Automating Repetitive Work Tasks

3 Workflows I've Automated for in-house teams. ① Ask Legal ② Procurement ③ Contract Review (not just the review!) 1. Ask Legal [or any department for that matter 🤷🏼♀️] You've heard me talk about legal teams and knowledge management. Long story short, your legal team is answering the same 20 questions over and over 😵💫 A simple way to save a CHUNK of time answering questions from the business (enabling them to go faster) ALL while having complete control & keeping a human in the loop? ↪️ Set up an 'Ask Legal' bot in your comms platform. ↪️ Sync it with your knowledge base (e.g GDrive/Notion/Sharepoint). ↪️ Set up your custom instructions (Want it to tag Bob on privacy questions only, specifically on a Tuesday? No problem).  ↪️ Don't want the answer to go straight out to the business without reviewing it first? Cool, turn on co-pilot mode. The result? 60-80% fewer repetitive queries. Your team focuses on the high value things that need a human lawyer. 2. Procurement Businesses have 100's of tools, but when departments don't speak to each other you end up with duplicate tools & subscriptions 😭 💵 🚽.  What if there was a way for the business to find out in <1 minute if there was a tool available that covered their needs, before needing to spend some hard secured department budget? Moreover, what if I told you, they could kick off the internal procurement process from the comfort of your comms platform? Team member : “Do we already have a tool for X?” in Slack/Teams ✅ Bot checks knowledge base (policies, procurement tool). ✅ If a match is found, it shares the approved tool & owner to contact. ✅ If not, the bot can ask the user for more info and direct them with next steps to kick off the procurement process from inside Slack/Teams. Ensuring your users ACTUALLY follow the process, without adding friction. Did I just see your CFO cry tears of joy? 3. Third Party Vendor Contract Review & Project Management Getting AI to redline a contract (as a first pass) is a huge win, but there's still the other pieces of the process missing, like: 🤷🏼♀️ The business figuring out IF legal review is even needed (according to company policy). 📨 The business actually submitting the contract to legal. 😩 Managing review capacity within the legal team. 🖥️ Getting the legal team to log & update the PM tool. The list never ends. Legal reviews only what actually needs their eyes, turnaround times improve, and the business stops pinging the team for “update pls?” in Slack : ) TLDR; Most legal teams are drowning in admin work that could be automated. I've built all of these using simple processes and tools (that I've found most businesses have). You also know I love a good Figma flow. So I’ve built them for all three of the above (see a sneak peak below). Want the entire thing? Comment "FLOWS" and I'll send them over. Also, tell me what you want to see - more of the above or step-by-step how-to build videos?

Instead of asking "what should I automate?" Focus on WHY you should automate and HOW it solves the data problem. Most data engineers automate the wrong things at the wrong time. Here's the framework I use after 8 years of building production systems: ✅ AUTOMATE WHEN: → Task runs daily/weekly → Human errors cause outages → Work blocks other priorities → Team growth = more manual work Examples: Reports, schema checks, alerts ❌ DON'T AUTOMATE WHEN: → Task happens quarterly → Requirements change weekly → Process isn't understood yet → Manual steps reveal insights My rule: If it’s done 3+ times, script it; 10+ times, automate it; fails 5+ times, redesign it. Automate what matters, when it matters—not everything! Here's how Airflow makes data automation ridiculously easy: 🎯 The Magic Triangle: → Scheduler: Triggers workflows on time → Executor: Distributes work to available workers → Workers: Actually run your Python code 💾 Smart State Management: → Metadata DB: Tracks every task run → Queue: Manages task priorities → Web UI: Visual monitoring & debugging 🔄 Why It Works: → Write Python DAGs once → Airflow handles the rest → Automatic retries & error handling → Parallel task execution → Visual dependency tracking Real Example: Instead of: ❌ Cron jobs that fail silently ❌ Manual dependency management ❌ No visibility into failures You get: ✅ Visual workflow monitoring ✅ Automatic failure notifications ✅ Smart task scheduling ✅ Easy debugging & restarting Image Credits: lakeFS The Bottom Line: Apache Airflow turns complex data workflows into manageable Python scripts. What's your biggest pipeline automation challenge? #data #engineering

