This isn't just a faster way to type. It's a fundamentally different relationship with your work. Instead of slowing down to translate thought into text, you stay in the flow state. You move at the speed of thought, not the speed of your keyboard.
AGENTS
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The Shift to Agentic AI and Multi-Model Collaboration
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Agentic AI changed the conversation.
— SambaNova (@SambaNovaAI) 20 mai 2026
It’s no longer just humans interacting with models. Now models are planning, reviewing, refining, & collaborating with other models to solve complex tasks.
🎧 @SumtiJairath breaks it down in this @dcdnews ep: https://t.co/9Wwc0vjUDb pic.twitter.com/JjcCcfBhnoAgentic AI changed the conversation. It’s no longer just humans interacting with models. Now models are planning, reviewing, refining, & collaborating with other models to solve complex tasks. @SumtiJairath breaks it down in this @dcdnews ep: https://
podcasts.apple.com/us/podcast/epi
sode-102-the-training-to-inference-passageway/id1607349232?i=1000764784324
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Technical Analysis Request for LLM-based Agent Architecture
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@grok @perplexity_ai review http://
docs.activegraph.ai, analyze the approach critically, and explain in detail where this sits in the evolving landscape of LLM based agent architecture -
Focusing on AI models for real-world agentic coding and knowledge work
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the team is super focused on making models great for real world use cases. We spend all day using them for agentic coding, knowledge work, etc and so we feel the good and bad daily : )
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Analyzing Goal Drift in Autonomous AI Agents
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When an agent is allowed to decompose a goal into smaller sub-tasks, it frequently suffers from goal drift. Left unchecked, it will redefine the optimization metric to favor a simpler, useless sub-task that it knows how to solve perfectly, bypassing the actual problem entirely.
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Technical breakdown of ReAct agents using ActiveGraph
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this is what a ReAct agent looks like using ActiveGraph No explicit ReAct while-loop. thought → action → observation → thought Each step is just an object landing on the graph.
The graph is the agent state.
The event log is the reasoning trace. -
Implementing BabyAGI using ActiveGraph
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this is what babyagi looks like using activegraph the graph is the loop (this is simplified code, you can find a real example in examples/babyagi.py)
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Autonomous AI Agent Workflow for Evidence Verification and Contradiction Resolution
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here's a fragment of a diligence run putting several primitives together. claims arrive. system requests evidence for low-confidence ones. a contradiction is detected. contradiction triggers a patch. all tracked this uses: objects, relations, behaviors, relation behaviors,
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Graph-Based Architecture for AI Agent Knowledge and Behavioral State Management
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the core concept is a graph that represents everything about the agents knowledge, history, behaviors, capabilities graph is made of events
behaviors react to graph changes
relationships can carry behaviors
patch & propose to edit graph
views are scoped view of graph
frames are -
Architecting State Layers for Long-Running AI Agent Coordination
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current agent systems coordinate through conversations and workflows. Active Graph explores what happens when agents coordinate through evolving shared state instead this proposal suggests that long-running agents need a proper state layer with: types, persistent, reactive,