I’m surprised how much is solved in AI by just having another AI. Examples:
Use one GPT to write, one GPT to edit
Use one model for ideas, one for tone Use o-family reasoning to lay out research tasks, GPT-family to execute Use one to suggest strategies, one to predict
AGENTS
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Using Multiple AIs Together to Solve Complex Tasks
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DataRobot Simplifies AI Delivery at NVIDIA GTC25
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How does DataRobot help simplify the delivery of AI? We’re at @nvidia #GTC25 and asked Prajakta Damle, SVP of Product, to share her thoughts👇#AI #agenticAI pic.twitter.com/6S3IRmJe0a
— DataRobot (@DataRobot) 20 mars 2025How does DataRobot help simplify the delivery of AI? We’re at @nvidia #GTC25 and asked Prajakta Damle, SVP of Product, to share her thoughts #AI #agenticAI
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o1-pro API Now Available for Complex Agentic Tasks
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By popular demand, o1-pro just landed in the API! Built for your toughest, most agentic problems—it uses extra compute to think harder and deliver consistently better answers.https://t.co/o0WAZImCd8pic.twitter.com/YnkM7uVwG0
— Romain Huet (@romainhuet) 19 mars 2025By popular demand, o1-pro just landed in the API! Built for your toughest, most agentic problems—it uses extra compute to think harder and deliver consistently better answers. https://
platform.openai.com/docs/models/o1
-pro
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Fullstack-Bench: Autonomous Coding with Feedback Loops
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Introducing Fullstack-Bench with @sujayakar314 of @convex
! https://
youtube.com/watch?v=FO0AN1
HftCE
… three ingredients for successful autonomous coding: 1. tight, automatic feedback loops
2. expressing everything in standard, procedural code
3. strong, foolproof abstractions -
Building AI Agents: The Next Development Challenge
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Now you just need someone to build those agents
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Agentic RAG Revolutionizes AI Workflows with Reasoning and Action
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Agentic RAG is changing AI workflows! AI is moving beyond simple retrieval—it’s now reasoning and executing actions. AI Agents retrieve, reason, and act LLMs choose the right tool for every task Persistent knowledge enhances AI decision-making APIs & functions
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Jensen Huang Unveils AI Breakthroughs at GTC25 Conference
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NVIDIA CEO Jensen Huang took the stage at #GTC25, sharing exciting breakthroughs in agentic AI, digital twins, robotics, and more. Missed it? Here’s a recap of the game-changing innovations and partnerships driving the future: http://
nvda.ws/3FO4V4x #HunanoidDevDay @NVIDIA -
Prompts Now Visible and Editable in Agent Node UI
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You can now see prompts attached to nodes, and edit them directly in the ui Agent building isn’t just engineering – there’s a big prompting/visual component to it, this is a step in that direction
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Confidence Thresholds and Computational Resource Trade-offs in AI
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– Yes, higher confidence thresholds meant more questions went unanswered – something we either need to accept, depending on the risk level, or be willing to throw more compute in when confidence is critical
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Risk Penalty Weights Impact on Model Performance
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They also experimented with different risk levels. On one end of the spectrum: no penalty for wrong answers. On the other: the penalty of wrong answers weighted 20x more than the reward for correct ones. What they found: