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Run a 12-week velocity-based training block where bar speed governs the weight on the bar for every lift, using a four-day split: day one acceleration, day two plyos and upper body, day three max velocity (flying 10 day, technically a rest day in the schedule), and day four the big absolute strength day with back squats.
Bake a super hook into a high-TAM post — e.g., on the 10,000 steps topic: 'in 2007, a group of Japanese researchers discovered a different walking protocol that can give you more results in less time'. A high-TAM post with a structured layout and no dead space (no shitty lines that could have been removed) has no reason not to go viral. Eugene spends a lot of time removing dead space and shitty lines from his scripts.
endorsed; virality claim is a strong heuristic, not a guarantee
Eugene Teo explains that for a traditional banger with high engagement, the first principle is a super high TAM (potential market reach). He recommends choosing topics your mum would recognise — like 10,000 steps a day or Diet Coke and aspartame — and approaching topic selection with an SEO mindset, identifying keywords that tick for the human brain, not Google.
A direct callout hook like 'you need to throw heavy shit' is a stronger hook for explosive training content than a generic 'here are five exercises that will make you really explosive and powerful' opener, which many creators fall flat with. Callouts make viewers who already do the behavior feel validated and viewers who don't feel curious.
endorsed; mechanism described rather than measured
Eugene Teo recommends leaning into being an active coach as a content strength, since influencers mostly have production studios rather than training studios with real hard-working live athletes. He suggests having a videographer follow you when training athletes, clipping the best raw footage, and posting it without heavy structure or hype edits — picking a session or two with an athlete each week.
Eugene Teo prefers to make actual sales conversions outside of short form content, on long form where viewers have more time to build trust. He suggests sending people to a YouTube video where they're more inclined to buy, or sending an email with a PDF guide where you can include an upsell — allowing many iterations of conversion funnels. Short form should be set up in your ecosystem as lead gathering, not conversion, though you could give a download off your story.
Eugene Teo recommends keeping content adaptation-focused or outcome-focused (e.g., explosiveness) rather than sport-niche focused, so it appeals across sports. Sport-specific content still reaches its target audience when framed around the outcome, and occasionally featuring a sport-specific example (swimmers, BJJ, wrestlers) is fine while keeping the framing adaptation-focused.
If the cells do something bad in the game, give them asynchronous stimulation; if they do something good, give them synchronous stimulation. Use the critic in the silicon decoder to predict surprise — figure out how far the action is from the silicon critic and how surprising it is — then scale feedback based on that surprise. If the critic expected an action to be good and it turned out really bad, the cells receive negative feedback. Scaling feedback by surprise modulates it in a more equal way so the system doesn't keep receiving negative feedback and can build connections over time. Positive and negative feedback are defined in relation to the game — if it does something bad in the game, asynchronous feedback is sent to reinforce not doing that action.
Maintain familiar patterns whenever possible when changing software for legacy users. Moving buttons, changing labels, or altering shortcuts forces users to stop and think, which slows them down. This could mean keeping core actions in the same place even if the surrounding UI changes, preserving keyboard shortcuts, or introducing new workflows alongside existing ones instead of replacing them immediately. If you have to break or substantially change a workflow, acknowledge it and help users adjust.
Try giving users control through gradual rollout, optional onboarding, or temporary access to familiar views to help them feel prepared instead of surprised. Roll out a new experience in phases, letting users opt in before it becomes the default, and allow them to switch back to the old version while they learn the new one. Feeling informed and in control reduces anxiety even when change is necessary.
The first step to making legacy users more comfortable is explaining changes clearly. Don't assume users will notice what changed or understand why it happened. Tell them what changed, why it changed, and what it means for their daily work. Even better, give them a roadmap for when things are changing so they know what's coming and can mentally prepare.
Hormozi advises Hasi to aggressively raise prices, since a 90% close rate means prices are too low.
The creator recommends tracking self-reported attribution, calling it probably the least used but possibly the most important method, because much of AI visibility's impact doesn't show up in analytics at all. Someone might see your brand name in a ChatGPT recommendation and then Google it afterwards, so analytics attributes the conversion to organic. In the creator's opinion, attribution is most meaningful when it comes from the perspective of the customer, so add a question like 'How'd you hear about us?' to your signup flow, checkout, or as a post-purchase survey — it's small but can be really valuable.
