💻 𝐓𝐚𝐛𝐧𝐢𝐧𝐞 𝐋𝐚𝐮𝐧𝐜𝐡𝐞𝐬 𝐀𝐠𝐞𝐧𝐭𝐢𝐜: 𝐍𝐞𝐱𝐭-𝐆𝐞𝐧 𝐀𝐈 𝐂𝐨𝐝𝐢𝐧𝐠 𝐏𝐚𝐫𝐭𝐧𝐞𝐫𝐬 𝐟𝐨𝐫 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 Tabnine has unveiled Tabnine Agentic, an evolution in AI-powered enterprise software development. This platform introduces autonomous coding partners that execute full development workflows while maintaining strict compliance with company standards and security policies. 💡 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅ Org-Native Agents: Powered by Tabnine’s Enterprise Context Engine, these agents understand repositories, tools, and policies to plan, execute, and validate multi-stage coding tasks securely. ✅ Adaptive & Autonomous: Agents automatically adapt to new codebases and workflows without retraining, handling tasks like refactoring, debugging, and documentation. ✅ Contextual Intelligence: Integration with internal systems—ticketing, logs, coding guidelines—ensures precise, context-aware results. ✅ Governance & Compliance: Centralized monitoring of permissions, usage, and context supports auditability across the enterprise. ✅ Flexible Deployment: Available as SaaS, private VPC, on-premises, or air-gapped setups, meeting stringent enterprise security standards. ✅ Transparent Pricing Model: Usage-based pricing with no hidden fees allows enterprises to maintain full control over LLMs, workflows, and environments. 𝐑𝐞𝐚𝐝 𝐌𝐨𝐫𝐞 : https://lnkd.in/eKD5YwyD #Tabnine #AI #EnterpriseAI #AgenticAI #GenAI #SoftwareDevelopment #CodingAI #OpenAI #LLM #AIWorkflow #AutonomousAI #EnterpriseAutomation

Anthropic’s Claude is an AI Agent that gets smarter as the number of skills you create and upload increases. Even more impressive is its ability to generate and troubleshoot very detailed skills from simple instructions. Proof? I’ve successfully completed the following — a fairly complex task — solely by interacting with Claude using natural language: Generating a Web of Data from a collection of relational tables. In other words, entity relationships originally represented in one form (n-tuples or tables) are transformed into another (3-tuples or triples) and then deployed using Linked Data principles. The result is that entities, entity types, and relationships are all denoted by hyperlinks that resolve to entity description documents in HTML (or any other negotiated document type). Why Should You Care? Fine-grained, structured data representations built from hyperlinks — whether public or private — are extremely powerful vehicles for accessing and sharing relevant information. Message quality improves exponentially when entities are named using hyperlinks that resolve to meaningful context. This simple mechanism eliminates procrastination and promotes engagement, since the trigger action is merely a mouse click (or a thumb press on a smartphone). This is how the World Wide Web era freed the world from the application-specific document constraints of the PC Era, and how the Agentic Web era takes it to new levels — shifting the focus to context enabled by Knowledge Graphs. Claude demonstrates this when used alongside our OPAL (OpenLink Software AI Layer) and Virtuoso platform combo for modern, sophisticated management of Data Spaces (databases, knowledge bases/graphs, filesystems, and APIs). I encourage you to watch the attached screencast and check the live links to additional information in the comments section. A massive change is happening right now, and it’s actually useful and highly impactful — with nothing to do with distractions like AGI or SGI. Fundamentally, individuals and enterprises are going to be profoundly affected, and upskilling is the single most important action at this crucial moment. Of course, if you engage with us at OpenLink Software, we’ll get you there pronto! #CDO #CDIO #CAIO #CIO #CTO #CMO #AI #Explainer #GenAI #KnowledgeGraphs #ClaudeSkills #SemanticWeb #VirtuosoRDBMS #OPAL #HowTo #LinkedData #MCP

IDEs are mature and polished for developers. But here’s the thing: AI doesn’t need to be limited to one developer, one environment. That’s why we built AdaL CLI. When AI Coding exists as a CLI, everything changes: ✅ It integrates directly with CI/CD pipelines for automated code review and feedback. ✅ It can spin up multiple AI Agents working in parallel across branches. ✅ It connects to different MCP servers and drives tools automatically. ✅ It has access to a richer context, encouraging better context management. With AdaL CLI, AI development becomes more scalable, structured, and powerful. 👉 Waiting list in the comment #machinelearning #artificialintelligence #sylphai #adalflow

