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Target brokerages to get 20 agents at a time because one realtor can send business every month plus refer others in their brokerage. Brokerage outreach is the highest leverage activity — the big 20X lever — and should not be treated as a small piece.
When a prospect objects to price, itemize the service differences the cheaper competitor lacks (insurance, mover level, included materials, caps) so the prospect sees more variables than they considered, then close by asking whether on-time delivery, nothing breaking, and a great experience are worth the extra $50.
Create a white glove offer for higher-end customers — that's what's missing from the business. Create a VIP moving level and a standard moving level, with realtors' referrals getting VIP service for the price of standard. Create internal certification levels for movers (e.g., level three mover) so different skill levels exist and you can staff all level three movers. Note: defining VIP by years of experience is harder to scale from a model perspective.
Go to the head of brokerages with 5-20 agents and offer a brokerage-level deal: free VIP upgrades for all their agents in exchange for being the first call for moves. Ask brokerages to integrate you into their vendor packet so you're the only mover on the list.
A coherent multi-step outreach method for reaching realtors: email got zero conversion, SMS gave traction; follow realtor leads on Instagram and DM them; use AI to find each lead's Instagram from the lead list; DM 10-20 leads per day; if a lead follows back, DM freely; use an auto-DM mentioning the realtor special; and consider two approaches (follow everyone and DM follow-backs, or automatically DM 10-20 cold leads).
The source describes corrected repetition as the fourth key: take action, make an adjustment, review with a coach, role play until your delivery is really good, then repeat. This includes recording yourself talking directly into the camera to see what you look and sound like, reviewing recorded interactions with a coach between field sessions, and sitting down with an expert who analyzes your tonality, delivery, and body language for personalized feedback.
Alex recommends taking recurring Q&A questions and turning them into content using an AI editor to cut them down, noting that 30-50 actual questions answered per week can become 30-50 posts a week just by capturing and editing.
Alex recommends Tina replace her three-to-four-hour unstructured sales calls with a structured webinar following an agreed framework, arguing that unstructured conversations waste time and lower the value per second for the customer.
Hormozi prescribes that if you want to be trustworthy, you have to do zero punishment — never misuse what someone shares with you. He also prescribes keeping your word, because breaking it when it benefits you short-term likely ends the relationship. When you have the opportunity to break your word for short-term benefit, recognize that doing so likely ends the relationship.
Hormozi recommends asking two questions before trusting someone: (1) do they have a track record of protecting what they've been given, and (2) does burning you cost them more than protecting you — i.e., is betraying you a bad deal for them? If someone gains more from protecting you AND has a track record of not burning you, it makes sense to go forward with the trust.
When comparing sandbox strategies, Brennan evaluates: (1) whether the setup is local or remote — whether it works with your copy of the codebase or must clone its own; (2) what scale you can get — locally you can run only a handful of agents before things get uncomfortable, while Kubernetes supports thousands; (3) the level of isolation — dangerously-skip-permissions on a laptop gives nothing, co-located agents in a VM can step on each other's toes, and Kubernetes gives each agent its own workspace; (4) the level of persistence — laptops and VMs give persistence by default, while ephemeral setups require saving files out or writing to external systems; and (5) what kinds of triggers can cause agents to run — local setups are typically manual prompt-typing, while cloud setups support schedu
Hormozi advises going where the reps are — like going where the fish are if you're fishing, since what you're fishing for is skills and network. Concretely: politics, go to D.C.; finance, go to New York; influencer/media, go to L.A. He stresses moving before you have strings tying you down, and calls geographical flexibility wildly underrated and a competitive advantage. Only once you're established — 'once you become Joe Rogan' — can you move to Austin and do whatever you want.
Implement a 'bring a friend' referral process: every time a customer comes in for a treatment, ask who they're bringing with them. Hormozi says this referral ask at every meeting will do the majority of the work on the demand-gen side and is the CS onboarding playbook, and that the marginal cost of a referral is nothing so even converting a third is worth it.
Run a weekly event with a big giveaway (a year of scans), give every attendee a scan while a VSL plays, then book in-person close meetings right after the event, with a Zoom fallback. The scan takes about 20 minutes, which is used to sell while the prospect watches the scan results, and a 5-7 minute wait is baked in before the close.
Hormozi lays out the event sales flow: have people watch a VSL and set an appointment for later that day for a close; run scan plus VSL at the same time so people understand what's going on while being sold the whole time; right after the scan plus VSL, have the close meetings. The scan itself takes 20 minutes, which can serve as the baked-in wait. V1 test is scan plus VSL straight to close; V2 is scan plus VSL, then an appointment as the next meeting, then VSL before the appointment and close.
The host advises costing out true delivery costs at different service levels and multiplying by 5-10x to set the price, with 5x as the minimum because costs may run over.
The host recommends adding a price lock guarantee: the quoted price holds as long as the client doesn't change anything. At higher price points, condition it on headcount not changing by more than 20% and no venue change. Verbally confirm scope boundaries (no venue change, no headcount change over 20%, no added services like AV, no date change) so the scope is confined; if the client breaks the constraints, have a different conversation and true up the price at the end. Frame it as a choice between being nickel-and-dimed on every change or one price that holds.
For B2B events where the audience is the customer, the host recommends a separate approach: do whatever it takes to get those events because the client fronts the cost of gathering 500-1,000 of your ICP; get a booth; secure a speaking slot (even 15 minutes); in the slot, present about 20 mistakes event planning buyers make, each with proof of one person saying you're awesome at that thing; offer the slides via a QR code on screen; the opt-in asks whether they run events and how many per year, which becomes a lead list; the CTA is simply 'do you want the slides?' rather than a pitch. Even closing 1% of the room yields ~15 clients, a 50% business increase.
conversion figures are the host's estimates
Create a custom tool called Dispatch Analysis to tell the head of research agent to spin off multiple sessions that will be coordinated for a custom harness. Build a custom orchestration harness in the server that delegates work for each ticker into a different session, then aggregates the results and gives a bunch of scores when all done. The main agent decides at some point: 'hey, I want to spin up 10 different sessions to do work' — using the custom tool to build the custom orchestration harness.
Leverage sub-agents to do the bulk of the work: spin up a financial expert analysis agent (extractor) and a risk evaluator analysis. Kick off a sub-agent to verify that the defined outcome is being met: it looks at the rubric, analyzes all the work done in the session and the artifacts created, and decides whether the scorecard was delivered, the numbers are well defined, and the memory update was correct. When sub-agents finish their work, they let the main agent know what was asked of them; the main agent then wakes up, gets that information, files everything it needs to do, and creates the deliverable.
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.
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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.
