Article 31TechniqueEvidence
Fable, Sol, and Terra: what really changed for builders
A dated radar of new AI portfolios and a method for choosing by task, risk, cost per outcome, evals, and verifiability.
Human authorship · Human knowledge + AI
Fernando Parreiras
Founder of Trustyu · Engineer · Solutions Architect
Fernando Parreiras is the founder of Trustyu, engineer and solutions architect. Connects technology, strategy and human development to build businesses, products and decisions with vision and purpose.
Trajectory
Technology with vision and purpose.
With more than 20 years of experience, Fernando Parreiras works at the intersection between technology, management and human development. As founder of Trustyu and creator of FORGE, he leads the design of architectures, digital products and AI systems that transform complex problems into safe, verifiable and sustainable solutions. His practice combines software engineering, solution architecture, product strategy, governance and team building. As an entrepreneur, advisor, author and strategic mentor, he shares knowledge so that business leaders and technical professionals can make better decisions without separating excellence, ethics, purpose and long-term impact.
- Solution architecture
- Software engineering
- AI Systems
- Product strategy
- Business and leadership
Published in Trustyu Forge
Ideas that become systems.
Article 13Career & EntrepreneurshipBusiness and P1–P6
AI lowers the cost of experimenting; it does not validate your hypothesis
How to use AI to prepare better experiments without promoting a synthetic persona, click, prototype, or opinion into market evidence.
Article 12Career & EntrepreneurshipBusiness and P1–P6
From idea to first revenue: P1–P6 for AI-first founders
A six-phase path to turn building speed into evidence of a problem, product, operation, and a real exchange of value.
Article 11TechniqueQuality, Evals and Contracts
The agent cannot be its own auditor
How to separate executor, checker, reviewer and approver to use AI in evaluation without creating circular authority or fictitious independence.
Article 10TechniqueEvidence
Evidence before adjective: how to defend claims about AI systems
A practical grammar to link claims to subject, property, mechanism, evidence, verifier, validity and limit of inference.
Article 09BusinessSecurity and Governance
Autonomy proportional to risk: where to delegate to AI and where to keep the human gate
A method for grading agent authority by effect, reversibility, sensitivity and uncertainty — without confusing capacity with permission.
Article 08TechniqueQuality, Evals and Contracts
Spec-driven without self-deception: coverage, contract, eval and OpenAPI are not the same proof
How to separate specification, contract, test, coverage and eval so that each green supports only the decision it actually verified.
Article 07BusinessBusiness and P1–P6
From Pilot to Value: Measure Accepted Change, Not Produced Tokens
A practical contract to measure AI pilots by accepted outcomes, quality, flow, total cost, and risk — not by tokens or artifacts.
Article 06Techniqueengineering harness
Engineering harness in practice: six plans to transform intention into evidence
A technical guide for organizing intent, policy, execution, verification, evidence, and feedback around AI agents.
Article 05BusinessBusiness and P1–P6
AI amplifies the business you already have
AI does not transform an organization alone: it enhances clarity, flow and learning — or accelerates ambiguities, queues and rework.
Article 04BusinessWhy FORGE
Why Trustyu created FORGE: from a founder's memory to a verifiable system
JARVIS's journey to FORGE and the six changes that transformed AI memory, decisions, and execution into a verifiable system.
Article 03BusinessBusiness and P1–P6
Accountable human, executing AI: the contract that organizes the squad
AI can perform more work without receiving accountability: clear roles, boundaries, verifiers, and evidence preserve accountability.
Article 02Techniqueengineering harness
The model is not the system: why powerful AI still needs a harness
Models change quickly; reliable results depend on context, tools, policies, verification, evidence and learning.
Article 01Career & EntrepreneurshipStart here
After the pyramid: what changes at work when AI performs more
AI amplifies execution, but purpose, learning, judgment and accountability depend on how we redesign work.