2025 saw a massive shift in how we perceive coding. It's 2026 now, and companies are still lagging behind. I used to think you needed developers to build products. Then I launched Searchable... And validated the entire idea with AI in 48 hours. At that level, I didn't need to know a single line of code. But if you're planning to replace real engineering work,  You'll need to create a proper plan of action. AI coding makes it easier than ever to build. But you still need to input clear ideas and know how it works. There are three levels of AI coding founders should understand: (See the visual for more details 👇) 1. Vibe Coding Level: Non-technical founders What it is: Turning rough ideas into working prototypes by describing what you want in plain English and letting AI handle the code. Business use case: → Validating startup ideas fast → Building landing pages, MVPs, internal tools → Testing demand before hiring engineers Tools to use: → Lovable - Product prototypes and signup flows → Bolt - Fast web app generation → Replit - Build and deploy without setup → Make - Connect tools and workflows 2. AI-Assisted Coding Level: Technical or semi-technical teams What it is: AI working alongside a human developer to speed up writing, debugging, and refactoring code. Business use case: → Building production-ready software faster → Improving developer output without growing headcount → Reducing bugs and repetitive work Tools to use: → Cursor - AI-first code editor → GitHub Copilot - Inline code assistance → Continue - Open-source AI coding assistant → Google Antigravity - Context aware completions 3. Agentic Coding Level: Advanced team and operators What it is: AI agents that can plan, write, test, and refine entire chunks of software from a single objective. Business use case: → Large feature builds → Legacy code refactors → Automating repetitive engineering tasks → Spinning up internal systems fast Tools to use: → Claude Code - Agent-driven deployment → OpenAI Codex - Autonomous coding tasks → Devin - Full software agent → Gemini CLI - Command-line agent workflows These tools let you validate first and hire second… Yet another way AI allows founders to move faster than ever before. If you’re building right now, this is leverage you can’t ignore. Are you familiar with AI coding? How are you using it?  Drop a comment below with your process.  At Searchable, we're using AI to build an autonomous SEO and AEO growth engine. It analyses, fixes, and scales websites to drive customers automatically. If you're a founder who wants to stay visible when people search with ChatGPT, Perplexity, or Google AI... This is built for you. Learn more and get started with a 14-day free trial here:  https://lnkd.in/epgXyFmi ♻️ Repost to share this breakdown with founders in your network.  And follow Chris Donnelly for more on building smarter. 

Everyone is chasing the next big breakthrough. But here’s the twist: Sometimes, the boldest move isn’t inventing something new. It’s automating what already works—then reinvesting that energy into your people. Let’s be honest. Most leaders get distracted by the shiny object. The latest AI. The next buzzword. The pressure to keep up can be overwhelming. But what if you stopped looking outward and started doubling down on what’s right in front of you? The processes that already drive results. The systems that keep your business running. The quiet routines that deliver real value, day after day. Here’s the reality most won’t admit: → Innovation isn’t always about invention. → Sometimes, it’s about optimization. → The real breakthrough? Freeing up your team’s time to do what only humans can do. So, how do you turn this idea into action? Identify Your Real Workhorses → What are the processes or tools your team uses every single day? → What produces consistent results—even if it isn’t flashy? Automate with Purpose → Don’t automate for the sake of it. → Ask: Does automation save time, reduce friction, and maintain quality? → If yes, map out the workflow. → Find the right tech (no need for the fanciest option). → Test it. Refine it. Make sure it works—every time. Reinvest in the Human Factor → Automation isn’t about replacing people. → It’s about giving them back their most precious resource: time. → Encourage your team to spend that time on: ↳ Building client relationships ↳ Solving complex problems ↳ Coaching peers ↳ Pushing creative boundaries Track the Impact → Don’t just measure cost savings. → Measure how much more your team can accomplish. → How much faster can you move? → How many more ideas get tested? → How much stronger is your culture? Here’s a brutal truth: If you automate what works, you create space for people to do what truly matters. That’s how you outpace the competition. That’s how you make room for growth that’s both profitable and sustainable. But most leaders won’t do this. They’ll keep piling on new tech, new projects, new distractions. They’ll miss the chance to build a team that’s energized, creative, and loyal. Here’s what I see in the field, every week: → The best companies automate the routine. → Then, they invest everything they save into developing humans. → Training. Mentorship. Recognition. → Space to think, experiment, and connect. It feels counterintuitive. But it works. So the next time your board demands “innovation,” ask yourself: → What can I automate today, so my people can do what only they can do tomorrow? If you want a practical framework to audit your workflows and spot what’s ready for automation, drop a comment. Let’s build smarter, more human businesses—starting now.

