Build intelligence.
Adapt to change.

Start by noticing what interrupts you and making one short block of work easier to protect. Attention Control offers focused practice selecting relevant information among competing signals.

Try this now: protect one 20-minute work block and count what actually pulls you away. Notice whether the main problem is external interruption, internal switching or uncertainty about what matters.

With AI, the same question matters: is its summary helping your goal, or drawing attention to the wrong things? Find the signal, coming soon, will practise checking that.

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Bring a real task to coaching. Decide what kind of thinking it needs — understanding, critical evaluation, decision-making or strategic action — and where AI should assist rather than replace the judgement you need to retain.

Try this now: before asking AI, write down what a good answer needs to contain. Then compare its response with your criteria and note what it added, missed or distorted.

Guidance is available through coaching.

Try keeping the current goal and next step visible instead of holding everything in your head. Coaching can help you simplify instructions, meetings or complex tasks.

With AI, an editable map can make relationships visible. You still need to check the links and understand the essentials without it. Relational Memory and Binding Memory will explore these skills.

Memory training nodes are coming later.

Consider how you revisit and retrieve what you learn. A coaching plan can combine recall practice, useful cues and a study routine that fits your week.

When learning with AI, try recalling before asking for a hint. Check later what you can explain without the tool; Knowledge Access will build on this approach.

Knowledge Access and memory nodes are coming later.

Choose one outcome, make the next action concrete and decide when to review it. Coaching can help you design a workable routine around competing demands.

With AI, compare possible paths and dependencies with your own prediction. Path Horizon will practise recognising when a plan should change.

Path Horizon is a future training node.

List the few options that are genuinely viable, decide what information could still change the choice, then set a review point rather than reopening the decision whenever a new possibility appears.

Try this now: ask what you would lose by spending another week exploring instead of acting. Execution, learning-by-doing and feedback also have value.

Separate model progress from action progress and state progress. A better plan can be valuable, but the next useful step should eventually change something outside the planning process or return information from the world.

Try this now: name one external action, what you predict will happen, and when you will review the result.

First identify where time goes: finding information, deciding what matters or choosing a response. We can explore a simpler workflow and an appropriate balance between speed and accuracy.

AI may shorten part of a task, but checking and correcting its answer also takes time. Compare the whole workflow at a similar standard of accuracy.

Faster game responses do not establish faster thinking generally.

Look at the conditions as well as the task: interruptions, workload, fatigue and emotional distraction. Coaching can help you try a practical routine and review how it works in that situation.

With AI, keep a way to check, pause or continue if the tool is wrong or unavailable. Emotional training tasks are a proposed practice context; everyday resilience needs its own evidence.

Resilience is an everyday outcome to evaluate, not a promised training effect.

Make the decision explicit, separate what you know from assumptions and compare a few meaningful alternatives. Coaching can help you build a decision routine and learn from the outcome.

Try this now: write down your current best option, one serious alternative and the evidence that would make you switch. Then revisit the decision after you obtain one new piece of information.

With AI, generate your own options first, then use alternatives and counterarguments to test them. You keep the final judgement.

Generative Search and Reasoning nodes are coming later.

Developing general intelligence is the central aim of Synergy IQ. Practise attention, memory and reasoning as a connected network, then use separate assessments to investigate whether progress extends beyond the games.

Also explore what becomes possible with AI. Assisted performance and retained understanding are valuable goals, measured separately from changes in general intelligence.

Synergy IQ has not been established to increase IQ or general intelligence.