ICF-certified coach, psychologist and independent researcher bridging psychology, structural intelligence, and AI systems. Developing tools and frameworks designed for deep analytical reasoning, cognitive alignment, and clean execution.
by Vladisav Jovanovic
Keep multi-shot AI video consistent from scene to scene. Lock character, product, wardrobe, lighting, camera, environment, and visual identity across generations, diagnose what drifted, and create the smallest repair prompt needed instead of restarting from scratch. Use it when individual shots look good but the sequence stops feeling like the same world.
by Vladisav Jovanovic
A grounded relationship analyzer that identifies behavioral patterns, emotional burdens, and whether repair leads to real change.
by Vladisav Jovanovic
Find the sentence your citations do not actually support. Audit research papers, reports, literature reviews, and AI-assisted drafts for unsupported claims, citation mismatch, weak evidence, stale sources, scope inflation, and conclusions that go beyond what the cited research can justify. Use it before submission, publication, peer review, or client delivery when the argument needs to survive evidence-level scrutiny.
by Vladisav Jovanovic
Turn a hiring opinion into an evidence-based decision. Audit candidate evaluations for vague impressions, inconsistent standards, hidden criteria, unsupported judgments, and evidence that is not actually relevant to the role. Use it after interviews or candidate review when you need to separate job-relevant evidence from confidence, charisma, familiarity, or gut feel.
by Vladisav Jovanovic
Pressure-test confusing situations, uncover hidden patterns, trace who carries the cost, and find what would actually change the situation.
by Vladisav Jovanovic
Find the cases your prompt was not designed to survive. Stress-test system prompts, agent instructions, workflows, and structured-output prompts for ambiguity, contradiction, scope drift, missing context, tool failures, schema pressure, and authority conflicts before users find the failures for you.
by Vladisav Jovanovic
Stop giving agents vague instructions and hoping they interpret them correctly. Turn a rough coding or AI-agent request into a bounded execution spec with clear scope, allowed actions, dependencies, acceptance criteria, verification steps, stop conditions, and escalation rules.
by Vladisav Jovanovic
Find the metric that looks healthy while the underlying reality is getting worse. Audit KPIs, OKRs, dashboards, and performance metrics for weak proxies, gameable targets, missing baselines, conflicting incentives, hidden burden, and cases where the number improves without the outcome improving. Use it when a metric is steering decisions and you need to know whether it still represents what actually matters.
by Vladisav Jovanovic
Find the requirements engineering would otherwise be forced to guess. Audit a PRD or feature specification for ambiguity, hidden assumptions, missing acceptance criteria, unresolved dependencies, edge cases, scope gaps, proxy metrics, and decisions that have not actually been made. Use it before implementation starts — while fixing the specification is still cheaper than fixing the product.
by Vladisav Jovanovic
Turn a report, strategy document, plan, or meeting transcript into a decision-ready gap map. Separate intended outcomes from current reality, identify what the evidence actually supports, expose missing constraints and unresolved risks, and clarify ownership and required action. Use it when a document contains plenty of information but still does not make the next decision obvious.
by Vladisav Jovanovic
Pressure-test marketing claims before customers, regulators, or competitors do it for you. Audit landing pages, ads, offers, and sales copy for overclaiming, weak proof, vague comparisons, credibility gaps, hidden conditions, and promises the available evidence does not actually support. Use it before publishing or scaling a campaign when trust depends on the claim surviving scrutiny.
by Vladisav Jovanovic
Assume the project failed — then work backward to find why. Identify fragile assumptions, dependency failures, ownership gaps, schedule traps, adoption risks, hidden workload, and warning signals that could reveal trouble while there is still time to act. Use it before kickoff or a major milestone when you want to discover failure paths before they become expensive.
by Vladisav Jovanovic
Pressure-test a business idea before you invest months building it. Examine the customer problem, demand, willingness to pay, alternatives, hidden costs, acquisition assumptions, operational risk, and the few beliefs that must be true for the idea to work. Use it when you have an idea but need to know what should be validated before you commit.
by Vladisav Jovanovic
Pressure-test a difficult decision before you commit. Expose hidden assumptions, tradeoffs, downside risk, reversibility, missing information, and what each option makes harder to undo. Use it when you are choosing between real alternatives and need a clearer decision — not another pros-and-cons list.
by Vladisav Jovanovic
Check AI-generated claims against the sources you provide. Identify which claims are supported, partially supported, unsupported, overstated, or contradicted, and show where stronger evidence is needed.
by Vladisav Jovanovic
Find the failure that could make your AI agent expensive, unsafe, or useless — before it executes. Audit hidden assumptions, missing scope, permissions, weak verification, likely failure paths, and the correction gate that should exist before the agent touches tools, data, workflows, or production systems.
by Vladisav Jovanovic
Audit an AI answer when you do not have a fixed source set. Find unsupported claims, hidden assumptions, false certainty, missing context, and what needs external verification before the answer should be trusted.
by Vladisav Jovanovic
Generate a polished README from what the repository actually contains, with verified commands and marked unknowns.
by Vladisav Jovanovic
Turn a branch diff into a factual PR brief: changes, risks, test needs, unknowns, and rollback signals.
by Vladisav Jovanovic
Turn a real bug into a minimal regression test that proves the fix and prevents recurrence.
by Vladisav Jovanovic
Turn failed agent runs into a diagnosis, minimal repro, correction check, and regression test.
by Vladisav Jovanovic
Turn a product, offer, or campaign goal into AI ad concepts designed to be tested, not just admired. Build creative hypotheses, hooks, visual proof, image and video generation prompts, controlled variants, and clear revision triggers so each creative can teach you something when it wins or fails. Use it when you need a repeatable creative-testing system instead of another batch of disconnected AI ad ideas.
by Vladisav Jovanovic
Fix the part that failed without destroying the parts that already work. Diagnose faces, hands, products, logos, text, backgrounds, lighting, composition, and style drift, then generate a minimal repair instruction focused only on the failed elements. Use it when an AI image is almost right and regenerating everything would risk losing the good parts.
by Vladisav Jovanovic
Turn “it seems to work” into a testable production decision. Convert an AI agent or automation workflow into a concrete acceptance-test matrix with observable pass/fail criteria across happy paths, tool failures, retries, permissions, partial success, human handoff, recovery, and regression cases. Use it before launch, client UAT, or production approval when failure needs to be visible before real users pay the cost.
by Vladisav Jovanovic
Stop evaluating your AI on easy examples it already knows how to pass. Build a hard evaluation dataset designed to expose real failure: hard negatives, ambiguity cases, counterfactual pairs, tool-state tests, leakage traps, edge cases, clean splits, and regression cases you can reuse after every change. Use it when you need evidence that a prompt, model, agent, or workflow actually improved — not just a few good-looking outputs.
by Vladisav Jovanovic
Turn an A/B test, pilot, or product experiment into a decision you can defend. Audit the hypothesis, success metric, guardrails, sample and measurement assumptions, confounds, segment effects, and evidence gaps — then define what would justify ship, iterate, rerun, or stop. Use it when a result looks positive or negative but you need to know whether the experiment actually earned the decision.
by Vladisav Jovanovic
Your agent can keep running long after its original plan has stopped making sense. Catch drift before repeated retries, stale assumptions, tool-state mismatches, and local fixes turn into a larger failure. Audit what the agent believes, what the tools actually returned, what has already failed, and whether its correction attempts changed anything. Finish with a clear operational decision: continue, verify, retry, revise, ask, escalate, or stop. Use it when an agent is already executing and you need to know whether it is recovering intelligently or just continuing more convincingly.