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AI Consulting and Expertiseβ€’β€’5 min readβ€’937 words

πŸ€– ChatGPT - Memory Hack

πŸ‘οΈ0reads (human + AI)πŸ€–0AI ingestions

πŸ€– ChatGPT - Memory Hack

This article describes a method to leverage ChatGPT's different persona models to achieve a form of memory persistence across interactions. It involves initiating a conversation with one model and then transitioning to another.

Key Points:

β€’ Using persona "4o" (fast helper) first allows for context transfer to persona "o3" (smart thinker).

β€’ "4o" possesses two years of conversational history which can be leveraged.

β€’ Initiating with a "who am I?" prompt in "4o" establishes the necessary context.

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πŸ”— Resources:

β€’ NorthstarBrain β†— - ChatGPT persona experimentation


πŸ’‘ Product Management - Evolving Role of PRDs

This article discusses the changing role of Product Requirement Documents (PRDs) in light of advancements in Large Language Models (LLMs). It highlights the increased context PMs now manage and the potential shifts in PRD creation.

Key Points:

β€’ LLMs significantly increase the amount of context PMs must handle.

β€’ The traditional PRD concept may change as LLM technology enables broader team contribution.

β€’ The role of PMs is evolving to manage a larger context with greater team collaboration.

πŸ”— Resources:

β€’ Ariel Jalali β†— - PM perspective on LLM impact
β€’ Eyal Toledano β†— - Discussion on PRD evolution


✨ Pokémon Go - Unexpected Origins

This article explores the surprising origin story of PokΓ©mon Go, suggesting its development was influenced by CIA technology initially intended for military surveillance.

Key Points:

β€’ PokΓ©mon Go achieved immense popularity, reaching 500 million downloads and $1 billion in revenue.

β€’ A claim is made linking its development to classified CIA surveillance technology.

β€’ The article hints at a hidden history behind the game's creation.

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πŸ”— Resources:

β€’ Shawn Chauhan β†— - Claims about Pokemon Go's origins


πŸ€– AI - Importance of MCPs in Workflows

This article highlights the significance of a particular type of AI solution (presumably Multi-Agent Collaboration Platforms or a similar technology denoted by MCPs) and recommends reading further to understand their value for enterprises. The original post lacks specificity on what MCPs are exactly.

Key Points:

β€’ The referenced material is deemed crucial reading for Q2 and Q3 of a given year within the AI space.

β€’ It is especially relevant for those considering implementing MCPs in their workflows.

β€’ It focuses on enterprise or defense applications.

πŸ”— Resources:

β€’ Jason Kneen β†— - Mentioned in relation to the important read.
β€’ Dromanocpm β†— - Source of the recommended reading.


πŸš€ AI Solutions - Distribution as Moat

This article argues that for AI and automation solutions, distribution, not the product itself, constitutes the primary competitive advantage due to the rapid pace of technological advancement.

Key Points:

β€’ Rapid technological change makes product differentiation short-lived.

β€’ Distribution networks and market reach become key factors for success.

β€’ Focusing on distribution strategy is crucial for long-term viability in the AI market.

πŸ”— Resources:

β€’ Aryan Mahajan β†— - Author of the perspective on distribution.


πŸ’‘ Future of Work - Fractional IC Roles

This article advocates for the normalization of fractional Independent Contractor (IC) roles, suggesting that AI will drive a gig economy of knowledge work. It also suggests that a significant portion of work time may be unproductive.

Key Points:

β€’ Fractional IC roles provide greater exposure to diverse problems and company cultures.

β€’ AI may facilitate the growth of gig-economy knowledge work.

β€’ A substantial amount of work time may be unproductive or "performative."

πŸ”— Resources:

β€’ Tal Weezy β†— - Author of the perspective on fractional IC roles.
β€’ Claire Vo β†— - Mentioned in relation to the topic


πŸš€ AI Advertising - Automated Creative Generation

This article describes an AI agent capable of creating effective advertisements by analyzing successful campaigns and identifying buyer triggers.

Key Points:

β€’ The AI agent outperforms expensive human creative teams.

β€’ It analyzes winning ads, identifies buyer triggers, and generates ready-to-launch creatives.

β€’ Its efficiency leads to improved ad conversion rates.

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πŸ”— Resources:

β€’ Aryan Mahajan β†— - Source of the AI advertising agent information


πŸ€– RAG Systems - Improving Performance

This article promotes a free email course designed to improve Retrieval Augmented Generation (RAG) systems, focusing on performance, quality, and user experience.

Key Points:

β€’ The course helps build better RAG systems beyond basic functionalities.

β€’ It enhances performance, quality, and user experience.

β€’ It provides a foundational framework for RAG system improvement.

πŸ”— Resources:

β€’ Free Email Course β†— - Course on improving RAG systems
β€’ jxnlco β†— - Source of the email course


πŸ’‘ Medical - Treatment for Hearing Issues

This article recounts a personal experience with persistent hearing problems and eventual successful treatment by a specialist.

Key Points:

β€’ Initial treatments with over-the-counter medications and antibiotics proved ineffective.

β€’ A specialist provided effective treatment, highlighting the importance of specialized care.

β€’ The successful treatment involved a freezing procedure (details not specified).

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πŸ”— Resources:

β€’ Dylan522p β†— - Author of the personal experience.


πŸ’‘ Political Commentary - Election Outcomes

This article presents a simplified mathematical observation about potential election outcomes given a specified distribution of political parties.

Key Points:

β€’ A simplified model is presented for election outcomes.

β€’ The model assumes two right-wing and one left-wing parties.

β€’ The observation emphasizes the need for straightforward political analysis.

πŸ”— Resources:

β€’ pkghosh99 β†— - Mentioned in the context.
β€’ Seth Abramson β†— - Mentioned in the context.
β€’ raindiary02 β†— - Mentioned in the context.


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Drix10
Written by Drix10

Co founder @ PartPilot | 1 x Acquired Founder | Canopy @ f.inc | Cybersec @ DSU | 2x International Hackathon πŸ†. Read more on drix10.com.