Automation Implementation Tips

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

You're a #CTO. Your board asks: "What's our ROI on AI coding tools?" Your answer: "40% of our code is AI-generated!" They respond: "So what? Are we shipping faster? Are customers happier?" Most CTOs are measuring AI impact completely wrong. Here's what some are tracking: - Percentage of AI-generated code - Developer hours saved per week - Lines of code produced - AI tool adoption rates These metrics are like measuring how fast your assembly line workers attach parts while ignoring whether your cars actually start. Here's what you SHOULD measure instead: 1. Delivered business value 2. Customer cycle time 3. Development throughput 4. Quality and reliability 5. Total cost of delivery (not just development) 6. Team satisfaction Software development isn't a typing competition—it's a complex system. If AI makes your developers 30% faster but your deployment takes 2 weeks and QA adds another week, your customer delivery improves by maybe 7%. You've speed up the wrong part. The solution: A/B test your teams. Give half your teams AI tools, measure business outcomes over 2-3 release cycles. Track what customers actually experience, not how much developers produce. Companies that measure business impact from AI will pull ahead. Those measuring vanity metrics will wonder why their expensive tools aren't moving the needle. Stop measuring how much code AI generates. Start measuring how much faster you deliver value to customers. What are you actually measuring? And is it moving your business forward? -> Follow me for more about building great tech organizations at scale. More insights in my book "All Hands on Tech"

How we saved 10+ hours weekly by giving finance a simple interface. Our finance team was processing invoices the same way for years: 1. Email attachments → 2. Manual download → 3. Print → 4. Physical signature → 5. Scan → 6. Manual data entry The entire cycle took 3-5 days. The request to "build a proper approval system" kept getting deprioritized—it felt like a multi-month project. We reframed the problem: We didn't need a complex system. We just needed to connect two things: the data from our accounting software's API and a simple list where the right people could click "Approve" or "Reject." What actually got built: • A single-page app that pulls unpaid invoices automatically • Logic that routes invoices over $5k to directors, others to managers • A comment field for rejections • A basic audit log showing who approved what and when What changed: ✅ Approvals now happen in under 24 hours ✅ The finance team stopped chasing paper trails ✅ Vendors get paid faster ✅ Every decision is logged automatically The takeaway: Sometimes "digital transformation" isn't about big platforms. It's about giving a team one less PDF to manage by building a simple, focused tool that sits on top of the data they already use. What's the most stubborn, repetitive task in your team's workflow? Often the highest-impact tools are the smallest ones that remove a single point of friction. https://uibakery.io/ #ProcessAutomation #FinanceTech #OperationalEfficiency #DigitalTransformation

Automation, AI workflow, or AI agent? To always 𝘬𝘯𝘰𝘸 𝘸𝘩𝘪𝘤𝘩 𝘰𝘯𝘦 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥, follow this 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬: Remember when I explained why many "𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴" shared on LinkedIn are actually 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 or 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 in disguise? Turns out: understanding the difference is only partially helpful. The real challenge is knowing 𝘸𝘩𝘪𝘤𝘩 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦. So I built this framework to help you decide. There are 6 key dimensions to consider - working in pairs: 𝐏𝐚𝐢𝐫 #1: 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠 ↔️ 𝐇𝐮𝐦𝐚𝐧 𝐈𝐧𝐯𝐨𝐥𝐯𝐞𝐦𝐞𝐧𝐭 aka. how decisions are made - and how much human intervention is required: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: You make ALL decisions upfront when designing your automation, which means that no human intervention is needed after. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: You set boundaries for the AI to operate within; humans occasionally review outputs or intervene when the system encounters edge cases. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: You set high-level goals, and AI determines its own path; this means humans need to provide ongoing feedback to ensure it makes the right decisions. 𝐏𝐚𝐢𝐫 #2: 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 ↔️ 𝐀𝐝𝐚𝐩𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 a.k.a which type of data the system should process - and how adaptable it has to be: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: Requires strictly predefined data formats with no deviation; breaks when encountering unexpected inputs and needs to be re-engineered when processes change. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: Handles mostly structured data with some variability allowed; can adjust to parameter variations within defined parameters but needs guidance for significant changes. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: Processes diverse unstructured data across multiple sources with varying formats; independently adapts to different inputs and shifting environments without reprogramming. 𝐏𝐚𝐢𝐫 #3: 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 ↔️ 𝐑𝐢𝐬𝐤 𝐓𝐨𝐥𝐞𝐫𝐚𝐧𝐜𝐞 a.k.a how predictable the outcomes must be - and what level of risk is acceptable: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: Delivers highly consistent, predictable results every time; ideal for mission-critical processes where errors cannot be tolerated and predictability is essential. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: Produces mostly reliable outcomes with occasional variations in edge cases; balances flexibility with guardrails to prevent major errors while allowing some adaptability. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: Creates outcomes that can vary significantly between iterations; optimized for scenarios where discovering novel approaches and adaptability outweigh the need for consistent results. How to use this framework: Always 𝘴𝘵𝘢𝘳𝘵 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘭𝘦𝘧𝘵 and move right only when necessary. 1. Start with automation 2. Move to AI workflows when you need more flexibility within guardrails  3. Only move to agents when you need high adaptability Don’t fall for the AI agent hype - most processes can be automated without agents.

