Recommendation · SocialThe coach critiques the student's 'Have you ever been to Seattle? You could come visit me' after four minutes with no prior flirting as a bit much of a jump. He models a smoother, more playful version: 'You've never been there? Okay, well, you got to come visit me in Seattle and then maybe you could be my host in Russia one day.' Tying a flirty invitation back to something relevant the woman said earlier (like her being from Russia) makes it more fun and smoother.
Coach Kyle · How to Talk to Women in Public: Real Infield Breakdown
Recommendation · SocialAsk what she's up to tonight to learn her availability, ask what she's doing for fun this weekend to set up the idea of hanging out later, and ask whether she's here with friends or by herself to understand her situation.
Coach Kyle · From Hi to Coffee Date (LIVE DEMO)
Recommendation · SocialIf she says she has to meet friends soon but offers to walk with you, accept the partial availability, pivot, and continue the interaction by walking with her.
Coach Kyle · From Hi to Coffee Date (LIVE DEMO)
RecommendationCharlie Guo recommends running 'git clone openai/codex' and then asking Codex about Codex to start building your own applications or integrations immediately, noting the Codex repo is a great reference source for AI engineers.
AI Engineer · Cooking with Codex — Charlie Guo & Gabriel Chua, OpenAI
RecommendationCharlie Guo recommends telling the agent in agents.md to use the linter for simple validation, but using hooks for more sophisticated or complicated output validation.
AI Engineer · Cooking with Codex — Charlie Guo & Gabriel Chua, OpenAI
RecommendationGabriel Chua recommends running an automation every Friday that reviews all your conversation threads to find skills you could improve, things you could add to the agents Markdown file, and skills you haven't been using that could be removed.
AI Engineer · Cooking with Codex — Charlie Guo & Gabriel Chua, OpenAI
RecommendationCharlie Guo recommends setting up automations in the Codex app under the Scheduled page so they spin out in their own thread, useful for one-time looks you archive afterward.
AI Engineer · Cooking with Codex — Charlie Guo & Gabriel Chua, OpenAI
RecommendationCharlie Guo recommends setting up an automation to triage Slack, email, and Linear notifications rather than manually checking each source, since manual tracking drowns you in notifications.
Also discussed by Adam Guild on The Official SaaStr Podcast: SaaS | Founders | Investors, Anish Acharya on The a16z Show, The a16z Show (unclassified source — not counted until classified). Not counted as independent sources.
AI Engineer · Cooking with Codex — Charlie Guo & Gabriel Chua, OpenAI
RecommendationGabriel Chua recommends cloning the openai/codex repo and asking Codex directly about the Codex AppServer to start building applications or integrations immediately.
AI Engineer · Cooking with Codex — Charlie Guo & Gabriel Chua, OpenAI
RecommendationCharlie Guo warns that not checking whether Codex was blocked by missing context or permissions before scaling up compounds broken workflows. He recommends diagnosing blockers first — what context or permissions Codex needed — and then encoding the fix into skills or plugins.
AI Engineer · Cooking with Codex — Charlie Guo & Gabriel Chua, OpenAI
Recommendation · MoneyKent advises that when a feature request comes without validation of why it exists, the product engineer should go back to the PM and ask to understand more about why it exists. Asking where a request is coming from helps shape the system used to build it.
AI Engineer · Build the Right Thing: Product Engineering (Part 1) — Kent C. Dodds, EpicProduct.engineer
Recommendation · MoneyKent advises hiring managers not to do coding challenges at all when hiring product engineers — and certainly not ones where candidates are not allowed to use an AI agent. He argues that banning AI agents in coding challenges signals an awful company to work at, because it contradicts what you're actually hiring the engineer to do.
AI Engineer · Build the Right Thing: Product Engineering (Part 1) — Kent C. Dodds, EpicProduct.engineer
Recommendation · SocialDuring the approach, stand about two feet away from her with feet close together, which keeps her comfortable.
Coach Kyle · A Realistic Approach at a Mall (Infield)
Recommendation · MoneySet up a Slack channel or some mechanism to collect all user feedback in one place. Create a feedback channel that aggregates Reddit threads, X, and all social media into one place. You don't have to answer everything in the feedback channel — that's not necessarily your job; the value is seeing where the rough edges and paper cuts are.
Also discussed by Rob Walling on Startups For the Rest of Us (unclassified source — not counted until classified). Not counted as independent sources.
AI Engineer · Build the Right Thing: Product Engineering (Part 2) — Kent C. Dodds, EpicProduct.engineer
Recommendation · MoneyKent C. Dodds advises that performance metrics must be brought up to at least the baseline line before adding new features on top — having performance metrics under the line will be a problem for your product. Measure performance metrics to make sure you're hitting your goals and are at the level you should be. For basic needs, focus on reliability, completeness, and ownership — these you absolutely have to nail and do right.
AI Engineer · Build the Right Thing: Product Engineering (Part 2) — Kent C. Dodds, EpicProduct.engineer
Recommendation · MoneyKent C. Dodds warns that shipping reverse-category features causes users to hate your product — it's the sort of thing you just really don't want in your product at all. He advises pushing back on stupid product ideas that are making users' lives awful and taking a stand and refusing to build features that make users' lives worse, adding that you should have a little respect for yourself even when you're not self-employed.
AI Engineer · Build the Right Thing: Product Engineering (Part 2) — Kent C. Dodds, EpicProduct.engineer
Recommendation · MoneyDelighters are like experiments — even if you think something is core to your identity, a delighter is not a basic, and you need to make sure you're covering your bases on the basics. For each experiment, decide how much you want to invest in making it solid and reliable, given there is only so much time and so many tokens. For delighter experiments, take a little less commitment from an architectural standpoint. Instead of integrating an experiment into the existing system, build it in a separate system where you can do a lot of learning, then find a way to bring it back into the full system once you have learned its shape.
AI Engineer · Build the Right Thing: Product Engineering (Part 2) — Kent C. Dodds, EpicProduct.engineer
Recommendation · MoneyKent C. Dodds advises that even shaped requests must be filtered, because every accepted feature impacts the system design you must maintain long term and slows down more important work. A request may solve a problem that seemed good to one person but wasn't generally applicable — verify broad applicability before building.
AI Engineer · Build the Right Thing: Product Engineering (Part 2) — Kent C. Dodds, EpicProduct.engineer
Recommendation · MoneyKent suggests tracking how often sessions start on time as a metric for whether the check-in solution solves the ultimate problem.
AI Engineer · Build the Right Thing: Product Engineering (Part 2) — Kent C. Dodds, EpicProduct.engineer
Recommendation · MoneyIndifference-category features are things the user decides don't make a difference — whether you have them or not, they don't care. Do not make an indifference-category feature a permanent part of your product. Get indifference-category features out of your product as quickly as possible because they are dead weight you have to maintain as an engineer. Maintaining an unused feature wastes engineering time — e.g., spending two weeks maintaining a feature nobody uses. Go to your boss or product manager and ask whether anybody is using a feature before spending time migrating or maintaining it. You need to understand the Kano model so you can make the case to your boss or product manager about which features are worth maintaining.
AI Engineer · Build the Right Thing: Product Engineering (Part 2) — Kent C. Dodds, EpicProduct.engineer