AI Fatigue: How to Measure Real Impact with Axify's AI Impact

In the GenAI gold rush, tools are everywhere. But quality? That’s the real treasure. We’re living in an era where every week brings a new GenAI tool, framework, or plugin. It’s exciting, yes - but also overwhelming. The temptation to chase every shiny new release is real. But here’s the truth: Tools don’t build excellence. People do. And excellence comes from clarity of purpose, thoughtful implementation, and relentless focus on quality. Before jumping to the next tool, ask: > Does it solve a real problem? > Will it improve the user experience? > Is it scalable, maintainable, and secure? "don’t become a tool fanatic. Be a quality fanatic." Build demos that impress - but build GenAI solutions that last. #GenAI #QualityOverHype #AIEngineering #InnovationWithPurpose #TechLeadership #AIProductivity #BuildWithVision #RandomThought #JustThinkingOutLoud

📊 New engineering impact metrics just released! (2/4) The latest CodeTogether release brings a big upgrade to our Engineering Impact Report, giving teams deeper clarity into GenAI use, productivity, and satisfaction. Here’s what’s new: 🔹 GenAI Usage Metrics – Understand how AI is influencing developer workflows 🔹 Throughput Metrics – See trends in completed stories and developer velocity 🔹 Satisfaction Metrics – Monitor the human side of engineering performance Whether you're managing delivery at scale or navigating GenAI adoption, these new insights help you connect the dots between tools, process, and team health. 📖 Full release notes here: https://hubs.ly/Q03LN41-0 #DevEx #EngineeringProductivity #GenAI #DevTools #TeamHealth #CodeTogether

OSS-Series #15: 𝗢𝗦𝗦 𝗪𝗲 𝗨𝘀𝗲: 𝗟𝗮𝗻𝗴𝗳𝗹𝗼𝘄 Building powerful AI workflows doesn’t have to start with code — and it definitely doesn’t have to rely on a SaaS black box. At Iridium Works, we’ve been using Langflow since 2025 to visually create, test, and run AI agents — locally and securely. https://lnkd.in/dudnsSZT 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗱𝗼𝗲𝘀 Langflow is a visual builder for AI agents, based on LangChain. It lets you connect LLMs, tools, APIs, memory components, and logic steps into structured, no-code pipelines. We use it to: - Build internal and client-facing AI agents - Connect those agents to self-hosted LLMs via Ollama or other models - Integrate 3rd-party tools like Firecrawl to enhance their real-world capabilities (e.g. web scraping, search, APIs) 𝗣𝗮𝗶𝗱 𝗮𝗹𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝘃𝗲𝘀 - Flowise - Dust - Relevance AI - Zapier AI (for lightweight agent workflows) 𝗠𝗼𝗻𝘁𝗵𝗹𝘆 𝘀𝗮𝘃𝗶𝗻𝗴𝘀 Compared to paid AI builder platforms with usage-based pricing and hosted runtime environments, we save an estimated €300/month, depending on agent complexity and usage. 𝗪𝗵𝘆 𝘄𝗲 𝗹𝗼𝘃𝗲 𝗶𝘁 Langflow brings transparency and flexibility to AI automation. We can rapidly prototype and deploy agents, all while keeping data local — whether we're using our own LLMs or connecting to external ones when needed. It’s perfect for teams who want to automate real business processes using AI — without losing control over data, execution, or integrations. 𝗪𝗼𝘂𝗹𝗱 𝘄𝗲 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱 𝗶𝘁? Absolutely ✅ If you’re serious about using AI to automate tasks, enhance workflows, or augment user experiences, Langflow is an excellent starting point — especially when paired with self-hosted LLMs like Ollama. Follow us to keep exploring the open source tools powering Iridium Works — from infrastructure to automation and AI. #opensource #langflow #aiautomation #aiagents #llmops #selfhosted #workflowautomation #digitalsovereignty #IridiumWorks #ossstack #langchain #nocodeai

Day 1 of the AI Agent Hackathon by Product Space! 🤖 Just wrapped up the kickoff call and I’m seriously excited to build an AI agent. Here’s what stood out to me today: 1️⃣ Problem Deep-dive: I had no idea that event hosting feedback was such a widespread issue. After diving into user feedback and use cases, it’s clear this problem impacts more people than I initially thought. 2️⃣ Agent Approach: Rather than just building another chatbot, I’m leaning toward a multi-agent system with specialized roles. Think of it as a "team" of agents, each tackling a different aspect of the problem! 3️⃣ Tech Stack Decision: After considering various options, I’m currently eyeing [e.g., LangChain/AutoGen/CrewAI] for orchestration. These tools seem like the best fit for my approach and should make the development process much smoother. Biggest insight from today? The most powerful AI agents don’t just automate tasks—they augment human decision-making, making us more efficient and informed in real-time. Tomorrow’s focus: Architecture planning and finalizing the tech stack. CTA: What’s one repetitive task you wish an AI agent could handle? Drop your ideas below—I might just integrate your suggestion into my next feature! 🙌 #hachathon #learningspace #langchain #chainofthoughts #llm #aichatbots

🎥 At AI for the rest of us 2025 conference Phil Nash from Langflow kindly demoed their low-code AI builder for agentic and RAG applications - and it’s brilliant. Langflow makes it effortless to design, connect, and deploy AI workflows visually - no boilerplate, just ideas turning into apps. 💫 As an OpenUK Ambassador, I’m especially happy to see Langflow stay open source - now growing under IBM ’s open innovation wing. Excited to share the demo below 👇 #Langflow #AIForDevelopers #LowCodeAI #AgenticAI #RAG #AIForTheRestOfUs #Community

