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  • Build Your First AI Agent with Gemini, n8n, Cloud Run
    Build Your First AI Agent with Gemini, n8n, Cloud Run

    By

    @miketamir

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    Build your first AI Agent with Gemini, n8n and Google Cloud Run https://
    buff.ly/zEKdNgk
    #AI #MachineLearning #DeepLearning #LLMs #DataScience

    → View original post on X — @miketamir

  • Mastering spaCy: Build NLP Solutions with spacy-llm
    Mastering spaCy: Build NLP Solutions with spacy-llm

    By

    @kirkdborne

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    Mastering spaCy — Build structured NLP solutions with custom components & models powered by spacy-llm: http://
    amzn.to/3QpPfXi v/ @PacktDataML [2nd edition] 𝓦𝓱𝓪𝓽 𝔂𝓸𝓾 𝔀𝓲𝓵𝓵 𝓵𝓮𝓪𝓻𝓷:
    Apply transformer models and fine-tune them for specialized NLP tasks
    Master

    → View original post on X — @kirkdborne

  • Cursor AI Accelerates Code Shipping Across Complex Codebases

    By

    @nvidiaai

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    .
    @cursor_ai is helping us ship 3× more committed code across large, complex codebases. By accelerating onboarding and automating workflows from code generation to debugging, we can scale development quickly — with measurable gains in both speed and quality. Learn more →

    → View original post on X — @nvidiaai

  • AgentLinter: Security Linter for Agent Configuration Files
    AgentLinter: Security Linter for Agent Configuration Files

    By

    @ki_young_ju

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    For vibe coders: claude.md is the law your agent must follow. If it's naive, the agent trusts everything and leaks everything to hackers. AgentLinter.com scans and fixes claude.md security issues in 30 seconds. It found 15 security issues in my agent. Highly recommend if you're running OpenClaw. Simon Kim (@simonkim_nft) AgentLinter is here! Is your agent sharp & secure? I built AgentLinter, a linter for CLAUDE.md and agent config files. Here's why. agentlinter.com Whether you're vibe-coding or agent-coding, your AI's output quality comes down to one thing: how well you wrote your CLAUDE.md. But managing these files properly? Way harder than it looks. 🎯 The Silent Failure Problem Vague instructions like "write good code" let the agent interpret however it wants. Output gets inconsistent, but nothing throws an error. The failure is silent. Anthropic's own docs say write "Use 2-space indentation" not "Format code properly." But as the file grows, spotting these with your eyes alone is nearly impossible. 🔐 The Security Problem People hard-code API keys and tokens directly into CLAUDE.md or TOOLS.md and commit them, way more often than you'd think. AgentLinter stats show 1 in 5 workspaces has exposed credentials. .gitignore doesn't catch secrets buried inside markdown files. 💥 The Consistency Problem Multiple config files = contradictions. SOUL.md says "be a friendly assistant," CLAUDE.md says "concise, direct tone." The agent gets confused. TOOLS.md references files that don't exist. Past 5 files, these conflicts triple. So I thought: CLAUDE.md is code. Code has ESLint. Why doesn't this have a linter? 🔍 What AgentLinter Does It diagnoses your agent config across 8 categories: 1) Structure: file organization 2) Clarity: instruction specificity 3) Completeness: missing definitions 4) Security: exposed secrets 5) Consistency: cross-file contradictions 6) Memory: session handoff 7) Runtime Config: gateway/auth settings 8) Skill Safety: dangerous shell commands & injection patterns Each scored 0–100 with concrete fix suggestions. Write "be helpful" and it tells you to specify response length, tone, and format. Find an API key? Instant CRITICAL alert to rotate. 🔒 Privacy-First & 100% Local Everything runs on your machine. Files never leave. Only the results are shared, and you can turn that off in settings. This matters — these files can contain system prompts, security rules, and personal context. Fully open source, MIT license, 100% free. 🛠️ Multi-Tool Support Works with Claude Code, Cursor, Windsurf, and Clawdbot. Detects CLAUDE.md for project mode, AGENTS.md or clawdbot.json for agent mode and adjusts diagnostics automatically. 🚀 Get Started with one line npx agentlinter Node.js 18+, no config needed. Run it, check your score, fix what needs fixing. Happy vibe-coding & happy agent life! 🤙 Website: agentlinter.com Github: github.com/seojoonkim/agentl… — https://nitter.net/simonkim_nft/status/2020004197693845716#m

    → View original post on X — @ki_young_ju, 2026-02-07 15:46 UTC

  • Agentic Architectural Patterns for Multi-Agent GenAI Systems
    Agentic Architectural Patterns for Multi-Agent GenAI Systems

    By

    @kirkdborne

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    New release v/ @PacktDataML @PacktPublishing "Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems" See it at http://
    amzn.to/3MaHy8T 𝕋𝕒𝕓𝕝𝕖 𝕠𝕗

    → View original post on X — @kirkdborne

  • Scikit-learn Cookbook (3rd Ed.) — 80+ Machine Learning Recipes
    Scikit-learn Cookbook (3rd Ed.) — 80+ Machine Learning Recipes

    By

    @kirkdborne

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    Scikit-learn Cookbook — 80+ recipes for #MachineLearning in Python with scikit-learn [3rd Edition]: http://
    amzn.to/4oDGOq7 v/ @PacktDataML 𝓒𝓸𝓷𝓽𝓮𝓷𝓽𝓼:
    Common Conventions & API Elements of Scikit-Learn
    Pre-Model Workflow and Data Preprocessing
    Dimensionality

    → View original post on X — @kirkdborne

  • Design Scalable Generative AI with LangChain on Google Cloud
    Design Scalable Generative AI with LangChain on Google Cloud

    By

    @kirkdborne

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    Generative AI on Google Cloud with #LangChain — Design scalable #GenerativeAI solutions with #Python, LangChain, and Vertex AI on Google Cloud: http://
    amzn.to/4frbkPA v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
    Turn challenges into opportunities by learning advanced techniques

    → View original post on X — @kirkdborne

  • Practical Guide to Building LLM and RAG Apps with LangChain
    Practical Guide to Building LLM and RAG Apps with LangChain

    By

    @kirkdborne

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    "Generative AI and RAG for Beginners: A Practical Step-by-Step Guide to Building LLM and RAG Applications with LangChain and Python" Get your copy at http://
    amzn.to/3MZZ9R5 Independently published: December 2025
    Print length: 255 pages

    → View original post on X — @kirkdborne

  • LangChain crash course for building OpenAI LLM apps
    LangChain crash course for building OpenAI LLM apps

    By

    @kirkdborne

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    #LangChain Crash Course — Fast track to building OpenAI LLM-powered Apps using #Python: http://
    amzn.to/3TFlQJT
    —————
    #LLMs #AI #DeepLearning #GenerativeAI #ML #MachineLearning #LLMOps #DataScience #DataScientist

    → View original post on X — @kirkdborne

  • The AI Agent Blueprint: Launch an Agent in 30 Days
    The AI Agent Blueprint: Launch an Agent in 30 Days

    By

    @kirkdborne

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    The AI Agent Blueprint — A Practical Playbook for Building Agentic Artificial Intelligence — Launch Your First Agent in 30 Days: http://
    amzn.to/4qHbpVP

    → View original post on X — @kirkdborne