That’s very easy to do, this has that already for support agents in loop
AUTOMATION
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Alembic Uses AI to Quantify Creative Marketing Impact
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Alembic isn’t just building better dashboards. They’re using AI to help CMOs see the full picture — quantifying the impact of every creative decision, from Super Bowl ads to surprise pop-ups. Why? Because today’s marketers are dealing with incomplete data.
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Causal AI and Spiking Networks Transform Marketing Into Data Science
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Marketing is one of the most human parts of business—so what happens when you bring advanced AI into it? Tomas Puig, CEO of @getalembic
, joined the NVIDIA AI Podcast to explain how causal AI and spiking neural networks are turning marketing into a data-driven science. -
Startups building death rays and automated robot farms
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Startups i talked to today:
— Andreas Klinger 🦾 (@andreasklinger) 3 juillet 2025
One doing a death ray (energybeam to destroy stuff)
One doing robot farms that automatically move, test, and kill mice
Member when cute social check-in apps where a thing? 😉 pic.twitter.com/V4MZPUvuCsStartups i talked to today: One doing a death ray (energybeam to destroy stuff)
One doing robot farms that automatically move, test, and kill mice Member when cute social check-in apps where a thing? 😉 -
n8n Workflows Repository for AI Automation
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here's the repo:
↳ https://
github.com/Zie619/n8n-wor
kflows
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n8n Database: 2000+ AI Workflows Across 365 APIs
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What a gem. Eliad Shahar curated an insane @n8n_io database with
2,000+ AI workflows (and growing fast) across 365 APIs & services! Also comes with blazing-fast docs for instant search. repo in ↓ -
PyPilot Dependent Tasks: Modular LLM-Backed Workflows
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With Dependent Tasks, you can compose modular, LLM-backed workflows effortlessly—no fragile scripting. Check out PyPilot’s docs and build workflows that think step-by-step! #AI #Python #PyPilot #Coding
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Task B: Summarize Text Using Keywords and Agents
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Define Task B: Summarize based on Task A
summary = http://
pypilot.run( f"Summarize the text using only these keywords: {keywords}", agents=[agent_b], result_type=str, context={'text': long_text}
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Task Orchestration Pipelines for Real-World AI Applications
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This ensures Task B only runs after Task A completes—creating clean, interpretable, and robust pipelines. Perfect for real-world use-cases: Keyword-based summarization Sentiment-guided user feedback Topic-powered content generation Context-aware chatbots
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Setting Up Multi-Agent AI Systems for Task Automation
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Setup agents for each task:
agent_a = pypilot.Agent(model="gpt-4o-mini", instructions="Extract keywords")
agent_b = pypilot.Agent(model="gpt-4o-mini", instructions="Generate summary using keywords")