01
Published
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.
Automatic publishing · America/Sao_Paulo
What has already come out.
What comes next.
The agenda is generated by the same contract that releases the index, feed and sitemap. Times indicate eligibility; deployment occurs on the first successful automatic cycle after the time.
02
Published
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.
03
Published
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.
04
Published
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.
05
Published
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.
06
Published
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.
07
Published
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.
08
Published
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.
09
Published
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.
10
Published
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.
11
Published
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.
12
Published
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.
13
Published
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.
14
Scheduled
From checklist to executable control: governance that actually changes the system
A practical contract to turn AI rules into properties, applicability, mechanisms, evidence, authority, and validity.
15
Scheduled
When AI fails, does the system learn or merely retry?
How to turn agent failures into regressions, controls, evals, and durable decisions instead of repeating retries across sessions.
16
Scheduled
From buying copilots to building an AI-first organization
How to turn individual gains from copilots into workflows, platforms, authority, evidence, and organizational learning.
17
Scheduled
The hidden cost of AI: rework, context, review, and operations
A total-cost calculation per accepted outcome that includes the model, context, tools, verification, rework, intervention, and incidents.
18
Scheduled
Long-running agents: context, handoffs, and progress across sessions
The continuity contract that preserves intent, state, decisions, evidence, and resumption when tasks span sessions.
19
Scheduled
Graph, loop, or hybrid? How to choose orchestration without handing control to the model
Criteria for choosing a chain, graph, loop, or hybrid with budgets, stop conditions, authority, and evidence defined by the system.
20
Scheduled
Your new portfolio should prove how you think, verify, and learn
A professional case-study model that shows the problem, decisions, use of AI, verification, outcome, limits, and learning.
21
Scheduled
Junior professionals in the AI era: how to grow when execution became cheaper
A learning path for using AI as a tutor and amplifier without outsourcing understanding, verification, judgment, and accountability.
22
Scheduled
Sandbox, capability and identity: task-based security in agentic systems
A security contract per task that separates workspace, sandbox, identity, capability, data, time, policy and evidence.
23
Scheduled
Taste, responsibility and clarity: the human skills that have become more valuable
When producing becomes cheaper, clarity, taste, critical thinking, responsibility and learning raise the standard of work.
24
Scheduled
Solo founder + AI squad: speed without outsourcing judgment
How a founder can orchestrate research, product, architecture and engineering with AI without outsourcing priority, risk or acceptance.
25
Scheduled
The first product is not just a product: it is the factory for the second
The first product must also leave standards, contracts, pipelines and learnings that reduce the cost and risk of the next one.
26
Scheduled
Context engineering: what deserves to enter, leave and survive the session
How to select, structure, retrieve, compress, externalize and expire context without confusing chat with durable memory.
27
Scheduled
Evals before release: how to transform expectations into executable criteria
Tasks, graders, datasets, trials, baseline and gates make an agent's expectations observable before releasing a change.
28
Scheduled
The right queue for AI: prioritize decisions and outcomes, not flashy automations
A method for prioritizing AI opportunities by outcome, frequency, friction, verifiability, risk and learning.
29
Scheduled
Seniority in the age of agents: fewer ready-made answers, more standard of proof
Seniority appears in framing, judgment, verification, ownership, multiplication and limits — not in the volume produced.
30
Scheduled
Design Partner is not a market: proximity to learn without inventing PMF
How to learn deeply from a Design Partner without turning intense collaboration into false proof of market or PMF.
31
Published
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.
32
Scheduled
Shadow builders: controls and evidence to operate AI-Native software
The RedAccess case converted into three model, discovery, negative tests and Evidence Gate for applications quickly created with AI.
33
Scheduled
AI writes, AI reviews: independence needs to be demonstrated
The review study IA-for-IA converted into authority segregation, protected checks, human approval and release provenance.
34
Scheduled
A security prompt is not a control: how to prove properties in AI-generated software
The paper on security prompts turned into a technical contract for specification, protected verifiers, tests, authority, and an Evidence Packet.