“Tal Raviv breaks down “building an agent” into four LLM primitives—simple chat threads, chat + tools, chat + tools + skills, and finally adding a file system (Memento) for persistent memory across sessions.”

Tal Raviv breaks down “building an agent” into four LLM primitives—simple chat threads, chat + tools, chat + tools + skills, and finally adding a file system (Memento) for persistent memory across sessions. This AI-driven approach cut a huge initial error rate to production-ready quality.

“Tal Raviv launched Familiar, an open-source app that captures your screen and clipboard every 4 seconds as Markdown so local AI agents can use live work context.”

#6 𝕏 Tal Raviv launched Familiar, an open-source app that captures your screen and clipboard every 4 seconds as Markdown so local AI agents can use live work context. #16 in Tal Raviv launched Familiar, an open-source app that captures your screen and clipboard every 4 seconds into markdown so local AI agents can use it as context.

“Tal Raviv likens AI taking over PM tasks to the Mixpanel/Amplitude self-serve analytics boom: while those tools let PMs spin up retention analyses and funnel charts without data-team requests, they also spawned flawed conclusions when events were misinterpreted and expert nua...”

#17 𝕏 Tal Raviv likens AI taking over PM tasks to the Mixpanel/Amplitude self-serve analytics boom: while those tools let PMs spin up retention analyses and funnel charts without data-team requests, they also spawned flawed conclusions when events were misinterpreted and expert nua...

“Tal Raviv calls for “context engineering as a team sport,” giving every team member’s AI assistant a shared knowledge base to speed onboarding and compound improvements.”

#15 𝕏 Tal Raviv calls for “context engineering as a team sport,” giving every team member’s AI assistant a shared knowledge base to speed onboarding and compound improvements.

“#8 in Colin Matthews spotlights Tal Raviv’s demo of a support agent that uses system prompts to call get_order and issue_refund via an application server, automating order status lookups and refunds for lost orders.”

#8 in Colin Matthews spotlights Tal Raviv’s demo of a support agent that uses system prompts to call get_order and issue_refund via an application server, automating order status lookups and refunds for lost orders.

“in Colin Matthews spotlights Tal Raviv’s demo of a support agent that uses system prompts to call get_order and issue_refund via an application server, automating order status lookups and refunds for lost orders.”

#8 in Colin Matthews spotlights Tal Raviv’s demo of a support agent that uses system prompts to call get_order and issue_refund via an application server, automating order status lookups and refunds for lost orders.

“Tal Raviv uses Anthropic’s Claude to automate his core PM workflows—drafting specs, prioritizing backlogs, and generating roadmaps—arguing that Claude now outperforms him so fully he might as well “give away his Legos.””

#22 𝕏 Tal Raviv uses Anthropic’s Claude to automate his core PM workflows—drafting specs, prioritizing backlogs, and generating roadmaps—arguing that Claude now outperforms him so fully he might as well “give away his Legos.” #23 in Carl Vellotti used Anthropic’s Claude to parse a week of his Slack messages and meeting transcripts, identify inefficiencies (like unnecessary meetings and redundant status updates), and codify his PM routines in a CLAUDE.md file.

“She’s teaming with Aman Khan and Tal Raviv for live OpenClaw & MCP builds to teach true AI Product Sense.”

#21 in Marily Nika, Ph.D warns that a rogue Chipotle burrito-bot demo exposed how AI products fail without steering guardrails. She’s teaming with Aman Khan and Tal Raviv for live OpenClaw & MCP builds to teach true AI Product Sense.

“Tal Raviv looped Claude into a weekend notification-design brainstorm by holding down the dictation button to feed it bottom-line points in real time, and Claude’s targeted questions kept their creative momentum flowing.”

#14 𝕏 Tal Raviv looped Claude into a weekend notification-design brainstorm by holding down the dictation button to feed it bottom-line points in real time, and Claude’s targeted questions kept their creative momentum flowing.

“in Tal Raviv Tal Raviv spent significant time “vibe coding” a landing page for Familiar and shares a theater-free, detailed account to challenge the industry’s obsession with framing “quick and easy” as the hallmark of AI-forward work.”

in Tal Raviv Tal Raviv spent significant time “vibe coding” a landing page for Familiar and shares a theater-free, detailed account to challenge the industry’s obsession with framing “quick and easy” as the hallmark of AI-forward work.