Optimizing Workflow Processes

Just created this comprehensive Pandas cheatsheet that I wish I had when I started my  journey! After seeing fellow practitioners struggle with the same pandas operations, I decided to create a simple yet powerful reference guide: "9 Must-Know Pandas Operations for Working with Data" This is - • Focused on real-world use cases, not just syntax • Includes time-saving tips I learned the hard way • Covers both basic and advanced features • Clean, visual layout for quick reference Key sections include: - Data Import/Export tricks - Efficient data selection methods - Statistical operations - Time series handling - String manipulation - Advanced features you might not know about Perfect for: • Data Professionals (Data Engineers, Data Scientists, ML Engineer, AI Engineers, and Data Analysts) • Tech Professionals working with Data Here are a few other commands that can help you with advanced operations - 1. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗦𝗲𝗰𝘁𝗶𝗼𝗻    - 𝚙𝚍.𝚌𝚘𝚗𝚌𝚊𝚝() for combining DataFrames    - 𝚙𝚒𝚟𝚘𝚝 vs 𝚞𝚗𝚜𝚝𝚊𝚌𝚔 operations    - 𝚍𝚏.𝚛𝚎𝚗𝚊𝚖𝚎() for column renaming    - 𝚍𝚏.𝚜𝚎𝚝_𝚒𝚗𝚍𝚎𝚡() and 𝚍𝚏.𝚛𝚎𝚜𝚎𝚝_𝚒𝚗𝚍𝚎𝚡() 2. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀    - 𝚍𝚏.𝚙𝚌𝚝_𝚌𝚑𝚊𝚗𝚐𝚎() for percentage changes    - 𝚍𝚏.𝚌𝚞𝚖𝚜𝚞𝚖(), 𝚍𝚏.𝚌𝚞𝚖𝚙𝚛𝚘𝚍() for cumulative operations    - 𝚍𝚏.𝚛𝚊𝚗𝚔() for ranking values 3. 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀    - 𝚙𝚍.𝚝𝚘_𝚍𝚊𝚝𝚎𝚝𝚒𝚖𝚎() for converting to datetime    - More datetime accessors like .𝚍𝚝.𝚖𝚘𝚗𝚝𝚑, .𝚍𝚝.𝚢𝚎𝚊𝚛    - Business day operations with 𝚙𝚍.𝚘𝚏𝚏𝚜𝚎𝚝𝚜 4. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀    - 𝚙𝚍.𝚌𝚞𝚝() and 𝚙𝚍.𝚚𝚌𝚞𝚝() for binning    - 𝚙𝚍.𝚐𝚎𝚝_𝚍𝚞𝚖𝚖𝚒𝚎𝚜() for one-hot encoding    - Window functions beyond .𝚛𝚘𝚕𝚕𝚒𝚗𝚐()    - Cross-tabulation with 𝚙𝚍.𝚌𝚛𝚘𝚜𝚜𝚝𝚊𝚋() 5. 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴    - 𝚍𝚞𝚙𝚕𝚒𝚌𝚊𝚝𝚎𝚍() method    - 𝚍𝚏.𝚠𝚑𝚎𝚛𝚎() and 𝚍𝚏.𝚖𝚊𝚜𝚔()    - 𝚍𝚏.𝚌𝚕𝚒𝚙() for limiting values 6. 𝗠𝗮𝘆𝗯𝗲 𝗮 𝗡𝗲𝘄 𝗦𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗻 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴    - MultiIndex operations    - Index alignment    - Cross-section selection with .𝚡𝚜() Have I overlooked anything? Please share your thoughts—your insights are priceless to me.

“What AI skill should my team and I actually learn right now?” I will scream this from the rooftops of NYC. ➡️ Learn agent delegation Target a dedicated workflow or task. Assign an AI agent said role, define the outcome, set constraints, and schedule review gates. Treat it like a junior teammate and give it work, while monitoring so you can review for accuracy. Here’s my do-this-now stack, and how I’d run it with a team ⏬ If you’re a beginner: Start with ChatGPT Agent Mode. Open a new ChatGPT chat and change the dropdown to ‘Agent Mode’. It can plan tasks, execute steps, and return cited outputs for market scans, vendor comparisons, executive briefs, and decision memos. Kick off the job, let it run, WATCH IT RUN, and then review the completion. If you’re more technical or ops-heavy: Use Claude Code when the work requires operating UIs or your computer - clicking through portals, filling forms, wrangling spreadsheets, saving down documents. Expect more upfront setup and ownership, so keep a step-by-step prompt checklist, add automatic reruns for failing steps, and update the checklist only when the site’s labels or paths change. If you’re living in Google Workspace: Turn on Google connectors (Drive, Gmail, Calendar) inside ChatGPT or Claude. Ask the model to find your team’s file, summarize threads, compare document versions, prepare for and schedule meetings, or draft from past emails. This lets your agent pull context and act on it without manual hunting. How to turn this into outcomes in 30 days ⏬ → Twice a week, use Agent Mode to produce a one-page brief with citations and a recommendation on a real business question. Track cycle time and data/citation quality, and, where relevant, use Claude Code to automate in parallel. At the end of the month, you should know where a few agents can tackle real work and have the data to support what to scale. #AIinWork

