Engineering Tools For Virtual Collaboration

🛠️ We just published a bunch of updates to Freeplay that make it *much* easier to manage the end-to-end workflow of building agentic AI systems that use tools. One of the biggest trends with AI product development that we’ve seen is an ongoing shift in roles, as subject matter experts learn to work alongside engineers on prompt engineering, evals and QA. We’ve been building Freeplay in part to support this shift — giving everyone on a product development team the ability to contribute and collaborate, even if they don’t write code. Tools (aka function calls) introduce a wrinkle. They’re essential for building AI products that take actions in other systems, and they're also intrinsically code. But we also see lots of teams who want to experiment, test and eval tool-using systems in a UI, either because it can help engineers move faster at times, or because non-engineering roles need to get involved. We've made some big updates to how Freeplay handles tools to solve these challenges — making it much easier for anyone on a product development team to collaborate on building great AI agents and other products that use tools. tl;dr on what's new (more details in the blog post, linked in comments): 🔄 Tool schemas can now be managed and versioned in Freeplay alongside prompts, and updates deployed to your code like feature flags 🏗️ Tools can be created in code OR the Freeplay UI, whichever workflow you prefer. Engineers maintain control while enabling collaboration. 🧑🔬 Anyone can experiment freely in our playground and see how models interact with various tools ↔️ The Freeplay SDK makes it seamless to swap between model providers – we reformat tools for you 📊 End-to-end dataset management, testing & evaluation support for tools makes it easy to run batch tests from the Freeplay UI or SDK, including evals that check attributes of tool behavior 🔎 Clear log rendering in our observability features. See how tools are being used without digging through stack traces. Engineers get the detailed data they need, while PMs & analysts can easily track agent behavior. Tool use is becoming the norm for AI product teams, and figuring out the right workflows will be key. We’re excited to see how these changes help. Blog post in the comments! Let us know what you think. 👇

🔧 Teams that work in different areas of technology have preferences or considerations they must think through. Mutable, Immutable? #Ansible or #OpenTofu? Scripting or Controllers? ⚠️ Problems tend to arise when we put on our one-size-fits-all hat. Trying to standardize around a single tool across all technical domains can be expensive and ineffective. The real key to success? 🔹 A flexible orchestration layer 🔹 Each team contributing with the most effective tool for their domain 🔹 Governance and consistency in outcomes 💡 Bottom Line: Focus less on tool standardization and more on outcome standardization. Build orchestration that's flexible enough to work with the tools that make your teams productive, and make it easier for those teams to use that tooling securely. ❓ What's been your experience balancing tool flexibility with governance needs? Itential Holly Holcomb #Automation #DevOps #Orchestration #NetDevOps #NetworkEngineer #ITStrategy

I've been exploring how to eliminate the "déjà vu in development" problem with deja-view - a semantic search tool built with Chroma that transforms GitHub issues into high-dimensional vectors. We've deployed it live to the Continue repo and I look forward to tackling parallels of open source development. The real game-changer comes when I get this integrated into our MCP to help identify issues current work will fix. Instead of just finding similar issues, you can now: discover semantically related problems → explore safely with Plan mode + MCP → understand context through AI file analysis → implement with full historical knowledge. This workflow turns Continue agnets from code generators into project historians who truly understand your codebase evolution. This represents the future of Continuous AI in software development - moving beyond keyword matching to semantic understanding. When AI can grasp not just what you're building, but what's been built before and why, we stop reinventing wheels and start building on the shoulders of our past solutions. The potential for automatic PR review enrichment and cross-repository semantic search is just the beginning. #ContinuousAI #vectorsearch #AInative

#RAG can really boost code search and generation 💻✨ But, when dealing with large enterprise codebases, a reliable and accurate solution comes with its own set of challenges. This system, shown in the diagram, processes code files, breaks them into meaningful chunks, generates descriptive text, and creates vector embeddings for efficient searching. Moreover, it does this continuously to support code changes. A simple vector similarity search often gives irrelevant results upon a user's query, so a two-step retrieval process is better. First, an initial vector search is applied, followed by an LLM-based refinement to accurately rank the results based on the query's context. Note that proper code chunking is crucial because it affects the quality of the code snippets you get back. While chunking text is straightforward, chunking code is more complex and needs advanced techniques to keep the code's semantics intact. To address this, CodiumAI has developed a comprehensive indexing pipeline capable of handling the scale and complexity of enterprise code.  The system includes a dedicated splitter for different types of files, which is also extendable (!!) to enable different dev teams to adopt the system for their internal format and practices. By effectively managing the challenges of #indexing and retrieval, we aim to bridge the gap between powerful language models and the realities of enterprise development.

