Peter Leonard

Even the most skeptical voices are starting to acknowledge the value of LLM generated code. Not as a replacement for deep expertise, but as a practical tool for acceleration, exploration, and iteration. When someone as famously disciplined about code quality as Linus Torvalds publicly acknowledges that AI generated code can be useful, it signals a shift worth paying attention to. The takeaway is not that AI writes better software than humans, but that good engineers are learning where and how these tools create leverage. https://lnkd.in/eFbNFqYN

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For solo founders and small teams, the leverage asymmetry is now absurd. One person in flow can out-ship a committee. The bottleneck moved from "can we build it?" to "do we have the clarity and uninterrupted time to build it?" Protect your focus like it's your most valuable asset. Because it is. #ClaudeCode #Convex #Greptile

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In case you missed it... My latest #TechTidesTuesday op-ed entitled, "What good navigation looks like: no machine sails itself" is well worth taking in given Amazon's ongoing AI agent reckoning. 👉🏾 https://lnkd.in/ddn3cR_J

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Funding: WisdomAI just raised $50M. The message behind it is bigger than the round. Not saying investors always get it right, but I see takeaways for data/AI leaders: 1. Analytics teams to extend from dashboarding to “decision readiness.” The BI/analytics category is moving toward real-time, context-aware decision support and increasingly, automation. Data leaders: Shift KPIs from “time-to-insight” to time-to-decision and evaluate tools on their ability to support agents with semantic modeling, workflows, and closed-loop learning. 2. BI isn’t going away-it's evolving. Dashboards aren't dying. BI/analytics insights are still selling, but insights give way to fast growth in decision loop solutions built on context that can be verified. Data leaders: As analytics move into governance, expect your BI/ Semantics / catalog ecosystems to converge. Plan your architecture accordingly. 3. Context is the make-or-break factor for AI. WisdomAI's Enterprise Context Layer is a key part of how they provide the queries to answer prompted questions. Data/AI leaders: invest in the semantic layer, unify metadata and policy-as-code to govern reasoning. This is where the next competitive gap will open. WisdomAI grew on the bet that context + trust will power the next generation of AI-driven action. The bigger story? We’re moving from AI that answers → to AI that actually understands and acts—safely. That’s why this raise matters. Read WisdomAI's release here: https://lnkd.in/gnkssiFU #DataToDecision #DecisionVelocity #EnterpriseAI #SemanticLayer #BITransformation #CDAO #AnalyticsModernization

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Serverless… just kidding 🤣 My GCP bill spiked 50,000% overnight, courtesy of a cute little Vector Search quietly running in the background like it owned the place. Sure it's 75$...but...50,000% rise in spend and that's not my stock portfolio!!! I've long had a habit of quick mental checklists, and one of them is a FinOps-lite pass, even for personal projects. Glad I did. Now I'm building a workaround. More on that later. A quick reminder for anyone building in the cloud: • Set spend alerts, even for personal projects. • Check what's actually running. Don’t let SaaS or PaaS lull you into a false sense of comfort. It’s like asking for extra legroom on a flight...it usually costs more than peanuts. • Revisit those "serverless" assumptions once in a while. • Keep a small playbook/runbook(minimal automation sets), even for experiments. Cloud is flexible. So is your bill. But is your wallet? #CloudComputing #FinOps #Serverless #CloudCostOptimization #GCP #DevOps #Engineering #CloudArchitecture

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Only 1% of companies say their AI investments have reached maturity, yet 3x as many employees as their leaders realize are already using gen AI for a *third* or more of their work. [1] That stat says a lot. It captures the messy middle we’re in. Nearly every company is investing in AI, with 92% planning to increase their spend over the next 3 years. [1] Most haven’t seen real returns yet. But, many employees are far ahead of leadership in experimenting and applying AI to their work. This tracks with what I’ve seen firsthand while building Liminary and speaking to folks in a variety of job roles as part of user research. There’s real energy from the bottom-up: 51% of frontline employees, 78% of managers and 85% of leaders are regular users of AI at work. [2] Yet, only 36% feel properly trained, and 54% say they’d consider using unauthorized tools if their corporate ones are inadequate. [2] It’s not that employees aren’t ready. It’s that many companies haven’t set them up for success. To me, this is the biggest leadership opportunity in AI right now, paradoxically by empowering more bottom-up exploration. That means training, but also visible executive buy-in and frameworks for experimentation that don’t require rogue usage. The companies that will pull ahead aren’t necessarily the ones with the most advanced internal models. They’ll be the ones that empower their people to use AI well. Are you seeing the same dynamic in your org, where employees are clearly pushing for more, and waiting for leadership to catch up?