This Claude setup takes 10 minutes and saves hours on content. Here's a quick step-by-step guide: Let me guess... You've been trying to post daily, writing from scratch every time, trying to keep up. Meanwhile all these other creators seem to produce content in half the time. You're both using the same tool (Claude). The only difference is they've set up a repeatable system that makes life 10x easier for them 10 minutes is all you need to do the same: Step 1️⃣: Create a Project for each content stream One project for your LinkedIn.  One for your newsletter.  One per client if you're managing others. Front-end as much information as you can in the Project instructions and files. Step 2️⃣: Upload your tone of voice documents Any PDFs or text files with your brand docs, past posts that performed, and examples of what sounds like you and what doesn't. The difference in output quality once Claude knows your voice is significant. Step 3️⃣: Turn on Memory By default, Claude forgets everything the moment you close the chat. Memory means your audience, content pillars, and positioning are loaded every session automatically. As time goes on, the outputs you get will improve as well with Memory toggled. Step 4️⃣: Connect your tools Toggle on any relevant connectors, like Notion and Google Drive. Claude can pull context from them and push outputs directly into your workflow. If you use anything outside those three, you can build custom connectors for your specific stack. Step 5️⃣: Build a Skill for your most repeated tasks A Skill is a saved workflow with one job. Train it the same way you'd brief a project: trigger, inputs, output format. Example: a hook-writing Skill trained on the best hook structures, in your voice, for your audience. Step 6️⃣: Repurpose systematically Take one strong post and feed it back in. Ask for three format variations: carousel, short text, story-led. Specify the audience and the pain point for each one. From just one idea, you can get multiple pieces of content and save yourself time. Step 7️⃣: Review before it goes live Three things to check before posting anything: - Does the hook stop the scroll? - Does the body deliver on what the hook promised? - Is there one clear takeaway? You can systemise a lot of the writing process. But you can't fully systemise judgement or taste. A quick manual review is what separates good content from AI slop. Trust me, it's 100% worth it to put some time aside and set this up. Once you do, the content process gets significantly faster without losing quality. If you want more breakdowns on how we build content systems like this, I break them down often in our weekly newsletter. Subscribe here: https://lnkd.in/eqJtR_Vf ♻️ Repost to help your network make better content Follow me, Nader Alnajjar, for more on personal brand and AI

It’s funny, but this harsh reality also highlights a serious truth: AI is powerful, but it’s not infallible. Algorithms can misinterpret context, miss nuance, or make mistakes that a human would never make. Blind trust can be dangerous, whether you’re eating a mushroom or making business decisions. So how can we question AI outputs and make better decisions? Here are a few strategies I use: Check the source – Where did the AI get its data? Is it reliable, up-to-date, and relevant to your situation? Cross-verify – Don’t take a single answer at face value. Look for supporting evidence or alternative perspectives. Consider context – AI can miss nuances that matter. Ask: “Does this recommendation make sense given my goals, constraints, and values?” Ask why, not just what – Probe AI suggestions: “Why is this solution recommended?” Understanding reasoning helps spot gaps. Add human oversight – Involve experts, mentors, or peers to validate outputs before acting. AI is a powerful partner, but decisions should still be human-led. Our judgment, skepticism, and experience are what turn insights into smart action. 💬 How do you validate AI recommendations in your work to avoid costly mistakes? #AI #CriticalThinking #Leadership #FutureOfWork #LearningAndDevelopment #TrustButVerify

