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Farza's standard advice for people asking how to start making videos about their company: take out your iPhone, click record, selfie mode, talk about what you're doing for 30 seconds, and post it to Instagram, TikTok, Reddit, YouTube, or wherever. Only about 1-2% of people actually take him up on it.
To differentiate in a crowded market, go nichier and nichier. If you like jigsaw puzzles and are also a micro-brew connoisseur, your point of differentiation is combining both — doing the jigsaw content while also talking about a Rogue IPA. Often the beer part takes off and that's where the money gets made, not the jigsaw — or both, or you become the only beer-jigsaw person.
hypothetical scenario for the outcome
Carmen Li recommends verifying provider claims before purchasing by checking UIDs and inventories and running performance benchmarks such as NVIDIA DGX, InferenceX, and MLperf. For forward capacity, she says providers' purchase orders and permits should be reviewed before committing to future delivery.
To audit your AEO readiness, give the agent your websites and a few AEO posts from X/LinkedIn, ask it whether the tips are useful, and have it prompt different LLMs cold (logged out) to produce an AEO research score. In the SaaStr case, cold-prompted agents surfaced the company as an answer 46 out of 50 times, but answers vary across agents — so expect discrepancies and test repeatedly.
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Route all replies from every outbound platform into a single unified response queue via each platform's API or MCP; start a timer when a prospect replies, auto-generate a draft follow-up based on the original outbound, start a second timer from your reply to their next reply to trigger reminders, and send a daily morning reminder listing prospects who haven't responded.
Do relentless competitor and lookalike tracking with outbound agents: feed the agent your full customer list, have it identify the top 3-4 competitors per historical customer with domain names, then feed those company names and domains to your outbound agents for targeting. If you have no outbound motion today, start with just two parts: load your customer list and load your direct competitors (or ask the agent who it thinks your competitors are).
Build a centralized 'prospector' queue hooked into inbound, outbound, renewals, and support tools so one agent can monitor all go-to-market activity in a single surface, preventing leads from falling through the cracks.
Their inbound agent daisy-chains steps: research the lead and build background history, generate a personalized prospectus/pricing page, directly answer the prospect's original question with package suggestions, and route to a custom meetings booker tied to the prospectus data.
Be proactive about privacy and trust — explain what data you access, how you use it, retention policies, and security. Products that proactively address privacy and trust concerns get more users to download, try, and spread the word. Communicate that data never touches the internet to build user trust for local-first products. Include a 'Trust and Security' link in your main navigation to build user confidence. Include an FAQ answering what data you collect and where the data lives. Trust and security emphasis will become a more common pattern across AI sites. AI apps and LLMs need so much data and context to be valuable that a new bar for trust, privacy, and security needs to be established.
prediction framed as 'would not be surprised'
Improve AI design results by leveraging the features that tools offer, such as skills and design-system context. Spend 10 minutes bringing your existing context (design system, components, skills) into the AI tool first, because it will save you hours in the long run. Add your design system and bring in your components as a skill in Magic Patterns. Give the AI guardrails via components so it can output complex experiences like a theme park in one or two shots. Updating a design system is as simple as prompting something like 'no trees, just lollipops and more chocolate rivers'.
Make the primary CTA the number one thing your landing page drives users toward. Make your CTA button large and readable — don't make users squint to find it. On the reviewed site the CTA was unclear, and a top-right button that just says 'Ronin' when the company is called Ego leaves visitors confused about whether it is a product or a model. If you have something important to communicate, it shouldn't matter the angle the user is looking at it — it should be painfully obvious. Make important text like 'free' large and obvious on pricing pages — there's a reason free is giant.
The host recommends proactively communicating data privacy and trust details (what data is accessed, how it's used, retention policies, security) to get users to download and try your AI product. He suggests asking yourself: what is the data we're going to have access to? How are we going to use it? What are our retention policies? Is it secure or not? The products that lean into this and go above and beyond to make users feel super comfortable win. He also says to communicate that the internet doesn't touch the data in any single way — that's what people really cared about.
The host recommends making the CTA obvious and clear on your landing page, labeling the CTA button with a clear description of what it does rather than just the product name (a button that just says 'Ronin' leaves him not knowing what Ronin is), and designing the page so the primary CTA (e.g., 'Download for Mac OS') is the number one thing the user is driven to click. He also suggests checking the page at 100% zoom to verify the CTA is clearly visible and readable, and scrolling through the entire page to verify the main CTA remains prominent and accessible — a main CTA so small it's barely visible at 100% zoom is a design failure.
Huberman describes evidence that ingesting one to three grams of EPA per day, in the form of fish oil capsules or liquid, is beneficial for brain and body health and can enhance focus and cognitive ability. He notes a category of supplements can greatly enhance the probability of offsetting depression, improve mood, and improve metabolic and cardiovascular function and focused work ability.
Huberman recommends testing a single supplement ingredient for about one week, provided it does not induce negative effects, while not varying anything else in your overall protocols so you can isolate its effect. He frames this as the way a scientist would design an experiment to identify which ingredients are most effective for your goal, and notes that lumping supplements together and taking them without testing individually is a less rational approach. Whether to immediately take magnesium threonate and apigenin together depends on individual circumstances; you can test one for a week, then the other, or combine them, and if neither works alone, he recommends trying them in combination. Many people take magnesium threonate, apigenin, and theanine together and derive great benefit, and
Huberman identifies a high-quality omega-3 fatty acid, typically taken as fish oil, as the single greatest single-ingredient supplement for mental health, physical health, and performance. If somebody has a limited budget and cannot afford a foundational supplement but can afford fish oil, he recommends prioritizing high-quality omega-3 fish oil above one gram EPA per day.
Tanmay Sah recommends extending traditional ML testing (assumptions, input data, behavior; held-out data with metrics like accuracy and AUROC) to the whole deployment system: test what happens when a tool fails, whether retrieval brings back the right information, whether human review catches mistakes, whether the agent stays within its permission boundary, and whether the system can recover. The logic of testing stays the same for agentic systems; only the boundary of what is tested gets bigger.
guest's recommendation
Evan Conrad argues that to make a compute market work, you have to operate the cluster like a hotel — run the whole thing — otherwise you can't do physical settlement. This means building out the entire operating capacity of a GPU cloud, and operating both the market and the clusters at the same time.
asserted
Evan Conrad argues that to solve compute market problems, you need to underwrite the value of the GPU cluster rather than underwriting the credit risk of the customer — otherwise the compute market will not work.
asserted
Start discovery calls by hunting for business problems, not product or situational issues — use broad questions to surface the deeper root cause behind the stated need. When a prospect is fixated on a surface-level need (e.g., 'we need sequence writing'), ask how they got there: 'Why are you struggling with X? Who wrote it? What are your open reads?' Prospects who state a specific need will almost always answer 'what prompted that need?' with a story — the fastest track to the bottom line. Sellers often avoid asking about the actual reason the prospect showed up out of fear, but asking directly is faster and better.
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.
