What if your job hunt could run like a data system? In this episode, I share the story of how I used three AI agents — Researcher, Writer, and Reviewer — to rebuild my job search from the ground up. These agents read job descriptions, tailor resumes, and even critique tone and clarity — saving hours every week. But this episode isn’t just about automation. It’s about agency. I’ll talk about rejection, burnout, and the mindset shift that changed everything: treating every rejection as a data point, not a defeat. Whether you’re in tech, analytics, or just tired of the job search grind — this one’s for you. 🔹 Learn how I automated resume tailoring with GPT-4 🔹 Understand how to design AI systems that protect your mental energy 🔹 Discover why “efficiency” means doing less of what drains you 🔹 Hear the emotional story behind building these agents from scratch Join the Discussion (comments hub): https://mukundansankar.substack.com/notesTools I use for my Podcast and Affiliate PartnersRecording Partner: Riverside → Sign up here (affiliate)Host Your Podcast: RSS.com (affiliate )Research Tools: Sider.ai (affiliate)Sourcetable AI: Join Here(affiliate)🔗 Connect with Me:Free Email NewsletterWebsite: Data & AI with MukundanGitHub: https://github.com/mukund14Twitter/X: @sankarmukund475LinkedIn: Mukundan SankarYouTube: Subscribe

Data interviews do not have to feel messy. In this episode, I share a simple AI Interview Copilot that works for data analyst, data scientist, analytics engineer, product analyst, and marketing analyst roles. What you will learn today: How to Turn a Job Post into a Skills Map: Know Exactly What to Study First.How to build role-specific SQL drills (joins, window functions, cohorts, retention, time series).How to practice product/case questions that end with a decision and a metric you can defend.How to prepare ML/experimentation basics (problem framing, features, success metrics, A/B test sanity checks).How to plan take-home assignments (scope, assumptions, readable notebook/report structure).How to create a 6-story STAR bank with real numbers and clear outcomes.How to follow a 7-day rhythm so you make steady progress without burnout.How to keep proof of progress so your confidence comes from evidence, not hope.Copy-and-use prompts from the show: JD → Skills Map: “Parse this job post. Table: Skill/Theme | Where mentioned | My level (guess) | Study action | Likely interview questions. Then give 5 bullets: what they are really hiring for.”SQL Drill Factory (Analyst/Product/Marketing): “Create 20 SQL tasks + hint + how to check results using orders, users, events, campaigns. Emphasize joins, windows, conditional agg, cohorts, funnels, retention, time windows.”Case Coach (Data/Product): “Run a 15-minute case: key metric is down. Ask one question at a time. Score clarity, structure, metrics, trade-offs. End with gaps + practice list.”ML/Experimentation Basics (Data Science): “Create a 7-step outline for framing a modeling problem (goal, data, features, baseline, evaluation, risks, comms). Add an A/B test sanity checklist (power, SRM, population, metric guardrails).”Take-Home Planner: “Given this brief, propose scope, data assumptions, 3–5 analysis steps, visuals, and a short results section. Output a clear report outline.”Behavioral STAR Bank: “Draft 6 STAR stories (120s) for conflict, ambiguity, failure, leadership without title, stakeholder influence, measurable impact. Put numbers in Results.”

AI, data, numbers—without uploads. Hash, mask, and redact PII, then run data analytics locally for time-saving and privacy. In this episode, we build a No-Upload AI Analyst that keeps your PII safe: HMAC SHA-256 hashing, masking, and redaction using policy presets and client-side transforms. We’ll: • Reframe the problem (insights > risk) • Set four hard constraints (no uploads, local preferred, policy presets, human-readable audit) • Use rules-first privacy + schema semantics • Walk the 5-step workflow (paste headers → pick preset → set secret → transform → analyze) • Show real-world cases (HIPAA/HITECH-aware analytics, FERPA contexts, product analytics) • Share a checklist + quiz + local Streamlit approach Perfect for data teams in healthcare, finance, education, and privacy-sensitive orgs. Key takeaways Stop uploading customer data. Transform it client-side first.Use HMAC hashing to keep joins without exposing raw emails/IDs.Mask for human-readable UI; redact when you don’t need the field.Ship a data-handling report with every analysis.Run the app locally for maximum privacy.Affiliate note: I record with Riverside (affiliate) and host on RSS.com (affiliate). Links in show notes. Links Blog version: (Free): https://mukundansankar.substack.com/p/the-no-upload-ai-analyst-v4-secure Join the Discussion (comments hub): https://mukundansankar.substack.com/notesTools I use for my Podcast and Affiliate PartnersRecording Partner: Riverside → Sign up here (affiliate)Host Your Podcast: RSS.com (affiliate )Research Tools: Sider.ai (affiliate)Sourcetable AI: Join Here(affiliate)🔗 Connect with Me:Free Email NewsletterWebsite: Data & AI with MukundanGitHub: https://github.com/mukund14Twitter/X: @sankarmukund475LinkedIn: Mukundan SankarYouTube: Subscribe

