FullStack

We’re hiring a Strategic Accounts Lead who leads with trust, not volume. 📈 This remote-first, Bay Area-based role is for the elite connector, someone who has spent the last decade building a "rolodex" of SF’s tech leadership. 🌉 You’ll leverage your deep local roots to translate our world-class delivery in AI and custom software into long-term, multi-million-dollar partnerships. 💎 Ready to build the territory from the ground up? 🚀 Apply here: 👇 https://lnkd.in/eJTjXCiJ #SFTech #StrategicSales #BayAreaJobs #AIServices #ExecutiveHiring

I came to this industry from the product side. Before I ever sold a services engagement, I was building things. Shipping things. Living with the consequences of decisions I made about what to build and when. That shaped how I think about what services should actually be. Here's the part that always sat uncomfortably with me. The standard services model is built on hours. Senior hours, junior hours, blended rates, ramp-up time. The whole conversation between buyer and seller revolves around capacity, not outcome. But no client I've ever met genuinely cared about hours. They cared about the checkout flow and shipping. The billing system is not breaking at month-end. The mobile app will be in the store by Q3. Hours were never what they were buying. Hours were our proxy because we couldn't agree on a better way to measure the work. For a long time, the model held because the volume of work was enormous, and the alternative was hard. Selling hours meant the client carried the risk of how much work it would actually take. That was easier for everyone to underwrite, even if it meant nobody was really being held to account for the outcome. A piece has been making the rounds this week arguing that the next trillion-dollar company will be a software company masquerading as a services firm. Most of our industry will read it defensively. I read it as overdue. The hourly model was always a workaround. It was the easiest way to keep the lights on while the industry figured out how to actually measure what mattered. AI isn't killing services. It's removing the excuse the industry has used for two decades to keep the conversation focused on hours rather than outcomes. The firms that thrive next won't be the ones protecting timesheets. They'll be the ones who finally have the tools to be accountable for what the client was actually buying. For those of us who came from product, this isn't a threat. It's the conversation we've been waiting to have.

I just built "Pizza Runner," a twist on the classic Snake game where you play as the snack running for your life! 🍕🐍 But the reason for the post isn't the game, it’s how it was built using the new Claude Cowork Artifacts. Claude recently released this feature, allowing anyone to use the Cowork environment to build quick, functional solutions. The shift here is that Cowork has access to local files, so the AI reads and writes directly to a project folder. Why this is a big deal: - The code lives on your computer, not in a chat history. - You don’t need to be a developer to use it. - You grant permission to a specific folder, so the AI doesn't touch your other files. If you’ve been sitting on an idea but didn’t want to mess with a code editor or a full "vibe-coding" setup, it's worth giving Cowork a try.

Two classes. Two professors. A completely different way of thinking about finance. As an MS Finance student The University of Texas Permian Basin , I’ve come to realize that finance isn’t just formulas and models, it’s a way of analyzing decisions under uncertainty. Professor Marcos Velazquez (Investments) has challenged me to think beyond returns, to really understand risk, market behavior, and disciplined investment thinking. Professor Umesh Jaiswal (Corporate Finance) has pushed me to approach financial decisions with structure from valuation to capital structure and long-term strategy. What stands out most is how both bring theory to life constantly tying concepts back to real-world application. Still early in the journey, but already seeing a shift in how I think, analyze, and approach problems. Looking forward to what’s ahead.

AI pilots don't fail because of technology. They fail the moment they succeed. Here's what I've seen happen more times than I can count. A team runs a 90-day pilot. It works. The metrics are good. The stakeholders are impressed. There's a presentation to leadership, genuine excitement in the room, enthusiastic support. Then the pilot "succeeds" and gets handed to a committee. The original owner gets reassigned because the work is "done." The metric that made the pilot legible gets replaced by a broader metric that nobody owns. The decision loop that moved fast during the pilot now requires approvals from people who weren't in the room. Six months later, it's still in "transition to production." This isn't a technology failure. The technology worked. This is an organizational failure, specifically the failure to treat AI scaling as a distinct leadership problem that needs its own owner, its own success metric, and its own decision authority. McKinsey has written about this. The language is different, but the pattern is the same: organizations that can run good pilots outnumber organizations that can scale them by a wide margin. The fix isn't complicated. It's just unpopular because it requires someone in the room to say: "I own this from pilot to production to scale. Not a committee. Me." Is there a named owner for your AI scaling work right now?