“AI needs more than chat” is probably the most important line here. Chat interfaces were never the end product. They were just the temporary UI until agents got real context.
AUTOMATION
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Launch of holaOS: An AI-Driven Operating System for Agentic Workflows
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holaOS Beta 0.1 got launched, bringing a new AI Workstream Management Layer on top of its Agent Computer foundation.
— 🚨 AI News | TestingCatalog (@testingcatalog) 12 mai 2026
holaOS Beta 0.1 is an operating system for long-running work and comes with:
– Workspaces with memory.
– Sub-agents running in parallel.
– A dashboard for… https://t.co/hbEVvv9zuA pic.twitter.com/xbr47hWISqholaOS Beta 0.1 got launched, bringing a new AI Workstream Management Layer on top of its Agent Computer foundation. holaOS Beta 0.1 is an operating system for long-running work and comes with:
– Workspaces with memory.
– Sub-agents running in parallel.
– A dashboard for -
Detailed Example of a Functional AI Workflow in Action
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Here is what a broken AI workflow actually looks like:
— Nico (@nicos_ai) 12 mai 2026
You wake up. OpenClaw has been running since 5am.
It read your emails, sorted your tasks, caught the production alert, drafted three replies, reordered your Linear board by what is actually blocking what.
Three hours of… https://t.co/wiVsA0ryac pic.twitter.com/MSgE7QY9NdHere is what a broken AI workflow actually looks like: You wake up. OpenClaw has been running since 5am. It read your emails, sorted your tasks, caught the production alert, drafted three replies, reordered your Linear board by what is actually blocking what. Three hours of
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Live demonstration of AI agent-based application development
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Live at #Rev26: @DominoDataLab CEO Nick Elprin builds a working Streamlit app on stage. Data file in, natural language prompt to an agent, deployed app out. The compliance layer stays. The friction doesn't.
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Multi-Agent Synergy for Scaling Test-Time Compute
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TMAS Scaling Test-Time Compute via Multi-Agent Synergy
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The Shift from Chat to Action in Enterprise AI Agents
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Enterprise AI is moving from chat to action. ServiceNow’s new Action Fabric matters because it gives AI agents governed ways to execute workflows, not just read data. That is the real test for 2026:
Can your agents act with permissions, auditability, and policy guardrails? -
AI-Integrated Technical Stack Overview
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Wavespeed for GPU (Photo AI + Interior AI)
Cloudflare for R2 storage and domain renewal
xAI for LLM AI API for all my sites
Backblaze for backups
Hetzner for VPS
Scrapingbee for scraping (mostly for Hotelist)
Google Cloud (also for Hotelist)
NameCheap (for like 4 domains left -
Governance Challenges in the Age of AI Agents
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The third lesson is the one every C-level team should pay attention to right now: In the age of AI, governance teams cannot act like the police. Agents, dashboards, and data products are multiplying fast.
If governance slows the business down, the business will route around it. -
Implementing long-term memory for AI agents
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顺便说下，这个长期记忆用的是 @evermind 😎 https://t.co/ID2FwO43wn
— 艾略特 (@elliotchen100) 12 mai 2026By the way, this long-term memory uses @evermind
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The Impact of Prompt Caching on LLM Agentic Workflows and Costs
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Prompt caching didn't even exist until <2 years ago Google: June 2024
Anthropic: August 2024
OpenAI: October 2024 Now for agentic workflows, 95% of tokens gets cached, without it the costs would be completely insane