Automating Repetitive Work Tasks

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

🎩 “How We Designed a Multi-Brand Design System” (https://lnkd.in/erc3mA4i), a fantastic case study by Ness Grixti on the pains of maintaining multiple systems, how to make a design system work seamlessly across multiple brands — with a multi-system token infrastructure in Figma, applied everywhere. Most teams eventually go through a “consolidation” effort — and that’s where struggles emerge as different systems have slightly different needs. Wise team created a system where the entire library is controlled by a top brand layer, which contained nested libraries for type, spacing and colors. I love Wise's approach of 1 system with 2 tracks to avoid duplications — typography and spacing on one track, and color on the other. Both pulled from shared primitives and brought together in a single Global Token library that houses all brands. As a result, designers can manage responsive type, spacing and interaction states across all color themes from one place. 🧱 Raw values core → for color, type, spacing without brand associations 🧅 Layered structure → primitives, scaling/device, sentiment, brands 🌈 Sentiment themes → for alerts, neutral, warning, success, proposition 📏 Accessible dynamic scaling → for type and spacing values 📦 Nested variables → scaling lives within responsive device library ♻️ Avoiding token explosion → tokens shared across brands + diffs If you'd like to dive deeper, I highly recommend to take a look at the Multi-Brand Design System Figma Kit (https://lnkd.in/eShgnPnW) by Pavel Kiselev, a practical guide and Figma kit on how to set up a design system in Figma for multiple brands, platforms or products — with full control over colors, typography and visual styles. And huge kudos to Ness Grixti (along with colleagues Henrique Gusso and Willem Purdy and the wonderful Wise team) for sharing the challenges, the failures and successes for all of us to learn from! 👏🏼👏🏽👏🏾 #ux #design

Few Lessons from Deploying and Using LLMs in Production Deploying LLMs can feel like hiring a hyperactive genius intern—they dazzle users while potentially draining your API budget. Here are some insights I’ve gathered: 1. “Cheap” is a Lie You Tell Yourself: Cloud costs per call may seem low, but the overall expense of an LLM-based system can skyrocket. Fixes: - Cache repetitive queries: Users ask the same thing at least 100x/day - Gatekeep: Use cheap classifiers (BERT) to filter “easy” requests. Let LLMs handle only the complex 10% and your current systems handle the remaining 90%. - Quantize your models: Shrink LLMs to run on cheaper hardware without massive accuracy drops - Asynchronously build your caches — Pre-generate common responses before they’re requested or gracefully fail the first time a query comes and cache for the next time. 2. Guard Against Model Hallucinations: Sometimes, models express answers with such confidence that distinguishing fact from fiction becomes challenging, even for human reviewers. Fixes: - Use RAG - Just a fancy way of saying to provide your model the knowledge it requires in the prompt itself by querying some database based on semantic matches with the query. - Guardrails: Validate outputs using regex or cross-encoders to establish a clear decision boundary between the query and the LLM’s response. 3. The best LLM is often a discriminative model: You don’t always need a full LLM. Consider knowledge distillation: use a large LLM to label your data and then train a smaller, discriminative model that performs similarly at a much lower cost. 4. It's not about the model, it is about the data on which it is trained: A smaller LLM might struggle with specialized domain data—that’s normal. Fine-tune your model on your specific data set by starting with parameter-efficient methods (like LoRA or Adapters) and using synthetic data generation to bootstrap training. 5. Prompts are the new Features: Prompts are the new features in your system. Version them, run A/B tests, and continuously refine using online experiments. Consider bandit algorithms to automatically promote the best-performing variants. What do you think? Have I missed anything? I’d love to hear your “I survived LLM prod” stories in the comments!

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. 

We cut proposal response time by 60% and reactivated 20% of lost deals. How? with just one move: better follow-up. We know that proposal decks get buried in inboxes. Buyers forget what you sent, AEs left chasing the same names for weeks. Not fun! So we looked for a way to rebuild the process. And we came across: AI Deal Rooms. It is basically a centralized room where the buyer can find everything related to the deal: - Proposal or pricing - Demo recap - Case studies - Next steps The AI layer makes the room dynamic: - Pulls in call notes automatically after meetings - Personalizes summaries and intros for each buyer - Tracks engagement (who viewed what, when, and for how long) - Triggers follow-ups when buyers reopen, share, or revisit the content Here’s how we run it now at SalesCaptain 👇 1️⃣ Meeting ends Call notes, key pains, and next steps are summarized automatically (PandaDoc natively, Fathom.ai, HubSpot meeting notes..etc) 2️⃣ A deal room opens instantly in PandaDoc Proposal, demo recap, and case study, all in one link. Each room starts with a short video from the AE (Loom, Tella) 3️⃣ Email goes out fast No “we’ll send it next week.” AEs hit send in minutes. 4️⃣ Signals start tracking We see when buyers open the room, how long they stay, and who else views it (PandaDoc) 5️⃣ Follow-ups trigger on actions - Pricing opened → Slack ping (Zapier). - Proposal revisited → “Still exploring?” check-in. - Old deal reopened → nurture sequence. The results we've seen so far are: → Proposal response rate up 30% → Lost deals re-engaged 20% more → Sales cycles shorter, cleaner, easier to track (the way sales cycles should be) Buyers get one link with everything they need. Reps stop chasing, and focus on closing. I’m seeing this setup change how fast teams follow up and how often buyers come back. If you haven’t tested AI Deal Rooms yet, it’s worth exploring. Shoutout to PandaDoc for this feature. #salesops #revops #salescycle #gtm #pandadoc

How we shrank 30-40 hours of weekly manual work into just 2-3 hours 🤯 (Automation Tip Tuesday 👇) This home services company was struggling with their invoice reconciliation process. They received numerous vendor invoices via email (PDF format) and needed to manually match them against jobs in ServiceTitan. Their team was stretched thin, discrepancies and overpaying were daily occurrences, and one day, they had enough. We worked on a three-step automated solution: Step 1: Finding the PDFs Zapier monitors the inbox for invoices. When it detects an invoice with a PDF attachment, it proceeds to Step 2. Step 2: Parsing the Data Nanonets uses AI to extract data from the PDF. Step 3: Data Comparison The extracted data is compared with jobs in ServiceTitan. Any discrepancies are added to a spreadsheet for internal review. 30-40 hours of weekly manual verification time is now just 2-3 hours. With instant discrepancy flagging, their system allows for better vendor management, improved billing accuracy, and more time for the team to pursue higher-value tasks. Which manual task that can be automated is currently taking up too much valuable time? If you’re thinking of one, it’s time we spoke. Book a free call (link in the comments 👇) and let’s see what we can do for your workflow. -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ These tips I share every Tuesday are drawn from real-world projects we've worked on with our clients at Flow Digital. We help businesses unlock the power of automation with customized solutions so they can run better, faster and smarter — and we can help you too! #automationtiptuesday  #automation #workflow

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

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.