Greg Kostello
@gregko.bsky.social
CTO, Huma.ai. Carbon-based LLM, known to hallucinate at times but knowledge current as of today. I'm here to learn more of how AI can unlock knowledge, solve challenges, and uplift humanity.
Everyone who wants to run should run. Let the voters decide. Letting the Democratic machine handle things is how we got Biden.
December 9, 2025 at 10:24 PM
Everyone who wants to run should run. Let the voters decide. Letting the Democratic machine handle things is how we got Biden.
Really appreciate hearing you tonight and also hearing from Nick Brown @nickbrownnow.bsky.social. Please keep up the good fight for our rights and working on protecting the constitution. Thank you for being on BlueSky!
February 12, 2025 at 1:57 AM
Really appreciate hearing you tonight and also hearing from Nick Brown @nickbrownnow.bsky.social. Please keep up the good fight for our rights and working on protecting the constitution. Thank you for being on BlueSky!
Here today. Very interesting to hear about agentic approaches from Bret Taylor and his perspective on OpenAI and their mission driven approach to benefit humanity. Thank you for creating this summit!
December 17, 2024 at 11:05 PM
Here today. Very interesting to hear about agentic approaches from Bret Taylor and his perspective on OpenAI and their mission driven approach to benefit humanity. Thank you for creating this summit!
Meanwhile, a "grader agent" evaluates each scenario's output, practicality, and costs.
Each of these agents acts independently of each other, with goals and objectives clearly defined.
Each of these agents acts independently of each other, with goals and objectives clearly defined.
December 1, 2024 at 4:52 AM
Meanwhile, a "grader agent" evaluates each scenario's output, practicality, and costs.
Each of these agents acts independently of each other, with goals and objectives clearly defined.
Each of these agents acts independently of each other, with goals and objectives clearly defined.
Another SME agent, based on the customer objectives, can be trained on decision-making criteria. The source-grounded knowledge bases are then used to perform "what if" scenarios, again with scoring.
December 1, 2024 at 4:52 AM
Another SME agent, based on the customer objectives, can be trained on decision-making criteria. The source-grounded knowledge bases are then used to perform "what if" scenarios, again with scoring.
To ensure these source-grounded knowledge bases have high-quality entries, another agent could evaluate each piece of data prior to being entered based on well-defined criteria and score it accordingly so that when retrieved, a score is used along with the reasoning for the score.
December 1, 2024 at 4:52 AM
To ensure these source-grounded knowledge bases have high-quality entries, another agent could evaluate each piece of data prior to being entered based on well-defined criteria and score it accordingly so that when retrieved, a score is used along with the reasoning for the score.
One challenge of plain RAG is getting the system to explain the answer, but an agentic RAG system composed of different agents can be tasked with decomposing the problem downn for higher accuracy and generating source-grounded knowledge bases. Each agent runs continuously to keep data evergreen.
December 1, 2024 at 4:52 AM
One challenge of plain RAG is getting the system to explain the answer, but an agentic RAG system composed of different agents can be tasked with decomposing the problem downn for higher accuracy and generating source-grounded knowledge bases. Each agent runs continuously to keep data evergreen.
We've also used agentic RAG to prepare data from multiple data streams and respond to questions with outstanding results. Performance is still lagging, especially with multiple data sources grow, but I believe that will improve if we leverage other techniques.
November 24, 2024 at 11:13 PM
We've also used agentic RAG to prepare data from multiple data streams and respond to questions with outstanding results. Performance is still lagging, especially with multiple data sources grow, but I believe that will improve if we leverage other techniques.
3. "Self-Reflective AI" - Agentic RAG adapts responses in real-time, refining accuracy and reducing the need for human intervention.
4. "Built for Complexity" - Its architecture enables handling of intricate, multi-layered problems, such as synthesizing insights from multiple sources.
November 24, 2024 at 11:08 PM
3. "Self-Reflective AI" - Agentic RAG adapts responses in real-time, refining accuracy and reducing the need for human intervention.
4. "Built for Complexity" - Its architecture enables handling of intricate, multi-layered problems, such as synthesizing insights from multiple sources.
1. "Dynamic Orchestration"—specialized, collaborative agents improve how complex queries are handled, distributing tasks for precision and scalability.
2. "Query Criticism in Action" - Multi-hop reasoning and iterative query refinement provide relevance and accuracy.
2. "Query Criticism in Action" - Multi-hop reasoning and iterative query refinement provide relevance and accuracy.
November 24, 2024 at 11:06 PM
1. "Dynamic Orchestration"—specialized, collaborative agents improve how complex queries are handled, distributing tasks for precision and scalability.
2. "Query Criticism in Action" - Multi-hop reasoning and iterative query refinement provide relevance and accuracy.
2. "Query Criticism in Action" - Multi-hop reasoning and iterative query refinement provide relevance and accuracy.