The biggest myth in AI today? That tools like LLMs, CoPilots, MCPs, and Agents will do the engineering for you. They won’t — because AI is engineering. LLMs. MCP. Agents. They’re all just that — tools. Yet many organizations are spending an extraordinary amount of time comparing, evaluating, and switching between tools — while missing the real essence of AI transformation. The real differentiator isn’t the toolchain. It’s the engineering mindset behind how those tools are used. Most organizations miss that AI is an engineering discipline — not a collection of experiments. It demands the same rigor as any mature system: design, development, testing, validation, rollout, and continuous optimization. Don’t go by leaderboards — they’re tested to work in controlled benchmarks, not in real-world, multi-system environments where context, latency, data, and cost all collide. And don’t fall for the misconception that AI will replace engineers. That’s a narrative being set — but having worked with top LLMs and chatbots, one thing is clear: they often fail when confronted with real engineering. Their code lacks depth, structure, and holistic system thinking. Tools never replace real engineering. They amplify those who understand it. Invest in the core. Invest in robust engineering practices. Upskill your teams. This will be your foundation in building scalable, responsible, and future-ready AI systems. Because tools will change. Frameworks will evolve. But engineering excellence — that’s what endures #aiengineering #ai #leanagenticai

My biggest takeaways from Fei-Fei Li: 1. Just nine years ago, calling yourself an AI company was considered bad for business. Nobody believed the technology would work back in 2016. By 2017, companies started embracing the term. Today, virtually every company calls itself an AI company. 2. The modern AI revolution started with a simple but overlooked insight from Fei Fei: AI models needed large amounts of labeled data. While researchers focused on sophisticated mathematical models and algorithms, she realized the missing ingredient was data. Her team spent three years working with tens of thousands of people across more than 100 countries to label 15 million images, creating ImageNet. This dataset became the foundation for today’s AI systems. 3. The human brain’s efficiency vastly exceeds current AI systems. Humans operate on about 20 watts of power—less than any lightbulb—yet accomplish tasks that require AI systems to use massive computing resources. Current AI still can’t do things elementary school children find easy. 4. Simply scaling current approaches won’t be enough. While adding more data, computing power, and bigger models will continue advancing AI, fundamental innovations are still needed. Throughout AI history, simpler approaches combined with enormous datasets consistently outperformed sophisticated algorithms with limited data. 5. Breakthrough technologies often start as toys or fun experiments before changing the world. ChatGPT was tweeted by Sam Altman as “Here’s a cool thing we’re playing with” and became the fastest-growing product in history. What seems like play today might transform civilization tomorrow. 6. Spatial intelligence is as crucial as language for real-world applications. In emergency situations like fires or natural disasters, first responders organize rescue efforts through spatial awareness, movement coordination, and understanding physical environments—not primarily through language. This is why world models that understand three-dimensional space represent the next frontier beyond text-based chatbots. 7. Physical robots face much harder challenges than self-driving cars, which took 20 years from prototype to street deployment and still aren’t finished. Self-driving cars are metal boxes moving on flat surfaces, trying not to touch anything. Robots are three-dimensional objects moving in three-dimensional spaces, specifically trying to touch and manipulate things. This makes robotics far harder than creating chatbots. 8. Everyone has a role in AI’s future, regardless of profession. Whether you’re an artist using AI tools to tell unique stories, a farmer participating in community decisions about AI deployment, or a nurse who could benefit from AI assistance in an overworked health-care system, you can and should engage with this technology. AI should augment human dignity and agency, not replace it—which means both using AI as a tool and having a voice in how it’s governed.

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

If you’re an AI engineer trying to understand and build with GenAI, RAG (Retrieval-Augmented Generation) is one of the most essential components to master. It’s the backbone of any LLM system that needs fresh, accurate, and context-aware outputs. Let’s break down how RAG works, step by step, from an engineering lens, not a hype one: 🧠 How RAG Works (Under the Hood) 1. Embed your knowledge base → Start with unstructured sources - docs, PDFs, internal wikis, etc. → Convert them into semantic vector representations using embedding models (e.g., OpenAI, Cohere, or HuggingFace models) → Output: N-dimensional vectors that preserve meaning across contexts 2. Store in a vector database → Use a vector store like Pinecone, Weaviate, or FAISS → Index embeddings to enable fast similarity search (cosine, dot-product, etc.) 3. Query comes in - embed that too → The user prompt is embedded using the same embedding model → Perform a top-k nearest neighbor search to fetch the most relevant document chunks 4. Context injection → Combine retrieved chunks with the user query → Format this into a structured prompt for the generation model (e.g., Mistral, Claude, Llama) 5. Generate the final output → LLM uses both the query and retrieved context to generate a grounded, context-rich response → Minimizes hallucinations and improves factuality at inference time 📚 What changes with RAG? Without RAG: 🧠 “I don’t have data on that.” With RAG: 🤖 “Based on [retrieved source], here’s what’s currently known…” Same model, drastically improved quality. 🔍 Why this matters You need RAG when: → Your data changes daily (support tickets, news, policies) → You can’t afford hallucinations (legal, finance, compliance) → You want your LLMs to access your private knowledge base without retraining It’s the most flexible, production-grade approach to bridge static models with dynamic information. 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d

