TechnBrains

AI adoption in real estate is exploding. The share of executives who say it's actually working just dropped to 1%. 𝗙𝗿𝗼𝗺 𝗿𝗼𝘂𝗴𝗵𝗹𝘆 𝟭𝟮% 𝗮 𝘆𝗲𝗮𝗿 𝗲𝗮𝗿𝗹𝗶𝗲𝗿 𝘁𝗼 𝗮𝗯𝗼𝘂𝘁 𝟭% 𝗮𝘀 𝗽𝗲𝗿 Deloitte 𝟮𝟬𝟮𝟲 𝗥𝗲𝗽𝗼𝗿𝘁. Adoption is soaring and perceived impact is collapsing at the same time. That sounds like a contradiction until you look closer. 9𝟮% 𝗼𝗳 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹 𝗿𝗲𝗮𝗹 𝗲𝘀𝘁𝗮𝘁𝗲 𝗳𝗶𝗿𝗺𝘀 𝗵𝗮𝘃𝗲 𝗽𝗶𝗹𝗼𝘁𝗲𝗱 𝗔𝗜. 𝗢𝗻𝗹𝘆 𝟱% 𝘀𝗮𝘆 𝘁𝗵𝗲𝘆 𝗵𝗶𝘁 𝘁𝗵𝗲𝗶𝗿 𝗴𝗼𝗮𝗹𝘀. Everyone's testing. Almost no one has made it work inside real operations. Here's the part that flips it. 𝗢𝗻𝗲 𝗺𝗶𝗱-𝗺𝗮𝗿𝗸𝗲𝘁 𝗯𝗿𝗼𝗸𝗲𝗿𝗮𝗴𝗲 𝗰𝘂𝘁 𝗶𝘁𝘀 𝗰𝗼𝘀𝘁 𝗽𝗲𝗿 𝗹𝗲𝗮𝘀𝗲 𝗳𝗿𝗼𝗺 $𝟯𝟮𝟬 𝘁𝗼 $𝟰𝟬, 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗲𝗻𝗲𝗿𝗴𝘆 𝗰𝗼𝘀𝘁𝘀 𝟭𝟵%, 𝗮𝗻𝗱 𝗵𝗶𝘁 𝟮𝟳% 𝗜𝗥𝗥 𝗼𝗻 𝗔𝗜-𝗳𝗹𝗮𝗴𝗴𝗲𝗱 𝗱𝗲𝗮𝗹𝘀. Same models everyone else has, but did one specific thing first which most teams skip. We broke down what separates the teams getting those numbers from the ones stuck in pilots, the full breakdown in the comments. #PropTech 

Your AI-built website works in the demo. Then a real user shows up and it starts breaking in ways you can't explain. Veracode found 45% of AI-generated code carried a known security vulnerability across more than 100 models tested. We get called in to fix these after they break in production, so we see the same five mistakes behind almost all of them. Here's how to avoid them:  1. 𝗗𝗲𝗳𝗶𝗻𝗲 "𝗱𝗼𝗻𝗲" 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗽𝗿𝗼𝗺𝗽𝘁. Scope you never wrote down is where re-prompting loops start.  2. 𝗦𝗮𝘃𝗲 𝗮 𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗲𝘃𝗲𝗿𝘆 𝗰𝗵𝗮𝗻𝗴𝗲, so you can roll back in seconds instead of rebuilding.  3. 𝗡𝗲𝘃𝗲𝗿 𝘀𝗵𝗶𝗽 𝗹𝗼𝗴𝗶𝗻𝘀, 𝗽𝗮𝘆𝗺𝗲𝗻𝘁𝘀, 𝗼𝗿 𝘂𝘀𝗲𝗿 𝗱𝗮𝘁𝗮 𝗼𝗻 𝗔𝗜 𝗰𝗼𝗱𝗲 𝗮𝗹𝗼𝗻𝗲. That's where the 45% lives.  4. 𝗖𝗼𝗻𝗳𝗶𝗿𝗺 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗲𝘅𝗽𝗼𝗿𝘁 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲. If you can't take it elsewhere, you don't own it, you rent it.  5. 𝗔𝗹𝘄𝗮𝘆𝘀 𝘁𝗲𝘀𝘁 𝘄𝗿𝗼𝗻𝗴 𝗽𝗮𝘀𝘀𝘄𝗼𝗿𝗱, 𝗲𝗺𝗽𝘁𝘆 𝗳𝗼𝗿𝗺, 𝘀𝗹𝗼𝘄 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻. The demo always works. Users don't. If you're stuck in the break-one-fix-another loop, or you want the next one built right the first time, that's the work we do: Fix what AI got wrong, or build it properly from the start, owned by you and made to last. #NoCode #AIDevelopment #AIbugsfixes

Worth five minutes if freight runs on a platform you don't own. Our Senior Software Engineer Syed Faisal's read on the vendor risk is right.  We'd add the technical reason these deals hurt. After an acquisition, the first thing to change isn't the roadmap, it's the connection your systems use to reach the platform. The new owner retires it quietly. Your workflow runs fine for months, then breaks the day it goes dark. The shippers who come through it intact built one layer of insulation early, so swapping the platform underneath is a config change, not a six-month rebuild. You can't control who buys your vendor. You can control how replaceable they are.