Let's talk about 𝐒𝐐𝐋 concepts that not only help in interviews but also make your day-to-day job as a Data Analyst easier. In my experience of facing multiple interviews and working with SQL daily, I've found a few concepts extremely valuable in real-world analytics: 𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬 (𝐂𝐓𝐄𝐬) These help simplify complex queries by breaking them into manageable parts. It makes your query readable and easy to maintain, especially when you're working in teams or on large projects. 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 (𝐑𝐎𝐖_𝐍𝐔𝐌𝐁𝐄𝐑, 𝐑𝐀𝐍𝐊, 𝐃𝐄𝐍𝐒𝐄_𝐑𝐀𝐍𝐊, 𝐋𝐄𝐀𝐃, 𝐋𝐀𝐆) These are game-changers. Instead of writing multiple subqueries, you can easily perform ranking, find running totals, compare rows, and calculate moving averages with one simple statement. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 (𝐍𝐞𝐬𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬) Subqueries allow you to perform complex operations step-by-step. They are great for scenarios where you need results from multiple queries combined into one. 𝐈𝐧𝐝𝐞𝐱𝐞𝐬 & 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Understanding indexing helps your queries run faster. For instance, creating an index on columns frequently used in JOINs, WHERE, or GROUP BY clauses drastically improves performance, especially in large tables. 𝐉𝐨𝐢𝐧𝐬 𝐯𝐬. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 (𝐖𝐡𝐞𝐧 𝐭𝐨 𝐔𝐬𝐞 𝐖𝐡𝐚𝐭) Many of us get confused about using joins or subqueries. Typically, JOINs are more efficient for large datasets, while subqueries can be simpler to write for smaller or one-time analyses. 𝐂𝐀𝐒𝐄 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐋𝐨𝐠𝐢𝐜 These are useful for categorizing your data without using multiple queries. A single CASE statement can simplify your logic and save processing time. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠𝐬 You should know how to effectively use GROUP BY along with aggregate functions like COUNT, SUM, AVG, MAX, MIN. Grouping data properly is fundamental to answering most analytical questions. 𝐃𝐚𝐭𝐞 & 𝐓𝐢𝐦𝐞 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬 Real analytics problems often involve time series data. Learn functions like DATE_TRUNC, DATE_PART, DATE_DIFF, DATE_ADD, and DATE_FORMAT to handle date-time data effectively. 𝐒𝐞𝐥𝐟-𝐉𝐨𝐢𝐧𝐬 & 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 Not all data lives neatly in one table. Self-joins help you analyze hierarchical data like employee-manager relationships or user referral systems. 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐃𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲 Knowing how to identify and remove duplicate records using ROW_NUMBER() or DISTINCT ensures accurate and reliable analysis. SQL isn't just about writing queries; it's about efficiency, readability, and solving real business problems. The above topics cover essential areas that have personally helped me improve my productivity and provided great value during interviews. Did I miss any important topic? Drop your suggestions below. Follow Shakra Shamim for more such posts.!

The difference between a good design doc and a great one is usually clarity. Technical writing should be crisp and to the point. So, it is always better to treat every sentence like it has a cost. After writing, cut aggressively. Remove extra words. Then check if a line can go. Sometimes even a full paragraph is unnecessary. One thing I always do is to start the doc with the conclusion; this way, the reader/reviewer knows where we are heading. This is contrary to how most engineers write docs - listing every approach first and only concluding at the end. That slows readers down. I avoid this because long explanations make people lose track; most readers want the conclusion quickly. So, always start with the answer and why it matters. Then add details and alternatives below for those who want depth. A habit that helps is a quick editing pass like this: - Remove filler words and repeated ideas. - Break long sentences into smaller ones. - Prefer bullets when listing options or steps. - Check if the first section clearly states the outcome. - Add a link or short explanation where a reader may pause. Empathy matters more than most people realize. Try to read your document as someone new to the topic. Ask yourself what might confuse them. Add the missing context. Add the helpful link. Let the ideas evolve naturally from problem to solution. This skill develops over time. Use simple language and fewer buzzwords. The goal is to communicate, not impress. Simple documents get read more. More readers means better alignment and better visibility for the work. Finally, always provide enough context. A short setup about the problem, constraints, and prior decisions goes a long way. It helps readers understand why the decision exists, and, of course, it prevents unnecessary back and forth later. Hope this helps.

