Brian Leonard

I’ve been writing a series of posts on the design principles behind Looping, a product I’m building to help teams run and evolve software over time. These principles shape the architecture underneath and have helped me prioritize clarity, composability, and long-term maintainability from the start. The latest post in the series is about Composable Services. In Looping, every service follows the same interface: it takes a hash of input and returns either Success() or Failure(). That consistency makes services easy to test, reuse, and chain together without resorting to sprawling conditionals or unpredictable side effects. The post walks through the structure, shows real examples, and touches on how it tracks source chains for observability and event logging. https://lnkd.in/ePkn-pF6 #ruby #rails #softwarearchitecture #buildinpublic

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The circles of Control, Influence, and Concern Early in our careers, we’re often told to “own the outcome.” As we grow, that ownership broadens beyond code or systems — it extends into people, mechanisms, and culture. Stephen Covey’s Circles of Control, Influence, and Concern provide a simple but powerful framework for reflection — one that we all live through daily. Circle of Control This is what you can directly act upon — your code, design quality, and decisions. It’s where you move fast because you don’t need permission to do what’s right for the system. For me, this means writing tenets, defining metrics, reviewing designs, and raising the bar on engineering standards. Circle of Influence This is where leadership without authority matters. You can’t force a change here — but you can shape it. Influence is built through clarity, trust, and data. When you help another org simplify a design, mentor an SDE into a stronger reviewer, or align leaders on shutting down a misaligned project — you’re operating here. Your success depends not on hierarchy, but on how consistently you demonstrate judgment, objectivity, and customer obsession. Circle of Concern This includes everything you care about but can’t control — like organizational priorities, macro-level business shifts, or leadership changes. The key here is emotional discipline. Those who spend energy worrying about concerns they can’t act on end up drained. Those who channel that energy back into influence — writing a clear doc, proposing a mechanism, or building alignment — create impact. Success isn’t about controlling everything; it’s about knowing where to invest your judgment, where to build trust, and where to let go. How do you decide which circle your current challenge belongs to?

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If you're keen on real AI use cases, this is actually quite powerful and I encourage you to read further (and help us build: https://lnkd.in/gsYnAfKK) At Hex, we've found our Threads product (https://lnkd.in/gJKCurJB) is the best way to summarize customer support load, because we're not limited to what's in the customer support portal - we can ask in terms of active users, customer size, feature adoption, and still get great answers. This is based on a strategy of keeping your data warehouse as a source of truth, and all related data work in one place - Hex - so that AI understands concepts in your business and makes smart connections between things. Arguably, ALL data analysis for a particular vertical is eventually best achieved in a tool like Hex, connected to curated data in your data warehouse. Wait - can't I do this directly in the customer support tool? Can't I connect an MCP server and get a functionality like this in one of my other AI enabled products? Maybe, but you have 100+ SaaS apps, so play this scenario out: - How many places are you going to sync your core data sets? - How many cross product integrations are going to exist, and how many of them are you going to enable? (100 choose 2 is ~5000) - In how many of these products will all users have seats? - In how many of these products will these seats have permissions to use AI features? (Our support tool just told me I don't have permission to ask the agent questions, probably because I don't have the right seat type, which means more $$) My bet is a on a world with ONE place for all analysis.

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This week’s lesson, don’t solely rely on a coding agent for a complex multi phase UX refactor. All the right plans, docs and guardrails will not stop it quietly defecating over your codebase.

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A little while ago I posted about Sacramento Labs being interested in acquiring software products and heard from a number of folks I hadn’t talked to in a while. The responses and conversations were genuinely great. (Original post here: https://lnkd.in/g-6hjy6q) After talking it through, Garrett Milster and I decided to formalize something we were already doing informally: we’re offering a $10,000 referral fee for any warm introduction to a company that we end up acquiring. A few notes for clarity: -The introduction needs to be a direct, warm introduction (email or message). -Public listings or cold forwards don’t qualify. -Referral fees are paid after closing and are contingent on a completed acquisition. -There’s no expectation of ongoing involvement unless explicitly discussed. If you come across a software business that feels like it might be a fit for us, feel free to make an intro.