The teams shipping faster than you aren't smarter. They just didn't hire. They augmented. They got Senior Engineers merged straight into their existing repo and rituals, shipped inside the same sprint they joined. No 6-week cycle. No onboarding lag. Swipe to see which model fits your build → #StaffAugmentation #SoftwareDevelopment #TechLeadership #TechnBrains

Worth a read for any engineering or product leader thinking about where AI fits into their own product strategy. Our PropTech Strategist, Zaid Tirmizi has sparked a real conversation on the Zillow–Gemini rental listings move, users are starting their search in a chatbot instead of on your site, and Zillow just showed what it costs to go along with that. #PropTech #AI #ArtificialIntelligence #SoftwareDevelopment #DigitalTransformation

Knowing how to code is now table stakes. Knowing what to build is the whole game. Boris Cherny at Anthropic put words to something we've believed for a while:  Job titles are dissolving. What matters isn't a résumé, it's which posture a person takes. A designer can be a sweeper. An engineer can be a prototyper. The résumé predicts almost nothing. There are five, and it's how we staff and manage teams at TechnBrains rather than by title: 𝗧𝗵𝗲 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲𝗿 spins up rough, throwaway versions early, so the idea is visible before anyone commits to it.  𝗧𝗵𝗲 𝗯𝘂𝗶𝗹𝗱𝗲𝗿 takes the version that survives and turns it into production code that holds.  𝗧𝗵𝗲 𝘀𝘄𝗲𝗲𝗽𝗲𝗿 goes back through and cuts what isn't earning its place, keeping the system small.  𝗧𝗵𝗲 𝗴𝗿𝗼𝘄𝗲𝗿 watches how real users behave once it's live and pushes toward adoption.  𝗧𝗵𝗲 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗲𝗿 keeps it secure, fast, and stable as usage climbs. Knowing the language is table stakes. Knowing which posture a moment calls for is what actually moves a product forward. But here's the part that matters most, and it's where we part ways with the pure-tech version of this. Postures tell you how something gets built. They don't tell you whether it's the right thing to build in healthcare, logistics, PropTech, construction, or fintech. Our teams carry deep expertise in those industries, the people who know where the compliance, workflow, and data traps actually sit. The postures decide how well something ships. The domain knowledge decides whether it was worth shipping at all. #SoftwareDevelopment #ProductDevelopment #EngineeringLeadership 

"We used AI, so we don't really need senior developers." That sentence has cost founders more money than almost anything else this year. AI made building cheap, but someone still has to catch the code that looks done and isn't. Maybe that's why 71% of developers say they review AI's output line by line, only 29% trust it as-is. Swipe for the checklist smart teams use before: #SoftwareDevelopment #SeniorDevelopers #TechLeadership

Stuck sprints usually aren't a tools problem, they're a scope problem. Tickets come in too vague for anyone to start, and that's what stalls the work. In The Digital Project Manager feature, Our CEO Kazim Qazi reveals why most agency backlogs really stall, and it isn't the reason teams keep spending on. He then breaks down the exact first move TechnBrains makes on every incoming request, before a human ever touches it, that quietly unblocks a stuck sprint. The full method is in the feature. Worth the read.

We'll go to any height for your next build. Literally. If you're staking it all on a build, it deserves a team that won't miss a beat. #softwaredevelopment #ITStaffAugmentation

Anthropic CEO Dario Amodei said it best: "AI models probably hallucinate less than humans, but they hallucinate in more surprising ways." We put that claim to the test on our workflows. We ran 7 leading LLMs: 𝗖𝗹𝗮𝘂𝗱𝗲 𝗦𝗼𝗻𝗻𝗲𝘁 𝟰.𝟲 𝗠𝗮𝘅, 𝗖𝗼𝗱𝗲𝘅 𝟱.𝟱, 𝗚𝗲𝗺𝗶𝗻𝗶 𝟯.𝟭 𝗣𝗿𝗼, 𝗚𝗲𝗺𝗶𝗻𝗶 𝟯.𝟱 𝗙𝗹𝗮𝘀𝗵, 𝗚𝗟𝗠, 𝗞𝗶𝗺𝗶 𝗞𝟮.𝟲, 𝗠𝗶𝗻𝗶𝗠𝗮𝘅 𝗠𝟯, through 4 real coding tasks and scored each on a 0–10 hallucination-risk scale. The gap between the safest and riskiest was wider than we expected, the lowest came in under 2/10, the highest topped 4/10. No model outright invented a fake Stripe or AWS API, but the spread between them tells you a lot about which one you can trust with less oversight. See the full 0–10 scores for every model, task by task, in the blog: [https://lnkd.in/diw6s4ZK] #GenerativeAI #AITesting #SoftwareEngineering