Unify Piping and Structural Design and Analysis — without losing a single data point. See how CAESAR II®, CADWorx®, and GT STRUDL® now work together to connect design and analysis in one intelligent workflow. 🔁 No rework: Piping and structural data transfer automatically—saving hours and eliminating manual entry errors. 🧱 Real-world accuracy: Structural stiffness replaces the old “infinitely rigid” assumption, producing results that reflect true field behavior. 👀 3D reference model: Overlay design and analysis models directly in CAESAR II to spot discrepancies, validate restraints, and plan design changes in context. 🔄 Smarter collaboration: When engineers send post-analysis models back, designers see updates instantly—reducing revision cycles and improving communication. ⚙️ Automated data exchange: CAESAR II and GT STRUDL share loads, geometry, and results directly—no manual re-entry of massive datasets. 🎥 Watch the Interoperability Video Here: https://lnkd.in/gnTbMWry This is what digital engineering should look like: seamless data flow, accurate results, and real collaboration between disciplines. #CAESARII #CADWorx #GTSTRUDL #Hexagon #PipeStressAnalysis #StructuralAnalysis #PlantDesign #EPC

➡️ 𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐂𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐒𝐜𝐫𝐮𝐦 𝐓𝐨𝐨𝐥 Your Scrum tool isn’t just software - it’s the backbone of your team’s rhythm.  Pick wrong, and workflows crumble. Pick right, and projects thrive. 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐠𝐮𝐢𝐝𝐞 𝐭𝐨 𝐦𝐚𝐭𝐜𝐡 𝐭𝐨𝐨𝐥𝐬 𝐰𝐢𝐭𝐡 𝐲𝐨𝐮𝐫 𝐭𝐞𝐚𝐦’𝐬 𝐃𝐍𝐀: ● Jira → Deep customization for complex projects. Best for teams living in Atlassian. ● Trello → Visual simplicity with Kanban. Ideal for small to medium teams. ● Azure DevOps → Microsoft-first? Perfect Scrum + CI/CD combo. ● Monday.com → Colorful, flexible workflows for collaboration across teams. ● ClickUp → One-stop hub for docs, tasks, and Scrum rituals. ● Asana → Lightweight, collaboration-driven, easy to adopt. ● Scrumwise → For Scrum purists who want simplicity + detailed metrics. ● Targetprocess → Enterprise-grade for scaling complex portfolios. ● VersionOne → Trusted for SAFe/LeSS enterprise frameworks. ● Pivotal Tracker → Iterative and software-focused. Great for continuous delivery. 🔹 The right tool isn’t “the most popular one” - it’s the one that fits your team’s size, culture, and goals. Follow Carlos Shoji for more insights on project management

A product leader asked me recently: 'Which digital tools actually move the needle?' After working with many product teams, here's my practical tech stack for 2025: 1/ Collaboration & Planning Notion for documentation ClickUp for project and task management Miro for visual collaboration 2/ Development & Testing GitHub for code management Amazon Web Services (AWS) for scalable and reliable hosting infrastructure Canny for feature requests 3/ Data & Analytics Mixpanel + Microsoft Clarity for product usage and analytics Google Analytics 4 for user behavior Databox for data dashboards 4/ Automation Tools Zapier for workflow automation Docsumo 📄 for document processing Pro tip: The goal isn't to use every tool. Pick ones that solve your biggest bottlenecks first. My team's approach: >List your top 3 time-consuming processes >Start with tools that solve these specific problems >Measure impact (time saved, error reduction) >Only then, expand your stack What's the one tool that transformed your product development process?

