Les Thomas

Ok Rust, there is a lot I genuinely love about your language design, but can we all acknowledge having self and Self as keywords that mean different things was a horrible decision. I don’t care what the rationalization was - having a key semantic characteristic differ by freaking capitalization is absurd. We shouldn’t sound like philosophers when we write code - “we each have multiple selves, and they represent different meanings”. Anyway, CodeQL has Rust support now, so for all of the security minded devs there is finally a global interprocedural analysis engine at your disposal.

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Have you been a victim of workslop 🪣? A recent HBR report on bad AI content at work really hit home for me. I've seen it everywhere and I know you have too. Workslop is that super polished-sounding content that looks impressive but says so little and lacks specifics in your work context that it offers no real value and even wastes time (HBR says AI slop is wasting $186 per employee per month). LLMs are incredibly impressive and are starting to make a dent on actual work (see OpenAI's latest research). But if companies rush to deploy any AI solution and don't think about training, metrics, or grounding it in their own work context, it's going to lead to wasted time and workslop. Why the rush? I've heard it firsthand in customer meetings: "We need AI or competitors will eat us alive." One report found 77% of CEOs feel competitive pressure to adopt it. So here are some my tips to get more out of AI deployments and reduce that workslop. 1. 📊 Your work context matters! Don't rely on a vanilla LLM or generic "enterprise chatbot."  It must be tuned to your work content, metrics, and data. Many chatbots only connect superficially to 3rd party content, or pull in personal files but not shared ones. So they aren't very effective. (Check out Dropbox Dash, we do this really, really well). 2. 🎯 Define your success criteria Know what you're solving. If you want fewer errors or faster delivery, measure that. Don’t just declare success because "AI is deployed." For software development, many companies see success when all devs are using an AI coding copilot. Or "generating code with AI". But for what purpose? Faster deployments, fewer bugs, improved feature velocity? That's what you need to measure. 3. 🛠️ Training and culture That blinking chatbot cursor is intimidating. In one survey, 49% of workers felt pressure to adopt AI, but 55% didn’t know how. The best deployments invest in training, shared playbooks, demos, and mentors. And don’t just do it once and think you are done. Ongoing measurement, monitoring, and training is needed. Also, look for AI tools that aren't just a chatbot. Products that blend seamlessly in the background. Not everything needs to be solved with a language model. 4. 🚀 High performers are going to highly perform and vice versa Ignore AI for a second. Top performers always seem to "figure it out" in the workplace. They learn the new tool and incorporate it into their workday. And so AI deployments are just new tools. It’s critical to watch and observe their output and use them as mentors (above). But others may simply take AI output and present it as their own. Workslop! And unless you guard against that (via reviews, performance management, feedback, and coaching), you're going to get more workslop. And that annoys and slows down everyone else. AI is nothing new here. Don't assume AI is some magic bullet and everyone's performance will rise. Don't just deploy it and forget it!

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The Andrej Karpathy interview is really, really great, I listened in its entirety and plan to listen again. Worth bearing in mind that our industry in general is incredibly optimistic, and the downside of this optimism is that we often can see where things are going long before the tech will be ready, see: self-driving cars in the 2010s, we all thought it was Right Around The Corner, it wasn't. Currently, people are talking about how LLMs are going to obviate entire classes of jobs, but in practice we all see them fail at very basic tasks, while we also underestimate the complexities of most jobs we claim AI will obviate. My own experiences using LLMs for coding assistance, chess coaching, and musical analysis is that they are currently good for small, focused tasks, but often fail at very basic sorts of analysis in completely unintuitive ways. Just this morning, ChatGPT advised a move that would have immediately lost me a queen in a practice game. We've all probably spun our wheels for hours coding when an LLM is going in circles. The whole interview is fantastic and well worth a detailed listen!

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We have all heard the saying "Never interfere with your enemy when they are making a mistake," which is good advice. But what more people should heed, since it is far more common: "Don't interfere with your teammate when they are saving your ass."

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I spent a few days at the Las Vegas Barber Expo recently, and it opened a door into a world I didn’t know much about. Barbering has its own rhythm and history—part art, part entrepreneurship, part neighborhood gathering place. What stood out most was the generosity of the people I met. They welcomed a newcomer like me without hesitation, eager to share stories about their shops, their customers, and the pride they take in their craft. It was a side of American culture that was new to me, and I left with a real respect for it. Experiences like that connect directly to what we’re building at SQUIRE. Our focus is helping small business owners—many of them barbers—run the kind of operation they want without losing the parts they love. Sometimes that means an AI Phone Agent that answers calls and books appointments when they’re too busy to pick up. Other times it’s automated payments and inventory tools that keep the back office from eating up their nights. Or analytics that help them spot trends and plan for growth. Or even the ability to build their own website without needing to hire someone. The goal isn’t flashy technology for its own sake. It’s giving shops the breathing room to concentrate on their craft and their customers, while the systems quietly handle the chores that usually drain energy and time. Spending time with the barbering community reminded me why this matters. Small businesses are a backbone of culture and connection, and helping them succeed—without losing what makes them special—is the kind of work I’m extremely proud to be part of. PS: For the engineering side of the story, we’re about to launch an engineering blog where we’ll share how we’re building Squire AI—from the architecture choices to the day-to-day challenges of bringing powerful AI to small businesses. If you’re curious about how we make these systems work behind the scenes, keep an eye out for that.

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What does it take to build a successful engineering organization? 🚀 It’s not just strategy decks or scaling headcount. It’s the messy, behind-the-scenes work—building clarity when there’s chaos, balancing autonomy with alignment, and constantly evolving your architecture and culture as you grow. I’ve spent the last few years learning (and re-learning) this. So, I wrote a 3-part blog series to share some nuggets. If you're leading (or hoping to lead) at scale, I hope there's something useful in here for you. Would love your thoughts, and even more—your own lessons 🙂 #engineering #leadership #lessonslearned

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I am an able-bodied son of a general category middle-class family. So when people talk about the challenges some face due to reservations and gender diversity hiring, I lie on the sweet spot of that most disadvantaged intersection in the Venn diagram. But through the years studying and working, I understood why we need affirmative actions for education and the workplace. To the people who feel that movements like reservations and feminism are sham, I'd like to present two anecdotes to you: 1. A person whom I know closely was forced out of her job, citing poor performance, when the rest of the team was all male. The other female employee who joined the team after the former left was also treated badly and left the team soon after. There was no harassment or explicit mistreatment, but there were signs of subtle sexism when working with colleagues who often tended to ignore requests for help or the doubt on quality of work provided by female members. 2. In my college, a junior approached me for help as he was struggling in his studies. The situation was worsened by the fact that, as he was coming from a reserved category, his batchmates maintained a distance in interactions. They weren't discriminatory, but just uncomfortable interacting with the person and therefore avoided close ties. From these incidents, I learnt how it's necessary to promote people in places where the stereotype doesn't want them to be. It is important to push for breaking norms even if the general public is non-discriminatory. You might not care about the rights of minorities today, you're neither for nor against them, but your lack of empathy will be taken as tacit support for discrimination and persecution today and in the future. When I see people arguing over how the draconian laws ruin men's lives over divorce settlements, I agree with them, but just for a particular case. To think that all men are suffering is a laughable rhetoric, and we have enough statistical evidence to prove otherwise. And so is the case for every discussion on how some minorities are being given benefits in society. There are some cases of exploitation, but there are also cases where rules promoting the rights of minorities are needed to protect them from exploitation. This is my serving of "food for thought" for you. #education #workplace #justice #equality 

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