“JP is one of the hardest working people that I have ever come across. His total dedication and passion for anything that he is working on is second to none. Probably his most understated ability is his ability to stay on task until the job is done more so that anyone that I have ever come into contact with. If anyone is thinking about working with JP there would be no better fit or potential candidate for any role that they see him in. Given the opportunity I would work with him again with ZERO hesitation. ”
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A consummate coach who time after time helps teams reach their maximum potential, Juan…
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I am excited to announce my promotion to Hiring Partner at Insight Global. This milestone holds significant meaning for me, especially as one of the…
I am excited to announce my promotion to Hiring Partner at Insight Global. This milestone holds significant meaning for me, especially as one of the…
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I get asked by industry outsiders (a lot) - if AI can read public records, why can't it produce insurable title instantly? Our new white paper…
I get asked by industry outsiders (a lot) - if AI can read public records, why can't it produce insurable title instantly? Our new white paper…
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Important read. We're here to dance with you, not for you.
Important read. We're here to dance with you, not for you.
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Kevin Iverson
Kevin Iverson
Senior technology executive with extensive experience building and scaling technical solutions and the organizations that create and support them.
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Troy Witthoeft
Leading engineering teams through growth is hard. Spotting who is doing it right is easy. Some survive, others thrive. Survivors - Stack more process on process to keep things from breaking. - Midnight war rooms are normal. - Maintenance windows and downtime for releases. - Senior devs as gatekeepers, not multipliers. - Every new hire adds drag. Thrivers - Bake knowledge into the platform. - Build automation tools. - New engineers ship to prod in week one. - Docs come from the code and pipeline. - Incidents teach, not blame. - Errors are clear and actionable. One model rewards heroics. The other makes them unnecessary.
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Bharat Kumar Kondapalli
𝐖𝐡𝐞𝐧 𝐲𝐨𝐮𝐫 𝐛𝐮𝐫𝐫𝐢𝐭𝐨 𝐛𝐨𝐰𝐥 𝐤𝐧𝐨𝐰𝐬 𝐲𝐨𝐮𝐫 𝐭𝐚𝐱 𝐛𝐫𝐚𝐜𝐤𝐞𝐭 I was reading summary of Chipotle’s latest earnings report released about 2 weeks ago and one statement from Chipotle's CEO stopped me: 60% of their core customers earn over $100,000 a year. 𝐌𝐲 𝐟𝐢𝐫𝐬𝐭 𝐫𝐞𝐚𝐜𝐭𝐢𝐨𝐧 𝐰𝐚𝐬 𝐩𝐮𝐫𝐞𝐥𝐲 𝐚𝐬 𝐚 𝐜𝐮𝐫𝐢𝐨𝐮𝐬 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫:How do they even know that? I don’t remember filling out a financial profile to get a side of guac. I just tap my card, grab my bowl, and walk out. But then, the engineer in me took over. I started thinking about...now, really- 𝐇𝐨𝐰? from a systems architecture and data-aggregation perspective. It hit me, The Architecture of a 𝐒𝐡𝐚𝐝𝐨𝐰 𝐏𝐫𝐨𝐟𝐢𝐥𝐞 that Chipotle isn't just a restaurant. In 2026, it’s a sophisticated data aggregator as well. Here is how that 60% figure likely moves through their pipeline: 𝙏𝙝𝙚 𝙩𝙧𝙖𝙣𝙨𝙖𝙘𝙩𝙞𝙤𝙣𝙖𝙡 𝙃𝙖𝙨𝙝: When you swipe your card, you’re generating a unique, anonymized identifier for them. 𝙏𝙝𝙞𝙧𝙙 𝙋𝙖𝙧𝙩𝙮 𝙀𝙣𝙧𝙞𝙘𝙝𝙢𝙚𝙣𝙩: Chipotle (and most major retailers) use 𝐃𝐚𝐭𝐚 𝐄𝐧𝐫𝐢𝐜𝐡𝐦𝐞𝐧𝐭 services from firms like Acxiom, Experian, or Cardlytics. They take that anonymized card ID and 𝐣𝐨𝐢𝐧 it with massive, external datasets including census data, property values, and credit-level spending patterns. 𝙏𝙝𝙚 𝙆-𝙎𝙝𝙖𝙥𝙚𝙙 𝙇𝙤𝙜𝙞𝙘: You must have already heard about K-shaped economy. By mapping where that card is used (high-end grocers, luxury retail and other restaurants) and the neighborhood of the cardholder (that's easy - based on the proximity you most shop at. That gives them most likely zip code you live in and average house value or rent prices there), they can statistically estimate household income with incredible accuracy without ever asking you directly about your income. Why this matters? From 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 to 𝐔𝐬𝐞𝐫𝐬 During the earnings call, CEO repeatedly referred to us not as customers, but as core users. He already got a lot of slack on media for that. As an engineer, that terminology shift is telling. It implies we are part of an ecosystem. Chipotle is using these insights to navigate a K-shaped economy..and how? Menu Changes: They aren't just raising prices; they’re introducing 𝐇𝐢𝐠𝐡-𝐏𝐫𝐨𝐭𝐞𝐢𝐧 add-ons specifically designed for that affluent 60% who are less price-sensitive. Resilience Testing: In other words, how much wiggle room you show in your spending with increased prices by testing 1-2% increase at a time or for trying new menu options in addition to your planned spending. That's predicting price elasticity. At the end of the day, we have to realize that every time we shop, we aren't just buying lunch. We are a walking, breathing data point for the companies. Is your lunch order more tech-forward than your actual job? What do you think? And did you think if you can limit companies do this to your data? opt-outs are there if you are interested...
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Gundars Kokts
Anthropic shipped multi-agent code review for Claude Code. Their internal numbers: substantive review comments went from 16% to 54% of PRs. Less than 1% of findings marked incorrect. If your team's code output has doubled but your review capacity hasn't, this is worth watching. More expensive than Claude Code GitHub Action though. https://lnkd.in/g_Pg-SiS
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