🧩 Solving BOM Complexity: Preventing Errors and Delays at Scale by Design to EBOM Governance Using New OpenBOM Team Settings Modern products are complex: mechanical, electrical, software—all living in different tools, managed by different teams. Here is the story told by one of the customers. A simple component, used across both systems, became a source of misalignment. In SOLIDWORKS, the component was treated as one part (SLDPRT) in the assembly. In Altium, that same switch appeared as separate components because of different electronic parameters. When the BOMs were exported and compared, they didn’t match. Procurement got confused, everything slowed down until it was resolved. Engineering lost time in conversations. Manufacturing waited, and the project was delayed This is a great example of problems OpenBOM is built to solve by providing a Collaborative Workspace which reminds you a Google Sheet on Steroids working with the complex Product Knowledge Graph, but providing a simple user experience connecting multiple pieces of product information together. The latest addition to OpenBOM is a Team Governance Model allowing to control how multiple users in your company or team are capturing data from their design to create an Engineering BOM. 🚀 In our latest blog, we walk through: ✅ The hidden risks of disconnected design BOMs ✅ How OpenBOM’s flexible data model brings structure to any product ✅ Why our CAD add-ins do more than export—they govern the data ✅ How Team Settings ensure company-wide consistency, even at scale Whether you're designing consumer products, robots, or medical devices—OpenBOM lets you move fast without compromising accuracy. 🎥 Pro tip: We even included a short demo of how OpenBOM Team Settings work in real CAD workflows. 📖 Read the full story: [Link to blog in the comment] #BOM #DigitalThread #OpenBOM #Engineering #Manufacturing #PLM #CAD #DataGovernance #DigitalTransformation #SolidWorks #Altium #ProductDevelopment

Semantic Search MCP Server Transforms GitHub Repository Access for AI Tools A developer has created a production-ready GitHub Semantic Search MCP Server that eliminates the need to clone repositories for AI code context, addressing specific limitations in tools like Cursor IDE. The system uses Cloudflare workflows to index GitHub repositories and provides semantic search capabilities through a live MCP endpoint accessible at https://lnkd.in/e3EpAnWy. The solution addresses a critical developer workflow issue where accessing recent code patterns requires manually cloning repositories since GitHub's search API is limited to public repos via Copilot chat and unavailable through GraphQL. The system supports both public and private repositories through GitHub token authentication, with indexing performance of approximately one hour per 1,000 files. Users can immediately access indexed repositories through their AI tools without local storage requirements. The technical implementation leverages Cloudflare's serverless infrastructure including D1 databases for workflow tracking, Vectorize for 384-dimensional cosine similarity searches, R2 buckets for tokenized code storage, and workflows for automated indexing. The system includes comprehensive metadata indexing for repository organization, branch management, and path-based filtering. Security considerations include RSA encryption for sensitive data at rest and self-hosting options for organizations with sensitive intellectual property. This implementation demonstrates practical serverless AI infrastructure that scales automatically while maintaining cost efficiency through throttled processing. The project establishes a replicable pattern for organizations needing semantic code search without the overhead of repository management. This showcases how Model Context Protocol can bridge existing development tools with cloud-native AI services, potentially transforming how development teams access and understand distributed codebases. 🔗https://lnkd.in/e2qeAHpW

In the chaos of startup life, we often overcomplicate. We chase the latest, shiniest tools, thinking they'll solve our problems. But they don't. They create noise. What if the answer was simpler? What if three basic tools could transform your team's performance? Here's what I have found that works: Trello Visual clarity in a world of confusion. Tasks become tangible. Progress becomes visible. Slack The digital heartbeat of your team. Where ideas flow and decisions happen in real-time. Notion Your collective brain, always accessible. Where knowledge lives and grows. These aren't just tools. They're amplifiers. They amplify focus, communication, and collective intelligence. The data speaks: - Teams waste 28% of time on email. We cut that by 25%. (McKinsey) - Collaborative teams are 5x more likely to perform highly. (i4cp) - Integrated project tools improve performance by 28%. (PMI) But tools alone aren't enough. It's how you use them that counts. We linked them. We made them talk to each other. A Trello update triggers a Slack notification, leading to a Notion document. Information flows. Knowledge compounds. Action accelerates. This isn't just about efficiency. It's about effectiveness. It's about creating an environment where great work can happen. Where your team can focus on what truly matters. Where leadership stays informed without micromanaging. Where investors see progress, not just promises. The question isn't "What tools should we buy?" It's "How can we simplify to amplify?" Start there. Start now. Your team is waiting. What's the one tool you can't live without? Share your thoughts.