🧠 “Your model isn’t failing. Your context is.” We’ve hit the limit of “chat with an agent, hope for the best.” In enterprise codebases, the win isn’t a smarter LLM, it’s context engineering: what the agent sees, when it sees it, and how that knowledge evolves. Here’s the playbook I’m seeing work across real teams: Treat specs as contracts, not artifacts • Research → Plan → Implement • Research = map the system (files, lines, data flows) • Plan = exact changes + tests to verify • Implement = execute with context < 40% utilized Practice intentional compaction • Persist a living “progress file” (what’s done, what’s pending, open questions) • Feed that to the next step/agent; archive the rest • Kill noisy blobs (logs, giant JSON) that don’t advance the next decision Make agents provable, not persuasive • Guardrails = unit/integration tests, CI, license checks • PRs originate from a reviewed spec; humans approve intent, agents execute • Version the context (not just code) so you can audit decisions Start where agents shine • Well-bounded subsystems, refactors, test gen, dependency upgrades • Expand scope only when review time ↓ and defect rate stays flat Hot take: “AI coding agents” will commoditize. Workflow and context will not. The orgs that win will version context like code, measure token → impact like infra, and align humans around specs, not chat transcripts. If you’re piloting agents: what broke first—tests, context, or trust? #AI #SoftwareEngineering #LLM #Agents #MLOps #DevEx #PlatformEngineering #EnterpriseAI #ContextEngineering

🔌 We've been building AI integrations all wrong! Every time ChatGPT needs Slack access, custom integration. Claude needs to read your database? Another custom build. Gemini wants to connect to Notion? You guessed it—custom again. This is the "AI integration mess" we've been living in. Magical Solution: Model Context Protocol (MCP) 🚀 Think of it as USB for AI. One standard. Infinite connections. The magic of MCP in 3 points ⚡ → Build ONCE, connect to ANY LLM → Your database speaks the same language to Claude, GPT, or Gemini → No more rebuilding integrations for every new AI tool The workflow 🔄 MCP Server (your integration) ↓ MCP Host (the application) ↓ MCP Client (any LLM) What you can do with MCP today 🛠️ ✓ Give AI access to your private databases ✓ Connect to Slack, GitHub, Notion seamlessly ✓ Build custom tools that work across all LLMs ✓ Create file system access in minutes The best part? You write your MCP server once, and every compatible AI assistant can use it immediately. No vendor lock-in. No redundant code. Just clean, standardized connections. If you're building AI products in 2025, MCP isn't optional anymore—it's infrastructure. Have you started exploring MCP yet? #AI #MCP #LLM #TechInnovation #DeveloperTools #Anthropic #OpenAI #OpenSource #AIIntegration

🚀 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗔𝗜 𝗴𝗮𝘁𝗲𝘄𝗮𝘆 𝗶𝗻 𝘂𝗻𝗱𝗲𝗿 𝟮 𝗺𝗶𝗻𝘂𝘁𝗲𝘀  Your AI tools deserve a happy ending. 𝘌𝘷𝘦𝘳 𝘧𝘦𝘭𝘵 𝘭𝘪𝘬𝘦 𝘺𝘰𝘶𝘳 𝘓𝘓𝘔𝘴 𝘢𝘯𝘥 𝘙𝘈𝘎 𝘥𝘢𝘵𝘢 𝘴𝘰𝘶𝘳𝘤𝘦𝘴 𝘢𝘳𝘦 𝘨𝘩𝘰𝘴𝘵𝘪𝘯𝘨 𝘦𝘢𝘤𝘩 𝘰𝘵𝘩𝘦𝘳? Different APIs, endless auth flows, and more “𝘸𝘩𝘺 𝘸𝘰𝘯’𝘵 𝘵𝘩𝘪𝘴 𝘤𝘰𝘯𝘯𝘦𝘤𝘵?!” moments than you can count? 😩 𝗠𝗲𝗲𝘁 𝗦𝘁𝗼𝗿𝗺 𝗠𝗖𝗣 — your enterprise-grade gateway that makes Large Language Models and AI tools actually talk (and cooperate). 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝘀𝘁𝗼𝗿𝗺-𝗹𝗲𝘃𝗲𝗹 𝗰𝗼𝗼𝗹 🌩️ ⚡ 𝗢𝗻𝗲 𝗚𝗮𝘁𝗲𝘄𝗮𝘆, 𝗭𝗲𝗿𝗼 𝗖𝗵𝗮𝗼𝘀: Connect Claude Desktop, Cline, or Claude Code — instantly. 🧩 𝟭𝟬𝟬+ 𝗩𝗲𝗿𝗶𝗳𝗶𝗲𝗱 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿𝘀: GitHub, Slack, Brave Search, Notion, Airtable & more. 🔒 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: OAuth2, API keys, encrypted creds — all built-in. 👀 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Watch every request, response, and “oops” in action. 🧠 𝗟𝗟𝗠 + 𝗥𝗔𝗚 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Context sharing for smarter, faster AI output. 𝗜𝘁’𝘀 𝗹𝗶𝗸𝗲 𝗭𝗮𝗽𝗶𝗲𝗿 𝗳𝗼𝗿 𝗔𝗜 — 𝗯𝘂𝘁 𝘄𝗶𝘁𝗵 𝘀𝘂𝗽𝗲𝗿𝗽𝗼𝘄𝗲𝗿𝘀. 𝘍𝘳𝘰𝘮 “𝘩𝘦𝘭𝘭𝘰 𝘸𝘰𝘳𝘭𝘥” 𝘵𝘰 𝘦𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦-𝘳𝘦𝘢𝘥𝘺 𝘪𝘯 𝘮𝘪𝘯𝘶𝘵𝘦𝘴. 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗔𝗜 𝗴𝗮𝘁𝗲𝘄𝗮𝘆 𝗶𝗻 𝘂𝗻𝗱𝗲𝗿 𝟮 𝗺𝗶𝗻𝘂𝘁𝗲𝘀: 🔗 https://tryit.cc/ykA81J Your AI workflow will thank you later. 😊 #StormMCP #AIIntegration #RAG #LLM #AIGateway #AIDevelopment #EnterpriseAI #Anthropic #MCP #Developers #AIAutomation #AItools #TechInnovation #SecureAI #FutureOfWork #APISimplified #Productivity #ZapierForAI #StormPlatform

