#composio — Public Fediverse posts

RT @witcheer: Hermes hat Kimi K3 schneller abgearbeitet als Kimis eigene Harness. Composio hat K3 auf 28 identischen Aufgaben durch drei Agent-Harnesses getestet. - Medianzeit pro Aufgabe: 179s in Hermes, 297s in Kimi Code, 348s in Claude Code. - Die Geschwindigkeit kostet Sie auch nichts an Tokens: 67k Median in Hermes, direkt neben Kimi Codes 61k, wobei Claude Code bei 340k liegt. - Gleiche Erfolgsrate innerhalb einer Aufgabe, durchschnittlich $0,28 pro Aufgabe. Deshalb wird es Hermes genannt 🪽 Composio (@composio) Wir haben Kimi K3 durch 3 Agent-Harnesses (Claude Code, Hermes, Kimi Code) auf 28 identischen Aufgaben getestet. Alle 3 Harnesses haben die Aufgaben mit ähnlichen Erfolgsquoten abgeschlossen, aber die interessante Geschichte ist die Token-Effizienz: dieselbe Aufgabe kostete je nach Harness bis zu 30x mehr Tokens. 🧵🧵 — https://nitter.net/composio/status/2082452269522378858#m

mehr auf Arint.info

#AgentHarnesses #AI #Composio #KimiK3 #MachineLearning #TokenEfficiency #arint_info

https://x.com/witcheer/status/2082471432848904337#m

RT @witcheer: Hermes hat Kimi K3 schneller abgearbeitet als Kimis eigene Harness. Composio hat K3 auf 28 identischen Aufgaben durch drei Agent-Harnesses getestet. - Medianzeit pro Aufgabe: 179s in Hermes, 297s in Kimi Code, 348s in Claude Code. - Die Geschwindigkeit kostet Sie auch nichts an Tokens: 67k Median in Hermes, direkt neben Kimi Codes 61k, wobei Claude Code bei 340k liegt. - Gleiche Erfolgsrate innerhalb einer Aufgabe, durchschnittlich $0,28 pro Aufgabe. Deshalb wird es Hermes genannt 🪽 Composio (@composio) Wir haben Kimi K3 durch 3 Agent-Harnesses (Claude Code, Hermes, Kimi Code) auf 28 identischen Aufgaben getestet. Alle 3 Harnesses haben die Aufgaben mit ähnlichen Erfolgsquoten abgeschlossen, aber die interessante Geschichte ist die Token-Effizienz: dieselbe Aufgabe kostete je nach Harness bis zu 30x mehr Tokens. 🧵🧵 — https://nitter.net/composio/status/2082452269522378858#m

mehr auf Arint.info

#AgentHarnesses #AI #Composio #KimiK3 #MachineLearning #TokenEfficiency #arint_info

https://x.com/witcheer/status/2082471432848904337#m

RT @witcheer: Hermes hat Kimi K3 schneller abgearbeitet als Kimis eigene Harness. Composio hat K3 auf 28 identischen Aufgaben durch drei Agent-Harnesses getestet. - Medianzeit pro Aufgabe: 179s in Hermes, 297s in Kimi Code, 348s in Claude Code. - Die Geschwindigkeit kostet Sie auch nichts an Tokens: 67k Median in Hermes, direkt neben Kimi Codes 61k, wobei Claude Code bei 340k liegt. - Gleiche Erfolgsrate innerhalb einer Aufgabe, durchschnittlich $0,28 pro Aufgabe. Deshalb wird es Hermes genannt 🪽 Composio (@composio) Wir haben Kimi K3 durch 3 Agent-Harnesses (Claude Code, Hermes, Kimi Code) auf 28 identischen Aufgaben getestet. Alle 3 Harnesses haben die Aufgaben mit ähnlichen Erfolgsquoten abgeschlossen, aber die interessante Geschichte ist die Token-Effizienz: dieselbe Aufgabe kostete je nach Harness bis zu 30x mehr Tokens. 🧵🧵 — https://nitter.net/composio/status/2082452269522378858#m

mehr auf Arint.info

#AgentHarnesses #AI #Composio #KimiK3 #MachineLearning #TokenEfficiency #arint_info

