years old. LLMs finally make it practical: models, artifacts, code, tests and documentation can stay connected.
Read articleCode from architecture: how LLMs write software without losing control
Code generation is not asking an AI to write a function. It works when architecture, rules, context and acceptance criteria become the input.
Read100% traceable test coverage: myth or reality
In AI SDLC, coverage is measured by traceability, not by lines of code. Every test is linked to requirements, use cases and implementation.
ReadDocumentation that does not go stale
Documentation becomes useless when it is written after the code. Model-driven AI keeps docs current by generating them from live artifacts.
ReadHow AI SDLC increases productivity by 5-10x
The growth comes from a clear shift: people stop creating every artifact manually and start reviewing, improving and approving generated artifacts.
ReadStory: how a startup shipped an MVP in 3 weeks
A three-person team moved from idea to working product in 21 days by generating requirements, architecture, code prompts, tests and docs as one flow.
ReadFrom 200 pages of requirements to architecture in one day
Large enterprise requirements usually create weeks of design work. A controlled AI transformation can compress that work into one focused day.
ReadWhy one senior engineer can cover the work of five
Not because other people are useless, but because LLMs remove routine. The senior focuses on decisions, validation and system integrity.
ReadLegacy reengineering: regain control of a monolith
When only two people remember how a system works, the business is at risk. AI SDLC can recover models and documentation from code.
ReadHow to reduce production defects by several times
Most expensive bugs start as gaps and contradictions in early artifacts. AI SDLC catches them before release.
ReadDeveloper onboarding in 2 days instead of a month
A new developer does not need to read code at night when the system has a current model, docs and traceability.
ReadRequirements without contradictions: how AI catches errors before code
The most expensive defects start in requirements. Automated validation makes them complete, clear and traceable.
ReadAPI documentation that writes itself
Swagger that always matches code is not a dream. It is the result of generating API docs from the same model as implementation.
ReadFrom MVP to production without technical debt
Most prototypes are rewritten later. AI SDLC helps build an MVP fast while keeping architecture, tests and docs from day one.
ReadHow much AI SDLC really saves: ROI explained
A practical ROI breakdown: productivity, lower operating costs, faster delivery and fewer late defects.
ReadHow to describe an app idea if you are not a programmer
A simple step-by-step guide for turning an app idea into clear requirements that AI tools or developers can use.
ReadVibe coding: how to build apps with AI without chaos
Vibe coding is fast, but without structure it becomes messy. Use requirements, versions and traceability to stay in control.
ReadHow to write a spec for Cursor, Codex and Claude Code
AI coding quality depends on input quality. Here is the structure of a technical spec that AI coding tools understand.
ReadUser story: what it is, how to write it, and 10 examples
A simple guide to user stories, INVEST criteria and acceptance criteria, with ready examples for product teams.
ReadHow to build an MVP alone over a weekend
A realistic solo-founder plan: cut scope, automate routine with AI and keep the artifacts you need for the next step.
ReadPRD in one hour: product requirements template with example
What a PRD is, which sections it needs, and how founders and product managers can write one quickly.
ReadSide project documentation: how to keep it alive
The minimum useful documentation for a personal project and how to keep it updated automatically.
ReadUse case diagram in simple words: guide and examples
What a use case diagram is, what it contains and how to build one for a product or online store.
ReadRAG in simple words: how AI searches by meaning
What Retrieval-Augmented Generation means, how semantic search works and why RAG answers with source links.
ReadPrompts for requirements generation: 12 practical templates
Ready prompts that turn a raw product idea into requirements, user stories, use cases and acceptance criteria.
ReadHow to recover documentation from existing code
What to do when code exists but documentation and diagrams do not. Reverse engineering can recover artifacts and keep them current.
ReadTest cases for beginners: how to cover an app with tests
What a test case is, how it differs from a checklist and how to write useful checks for a feature.
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