I just taught Claude to directly query my CRM. Complex workflows became single prompts: A month ago my network kept talking about something called Model Context Protocol (MCP). Initially abstract, I understood it simply as: MCP lets AI models directly access your existing tools and databases. Think of it like the invention of USB: → Before USB: Multiple incompatible ports → After USB: One universal connection → Before MCP: Custom data integrations → After MCP: Universal plug-and-play AI connectivity Then a week ago I got an email from my personal CRM provider Clay that they had support for MCP. Historically, CRMs have acted as passive databases, requiring manual interactions to deliver insights. Here is what I used to do when I wanted to know who within my network had changed roles recently: OLD PROCESS: → Log into Clay CRM, export contacts as CSV → Clean and format data in a spreadsheet → Copy-paste formatted data into Claude → Manually instruct Claude to analyze job changes → Copy Claude’s insights back to Clay → Update contact records individually → Manually set follow-up tasks for each contact NEW PROCESS: → Simply instruct Claude: “Identify contacts in my network who recently changed jobs, showing their old and new positions and when I last interacted with them.” → Claude directly accesses Clay via MCP → Finds contacts who’ve recently changed jobs → Instantly provides a detailed, actionable list The results aren't perfect, but they turned a previously tedious process into an effortless query. The technical setup took 5 minutes: → Generated a Clay API key → Connected through Clay’s Smithery page → Installed Node.js locally → Ran one terminal command → Restarted Claude, confirming integration MCP's power comes from three shifts: → From isolated silos to interconnected intelligence → From sequential tasks to seamless orchestration → From human middleware to direct and automated interactions While it is early days, I believe we are scratching the surface with what is possible. I'm now working with several of our portfolio companies to explore how we can do deeper AI integrations. In an age where everyone has access to similar AI tools, the real competitive advantage isn't the tool itself. It's how deeply you embed it into your workflows.

Most people think the risk with AI is that it sometimes says something dumb. I think the bigger risk is the opposite: it says something clean, confident, and well-formatted… and it’s quietly wrong.   A recent Microsoft Research study stress-tested frontier models (including GPT-5) and ran a deceptively simple experiment: remove the image that a question depends on (like a chest X-ray) and see what happens. Models still answer above random chance. That doesn’t mean the model “saw” anything — it can often “pass” by leaning on shortcuts and priors.   Now swap “X-ray” with “the attachment,” “the pricing spreadsheet,” or “Section L.”   This is where a lot of AI-assisted RFP workflows break: upload the solicitation, run one prompt, and accept the generated outline/compliance matrix with no verification loop. The output looks professional. But without traceability (show me the exact source text) and a human correction loop, you can get an illusion of compliance — not real compliance.   To be clear, this is not an anti-LLM take. Models will keep improving. My point is architectural: reliability comes from workflow design, not blind faith in a single pass.   I wrote a deeper article outlining the predictable failure modes (misinterpretation, omission, and document artifacts) and what a safer workflow looks like: traceability, red-flags, and fix-in-place review.  

Not every problem needs the same type of AI agent. Most people try to build AI agents first. Experienced builders start with patterns. Some tasks need memory. Some need tools. Some need planning. Others need human approval. The real skill in Agentic AI is knowing which agent pattern to use and when. This cheat sheet breaks down the core AI agent patterns used in modern AI systems: • Memory Agents - maintain long-term context across conversations and workflows. • Tool Agents - connect LLMs with APIs, databases, and real-world actions. • Planner Agents - decompose complex goals into structured execution steps. • RAG Agents - retrieve trusted knowledge before generating responses. As systems scale, more advanced patterns appear: • Autonomous Agents - run continuous workflows with minimal human input. • Multi-Agent Systems - specialized agents collaborate to solve complex problems. • Reflection Agents - evaluate and improve outputs before final delivery. • Human-in-the-Loop Agents - add approvals and governance for critical decisions. The key insight: AI agents are not magic. They are architectures built from repeatable design patterns. Start by identifying signals in your problem. Choose the right pattern. Then add tools, memory, and guardrails. That’s how real agentic systems move from demos → production. Save this if you’re building AI agents, exploring Agentic AI, or designing intelligent workflows in 2026.

It took me a full weekend to write my first weekly newsletter. Now it takes less than an hour 👇🏼 Next week, I will publish issue 100 🚀 What started as an experiment is now part of my weekly routine. The biggest improvement did not come from asking AI to write more. It came from turning the process into a repeatable workflow. My newsletter has three fixed sections: - Open Source of the Week - New Learning Resources - Book of the Week I provide the links, and the newsletter-builder handles the workflow: - Research the sources - Draft each section in a fixed format - Apply a voice pass based on previous issues - Assemble and validate the final Markdown draft Three specialized sub-agents handle the safeguards around that work: - A status logger tracks drafts and locks published issues - A history tracker flags books or projects I have featured before - A backlog manager queues future resources and records them when they are used The workflow also checks links, validates the issue structure, and keeps its tracking state synchronized after every save. I now use similar specialized agents across many of my personal projects - from managing Git branches and commits to project tracking, slides, and presentations. The lesson: the biggest time savings did not come from a better summarization prompt. They came from designing a system around the entire workflow. Which recurring workflow would you automate first? #ai