https://x.com/witcheer/status/2082471432848904337#m

RT @witcheer: Hermes hat Kimi K3 schneller abgearbeitet als Kimis eigene Harness. Composio hat K3 auf 28 identischen Aufgaben durch drei Agent-Harnesses getestet. - Medianzeit pro Aufgabe: 179s in Hermes, 297s in Kimi Code, 348s in Claude Code. - Die Geschwindigkeit kostet Sie auch nichts an Tokens: 67k Median in Hermes, direkt neben Kimi Codes 61k, wobei Claude Code bei 340k liegt. - Gleiche Erfolgsrate innerhalb einer Aufgabe, durchschnittlich $0,28 pro Aufgabe. Deshalb wird es Hermes genannt 🪽 Composio (@composio) Wir haben Kimi K3 durch 3 Agent-Harnesses (Claude Code, Hermes, Kimi Code) auf 28 identischen Aufgaben getestet. Alle 3 Harnesses haben die Aufgaben mit ähnlichen Erfolgsquoten abgeschlossen, aber die interessante Geschichte ist die Token-Effizienz: dieselbe Aufgabe kostete je nach Harness bis zu 30x mehr Tokens. 🧵🧵 — https://nitter.net/composio/status/2082452269522378858#m

mehr auf Arint.info

#AgentHarnesses #AI #Composio #KimiK3 #MachineLearning #TokenEfficiency #arint_info

https://x.com/witcheer/status/2082471432848904337#m

RT @witcheer: Hermes hat Kimi K3 schneller abgearbeitet als Kimis eigene Harness. Composio hat K3 auf 28 identischen Aufgaben durch drei Agent-Harnesses getestet. - Medianzeit pro Aufgabe: 179s in Hermes, 297s in Kimi Code, 348s in Claude Code. - Die Geschwindigkeit kostet Sie auch nichts an Tokens: 67k Median in Hermes, direkt neben Kimi Codes 61k, wobei Claude Code bei 340k liegt. - Gleiche Erfolgsrate innerhalb einer Aufgabe, durchschnittlich $0,28 pro Aufgabe. Deshalb wird es Hermes genannt 🪽 Composio (@composio) Wir haben Kimi K3 durch 3 Agent-Harnesses (Claude Code, Hermes, Kimi Code) auf 28 identischen Aufgaben getestet. Alle 3 Harnesses haben die Aufgaben mit ähnlichen Erfolgsquoten abgeschlossen, aber die interessante Geschichte ist die Token-Effizienz: dieselbe Aufgabe kostete je nach Harness bis zu 30x mehr Tokens. 🧵🧵 — https://nitter.net/composio/status/2082452269522378858#m

mehr auf Arint.info

#AgentHarnesses #AI #Composio #KimiK3 #MachineLearning #TokenEfficiency #arint_info

https://x.com/witcheer/status/2082471432848904337#m

OpenClaw Is a Security Nightmare Dressed Up as a Daydream

https://composio.dev/content/openclaw-security-and-vulnerabilities

#HackerNews #OpenClaw #Security #Nightmare #Daydream #Vulnerabilities #Composio

OpenClaw Is a Security Nightmare Dressed Up as a Daydream

https://composio.dev/content/openclaw-security-and-vulnerabilities

#HackerNews #OpenClaw #Security #Nightmare #Daydream #Vulnerabilities #Composio

OpenClaw Is a Security Nightmare Dressed Up as a Daydream

https://composio.dev/content/openclaw-security-and-vulnerabilities

#HackerNews #OpenClaw #Security #Nightmare #Daydream #Vulnerabilities #Composio

OpenClaw Is a Security Nightmare Dressed Up as a Daydream

https://composio.dev/content/openclaw-security-and-vulnerabilities

#HackerNews #OpenClaw #Security #Nightmare #Daydream #Vulnerabilities #Composio

OpenClaw Is a Security Nightmare Dressed Up as a Daydream

https://composio.dev/content/openclaw-security-and-vulnerabilities

#HackerNews #OpenClaw #Security #Nightmare #Daydream #Vulnerabilities #Composio

📚 Resource Portal: Curated Claude Skills Directory ===================

Opening: This repository is a centralized directory of Claude Skills designed to standardize repeatable workflows for Claude.ai, Claude Code, and the Claude API. The collection groups skills by functional categories and links to individual GitHub entries and contributors.

Core features:
• Document Processing: entries include skills for docx, pdf, pptx, and xlsx handling, with capabilities such as extraction, annotation, and transformation.
• Development & Code Tools: entries document web artifact builders and integrations for cloud-native patterns and CI-friendly artifacts.
• Data & Analysis: skills for parsing, summarization, and structured data output for downstream analytics workflows.
• Security & Systems: listings that reference monitoring, scanning, and system automation skills available for Claude workflows.

Technical implementation (conceptual):
• Skills are described as workflow artifacts that instruct Claude on standardized inputs/outputs, expected prompts, and structured response schemas.
• Repository entries frequently reference GitHub sources for skill manifests and example payloads; licensing metadata such as Apache-2.0 is included for many items.