AI is changing the way we code but reproducing algorithms from research papers or building full applications still takes months. DeepCode, an open-source multi-agent coding platform from HKU Data Intelligence Lab, is redefining software development with automation, orchestration, and intelligence. What is DeepCode? DeepCode is an AI-powered agentic coding system designed to automate code generation, accelerate research-to-production workflows, and streamline full-stack development. With 6.3K GitHub stars, it’s one of the most promising open coding initiatives today. 🔹Key Features - Paper2Code: Converts research papers into production-ready code. - Text2Web: Transforms plain text into functional, appealing front-end interfaces. - Text2Backend: Generates scalable, efficient back-end systems from text prompts. - Multi-Agent Workflow: Orchestrates specialized agents to handle parsing, planning, indexing, and code generation. 🔹Why It Matters Traditional development slows down with repetitive coding, research bottlenecks, and implementation complexity. DeepCode removes these inefficiencies, letting developers, researchers, and product teams focus on innovation rather than boilerplate implementation. 🔹Technical Edge - Research-to-Production Pipeline: Extracts algorithms from papers and builds optimized implementations. - Natural Language Code Synthesis: Context-aware, multi-language code generation. - Automated Prototyping: Generates full app structures including databases, APIs, and frontends. - Quality Assurance Automation: Integrated testing, static analysis, and documentation. - CodeRAG System: Retrieval-augmented generation with dependency graph analysis for smarter code suggestions. 🔹Multi-Agent Architecture DeepCode employs agents for orchestration, document parsing, code planning, repository mining, indexing, and code generation all coordinated for seamless delivery. 🔹Getting Started 1. Install DeepCode: pip install deepcode-hku 2. Configure APIs for OpenAI, Claude, or search integrations. 3. Launch via web UI or CLI. 4. Input requirements or research papers and receive complete, testable codebases. With DeepCode, the gap between research, requirements, and production-ready code is closing faster than ever. #DeepCode

𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗠𝘆 𝗣𝗠 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝗻 10 𝗠𝗶𝗻𝘂𝘁𝗲𝘀 - 𝗛𝗲𝗿𝗲’𝘀 𝗘𝘅𝗮𝗰𝘁𝗹𝘆 𝗛𝗼𝘄 👇 Product teams waste 17% of their time on documentation and comms (McKinsey). And I automated the most tedious part for me using Lovable - with Zero code. 👉 Turning detailed PRDs into internal launch comms, Notion posts, stakeholder briefs, and checklists. It was eating up hours every week, across product and growth team. So I thought, what if we just automated it? 🤔 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁: ✅ Reads our PRDs ✅ Extracts the key details ✅ Generates a Notion-ready launch post ✅ And even creates a structured PM checklist No code. Just a few smart prompt blocks. Now this agent saves our team 4–6 hours per launch, and keeps everyone aligned without the usual back-and-forth. In this post, I’m breaking down exactly how I built it step by step: - My exact 10-step framework. - Battle-tested prompts you can copy. - Common pitfalls (and how to avoid them). 👉 Swipe through to see how you can build your own AI teammate too. P.S. Should product teams have an "AI Agent Manager" role by 2025?

