Nick Tudor

Building in public isn’t about showing off: it’s about listening. In early-stage startups, the riskiest assumption isn’t “can we build this?”: it’s “will anyone care?” That’s why building in public matters. It’s not just about posting updates, it’s about: - Engaging with future users while you’re still shaping the product - Validating whether what you’re building solves a real problem - Building traction early, before you’ve scaled operations Tools like https://posthog.com/ make this process real. They don’t just track clicks; they help founders see how people actually use the product and, even better, involve users directly in prioritising features. The result? Instead of building what the founder dreams of, you build what people want (and are willing to pay for). In a world where AI accelerates the building process, this feedback loop becomes even more critical. Speed without validation just gets you to the wrong outcome faster. #BuildInPublic #ProductMarketFit #Startups #MVP

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To all my founder & non-developer friends - Just do these two things this weekend and come back stronger on the other side next week! 1. Get started with Claude Code. It's dead simple. Sign up with Claude → Install the CLI on your local machine → Done. Just 2 steps. Bonus: They're giving away 1 month of Pro subscription free! So no excuses. :) https://lnkd.in/gvunaqhQ 2. Use Meta Prompting. Ask AI to teach you how to communicate with it better. It's the holy grail of learning to learn! Example: "Teach me how to write better prompts for building a world-class landing page. What should I specify? What details matter? Then help me write the perfect prompt to create an Apple-style product page with all the animations, effects, typography and brand ethos." By following just these two, you'll be surprised how far you can go and the range of things you can achieve. All the backlogs collecting dust. The bandwidth issues you face with the engineering team. The ideas that strike you overnight — you can now bring them to life before filtering them out. Your engineering, product, design, and marketing teams will love you. You'll start co-creating with your team instead of standing by while they execute. You'll feed them sharper inputs and let them do what they do best. A few examples to get started:  → Create a landing page like Apple's world-class website  → Generate a detailed report on your website's performance last week  → Prototype a new feature for your  website  → Analyze your customer support queries for insights  → Clone your writing style for brand consistency→ Extract key information from emails and attachments Basically, you're becoming a 1–5× developer, while your engineering, product, and design teams aim to become 10–20×!

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Always fun to host engineering dinners with Sumeet Vaidya and Crafting. Few takeaways for me: 1. We are living through one of the most dynamic periods in software engineering. Everyone by now knows to heavily leverage “Plan” mode upfront when coding with AI - but now we are hearing there’s even more time/tokens/turns spent on “Ask” mode before writing the Plan. And PRs of plans! We are literally flying up the abstractions curve. 2. Documentation used to be a luxury, but it is now cheap and easy. Documentation is increasingly agent-first, and markdowns in the repo seem to work a lot better than Confluence/Notion. 3. Historically the magic of GitHub was how it captured the collaboration context - PRs, discussions, reviews, commit history. Today so much of that context sits in prompt/chat history which does not go in GitHub or anywhere else! Feels like this needs to be solved. Openclaw’s memory system and Granola are both inspirations.

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Most product failures don’t happen in development. They happen before it. Wrong assumptions, unclear value, vague requirements. That’s why at Railsware we use BRIDGeS, both for our own and clients' products. This framework of ours helps make sense of messy product ideas and turn them into decisions that stick. I walked through how it works (and why it saves teams months of chaos) with Noah Labhart on the Code Story podcast. 🎧 https://lnkd.in/e_r8CST2

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👉 Why we led Vanta’s $150 million Series D Vanta is working to solve one of the most persistent challenges in enterprise software: how to prove trust in real time. Their AI-powered platform is transforming security and compliance from static checklists into continuous, automated workflows. “Christina and the Vanta team have created something truly special,” said Matthew Witheiler, head of Late Stage Growth. “We look forward to seeing their impact grow as they expand into new markets and push the boundaries of what’s possible for trust management in the age of AI.” https://lnkd.in/eRykcJEG

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Garry Tan, president and CEO of Y Combinator, recently posted about why Ruby on Rails plus Claude Code is a "crazy unlock." Rails is designed around strong conventions, predictable patterns and expressive DSLs. That combination means an LLM has far less ambiguity to fight through before it can be useful. As he put it: "Rails was designed for people who love syntactic sugar, and LLMs are sugar fiends." And it got me thinking about Django. Django has some convention. The framework will tell you roughly where your models and views and URLs live. What it won't tell you is where your business logic goes. Or how your views and models should be structured. Those questions get answered differently by every team, in every project. The thing about those decisions is that you don't really know if they were good ones until years later. A pattern can feel perfectly sensible in year one and leave you maintaining a small archaeology project by year five. That experience is what pushed me to write Django RAPID Architecture. RAPID adds convention where Django leaves things unsaid. It gives you a clear horizontal structure: data, interfaces, readers, actions. It makes it obvious where different kinds of code belong. And with django-readers, the specs layer gives you a compact, expressive way to describe what data a view needs and how it should be shaped: something close to a DSL. Well, it turns out that Garry was right. When the codebase has already made the structural decisions, a model doesn't have to reverse-engineer your team's habits before it can be useful. With a bit of guidance, the shape of the thing is immediately visible. And LLMs love it. I've seen agents one-shot large, complex features, and the code is exactly as I would've written it. That's the deeper point in Garry's post. It's not really about Rails specifically. The most AI-native codebases are the ones that are already legible, where the important decisions have been baked into the structure itself. So RAPID isn't just for humans, it's for agents too. If you want help moving your Django codebase in the direction of these huge AI productivity gains, get in touch: https://lnkd.in/eiRtAepq And if you just want to learn more about RAPID: https://lnkd.in/ejkF7DMB

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