AI, data, and analytics pick three cookable dinners from the ingredients and appliances you already have—no grocery run. We use AI, data, and a rules-first analytics score to rank real meals you can make tonight with what’s in your pantry. A lightweight rules engine avoids AI hallucinations; Chef-AI adds safe swaps and one-line directions. You’ll learn a copy-paste AI prompt, how to reduce waste, and how analytics rank time, fit, and vibe. 3 bullets (skimmable): Rules > raw AI for reliable, cookable resultsAnalytics score to rank fastest/best-fit mealsCopy-paste prompt for 3 ideas in under a minuteYou’ll learn Why a rules engine beats raw AI for reliable, cookable recipesHow an analytics score prioritizes the best matches fastA copy-paste AI prompt that returns 3 make-tonight ideas in under a minuteHow to reduce waste and keep weeknight meals simple & tastyTry this prompt: I have [3–5 ingredients] and these appliances: [list]. Suggest 3 meals I can make in under 30 minutes. If something’s missing, suggest simple pantry substitutions. Keep it realistic and give one-line directions for each. Quick quiz True or False — If you only rely on AI, it may assume tools you don’t have and suggest impossible recipes. Answer: True. Start with rules; use AI for riffs and swaps. Discussion question When you’re deciding on dinner, do you want structure (reliable classics) or creativity (something new)? Reply on Substack or X—I'll share the poll next week. Resources & links Blog Link: https://mukundansankar.substack.com/p/pantry-plate-the-aifirst-way-to-decideKey takeaways Put rules before AI for cookable results.One clear AI prompt can end dinner indecision in minutes.AI is a partner, not the chef.Affiliate partners (links below): RSS: your podcast, get free transcripts, and earn ad revenue with as few as 10 monthly downloads. Sign up here.Sider AI. AI-powered research and productivity assistant for breaking down job descriptions into keywords. Try Sider here.Riverside FM: Record your podcast in studio-quality audio and 4K video from anywhere. Get started with Riverside here.Affiliate disclosure: Some links may be affiliates. If you use them, I may earn at no extra cost to you. Answer: True. Keywords: ai, ai meal planner, data, data analytics, analytics, time-saving tools, pantry, dinner ideas, recipe generator, meal planning

I first built an AI that thinks like an analyst. Now I have built a better AI Data analyst for the practical use of AI. This episode breaks down the simple rebuild: start with a clear objective, pick 5–8 focus columns, and ship a one-page Markdown brief. You’ll also get a 3-minute quiz (10:33), a Substack discussion (17:04), and a 9-step checklist you can use today. What you’ll learn How to start with a clear business goal (not charts)Why focusing on 5–8 columns increases signalHow a 1-page brief moves work faster than a dashboardQuiz & Discussion Take the Lightning QuizJoin the Substack discussion: https://mukundansankar.substack.com/(Tell your day-two story, your one metric, and your 5–8 focus columns.)Listener Checklist Copy/paste: 1) Objective (one line) 2) 5–8 focus columns 3) 10 questions + why 4) Quick data health checks 5) Export 1-page brief 6) Share in Slack/Notion/Jira 7) Run 2–3 quick analyses today 8) Log learning + next decision 9) Repeat tomorrow Links Blog version: (with Medium membership): https://medium.com/data-science-collective/i-built-an-ai-that-thinks-like-a-data-analyst-then-it-went-viral-so-i-made-it-smarter-1f3206a8254b(Free): https://mukundansankar.substack.com/p/i-built-an-ai-that-thinks-like-aSubstack Note (comments hub): https://mukundansankar.substack.com/notesTools I use for my Podcast:Recording Partner: Riverside → Sign up here (affiliate)Host Your Podcast: RSS.com (affiliate )Research Tools: Sider.ai (affiliate)🔗 Connect with Me:Free Email Newsletter: https://data-ai-with-ms.kit.com/bae4d0c550Website: https://mukundansankar.substack.com/Twitter/X: @sankarmukund475LinkedIn: https://www.linkedin.com/in/mukundansankar/YouTube: https://www.youtube.com/@MukundSankar