This AI Workflow Automates Networking (n8n + ChatGPT): When I was networking, I felt overwhelmed. Not only was talking to strangers way out of my comfort zone... But keeping track of new contacts, existing contacts, and previous conversations was overwhelming. Not to mention trying to figure out what the heck to even say to these people. If you're struggling with any of those things? This video is for you. I'm going to teach you how to use a combo of n8n + ChatGPT to build a workflow that automates everything outlined above. We work with hundreds of private clients every year in our job search coaching program. That experience has confirmed two things: 1. Networking is far and away the most effective way to get hired right now 2. Most people don't have a good system for networking, and don't get traction as a result This video is going to show you how to set up and automate a crazy effective networking system, including: ✅ A "Second Brian" For Your Networking Efforts You need a central hub to store all of the information from your networking - names, emails, interests, dates of last convos, notes, etc. Here's a screenshot of a version of what I used for my job search (there's a link grab a free copy of this template in the YouTube video description). This n8n workflow is going to automate everything for you after you add a new contact to your sheet. 🤖 Automated Updates The workflow is set up to scan your email at regular intervals looking for messages from your networking contacts. When it finds them? It uses that context to do all of the tracking and brainstorming for you. ✏️ Automated Conversation Notes The workflow is also going to turn emails from your contacts into short summaries so you never forget what you spoke about. The summaries will automatically update with every email your contact sends you. 🧠 Automated Next Steps (Adding Value) When a conversation is updated, the workflow will brainstorm ways that you can add value to your contact. Then it will upload those ideas in a "Next Steps" column so you can easily locate them and take action. 🗓️ Automated Follow Up Deadlines Finally, the workflow will recommend follow up deadlines that are in line with the conversation and the next steps you're taking. Did your contact ask for a PDF you mentioned? It'll tell you to send that today. But if they told you try a 2 week course on AI fundamentals? It'll recommend following in, say, 2.5 weeks. The best part? You do NOT need to be technical or no how to code to set this up. The whole thing should take you about an hour and you'll have an automated networking system to supercharge your job search. Also, the video comes with: ✅ A free copy of my Google Sheet networking track ✅ A free copy of the n8n template that you can plug and play ✅ The exact ChatGPT prompts I spent hours dialing in for this use case All for free in the video description: >> Click here to watch the full video: https://lnkd.in/eCW5EMZ8

Anthropic’s Model Context Protocol (MCP) lays the foundation for how LLMs interact with tools and data through structured protocols. But most discussions stop at theory. This graphic shows what it 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 looks like to build and operate MCP-compatible servers across real-world use cases. Here are 5 production-ready MCP servers that automate day-to-day tasks: → 𝗙𝗶𝗹𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗠𝗖𝗣 Interacts with your local files: read, write, move, search, and fetch metadata. Critical for on-device workflows. → 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗿𝗶𝘃𝗲 𝗠𝗖𝗣 Extends those same capabilities to cloud storage. Enables LLMs to search, access, and organize cloud documents. → 𝗦𝗹𝗮𝗰𝗸 𝗠𝗖𝗣 Let agents read, post, and reply inside Slack. Useful for AI-powered meeting assistants and notification engines. → 𝗦𝗽𝗼𝘁𝗶𝗳𝘆 𝗠𝗖𝗣 LLMs can queue songs, recommend music, and manage playback through API calls. → 𝗡𝗼𝘁𝗶𝗼𝗻 𝗠𝗖𝗣 Enables Claude or any LLM to manage your task list inside Notion — from reading tasks to marking them complete. Each server follows a clear lifecycle: Client Request → Credential Validation → Tool/Operation Identification → Execution → Logging + Error Handling MCP isn’t just a spec. It’s the protocol that bridges GenAI models with the real world — turning LLMs from chatbots into autonomous operators.