The future isn’t something we wait for - it’s something we build. These 5 tools from Product Hunt’s September 27, 2025, launch are paving the way: 1. GitHub Copilot CLI  Copilot’s response to Claude Code and Codex CLI (GitHub, Mike Kerzhner, Idan Gazit) 2. Recal Dev  Like Resend, but for everything Calendars & Scheduling. (Jona Schwarz, Torben Köhler) 3. Zennbox  Save & organize your resources. Take notes with AI. (Bhuwan Mahato) 4. Toopost  A New Era of RSS Powered by AI. (Toopost, Andrei Babrounik) 5. Git Pushups  Do pushups, or we block your code. Which tool inspires YOU to think bigger? Share your thoughts below. Follow me for more insights into tech trends and startup innovations. #ProductHunt #Tech #AI #Innovation #SocialMedia

The first 80% with AI feels effortless. The last 20% feels impossible 😉 But it’s the last 20% that makes it valuable. I’ve lived this myself, and I see it happening everywhere right now. Anyone can grab an AI tool and spin up an app or a prototype. The first 80% comes together almost effortlessly; you feel like you’ve cracked the code. 😩 And then you stall. Why? Because the last 20% is where the real work lives. Debugging. Integrations. Edge cases. Security. Hosting. Making it not just a demo, but production-ready. That’s the gap between a clever experiment and a real business solution. A client I spoke with recently said it best on our very first call: “We can get about 80% of the way there, but we’re having a hard time getting that last 20% done. ” In their case, the challenge wasn’t just building a prototype, it was orchestrating multiple systems into one seamless solution. AI got them moving quickly, but it couldn’t finish the job. That’s where the expertise comes in. And, that’s where KASE comes in. We bridge the gap: leveraging AI acceleration to get you moving fast, then applying hard-won expertise to close the loop and deliver something that truly works. 👉 Working on an AI prototype that’s 80% there? Reach out to learn how KASE can help finish your project. #AIforBusiness #ProductLeadership #AIAdoption

We had the chance to meet Irosha de Silva the Co-Founder & CEO of Marketrix AI, a company on a mission to make software self-supporting. 🚀 Marketrix AI is building an AI agent that autonomously learns any web-based application by simulating real user behavior. The result? Real-time, contextual, and interactive support, directly embedded into the product. No more static guides, clunky manuals, or endless help desk tickets. Stories like this remind us why we started Project Stories to amplify the journeys of founders who are reimagining industries and pushing the boundaries of what’s possible. 🌍✨ MLOps community Event in San Francisco, CA, early September. #FounderStories #AIInnovation #FutureOfSoftware #StartupJourney #ProjectStories #TechForGood

🚀 Transform your AI career in 2025—for FREE! Whether you're building your first AI agent or scaling production systems, here's a curated list of the hottest tools and frameworks dominating the industry right now: 🔹 LLM Frameworks: LangChain, LlamaIndex 🔹 Multi-Agent Systems: CrewAI, AutoGen 🔹 Automation: n8n (workflow magic) 🔹 Vector Databases: Pinecone 🔹 AI App Building: Streamlit, Gradio 🔹 Local LLMs: Ollama 🔹 Monitoring: LangSmith 🔹 Essential Skills: Prompt Engineering ✨ All 100% free resources from trusted platforms. 📌 Bookmark this for later. Start with ONE tool and build consistency. Even 30 minutes a day compounds into real expertise. 👉 Full resource links in the comments! #AI #MachineLearning #LangChain #Automation #LLM #FreeResources #CareerGrowth #AITools #TechSkills #2025

What it really takes to deploy AI across an org (my week-long note taking debrief from Gumloop AI Champions Sessions): After a full week with teams from #Anthropic, #Cursor, #Gumloop, and #Vercel, a few truths stood out: 5 themes that kept repeating -Traditional dev workflows are dying → work shifts from typing code to thinking in plans + agents. -AI First org design beats “bolt-on AI.” Rethink ops, don’t just add a tool. -Prototype > PRD → ship a test, then align. -Communities > mandates → internal “AI FOMO” spreads adoption. -Beyond summarization → real value comes from embedded, end-to-end workflows. Session Highlights: Cursor: Tiny, empowered teams; even non-engineers query and contribute. Org design changes when anyone can “ask the codebase.” Vercel: Prototype-first reality—built a complete signup page in <45s with v0; design systems keep brand consistent at speed. Gumloop: Don’t jump straight to agents. Reliable workflows come first. Create AI FOMO with prepped hackathons + loud amplification of wins. Anthropic: most pilots fail for predictable reasons. Don’t teach basics, instead teach people to do their jobs differently, and measure specific outcomes (e.g., 50% faster ticket resolution by triaging with an agent). What is the playbook going forward? -Start with a prototype tied to one metric. -Operationalize a repeatable workflow before automating with agents. -Build an internal community: teach, run a focused hackathon, then broadcast wins. -Measure what matters (cycle time, resolution rate, $ impact) and scale what proves out. Huge thanks to all the teams pushing this forward & for sharing insights into your capabilities this week in the Gumloop AI Champions Sessions. If you’re driving AI change inside your company, happy to share my notes from the sessions. #AI #Automation #AIOps #Product #DevTools #ChangeManagement #Gumloop #Anthropic #Cursor #Vercel