an ai agent isn’t just a model with a prompt. it’s actually a layered system. memory: without it, the agent is basically a goldfish. it forgets past steps and keeps repeating the same mistakes. planning: the ability to break a big task into smaller actions. without it, the agent

most assumptions survive because they’re never properly tested. breakthrough builders understand that systematic validation isn’t about being right - it’s about being wrong faster and cheaper than the competition. this prompt teaches you to think like a validation architect so

you better start learning ai now.

great video about context engineering.

most companies think ai is some magic switch. launch a model → instant advantage. but honestly? ai isn’t magic—it’s just engineering. without proper data governance, models literally starve. without real success metrics, teams end up chasing vanity wins. without ops

agents aren’t magic most are just wrappers on top of apis what matters is orchestration: – memory – context management – fallback rules without that, your “agent” is a toy

here’s the paradox: models move way faster than laws. a regulator might take 2 years to draft a framework. a fine-tuned model can be deployed in 2 weeks. that time gap creates this vacuum. and in that vacuum, responsibility has to land somewhere. but who actually carries it? –

is the to-do list the best motivation thing ever?

self-esteem is really important nowadays.

another great read about prediction markets which are gaining popularity now.

in business strategy, one of the most powerful lenses you can apply is asking: is this game zero-sum, or positive-sum? a zero-sum game means that every unit of value you gain comes at someone else’s expense. think of poker: the pot is fixed, your win equals another’s loss. in