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3d •
HR is a quiet goldmine, but only if you build it the right way
Look at what’s happening in HR right now. Nearly half of organizations expect to use AI in HR in 2026, and most CHROs expect even more of it. At the same time, over half of companies build their AI strategy without HR involved. So there’s demand, and the people who own the problem often don’t have the technical help. That’s the gap. HR also carries something most niches don’t: legal weight. New York City, California and Illinois already have rules in force on automated hiring tools, and Colorado and the EU have pushed their dates back to January 2027 and December 2027. Those dates have shifted before, so check them when you pitch. The point is that an HR buyer wants someone who understands that “it works” isn’t enough. They need “it works and we can show we checked.” What sells well and stays safe: Onboarding workflows that trigger tasks for IT, managers and new hires. Policy Q&A bots that only answer from the uploaded handbook and say “I’m not sure” when it isn’t covered. Survey comment summaries with names removed. Interview scorecards and structured question banks. Job description drafting with a bias check pass. What to avoid building, however much a client asks: Anything that automatically rejects candidates with no human review. Anything that ranks people without showing its reasons. Tools that claim to read emotions from video or voice. The EU AI Act has banned inferring emotions at work since February 2025. How to pitch it. Don’t lead with the tech. Lead with the cost of what’s going wrong: hours lost, mistakes, risk. People move faster to avoid a loss than to chase a gain. Say something like: “I build HR automations with a human review step and a log, so you save the time and can still show how every decision was made.” That one line separates you from everyone selling “AI hiring magic.” Build in the boring protections too. Keep a log of every action, add a human checkpoint where people’s careers are affected, and let the system say “I don’t know” instead of guessing.
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Aug 9 •
This $3.5K Dashboard Build Is Now My Template for Every Consulting Client
Just shipped a BI dashboard for XXXM Consultants — took their 5 scattered Smartsheets and put everything in one place. Sharing the breakdown because this is the template I'm running for every consulting firm with the same problem. What it is: One dashboard pulling from 7 Smartsheets: Leads, Employees, Invoices across two entities and multiple years. Syncs every morning at 8am. No manual work after launch. Why they needed it: Their team was stuck in spreadsheet hell. Sales reps couldn't tell which deals were due. HR had to dig for performance history. Finance was manually converting INR to USD on invoices. Client info lived in three different places. Every decision took hours. They just needed one place where everything was there. Here's what it does: HR Employee profiles: role, department, location, certifications, promotion history. Performance reviews from both appraiser and self-review. Salary growth tracked year over year. Recruitment pipeline shows the flow from applied - shortlisted - hired. Current Work tab shows who's assigned to which client, linked back to their profile. Sales Their entire pipeline from 8 stages (New Lead through Closed Won/Lost). Color-coded by stage using their brand colors. Shows deal value, win probability, next follow-up. Sorted so reps see urgent deals first instead of scrolling through closed ones. Clients Every client's Active/Not Active status updates automatically. It checks three signals: a won deal, an invoice this year, or a renewal coming up. No manual updates. It just works. SOW tracking shows services cost, effort, what you're delivering. Client timeline shows the journey: lead - won - invoiced - expanded - renewed. Team tab shows who's actually working on each client. Finance Revenue in USD and INR across both entities. Fiscal year breakdowns (Apr–Mar) with year-over-year growth. Revenue by client, by service. FY26-27 projections next to actual numbers so they can actually plan. as well add some ss of work for better understanding!!
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Jul 20 •
Welcome! Introduce yourself + share a pic of your workspace 🎉
Let's get to know each other! Comment below sharing where you are in the world, a photo of your workspace, and something you like to do for fun. 😊
12d •
What happens to your judgment when AI hands you a “recommended” list
Researchers at the University of Washington ran a simple test. They took the same resumes and changed only the names. Resumes with white-associated names were chosen for the next step 85% of the time, while Black-associated names were chosen 9% of the time. Same resumes, different names. Fair enough, you might think. A person will catch that. A newer study tested exactly that. It put 528 people through 1,526 resume screening scenarios. When they had no AI, or an AI built to be neutral, they picked white and non-white candidates at about equal rates. When they worked with a moderately biased AI, their picks tended to follow its lean. Coverage of the study put the agreement at about 90% when the AI was biased. In plain terms, the AI’s bias became theirs. This is automation bias, and it works through anchoring. Once a confident list shows up, your brain stops building its own answer and starts judging the AI’s. You feel like you’re reviewing, but you’re mostly agreeing. Here’s a workflow that protects you. I call it “you first, AI second.” Write down your own scoring criteria for the role before you read a single resume. Five criteria is plenty. Score each candidate yourself against those criteria, with names and photos hidden if you can. Only then ask AI for a summary. Make the AI show its evidence. Paste this: “For each candidate, list how they match each of these criteria: [your criteria]. Quote the exact resume line that supports each match. If there’s no evidence, write ‘no evidence.’ Do not rank the candidates. Do not comment on name, age, gender, schools, or employment gaps.” Compare your scores to the AI’s. Where you disagree is where to spend your time. Also note that the researchers pointed out that removing names alone may not solve the problem, so keep checking results over time, not just inputs. AI is useful for reading 200 resumes. It shouldn’t be the first voice in your head. When you use AI to screen or summarize, do you form your own view first, or read its summary first?
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14d •
What happens to your judgment when AI hands you a “recommended” list
Researchers at the University of Washington ran a simple test. They took the same resumes and changed only the names. Resumes with white-associated names were chosen for the next step 85% of the time, while Black-associated names were chosen 9% of the time. Same resumes, different names. Fair enough, you might think. A person will catch that. A newer study tested exactly that. It put 528 people through 1,526 resume screening scenarios. When they had no AI, or an AI built to be neutral, they picked white and non-white candidates at about equal rates. When they worked with a moderately biased AI, their picks tended to follow its lean. Coverage of the study put the agreement at about 90% when the AI was biased. In plain terms, the AI’s bias became theirs. Recruiters Follow AI's Biased Hiring Recommendations 90% of the Time, Research Says This is automation bias, and it works through anchoring. Once a confident list shows up, your brain stops building its own answer and starts judging the AI’s. You feel like you’re reviewing, but you’re mostly agreeing. Here’s a workflow that protects you. I call it “you first, AI second.” Write down your own scoring criteria for the role before you read a single resume. Five criteria is plenty. Score each candidate yourself against those criteria, with names and photos hidden if you can. Only then ask AI for a summary. Make the AI show its evidence. Paste this: “For each candidate, list how they match each of these criteria: [your criteria]. Quote the exact resume line that supports each match. If there’s no evidence, write ‘no evidence.’ Do not rank the candidates. Do not comment on name, age, gender, schools, or employment gaps.” Compare your scores to the AI’s. Where you disagree is where to spend your time. Also note that the researchers pointed out that removing names alone may not solve the problem, so keep checking results over time, not just inputs. AI is useful for reading 200 resumes. It shouldn’t be the first voice in your head.
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