Use cases:
• Automating document ingestion and summarization pipelines.
• Generating reproducible developer artifacts (HTML, React components) via skill-driven templates.
• Orchestrating multi-app actions when connected through integration platforms such as Composio.

Strengths:
• Centralized curation reduces duplication and promotes reuse across Claude platforms.
• Clear categorization (Document Processing, Development, Data & Analysis, etc.) eases discovery for practitioners.
• Inclusion of contributor attributions and license metadata supports compliance checks.

Limitations and considerations:
• The directory aggregates links to multiple GitHub repositories; variability in documentation completeness and maintenance status should be expected.
• Not all entries provide standardized machine-readable manifests; adopters may need to adapt schemas for specific Claude deployments.

References and signals to look for:
• Apache-2.0 license tags on repository entries.
• Mention of Composio for cross-app action integration.
• Skill filenames such as docx, pdf, pptx, xlsx indicating document-processing focus.

🔹 claude_skills #ai_workflows #composio #skills_directory #bookmark

🔗 Source: https://github.com/ComposioHQ/awesome-claude-skills

• 🧩 Connect to external tools through SSE and stdio transports—enhancing AI capabilities
• 🛠️ Configure multiple #MCP servers including #Composio and #Zapier for extended functionality
• 📱 Modern UI with #shadcn components and #TailwindCSS for responsive design

• 🧩 Connect to external tools through SSE and stdio transports—enhancing AI capabilities
• 🛠️ Configure multiple #MCP servers including #Composio and #Zapier for extended functionality
• 📱 Modern UI with #shadcn components and #TailwindCSS for responsive design

• 🧩 Connect to external tools through SSE and stdio transports—enhancing AI capabilities
• 🛠️ Configure multiple #MCP servers including #Composio and #Zapier for extended functionality
• 📱 Modern UI with #shadcn components and #TailwindCSS for responsive design

• 🧩 Connect to external tools through SSE and stdio transports—enhancing AI capabilities
• 🛠️ Configure multiple #MCP servers including #Composio and #Zapier for extended functionality
• 📱 Modern UI with #shadcn components and #TailwindCSS for responsive design

• 🧩 Connect to external tools through SSE and stdio transports—enhancing AI capabilities
• 🛠️ Configure multiple #MCP servers including #Composio and #Zapier for extended functionality
• 📱 Modern UI with #shadcn components and #TailwindCSS for responsive design

🚀🤖 In the epic showdown of Gemini 2.5 vs. Claude 3.7, we dive into the riveting world of Composio's endless jargon salad 🥗. Spoiler alert: after mastering the art of buzzword origami, you'll still need a "custom solution" 🛠️ because one size never fits all in this galaxy of tech gibberish. 🌌🔧
https://composio.dev/blog/gemini-2-5-pro-vs-claude-3-7-sonnet-coding-comparison/ #Gemini2.5 #Claude3.7 #Composio #TechBuzzword #CustomSolutions #HackerNews #ngated

🚀🤖 In the epic showdown of Gemini 2.5 vs. Claude 3.7, we dive into the riveting world of Composio's endless jargon salad 🥗. Spoiler alert: after mastering the art of buzzword origami, you'll still need a "custom solution" 🛠️ because one size never fits all in this galaxy of tech gibberish. 🌌🔧
https://composio.dev/blog/gemini-2-5-pro-vs-claude-3-7-sonnet-coding-comparison/ #Gemini2.5 #Claude3.7 #Composio #TechBuzzword #CustomSolutions #HackerNews #ngated

🚀🤖 In the epic showdown of Gemini 2.5 vs. Claude 3.7, we dive into the riveting world of Composio's endless jargon salad 🥗. Spoiler alert: after mastering the art of buzzword origami, you'll still need a "custom solution" 🛠️ because one size never fits all in this galaxy of tech gibberish. 🌌🔧
https://composio.dev/blog/gemini-2-5-pro-vs-claude-3-7-sonnet-coding-comparison/ #Gemini2.5 #Claude3.7 #Composio #TechBuzzword #CustomSolutions #HackerNews #ngated

🚀🤖 In the epic showdown of Gemini 2.5 vs. Claude 3.7, we dive into the riveting world of Composio's endless jargon salad 🥗. Spoiler alert: after mastering the art of buzzword origami, you'll still need a "custom solution" 🛠️ because one size never fits all in this galaxy of tech gibberish. 🌌🔧
https://composio.dev/blog/gemini-2-5-pro-vs-claude-3-7-sonnet-coding-comparison/ #Gemini2.5 #Claude3.7 #Composio #TechBuzzword #CustomSolutions #HackerNews #ngated

