Ideas
1- EVERYBODY Will Have Personal Agents 2- Company Memory Becomes an Asset 3- Different LLM Form Factors 4- LLM → Commodity / Agent/Harness → The Differentiator 5- APPS → Tools/Skills, USERS → Agents 6- Security/Governance Embedded in Harness 7- Agent Discovery & Collaboration 8- Testing/Hardening/Benchmarking Becomes Mainstream
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His name is Mr No. It has a male character.His skills are explained in the attached pdf. Link to detailed PDF file.
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The IOA Blueprint Architecting the Future of Agentic Collaboration. (Link To PDF – a Must read) We are rapidly moving beyond the traditional Internet and the Internet of Things. Welcome to the era of the Internet of Agents, a foundational framework designed to seamlessly interconnect, dynamically discover, and collaboratively orchestrate autonomous AI agents at scaleUnlike the human-centric web or the trigger-based Internet of Things, the Internet of Agents is built for agent and knowledge connectivity….
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After listening to those impressive comments from Sam and Ali: Take aways
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The graph visualizes the performance of all fifteen assets from November 2023 to October 2025:
Updated Ranking of 15 Assets (Nov 2023 – Oct 2025)The table below provides the full, final ranking, with the new assets slotted based on their performance (starting point Nov 2023=100$).
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Joule and Just Ask connect to address user queries. Questions may go to different models based on customer data. Challenges arise from ambiguous data models and user expectations for immediate answers based on real data.
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“…In his latest presentation at BTC Prague, Michael Saylor presented “his bitcoin prophecy”, predicting that bitcoin will reach a price of USD 21 million per coin by 2046 (21 years from now). While USD 21 million sounds crazy right now, it is important to put this into perspective. This price would imply an annual return of 28.5% over the next 21 years. If we consider that over the last 21 years, Apple’s stock has returned…
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Why a vector Database is a must?A vector search engine is a type of search engine that uses vector representations, also knownas embeddings, to search for relevant information. Unlike traditional keyword-based search engines, vector search engines use mathematical techniques to represent and process the meaning of words, phrases, sentences, or documents in a high-dimensional vector space. The key idea is that semantically similar items have similar vector representations. Vector search engines are particularly useful for…
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Some take aways from Nassim Taleb “Statistical Consequences of fat tails” book. A must read for a next generation thinker.
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