 𝘞𝘩𝘺 𝘠𝘖𝘜𝘙 Automation 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 𝘸𝘪𝘭𝘭 𝘍𝘈𝘐𝘓 𝘪𝘯 6 𝘮𝘰𝘯𝘵𝘩𝘴 (𝘢𝘯𝘥 𝘩𝘰𝘸 𝘵𝘰 𝘧𝘪𝘹 𝘪𝘵) I just audited a $50M company's automation framework. Result? 73% of their tests were failing randomly. Their CTO asked me one question: "How did we go from 10 passing tests to complete chaos?" The Brutal Truth: 85% of Selenium Projects Die the Same Death Month 1: "Our automation is amazing!" Month 6: "Why does everything break when we deploy?" Here's what kills every framework (and the fix): 1️⃣ The "Everything in One Folder" Disaster ❌ Death pattern: UI, API, utils all mixed together ✅ Fix: Dedicated packages → UI, API, POJO, services separated Reality check: Good teams onboard new devs in 2 hours, not 2 weeks. 2️⃣ The "Hardcoded Hell" Problem ❌ Death pattern: URLs, data, timeouts scattered everywhere ✅ Fix: Environment property files + externalized test data Game changer: Switch DEV→QA→PROD with one command. 3️⃣ The "No POJO = No Scale" Trap ❌ Death pattern: Raw JSON strings, manual API payloads ✅ Fix: Request/Response POJOs + schema validation Impact: API tests become 10x more maintainable. 4️⃣ The "Debug Nightmare" Issue ❌ Death pattern: "Test failed" with zero context ✅ Fix: Extent Reports + screenshots + API logs Truth: Debug time drops from 2 hours → 5 minutes The Framework That Actually Scales I've built a production-ready structure that includes: 🏗️ Proper separation of UI/API/POJO layers 🔧 External configurations for all environments 📊 Rich reporting with screenshots & metrics 🚀 CI/CD ready with Docker & Jenkins support 🎯 BDD structure that business teams understand The Bottom Line: Stop building "quick automation scripts." Start building software systems that scale. Your framework should work at test #10 AND test #1000. Want the complete folder structure? 👇 Comment "𝑭𝑹𝑨𝑴𝑬𝑾𝑶𝑹𝑲" and I'll send it to your inbox! Found this helpful? Share with someone struggling with flaky tests! 🚀 -x-x- Full Stack QA & Automation Framework Course with Clearing SDET Coding Rounds: https://lnkd.in/g7tn6Uif #japneetsachdeva

~30% of my pipeline comes from Closed Lost opportunities. So when an opportunity is Closed Lost, don’t let it go cold. If you have a sales engagement tool, set up an automation rule to auto add the primary contact into a Closed Lost Cadence, if not, just do this manually. Here’s an example cadence: 🔹 Step 1 (30 days post-CL) → Manual email (personalised) Summarise their focus, why the deal was lost, and let them know you’ll stay in touch. 📩 Example: "Hey Billybob, really enjoyed working with you and learning more about [initiative], like increasing conversion rates from 12% → 15% and driving $100K pipeline per AE. Appreciate other priorities took precedent, but I’ll stay in touch until timing makes sense". 🔹 Step 2 (55 days post-CL) → Automated email (deposit) Share a relevant resource. 📩 Example: "Pipeline is a challenge for most teams - thought this 30MPC webinar on account segmentation might be useful". 🔹 Step 3 (80 days post-CL) → Evaluate next steps Any team growth? Leadership changes? Priority shifts? No change → Stay in Closed Lost cadence. Key changes → Move to a prospecting cadence & re-engage. 🔹 Step 4 (105 days post-CL) → Phone call + LinkedIn touch (check-in). 🔹 Step 5 (130 days post-CL) → Automated email (new product update). 📩 Example: "See how Salesloft Rhythm incorporates AI into workflows to prioritise prospects most likely to convert into meetings [link]". 🔹 Step 6 (155 days post-CL) → Call (check-in). 🔹 Step 7 (180+ days post-CL) → Final review & decision No movement/changes? Pause outreach or move to a light nurture cadence. New priorities? Add to outbound cadence with a tailored approach. The goal? Stay relevant without being intrusive - so when timing aligns, you’re already on their radar. Are you keeping tabs on your Closed Lost Opps, or letting them slip? #sales #cadences #closedlost