🚀 Comprehensive Guide to Building with #Groq API

🔧 Key Components:
• Complete examples for building chatbots, RAG systems & #SQL applications using #LangChain, #LlamaIndex & #DuckDB
• Integration tutorials with popular tools like #Streamlit, #Portkey, #JigsawStack & #E2B
• Ready-to-use #Replit examples for quick experimentation with different #LLM implementations
• Step-by-step guides for setting up #CodeGPT in VSCode with #Groq

💡 Featured Implementations:
• Text-to-SQL applications with JSON mode & function calling
• Presidential speeches RAG with #Pinecone
• Stock market analysis using #Llama3 function calling
• Newsletter summarizer using #Composio
• #CrewAI machine learning assistant

🛠️ Perfect for developers looking to leverage Groq's lightning-fast inference speeds in production applications.

📖 Full documentation & examples available at: https://github.com/groq/groq-api-cookbook

🚀 Comprehensive Guide to Building with #Groq API

🔧 Key Components:
• Complete examples for building chatbots, RAG systems & #SQL applications using #LangChain, #LlamaIndex & #DuckDB
• Integration tutorials with popular tools like #Streamlit, #Portkey, #JigsawStack & #E2B
• Ready-to-use #Replit examples for quick experimentation with different #LLM implementations
• Step-by-step guides for setting up #CodeGPT in VSCode with #Groq

💡 Featured Implementations:
• Text-to-SQL applications with JSON mode & function calling
• Presidential speeches RAG with #Pinecone
• Stock market analysis using #Llama3 function calling
• Newsletter summarizer using #Composio
• #CrewAI machine learning assistant

🛠️ Perfect for developers looking to leverage Groq's lightning-fast inference speeds in production applications.

📖 Full documentation & examples available at: https://github.com/groq/groq-api-cookbook

🚀 Comprehensive Guide to Building with #Groq API

🔧 Key Components:
• Complete examples for building chatbots, RAG systems & #SQL applications using #LangChain, #LlamaIndex & #DuckDB
• Integration tutorials with popular tools like #Streamlit, #Portkey, #JigsawStack & #E2B
• Ready-to-use #Replit examples for quick experimentation with different #LLM implementations
• Step-by-step guides for setting up #CodeGPT in VSCode with #Groq

💡 Featured Implementations:
• Text-to-SQL applications with JSON mode & function calling
• Presidential speeches RAG with #Pinecone
• Stock market analysis using #Llama3 function calling
• Newsletter summarizer using #Composio
• #CrewAI machine learning assistant

🛠️ Perfect for developers looking to leverage Groq's lightning-fast inference speeds in production applications.

📖 Full documentation & examples available at: https://github.com/groq/groq-api-cookbook

🚀 Comprehensive Guide to Building with #Groq API

🔧 Key Components:
• Complete examples for building chatbots, RAG systems & #SQL applications using #LangChain, #LlamaIndex & #DuckDB
• Integration tutorials with popular tools like #Streamlit, #Portkey, #JigsawStack & #E2B
• Ready-to-use #Replit examples for quick experimentation with different #LLM implementations
• Step-by-step guides for setting up #CodeGPT in VSCode with #Groq

💡 Featured Implementations:
• Text-to-SQL applications with JSON mode & function calling
• Presidential speeches RAG with #Pinecone
• Stock market analysis using #Llama3 function calling
• Newsletter summarizer using #Composio
• #CrewAI machine learning assistant

🛠️ Perfect for developers looking to leverage Groq's lightning-fast inference speeds in production applications.

📖 Full documentation & examples available at: https://github.com/groq/groq-api-cookbook

🚀 Comprehensive Guide to Building with #Groq API

🔧 Key Components:
• Complete examples for building chatbots, RAG systems & #SQL applications using #LangChain, #LlamaIndex & #DuckDB
• Integration tutorials with popular tools like #Streamlit, #Portkey, #JigsawStack & #E2B
• Ready-to-use #Replit examples for quick experimentation with different #LLM implementations
• Step-by-step guides for setting up #CodeGPT in VSCode with #Groq

💡 Featured Implementations:
• Text-to-SQL applications with JSON mode & function calling
• Presidential speeches RAG with #Pinecone
• Stock market analysis using #Llama3 function calling
• Newsletter summarizer using #Composio
• #CrewAI machine learning assistant

🛠️ Perfect for developers looking to leverage Groq's lightning-fast inference speeds in production applications.

📖 Full documentation & examples available at: https://github.com/groq/groq-api-cookbook