Jeff recommends visualizing your plate as a clock and focusing on two times: 9 o'clock and 9:20. Fill the largest portion with fibrous carbohydrates like leafy, crunchy green vegetables; the second largest portion with protein, whether vegan or meat-based; and the smallest portion with starchy carbohydrates. He explains starches get the smallest share because people tend to overeat them when given free rein, but stresses they should not be avoided entirely since they are an important part of a healthy balanced nutrition plan. Include fat incidentally by cooking with it rather than piling it on. Done this way, counting calories is unnecessary, and when nutrition is in check, six-pack abs become much easier to see.
The host advises not letting the shoulder position drift forward in dips: falling forward pitches the shoulder more and more as you descend, putting excess stress on the anterior shoulder capsule and structures inside the joint. Keep your shoulders back and chest out always, and make the distance between ears and shoulders greater by pulling the shoulders down (the same cue as the pull-up). Set up by pushing down and engaging the serratus rather than sinking into the shoulders. Control depth to about 90 degrees of elbow flexion instead of going all the way down, which he calls 'influencer fitness' and bad advice; pushing the chest forward allows a better stretch even at controlled depth. On the way up, actively push high rather than letting the shoulders drift back up, and perform a 'dip'
The host advises not skipping the pull-up but fixing it: before the first rep, create extra distance between shoulder and ear by pulling down into a more stable position; on the way down, maintain tension through the scapula and shoulder girdle and treat every rep a little slower; don't avoid full elbow extension at the bottom; and never let the shoulders ride up, which reintroduces traction stress. With these changes the pull-up can stay in the program and build a much bigger back.
The host advises tucking the elbows in closer and dropping them at a 45-degree angle to the sides, which feels much better and takes stress off the bicep tendon. Flaring the elbows out causes more internal rotation at the bottom, lets the bar drift forward, and aggravates the bicep tendon. Going extra wide in grip prevents pinching the shoulder blades back, which is needed for a proper bench press; going more narrow lets the shoulder blades pinch back more, allows the arms to drop back, and keeps them there when pressing. Leading with the shoulders loses scapular retraction and sets off a cascade of issues.
The host advises getting away from a wide grip on the bar when overhead pressing, because a wide position causes the elbows to drift out to the side and out of the scapular plane. The scapular plane is oriented forward, not out to the side, so the arms should sit slightly in front of the body. Narrowing the hand positioning brings the elbows more in front of the body, adds a little more tricep contribution, and helps you get overhead in a much more free and mobile position. With dumbbells, don't flare the elbows out to the side; bring them in to around 45 degrees and press from there. Since shoulder problems are sometimes structural and sometimes just a lack of stability rather than a torn element, press one dumbbell at a time to create a stacked joint in the shoulder for more stability:
When handing writing work to AI, load the project with a best practices guide, a stack of old scripts, and a style guide first, or the output will not come back good.
The speaker sends one message in Slack to his AI agent to start a new outreach campaign. The agent interviews him first to nail down exactly what kind of prospects he's after, vets every domain against his quality criteria, posts contact info, and drops hundreds of ready-to-send drafts straight into his Gmail. The only thing left for him to do is log in, open a draft, and hit send — a guardrail in case the AI sends something he wouldn't approve of.
stated
Use saved training trajectories and a controlled intervention to explain a failure, separating equilibrium theory from observed empirical stability.
Demonstrate all 3 repaired bugs and defend why a passed gradient check is necessary but not sufficient for generalization.
Use the tractable fixture and posterior predictive results to separate approximation, sampling and model errors; justify one revised modeling assumption.
Choose sources → to get a library of your own and a daily brief.
Map each implemented component to the original paper and explain 2 reduced-model failure modes without claiming full-scale reproduction.
Use calibration and shift measurements to justify a monitoring or abstention threshold, and give an explicit confounding counterexample to causal feature-attribution claims.
Defend a causal claim or refusal using the DAG, numerical estimates and a counterexample showing why predictive model fit cannot establish identification.