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I was CPO at Blend before their $4 billion IPO. We rejected the “forward deployed engineer” Palantir model that everyone is copying now. Here’s why: Blend could have followed the forward-deployed engineer approach. Both our CEO and CTO were early Palantir. They'd seen it work. Instead, we built a SaaS product with core components. Move fast, iterate, use data to learn. Not custom code for every client. That Palantir model is having a resurgence. Now Sierra, Decagon, and others are hiring forward-deployed engineers. But it’s not the right approach for every company. It’s fundamentally the wrong answer for Assembled’s customers. Here's what we understand about support teams and why we’re not going the forward deployed route: Customer Support teams are used to getting no resources and no support. On the surface, you'd think they'd be excited about engineers building custom solutions. "Finally, someone's offering us developers!" The reality is support teams are terrified of getting help they can't afford to keep. Support teams are wary because they know the math. They don't have budget for expensive, scarce resources. At some point the shoe drops. The engineers disappear when priorities shift. And custom solutions break. Support is left holding the bag with something they can't maintain. They're resource-starved by design. The idea of a dependency on resources they can't control is terrifying. So instead, the goal should be to give support teams the ability to manage, change, and solve their own problems. Not dependencies on engineers they can't keep. Smart, competent people who teach them to do it themselves. Palantir wasn't even a product company for years. They were a custom development shop with reusable components. Show up, build something bespoke, maybe extract learnings. That's not scalable for any but the largest support teams who need sustainability. Forward-deployed engineers are the new shiny object. I wasn’t a fan of it at Blend. And I’m not a fan today either. What I see in the customer support space is that teams need empowerment, not dependency. I want the teams we work with to be able to say: "I love that you’re partnering with me. And I love that I can do this myself too." That's the difference between building a product and building a consulting practice.

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It's a big day at Honeycomb! We're announcing major advancements in Honeycomb Telemetry Pipeline that unlock a new era of observability—one without tradeoffs. 🔥 Over the past several years, conversations around observability have revolved around challenging tradeoffs: retention versus fidelity, availability versus speed—and always, the constant pressure of cost. With the rise of OpenTelemetry shifting telemetry ownership from vendors to engineering teams, it was no surprise to see telemetry pipelines quickly emerge as essential infrastructure. Yet—as standalone solutions—these pipelines reinforce divisions between the producers and consumes of telemetry data (but this time, more toxically, often within the same organization). Honeycomb is committed to a different approach: deeply integrating telemetry pipelines into the observability experience itself. ✨Enhance✨ is the first step toward this vision, eliminating the traditional tradeoffs in data management. Teams now retain easy, cost effective access to all their telemetry, even (especially! 💥) the bits they didn’t expect to matter. By aligning telemetry data management directly with data usage patterns, we're able to foster positive feedback loops across engineering teams and lay the groundwork for a more integrated, insightful, and collaborative future. Welcome to an era of true lossless observability. It's gonna be exciting. 🚀 Learn more from our press release: https://lnkd.in/dqgCZDyy

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Real ecosystem influence happens inside engineering. Most people think partnerships stop at go-to-market. They don’t. In this clip, Rohan Karmarkar explains how partner feedback flows directly into Amazon Web Services (AWS) engineering teams. Solution Architects working with ISVs and SIs collect real-world input on what capabilities are needed. That feedback doesn’t sit in a document - it gets prioritized, roadmapped, and built. And in many cases, it goes even further into true co-engineering between AWS service teams and partner engineering organizations. That’s when partnerships move from alignment to innovation. Where have you seen partnership drive real product innovation? Abhisheikh Lahoti Jessica Rudy Allison Johnson

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I wrote about how product managers should deliberately decide which features should be built to an A grade and which ones should be built to a B or even only a C grade. Making the resource tradeoffs to optimize business outcomes is a core job of product management, and building every capability to an A grade is a poor use of scarce resources. https://lnkd.in/gTXBfNYE