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We don’t talk enough about this kind of leadership. Last week, forgecode shipped a feature that degraded response quality. Instead of hiding it, they: - Admitted the mistake publicly - Explained what went wrong - Fixed it promptly In the AI space, it’s easy to chase growth at all costs. But accountability is what builds real trust. The companies that own their mistakes and put customers first? They’re the ones that will win long-term. We need more of this energy in tech.

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I thought this was a good and very reasonable talk by Eleanor Millman on how to prioritize projects for a platform engineering team: https://lnkd.in/gaYtUHRH A few insights I took from it: - Unlike product features which can be more directly tied to revenue, platform engineering work is a couple steps removed. But that doesn't mean it's unimportant! Far from it, platform engineering projects can make everyone at the company more efficient, able to produce higher quality software, etc. - While "urgency" is always going to be a factor, you don't want to just be putting out fires, you want be able to prioritize work which is "high impact" ... even if that impact isn't felt immediately. - Good rule of thumb: prioritize "highest impact for lowest effort". - The talk has some ideas on what factors to choose for impact, and how to blend them. For instance "speed of development" is one factor, "cloud cost optimization" might be another. The weighting of different impact factors can change over time, depending on the needs of the business. I would say that a lot of companies don't have much methodology here but I can really see the value in codifying it. You can always change the methodology or the weighting if it's spitting out results that don't pass the smell test or you really feel it is leading the company astray. Clarity can give the org more freedom to put resources behind projects that would otherwise never be taken on. When things are unclear, prioritization still happens implicitly, but the decisions tend to be a lot more random and fear-based and the org is worse off as a result.

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"Because engineers had been through brainstorming and story mapping, they were able to identify the most promising direction and find a way to implement one key piece of it pretty quickly." Want to get your engineers more involved in continuous discovery? Learn how Ellen Juhlin, Senior Director of Product Management at Orion Labs, successfully brought engineers into the discovery process. Key takeaways from Ellen's experience: 🎯 Start by inviting engineers to customer interviews as listeners and note-takers 💡 Include engineers in brainstorming and solution mapping sessions 🗺️ Use assumption mapping to help engineers focus on validating ideas 🔄 Don't worry about following a linear process - iteration is key 🎯 Begin with just one or two engineers rather than the whole team 🤝 Keep engineers connected to business outcomes Check out the comments for a link to the article. ❓ What's one small step you've taken to include engineers in discovery work? Share your thoughts in the comments below.

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PostHog is perfect for engineers. PMs need a different lane. PostHog nails what engineers care about. But sticky adoption comes from deep hypothesis testing and measurement - work that lives squarely with PMs. Engineers should be shipping. Context-switching between code and funnels is a tax. 👉 Our take: -Engineers wire up the data. - PMs own the hypotheses - what’s working, what isn’t, and why. - That job can easily be full-time. 🌟 So we’re building a PM-native layer on top of PostHog: - Write a hypothesis → instantly pull the sessions/users that prove or disprove it - Measure intent, hesitation, and friction (not just clicks) - Trigger chatbots/pop-ups at the right moment - Map replays to customer conversations for fast follow-ups PostHog stays the engine. We’re giving PMs the perfectly handcrafter flow for hypothesis testing around adoption If you’re a PM using PostHog today, what’s the one workflow you wish existed?

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rqlite -- and the way I develop it -- is fundamentally changing. LLMs are in the loop -- from ideation to Pull Request. https://lnkd.in/ejwXGddR

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San Francisco’s problems aren’t monocausal, but housing scarcity makes almost every problem harder. I wrote a short explainer on the city’s zero-sum politics and anti-growth nostalgia. Link in the comments.

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