Model Context Protocol (MCP): The Universal Language for AI Tool Integration What is MCP? The open standard revolutionizing how AI assistants connect with external data and tools. Think USB-C for AI—one universal interface for any model to access any data source. The Problem It Solves: Before MCP, every AI integration meant custom code for each LLM provider. MCP eliminates this fragmentation with a standardized protocol. *Core Architecture* Client-Server Model: - MCP Hosts (AI assistants, IDEs) connect to servers - MCP Servers expose resources, tools, and prompts - Bidirectional communication with structured protocols Three Key Primitives: Resources: Contextual data (files, databases, APIs) - URI based addressing - Dynamic discovery Tools: Functions AI can invoke - Execute code, query databases, call APIs - Structured schemas with validation Prompts: Reusable templates - Pre-configured workflows - Context aware suggestions *Why It Matters* - Universal Compatibility - Write once, use with any LLM - Enterprise Security - Local execution, OAuth 2.0, granular controls - Production Ready - TypeScript & Python SDKs, built-in error handling *Real-World Use Cases* - Development: AI with GitHub, databases, deployment access - Enterprise: Unified CRM, ERP, data warehouse connections - Research: Multi-source queries via natural language - Automation: Cross-platform tool chains *Technical Edge* Unlike basic function calling (which is model-specific), MCP works at the infrastructure level. And unlike orchestration frameworks like AutoGen or LangGraph, MCP is the transport layer—those frameworks can actually use MCP servers as their tools. *Getting Started* Resources: - Specification: modelcontextprotocol.io - Python SDK: pip install mcp - TypeScript SDK: npm install @modelcontextprotocol/sdk - Pre-built servers: GitHub, Postgres, Slack, Google Drive *Bottom Line* MCP is foundational infrastructure for agentic AI. Standardized, secure system integration—built for the next decade. - For Engineers: Build tools once, use everywhere - For Platform Teams: Standardize AI data access - For Product Teams: Ship AI features faster #MCP #ModelContextProtocol #AgenticAI #AIInfrastructure #LLM #AIEngineering #OpenSource #EnterpriseAI #DeveloperTools #AIIntegration

# 𝟮.𝟰 Tools exist. Resources exist. But WHO GOVERNS how the AI uses them? Answer: 𝗣𝗿𝗼𝗺𝗽𝘁𝘀. Prompts are embedded instructions within the server architecture. They establish behavioral guidelines for how your AI SHOULD interact with Tools. REAL COMPARISON: Unprofessional approach: AI creates a GitHub issue as: "Fix button" Professional approach: AI creates a GitHub issue as: "Fix Login Button - Steps: 1. Open login page 2. Click button 3. Error appears. Expected: Button works. Actual: Error displays." THE DIFFERENCE? PROMPTS. The server instructs your AI: "When creating issues, ALWAYS include: Steps to Reproduce, Expected Behavior, Actual Behavior, Environment Details." WHAT PROMPTS DELIVER: --> Quality standardization across all outputs --> Consistency in format and structure --> Predictable, reliable AI behavior --> Maximum ROI from your Tool implementations WITHOUT PROMPTS: --> Variable output quality --> Inconsistent formatting across tasks --> Unpredictable AI decision patterns --> Underutilized Tool potential WITH PROMPTS: --> Enterprise-grade output standards --> Uniform professional results --> Reliable, predictable operations --> Complete Tool utilization CRITICAL INSIGHT: Well-architected servers with clear Prompts are invaluable assets. They enforce quality. They ensure consistency. They scale reliably. Part # 2.5 coming next: The complete integration - how Tools, Resources, and Prompts work in concert #MCP #AI #Protocol #MachineLearning #SoftwareArchitecture #BestPractices #Developers #SystemDesign

