Introducing MotiaDev: A unified backend framework for APIs, jobs, workflows, and AI agents

Wassette redefines how AI agents access and run tools. Built on WebAssembly and the Wasmtime runtime, Wassette enables agents to autonomously fetch and execute OCI-hosted components with secure sandboxing and fine-grained permissions. It’s a powerful leap toward scalable, modular, and secure AI workloads—fully open source and ready to integrate with MCP-compatible agents. Full article available at: https://msft.it/6049snaxh

DeepSeek just dropped V3.1 and it's a complete game-changer for AI agents! This isn't just another model update - it's the first AI system that can actually THINK when it needs to and execute fast when it doesn't. One model, two modes: hybrid inference that switches between deep reasoning and rapid responses. The performance gains are staggering. DeepSeek-V3.1-Think solves complex problems faster than their previous reasoning model, while the agent capabilities have been completely transformed. We're talking about real improvements in multi-step reasoning, tool usage, and handling complex search tasks that actually matter for business applications. What caught my attention most? The pricing strategy. They're keeping current rates until September 2025, giving enterprises time to integrate before any changes kick in. Smart move that shows they're thinking long-term adoption, not quick profits. The open-source weights are already available, and the API now supports both Anthropic format and strict function calling. This means developers can start building production-ready agent workflows today without vendor lock-in concerns. The benchmark results speak for themselves - significant improvements across SWE-bench and Terminal-Bench, the gold standards for measuring real-world coding and system administration capabilities. This feels like the moment when AI agents transition from impressive demos to essential business tools. The efficiency gains in thinking processes alone could reshape how we approach complex problem-solving in enterprise environments. #AI #MachineLearning #ArtificialIntelligence #AIAgents #DeepLearning #Technology #Innovation #OpenSource #APIs #BusinessIntelligence #Automation #TechNews #FutureOfWork #SoftwareDevelopment #DigitalTransformation

Time for the next version of Aici VS Code Extension - now it supports: /update — AI automated file updates /commit — AI-generated git commit messages /plan — AI file change plans /build — AI-assisted build commands with error fixes https://lnkd.in/gSJt2Fuv

An API that works isn’t necessarily a successful API. Success = 𝐀𝐃𝐎𝐏𝐓𝐈𝐎𝐍. Think of an API like a product — it’s only successful if people want to use it. Some of the most widely adopted APIs today: 𝐆𝐨𝐨𝐠𝐥𝐞 𝐌𝐚𝐩𝐬 𝐀𝐏𝐈 – made geospatial data accessible and easy to integrate. 𝐒𝐭𝐫𝐢𝐩𝐞 𝐀𝐏𝐈 – turned complex payments into a few lines of code. 𝐓𝐰𝐢𝐥𝐢𝐨 𝐀𝐏𝐈 – unlocked SMS, voice, and communication features with simple endpoints. 𝐎𝐩𝐞𝐧𝐀𝐈 𝐀𝐏𝐈 – opened up powerful AI tools to developers everywhere. 𝐆𝐢𝐭𝐇𝐮𝐛 𝐀𝐏𝐈 – streamlined CI/CD and developer workflows. APIs aren’t just technical assets. They’re 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧-𝐝𝐫𝐢𝐯𝐞𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐞𝐦 𝐰𝐢𝐭𝐡 𝐲𝐨𝐮𝐫 𝐞𝐧𝐝 𝐮𝐬𝐞𝐫𝐬 𝐢𝐧 𝐦𝐢𝐧𝐝, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐲𝐨𝐮𝐫 𝐬𝐲𝐬𝐭𝐞𝐦.

🚀 Biweekly Update #4 — Strengthening APIs & AI-Driven Error Handling Continuing from last week’s progress on clean APIs and workflows, this week focused on eliminating hardcoding, building a consistent error-handling framework, and integrating AI for smarter responses. 🔑 Key Progress & Learnings: 1️⃣ Removed Hardcoding → Refactored configurations to replace residual hardcoded values, making the system environment-agnostic and flexible. 2️⃣ Error Handling System → Developed a standardized framework for success & failure responses across APIs, ensuring consistency for consumers. 3️⃣ AI for Generic Concepts → Leveraged AI to accelerate boilerplate generation and simplify recurring development patterns. 4️⃣ Ollama AI Integration → Added AI-driven error summarization to generate concise, human-readable messages for Stripe client/server errors, with fallback mechanisms to ensure production stability. 5️⃣ Feature Development → Merged feature/create-session-api (PR #2), expanding payment workflows and centralizing session handling. 💡 Biggest takeaway: Consistency and clarity go hand-in-hand—AI can supercharge workflows, but stability and reliability for API consumers must always come first. 📌 Next Steps: The focus now shifts to expanding session lifecycle APIs, integrating database persistence for transactions, and gradually enhancing transaction flows with versioning and status tracking to make the system more resilient. #BackendDevelopment #SpringBoot #APIDesign #MicroservicesArchitecture #ErrorHandling #StripeIntegration #AIIntegration #CleanCode #ProductEngineering

🔽 This video dives into the intelligent architecture behind the PyAirbyte MCP remote server. You'll see how robust contextualization—using OpenAI's file storage with context from our 600+ connectors—ensures the AI generates accurate, best-practice PyAirbyte code. Understanding this technical foundation means you can trust the AI-generated code, accelerate your workflows, and build with confidence. 👍 https://lnkd.in/ggUnvxHb

This blog post describes the development of an AI agent and an MCP server for Jaqpot. These new functionalities enable users to communicate with Jaqpot through natural language and make it accessible to MCP-compatible clients and AI assistants such as Claude.

Opsera's Codeglide Launches Real-Time MCP for AI Trend: Codeglide.ai turns legacy APIs into real-time MCP servers for AI, cutting integration time by 97% and costs by 90%. Why it matters: Legacy overhangs are AI blockers—real-time API mashups are leapfrog paths. Question: What latency would you cut if you didn’t build custom adapters? 🔁 Repost if modernization isn’t monolithic 🔔 Follow me for lean AI integration 🚀 Lift AI on existing stacks

Tried out the remote dbt MCP server with Google’s Agent Development Kit. dbt doesn’t just transform data — it can become the context layer that makes AI agents reliable and trustworthy. repo link in the comments.

Whenever you deploy the LLM Application make sure you trace each and every request, response. It tell you things like: - input, output tokens - Cost for LLM Models (most important) - Tracing for Tool Calling Also it is recommended to use the Pool of the Prompts for different Scenario for Answer Generation or task execution. All these things are possible with Langfuse #MLOPS #GenerativeAI #ArtificialIntelligence