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Why is Anthropic hiring engineers like crazy, even though Claude is writing all the code? Boris is the head of Claude Code, this was his response: They still need engineers to prompt, talk to customers, prioritize work, and coordinate with other teams To most people, that sounds more like the scope of a PM But of course, a high-judgment engineer is great at these things as well The lines between job descriptions are blurring

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How I built a small business app for my wife's family Tl;dr, 1) Use Claude Artifacts to build basic functionality 2) Use cursor / claude code to build it out fully 3) Make mistakes and refactor to different technologies more than I probably should. Long version: 1) Use Claude artifacts to make a simple form 2) port it into GitHub using create react app, connect to vercel (super easy!) 3) get it working (can create and share receipt images, woohoo! In laws love it) 4) move to vite (apparently CRA is old and AI used it) 5) move to nextjs to have a semi backend 6) convert js to typescript (that took awhile) 6.1) Revert at least once due to urgently needed new features. 7) Add a database using Neon 8) Decide not use to Neon, use Prisma instead (because AI suggested that) 9) decide not to use Prisma, use railway instead 9.1) Also switch from Prisma ORM to postgres.js 10) fight with AI not to use "unsafe" 11) build different data views and UI for all database operations (add, edit, delete) 12) Add multiple tables and build out more advanced functionality (like expenses!) 13) learn how to backup and migrate databases 14) create a dev and prod instance in GitHub + railway, start pushing to dev for testing instead of prod 15) eventually try to make a pdf report, fail, build f# backend to make pdf 16) Learn type algebra to and build a super elegant user permission management system. 16) Start moving APIs to f#, make elegant schema system 16.1) Break UX, then fix it. Users slightly confused but they're my in-laws so they'll be ok. 17) Accidentally wipe my local database (it was my fault, not AI's), then restore it from a backup. You are here Next steps  - build ability to dynamically build forms. - find another small client - teach AI to build with me Lessons : I find that AI doesn't create bad code, it just has generic assumptions. As I guide it to have better assumptions it gets me to something that I find elegant and easy to work with. Still it starts messy and I have to massage it to something clean. I also find that I move faster if I think first, but often I like to skip that and do first. This loses time (note to self).

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Highly recommend watching this episode from Lenny Rachitsky's podcast with the one and only Jeanne DeWitt Grosser (COO of Vercel) on the future of GTM in an AI world. She provides a deep dive into how Vercel is using AI to benefit their sales org today. Must watch.

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I'm helping a few friends find technical co-founders, early-stage CTOs, and heads of engineering. These are exited founders and people with real traction and backing from top VCs.  If you are a ridiculously talented engineer, you owe it to yourself to take a big swing in this generational paradigm shift. I can't guarantee success, but I can promise long hours, high stress, meaningful work, ownership, agency and insane growth. Which is to say, it'll be a hell of a ride. US only for now. If this is interesting, hit me up. Note: I'm not a recruiter, I'm truly just helping some people I think are talented.

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Two of AWS’ Sr. Principal Engineers, Nicholas Matsakis and Marc Bowes, take us inside the development of Aurora DSQL in a fascinating new blog. The post shows how the team scaled write operations without two-phase commit, overcame garbage collection hurdles, and ultimately embraced Rust for both data and control planes. What I find particularly interesting is how the team's journey with Rust evolved. They started cautiously, using it for a single component, and ended up rewriting almost everything in Rust. It's a reminder that sometimes the hard choices - like adopting a new language - can unlock tremendous value. The full post is well worth your time. It's not just about database internals, but also about team dynamics, a culture of learning (and questioning established approaches), and making difficult architectural decisions. Read it here: https://lnkd.in/eMhxBu9e Now, go build!

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This might be obvious but I find that the current models are not [default] smart enough to ask clarifying questions or to help the human pause. Instead the human has to approach with questions, otherwise these models are built as 0 shot execution engines. So many times I'll ask the agent to do something, and then I'll trace my steps back and then ask a question about whether it was the right thing to do and then it will tell me actually no it wasn't the right approach. This is the critical thinking that needs to be built in as it approaches any task. It is fundamental to building any domain agents. I don't believe a single augmented LLM is going to be enough and instead an evaluator/optimizer workflow will be at the core of this. #ai #smartbutdumb

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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