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Mention your work indirectly (e.g., 'I use WhatsApp a lot for my business') so she asks what you do, letting you present it in an attractive way. When she asks, deliver a demonstration of higher value in your answer.
After getting the number, text later, ask how dinner's going, talk about what you have going on, and then meet up — even if it's just grabbing a drink or meeting at a venue.
Ask 'What are you doing later?' near the end of the interaction to set up the meetup plan. If she's unsure of her plans, ask whether she'll be in the area so you can gauge feasibility of meeting up.
When passing an interaction to Figma Make, use the intro animation and keep the transitions as-is (honor the transitions).
When asking an LLM to reproduce an animation, request a full breakdown including duration, repeat behavior, value ranges, and easing curves.
Vlad recommends connecting agents across frameworks via a platform that handles runtime-binding, persistency, channels, filtering, security, and observability for you; use a registry so agents can discover each other automatically, do message filtering within the platform rather than the client side, and keep humans in the loop by letting your agent invite you into every conversational space.
Rogan says the first step to engineering society positively is turning your back on the state: anyone could drive a truck to a dumpster and clean up trash instead of waiting for the federal government, give water to people out there (even ones that seem terrifying or possessed), and help without filming it or doing it for any reason.
Philipp Schmid advises that environments alone are not enough: the model needs the right context, information, or tools to work inside those environments. You can provide sources to the environment: GCS buckets, GitHub repositories, or inline files.
Philipp Schmid recommends reusing environments: in a multi-turn conversation you can use the same environment from your previous turn for a new interaction (e.g., research the latest tech news and create a file, then follow up in that same environment); you can share environments between different agents — three agents working inside the same environment can share context through the file system — and you can use sub-agents to pick up work another agent has already done.
Joe Liemandt argues the single best use of AI for education is giving kids individualized lesson plans based on their level to catch them up to grade level — not just giving kids chatbots.
Michael Lee recommends restructuring the organization to leverage machines that work 24-7 and scale with electricity, rather than just giving small AI tools to each human.
Dru Knox describes turning any skill into an automated workflow via a command called teslaunch, which takes a skill that codifies a workflow, sandboxes the agent with appropriate permissions, makes it long-running, and gives it GitHub tokens so it can put up a PR and respond to comments.
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Dru Knox recommends giving the agent CLI and API access and a way to click through your product in order to build it.
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Dru Knox recommends tracking manual takeovers and human PR comments as metrics to measure progress toward a software factory. Over time, you want to see more PRs initiated without human input as a sign that you're automating more. During the transition, keep quality constant or accept only a very small dip, expecting better quality in the code produced as the ultimate payoff.
When the agent needs to follow a strict rule, state it with no-exceptions language in the guidelines, for example 'we always use our AppHutter component for navigation, no exceptions'.
Prompt the agent to apply your library to a screen and observe whether it reaches for your components, uses your tokens, and follows your conventions without them being included in the prompt.
Eugene Teo advises the host's team to cut posting frequency, arguing that a small number of posts vastly outperform the rest, so the time spent on low performers should go into making more of the exceptional ones.
advice attributed to Eugene Teo; based on the team's own performance data
Eugene Teo recommends hiring a videographer to follow you while training athletes and clipping the best raw moments for social media, posting raw unstructured fly-on-the-wall training footage rather than super hype Mr Beast-style edits, and publishing one or two training sessions with an athlete each week as long-form content.
Eugene Teo advises the host to experiment with increasing high-TAM content volume in the short term, since the host hasn't done much of it yet.
Eugene Teo recommends inviting social media creators with their own brands and audiences to your facility to train and film the session, filming a live assessment session putting a guest through your periodic testing battery (e.g., the 13 tests done every 13 weeks) and showing the results, and being prepared to invest financially in creator collaborations — flying them out and taking good care of them so they have an awesome experience.
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