Beat instant rejections. Use an AI resume audit to pass ATS filters and turn silence into interviews—clear steps, a one-week plan, and a free checker. AI job search without the guesswork. In this episode I use a tiny AI resume & portfolio audit to beat ATS filters—what to highlight, what’s missing, and how to rewrite one project so a hiring manager actually cares. It’s personal, practical, and ends with a one-week plan you can apply today. You’ll learn • How modern ATS screeners work—and why they’re fast (and unforgiving) • The simple AI workflow behind my ATS simulator (no hype, just outcomes) • Three lessons from failing my own test—and what actually moved the score • How to translate your story so it passes the bots and reaches humans • A one-week action plan to raise your odds on your next application Key takeaways • ATS = gatekeeper. If you don’t pass it, humans may never see you. • Match keywords exactly from the JD—“close enough” doesn’t count. • ATS-friendly formatting beats fancy templates that break parsing. • Quantify outcomes so machines and recruiters see impact. • Test before you apply with an ATS checker/simulator. Try this today (no code) Paste into your AI tool of choice: “Here’s my resume + 3 project summaries and the job description I’m applying to. 1) What should I highlight to match the JD? 2) What am I missing? 3) Rewrite one project to emphasize measurable business outcomes in 2–3 bullets.” One-week plan Day 1: Baseline ATS check; log gaps. Day 2: Map exact JD keywords to your resume/projects. Day 3: Rewrite top project in outcome language (numbers first). Day 4: Fix formatting (simple headings, standard section names). Day 5: Add two quantified wins; remove tool-only bullets. Day 6: Align portfolio links to the role (pin your best two). Day 7: Re-test; apply to three roles; track results. Resources Full story + DIY steps: https://medium.com/data-science-collective/when-an-ai-tool-i-built-evaluated-my-resume-i-learned-what-100-rejections-never-taught-me-8e8eea1f3d8fRecommended: use any reputable ATS checker to preview parsing before you apply.Affiliate Disclosure This episode may contain affiliate links. If you purchase via these links, I may earn a small commission at no extra cost to you. Thanks for supporting the show. Affiliate partners (links below): RSS: your podcast, get free transcripts, and earn ad revenue with as few as 10 monthly downloads. Sign up here.Sider AI. AI-powered research and productivity assistant for breaking down job descriptions into keywords. Try Sider here.Riverside FM: Record your podcast in studio-quality audio and 4K video from anywhere. Get started with Riverside here.Do this next Run your resume through an ATS checker this week. Find the gaps. Fill them. If this helped, share with a friend who’s job hunting and follow/subscribe for more real-world AI workflows.

Description: Join Mukundan Sankar as he explores the challenges of delivering an effective elevator pitch and how AI can assist in crafting one. Mukundan shares personal anecdotes and demonstrates AI-generated pitches tailored for different career stages. Key Takeaways: The importance of a well-crafted elevator pitch How AI can personalize pitches for different roles Real-life examples of AI-generated pitches Resources: 1]Elevator Pitch AI Code Mukundan's Blog Post: https://substack.com/home/post/p-170400977 2] Thinking about starting a podcast but worried it’ll take forever to grow? Here’s the thing — you don’t need a huge audience to get started or to earn money. I run my show on RSS.com, and it’s the simplest way to get your podcast live on Spotify, Apple, Amazon, YouTube, iHeartRadio, Deezer, and more — all in one step. Their analytics tell me exactly where my listeners are tuning in from, so I know what’s working. And here’s the best part — with their paid plan, you can start earning revenue through ads with as little as 10 downloads a month. That’s right — you don’t need to wait for thousands of listeners to start monetizing. Start your podcast for free today at RSS.com. (Affiliate link — I may earn a commission at no extra cost to you.) 3] 💡 Sider.ai– Your AI Copilot for Productivity: Sider.ai is the all-in-one AI assistant that works inside your browser, letting you research, write, summarize, and brainstorm without switching tabs. Whether you’re prepping for an interview, drafting your next pitch, or refining your business plan, Sider.ai can supercharge your productivity. It’s like having GPT-4 on standby, ready to help you think faster and write better. Try Sider.ai today and see how much more you can accomplish in less time. (Affiliate link — I may earn a commission if you sign up.)

Summary In this episode, Mukundan Sankar discusses the importance of analytics for Newsletter creators using Substack, emphasizing how understanding traffic sources can significantly enhance newsletter growth. He breaks down various traffic types, including direct, email, referral, and social media, and provides actionable strategies for optimizing each source. The conversation also highlights common mistakes creators make with their analytics and offers the next steps for leveraging data effectively to engage audiences and grow subscriptions. Chapters 00:00 Introduction to Content Creation and Analytics 00:00 Understanding Substack and Its Analytics 01:11 The Importance of Traffic Analytics 03:01 Exploring Traffic Sources 09:58 Leveraging Traffic Analytics for Growth 12:54 Common Traffic Mistakes and Next Steps 14:57 Conclusion and Future Updates Takeaways Analytics provide insights into audience engagement.Understanding traffic sources is crucial for growth.Direct traffic indicates loyal audience members.Email traffic reflects the effectiveness of subject lines.Referral traffic can introduce new readers to your content.Social media can convert casual readers into subscribers.Data must be acted upon to be valuable.Avoid focusing solely on top-line metrics.Experimentation is key to finding effective strategies.Set measurable goals to track progress. Links: Website: https://mukundansankar.substack.com/