AI tools have transformed how developers code — but how do we measure that transformation? 🤔 That’s the problem GitKraken Insights aims to solve. The new platform helps teams understand how AI impacts developer productivity, code quality, and workflow efficiency — all while respecting developer privacy. It combines DORA metrics, AI impact tracking, and developer feedback to reveal where AI truly adds value — and where friction still exists. For open-source maintainers and contributors, this could mean smarter data on how automation, Copilot, and AI-based reviews influence contribution velocity and technical debt. Transparency without surveillance. Context without bias. Insights that elevate developer experience for everyone involved. #OpenSource #GitHubInsights #GitKraken #DeveloperExperience #AIMetrics #SoftwareEngineering

🚨 𝐓𝐨𝐩𝐢𝐜: “𝐓𝐡𝐞 𝐀𝐈 𝐔𝐩𝐠𝐫𝐚𝐝𝐞 𝐍𝐨𝐛𝐨𝐝𝐲’𝐬 𝐑𝐞𝐚𝐝𝐲 𝐅𝐨𝐫 — 𝐀𝐠𝐞𝐧𝐭 𝐓𝐨𝐨𝐥𝐬 & 𝐌𝐂𝐏” 𝐒𝐡𝐨𝐜𝐤𝐢𝐧𝐠 𝐅𝐚𝐜𝐭: Your favorite LLM is basically useless without 𝐭𝐨𝐨𝐥𝐬. And thanks to MCP (Model Context Protocol), those tools are becoming an entire interconnected ecosystem — like giving AI its own app store, but with way more power and way more ways to break things. Here’s what’s actually changing (and why it matters): ✅ 𝐓𝐨𝐨𝐥𝐬 = 𝐚𝐧 𝐀𝐈’𝐬 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 No tools → no actions. With tools → AI can call APIs, fetch data, run code, update systems, automate workflows. This is how LLMs turn into agents. ✅ 𝐓𝐰𝐨 𝐤𝐢𝐧𝐝𝐬 𝐨𝐟 𝐭𝐨𝐨𝐥𝐬 Tools that help AI 𝐤𝐧𝐨𝐰 things (retrievers, search, RAG) Tools that help AI 𝐝𝐨 things (APIs, workflows, systems) This is the foundation of action-ready agents. ✅ 𝐀𝐠𝐞𝐧𝐭 𝐭𝐨𝐨𝐥𝐬 = 𝐀𝐈 𝐝𝐞𝐥𝐞𝐠𝐚𝐭𝐢𝐧𝐠 𝐭𝐨 𝐀𝐈 Agents can call other agents as tools. Meaning they can break down tasks, hand off work, and coordinate — without losing context. ✅ 𝐌𝐂𝐏 𝐦𝐚𝐤𝐞𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐥𝐞 No more messy custom integrations. One standard, plug-and-play way for models and tools to talk. Think “USB for AI capabilities.” ✅ 𝐁𝐮𝐭 𝐌𝐂𝐏 𝐮𝐧𝐥𝐨𝐜𝐤𝐬 𝐧𝐞𝐰 𝐫𝐢𝐬𝐤𝐬 Malicious tool definitions Privilege escalation Data leaks Shadowed tools When AI can act, mis-actions become dangerous. ✅ 𝐓𝐡𝐞 𝐤𝐞𝐲 𝐢𝐬 𝐜𝐥𝐞𝐚𝐧, 𝐩𝐫𝐞𝐜𝐢𝐬𝐞 𝐭𝐨𝐨𝐥𝐢𝐧𝐠 Small tools. Clear docs. Strict schemas. Validated outputs. Because sloppy tools = sloppy agents. 💀 Reality Check If you still think “agents” means “ChatGPT with extra steps,” you’re missing the real shift. We’re building 𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝, 𝐭𝐨𝐨𝐥-𝐝𝐫𝐢𝐯𝐞𝐧 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐰𝐨𝐫𝐤𝐟𝐨𝐫𝐜𝐞𝐬 — where models, tools, and protocols operate like a full operating system. 💬 Your Turn What’s the first tool capability you’d give an agent — data access, automation, or code execution? Comment below 👇 #ArtificialIntelligence #AIAgents #MCP #AgentTools #AITools #ModelContextProtocol #GenerativeAI #Automation #FutureOfWork #TechTrends #NoFluffAI