Alan Kent

Many questions on the differences betweeen agent platforms. So I asked Opus in Claude code to skip the marketing content and compare Vertesia’s platform with Langchain Deep Agents, using the public documentation and source code of both. Here are its views

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As COO at www.jaiinfoway.com this is an important signal for where agentic AI is heading. Non technical implementations are catching up fast but the real story is iteration speed and learning loops. Building Cowork entirely with Claude Code shows how agents are becoming first class workers not just assistants. At www.jaiinfoway.com we see this shift clearly as enterprises move from copilots to autonomous task execution with guardrails. UX will evolve tooling will mature but the direction is set. Curious how others here see non technical users adopting agent driven workflows this year. Are we ready for agents as coworkers. #Jaiinfoway #AgenticAI #Claude #AIAgents #EnterpriseAI #FutureOfWork #AIProductivity #AITransformation #AILeadership

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So which models produce HTML that has the least accessibility errors, and at what cost? Mapped it out here (bottom left is the best). This is helpful for understanding which models are most compliant for those with disabilities and do it efficiently. Qwen and Gemini 2.5 Flash lead the pack. Hat tip to Ben Ogilvie for pointing me to this: aimac.ai

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Dashboards don’t create accountability. They create meetings. Charts get shared. Everyone nods. Action items get “noted”. Same problems. Next quarter. Accountability doesn’t come from visibility. It comes from systems — where ownership is clear and decisions can’t be ignored. I wrote a short note on why dashboards feel productive but rarely change behaviour — and what actually does. If your org has great dashboards but the same issues every review… this will sound familiar. Dashboards Don’t Create Accountability — Systems Do: https://lnkd.in/gCGpm-VU

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Part 2 of my ongoing series From Vibe Code to Production-Ready is live! This one dives into Claude, Gemini, and MCPs (Zen + Serena) -- not just as shiny tools, but as a framework for moving from quick prototypes into reliable systems: Command patterns, context, and the practices that make "vibe coding" scale. Part 1 explored why vibe coding eventually breaks down, and Part 2 is the bridge: where experimentation starts becoming engineering. Part 3 will explore agent command patterns: Compacting, Context, and Claude Code Ops Read here: https://lnkd.in/e8-dnaYC #AI #MCP #ClaudeAI #Gemini #AgenticAI #Engineering #ProductDevelopment #VibeCoding #FromPrototypeToProduction #TechLeadership

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I'm incredibly excited for this talk. Top K queries are the backbone of search engines, which have historically been NoSQL databases unable to execute JOINs. We're fixing this with ParadeDB. Highly recommend attending, I promise you'll learn something!

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We’re excited to partner with Cognition, the stellar crew behind Devin 🤖 and Windsurf 🌊, to make SWE agents more effective. Here’s the workflow: ✨ CoreStory extracts the hidden business logic, requirements, and complex behaviors in legacy codebases into a persistent spec. ⚡ You pass that spec as context and tasks to Devin, Cognition's Autonomous AI Software Engineer. 🚀 With the spec to navigate, the AI agent gets more powerful. It can modernize entire systems, or use the spec to field even more tickets with higher confidence. This is what the future of software engineering looks like: Human devs + AI agents, working side by side, with CoreStory fueling the spec. #AI #Agents #SpecDrivenDevelopment #LegacyModernization #CoreStory #Cognition

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Interested in learning more about Foundry Local https://foundrylocal.ai and how to build RAG-style systems that run locally? I’ve been exploring patterns for local-first retrieval, generation, and agent workflows, focusing on privacy, performance, and developer control. If you’re experimenting with RAG beyond cloud-only setups, these samples might be useful: • Local RAG – a simple, practical starting point for retrieval‑augmented generation running locally https://lnkd.in/e8DaV8ae • Local CAG – moving beyond classic RAG into contextual and agent‑driven generation patterns https://lnkd.in/eb5u_QXc • Local Hybrid Retrieval (ONNX) – combining lexical + vector search with optimized local inference https://lnkd.in/eh4mMwSi These projects are all about understanding the building blocks: local models, embeddings, hybrid retrieval, and how they fit into modern agent architectures with Foundry Local. If you’re curious about edge AI, local-first agents, or privacy-aware RAG, feel free to explore, fork, or share feedback. #AI #RAG #FoundryLocal #AgenticAI #LocalAI #EdgeAI #Developers

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