can be used to buy credits for the pypilot agents
AGENTS
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Digital Twins Transform Professional Productivity and Workforce
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Want to double your productivity? Digital twins—perfect AI replicas trained on your data and communication style—will transform work by attending meetings and answering emails while you focus on what matters. Over the next few years, most professionals will have digital twins
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Sakana AI AB-MCTS: Multiple AI Models Collaborating to Solve Complex Problems
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Sakana AI debuted AB-MCTS, an algorithm that lets different AI models work together, using their strengths and mistakes, to solve complex problems It used ChatGPT, Gemini, and DeepSeek to solve 30% of ARC-AGI-2 puzzles vs just 23% for top solo models
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Fundamental Launches Shortcut: AI Spreadsheet Agent Solving Tasks 10x Faster
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Fundamental dropped Shortcut, a "superhuman" spreadsheet agent, in early preview
— Rowan Cheung (@rowancheung) 3 juillet 2025
It one-shots most knowledge work tasks on Excel, also scoring >80% on Excel World Championship Cases in about 10 min.
That's 10x faster than humanspic.twitter.com/FZ5EYWNgPGFundamental dropped Shortcut, a "superhuman" spreadsheet agent, in early preview It one-shots most knowledge work tasks on Excel, also scoring >80% on Excel World Championship Cases in about 10 min. That's 10x faster than humans
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Genspark Launches AI Docs for Agentic Productivity Suite
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Genspark continues to expand its agentic productivity suite — this time with AI Docs
— Rowan Cheung (@rowancheung) 3 juillet 2025
Just like AI Slides and Sheets, the Docs offering creates full documents for the user using nothing but simple text instructions
Also supports rich text and markdownpic.twitter.com/yS9J6XU9QYGenspark continues to expand its agentic productivity suite — this time with AI Docs Just like AI Slides and Sheets, the Docs offering creates full documents for the user using nothing but simple text instructions Also supports rich text and markdown
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Replit Agent Enhanced with Dynamic Intelligence and Extended Thinking
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Replit Agent now includes Dynamic Intelligence with extended thinking, high-power models, and web search It enables the agent to think deeper, reason better, and surf the web to solve challenging, open-ended problems like app performance optimizations, and more
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LLM Agents: Custom Setups, Validators, and Structured Output
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Why this works so well: Agents – custom LLM setups Result types – ensures structured output Validators – guarantees scores between 0–1 Context passing – clean API, solid design
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Create Your Agent with GPT-4o Mini Sentiment Assessment
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Step 1: Create your agent
import pypilot as pypilot from pypilot.tasks.validators import between optimist = pypilot.Agent(model="openai/gpt-4o-mini")
This agent uses GPT‑4o mini to assess sentiment. -
Sentiment Classification Function with AI Agents
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Step 2: Define the sentiment function def sentiment(text: str) -> float:
return http://
pypilot.run(
"Classify the sentiment of the text as a value between 0 and 1",
agents=[optimist],
result_type=float,
result_validator=between(0, 1),
context={"text": text},
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AI Agent Framework Comparison Guide for Developers
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🚀 Choosing the right AI Agent Framework just got easier.
— Giuliano Liguori (@ingliguori) 3 juillet 2025
From LangChain to AutoGPT, Semantic Kernel to Crew AI—this chart nails the features, goals & use cases.
🧠 Whether you're building autonomous tools or graph-based systems, this saves you hours.
Visual by @vishnunallani… pic.twitter.com/hLQnq4ndDTChoosing the right AI Agent Framework just got easier. From LangChain to AutoGPT, Semantic Kernel to Crew AI—this chart nails the features, goals & use cases. Whether you're building autonomous tools or graph-based systems, this saves you hours. Visual by @vishnunallani