Top 6 AI tools for design & workflow in 2026 👇 Yes, not all of them are “design tools.” Yes, that’s exactly the point. I spent time exploring tools beyond just UI screens… Because real product work is not just design anymore. It’s workflows. Automation. AI orchestration. Here are 6 that actually matter right now: 1. Paperclip AI https://lnkd.in/dXkCrnbe Local-first AI for organizing research, notes, and work items. But it goes deeper. It acts like an orchestration layer for AI agents. Goals. Budgets. Audit logs. Agent “heartbeats.” If you deal with messy research or multi-step thinking, this is insanely powerful. 2. Flowstep https://flowstep.ai Prompt → UI designs. It generates wireframes and full interfaces on an infinite canvas. You can iterate fast. Refine layouts. Explore ideas visually. Feels like Figma + AI had a smarter child. 3. Moonchild AI https://moonchild.ai Turn PRDs into actual UI screens. It helps with: User flows UX problem solving Moodboards Design systems This is not just generation. It’s structured product thinking. 4. Dify https://dify.ai Visual builder for AI apps. Drag. Drop. Deploy. You can create: Chat apps Text-generation tools Custom AI workflows If you ever wanted to ship your own AI product without heavy coding, start here. 5. Flowise https://www.flowise.io Low-code builder for LLM workflows. Think: Connecting multiple models Creating agent flows Shipping APIs fast Great for prototyping AI features inside real products. 6. n8n https://n8n.io Automation on steroids. Connect apps. Trigger workflows. Automate repetitive ops. Designers ignore this. Smart designers don’t. Because real impact = design + systems. Here is the shift most designers are still missing. The future is not just UI design. It’s: Design + AI Design + automation Design + systems thinking Tools like Flowstep and Moonchild help you design faster. Tools like Dify, Flowise, and n8n help you build smarter. And tools like Paperclip help you think better. AI will not replace designers. But designers who understand workflows will replace designers who only push pixels. Use these tools for: Speed Exploration Systems thinking Execution Not just aesthetics. Because in 2026… The best designers are not just designing screens. They are designing how things work. If you had to pick ONE tool to explore this week, Which one are you trying first?

Many people I talk to have heard of coding agents and are interested in using them, but don't know: 1. What current coding agents can do 2. How users can prompt agents effectively To help out with this, I wrote a blog on 8 use cases for coding agents, with example prompts: https://lnkd.in/gdizhYMX The first two use cases are familiar ones, (1) fixing bugs and (2) adding features. This is the common "resolve github issue" usecase tested in benchmarks like SWE-Bench. One nice thing about good agents is that they can implement a change and test it. Here's an example prompt. Another thing people are using agents for a bit is (3) creating new apps from scratch. Here's are two examples for a frontend app and email sending script that I successfully created with OpenHands. In this case I prompt about some design decisions, like what framework to use. The next three are actually my favorites: (4) fixing failing CI tests, (5) fixing merge conflicts, and (6) writing docs. These tasks every developer needs to do but noone wants to do. It's been a huge boost to be able to ask the agent to resolve these issues for me; it generally works well! Coding agents can also (7) help with deployments by spinning up cloud resources. Obviously you need to carefully supervise the agent to make sure that it doesn't break anything, but with careful credentialing and review of infrastructure as code it can make deployment much easier! Finally, I have been using coding agents a lot for (8) data analysis tasks. I asked OpenHands to create a script to monitor commit activity on our repo, and the resulting graph is in the blog. One final easter egg, I actually asked OpenHands to make the header figure at the top of this thread too! I asked it: * Download appropriate from fonts-awesome * Arrange them 2 rows and 4 columns center-justified with the text * Make them rainbow colored * Write to png If any of these use cases sound interesting, I'd encourage you to read the blog and try out OpenHands, a general software development agent that can help with these tasks: * Download now: https://lnkd.in/g4VhSi9a * Sign up for the web app: https://lnkd.in/gJ-_SFv2