Nick Walter

What a cool idea from Valerie Harrison... She made a quick remark at an internal session about using AI for HOA docs which sent me down a path. Our community has 15+ years of docs that have been amended and changed as more development has happened. It's very hard to figure out what the rules are about something at this point. Put those docs into NotebookLM and now we have an AI agent to ask! Technology is so cool when it solves a problem.

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He's right. Typescript is the correct thing to learn in 2025. Knowing JS isn't bad; there's a lot of code written in it, but TS is the better language to work in, especially for new projects. I ran OKC.js for about a decade, and I've given numerous technical talks on JS (possibly the most in Oklahoma) over the years and I am co-signing this, you need to know it now.

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When it comes to selecting the right DXP, things can go off the rails. But don’t lose your caboose. Dan Drapeau has a two-track evaluation model that can keep you rolling – full steam ahead. In today's Guest Critic article, Dan proves why he’s a masterful conductor on the digital experience train. As he explains, the DXP competitive landscape has converged across architectural models, and evaluation approaches haven’t evolved at the same pace. Dan’s observation is that single-track processes are still chugging along – focusing on feature breadth and demo cohesion and favoring pre-composed suites. In his experience, what we really need is a more balanced approach that separates platform evaluation from ecosystem architecture. How do you leave the station? By running two structured tracks, which he outlines in detail. This helps vet not only current capabilities but also long-term durability relative to core considerations like vendor lock-in, CDP and DAM strategies, and the ever-evolving target of AI. In his post, Dan details the operational requirements, where the complexities exist, and how to synthesize results. He even digs into the critical topics of validation and stress testing, and distills it all into a handy checklist to help guide your DXP evaluation. As he says, composable and pre-composed DXPs represent architectural strategies along a spectrum, and these pointers can help keep you on track. “A structured two-track evaluation approach ensures that whichever model is selected, it is chosen for structural alignment and long-term outcomes rather than surface cohesion.” If you’re currently evaluating DXPs – or considering it in the future – this is a fantastic read from a sharp practitioner. Check it out on CMS Critic: 🔶 https://lnkd.in/e-XB3Pig #dxp #digitalexperience #digitalexperienceplatform #softwareevaluation #dam #digitalassetmanagement #cdp #customerdata #customerdataplatform #cmp #contentmarketingplatform #ai #artificialintelligence

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After exiting Drivably - Acquired by ACV Auctions (NASDAQ: ACVA), I came to Utah to look for the companies Trent Mano is asking about. What startups were all substance, no hype? What startups were simply creating motion, not heat or noise? Local awareness of your startup can be driven by community ties (e.g. you have local investors), product type (e.g. you're building consumer or startup B2B SaaS), or actual traction (this is or should be your real goal). Hype, heat, and noise can help you secure your initial product, investors, and customers. Some social awareness is desirable: you want awareness with the customers who will give you ARR, the investors who will give you runway, and with the talented people who will build the company when you're not in the room. But too often, heat and noise are the unintended byproducts of systems designed to produce something else entirely. Light. Motion. Acceleration. What kind of systems are we trying to build here? For many startups, broad social awareness is NOT what gets you to the magical product, true fans, and TAM dominance. What gets you there is focus. Focus on uncomfortably narrow, intensely specific groups of people that no one else is focused on the way you are. It requires getting used to flying under a general purpose radar. Getting used to explaining, "So what exactly do you do again?" For much of Savi's life, we've been so far under the general purpose radar, we weren't just in stealth mode, we were in exile. Turns out, exile is the perfect place to build something dangerous for a small group of people. Exile is where Savi sat down with fast-growing Utah brands and operators like Swig, Gourmandise, and Savory Fund, and designed and built the vision AI operating system for restaurant and retail. Exile is where we found Next Coast Ventures and other investors that shared our conviction about AI transforming Main Street businesses. So, get on the radar that matters for you. Focus on the small, sub-scale groups of people that matter to your startup. And maybe your time off-the-radar will be the reason you get on-the-radar in the ways that matter.

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Do you buying technical debt? If your agency just takes your project and passes it to someone else, you’re buying tech debt you’ll pay for later. Real developers don’t just code, they solve problems, debug, and care about your business. Middlemen? They sell promises, not quality. Your app might work for 10 users… but crash when you hit 1,000 ( like as shows in this image ) Want a product that scales, works under pressure, and actually solves problems? Go straight to developers who care. Stop paying for people who sell projects. Start paying for people who deliver solutions 🌟 . #SaaS #MVP #Startup #Founders #ProductDevelopment

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Hot take 🔥. Vanilla CSS still holds its ground, even with the rise of Tailwind CSS and other frameworks , especially from an AI-assisted development perspective. Why? AI can directly leverage the latest modern CSS capabilities, many of which are abstracted or limited by utility-first frameworks. Tools amplify fundamentals , they don’t replace them. Vibe coding works best when the basics are already mastered. #SoftwareEngineering #FrontendDevelopment #CSS #WebDevelopment #AIDevelopment #TechLeadership #EngineeringBestPractices #Upskilling #DevCulture

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During the "Ambient Agents" meetup last week, Brace Sproul gave us a walkthrough of Open SWE, a coding agent built on Langgraph. Open SWE monitors GitHub issues. When it gets a new one assigned, it kicks off a multi-agent architecture with a Manager, Planner, and Programmer. It can receive messages from users at any time during the process and incorporate them into the state. Check out the slides for diagrams explaining each agent: https://lnkd.in/g3PnNHqG And the code: https://lnkd.in/gY68TKYy I found the takeaways really interesting: * Summarization is hard: Especially with code changes that can get quite long, you literally can't send the entire context history to the LLM, and highly detailed old diffs may distract it. IIRC, the agent sends the last 20 messages, and summarizes the changes before. * Agentic > hard coded: Open SWE tends to use the agentic approach of "here are your tools, pick which one to use" for almost all tasks. Except for one.. * Explicit error handling: When an error happens, Open SWE stops everything and focuses on resolving the error. Otherwise, it could get too far afield and be unable to get back to a good place to solve the error. (Hopefully I didn't mis-represent anything I heard!) #Langchain #Langgraph

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Check out this a brief demo of a small tool I have been building in the last couple weeks that turns dense federal appropriations PDFs into something regular folks can understand. Right now the pipeline looks like this: • A Next.js frontend uploads bills via presigned URLs straight into MinIO (S3-compatible storage). A FastAPI backend pulls the PDF, runs a parser that merges table cells and surrounding prose into structured rows, attaches page and bounding box metadata, and stores everything in PostgreSQL. • It then uses the OpenAI API to suggest cleaner program names and short summaries for each funding line. • From there the API can audit the table, generate diffs between fiscal years, export CSVs, and render highlighted snippets of the original PDF for context. Everything runs under Docker Compose: Postgres for relational data, MinIO for documents and images, FastAPI for the API, and Nginx as the single public entry point. The goal is to make it much easier to see what is actually being funded, compare versions of a bill, and link every number back to the source text. It's also a pretty neat use-case for use LLMs to create accessible tools that benefit everyone and break down barriers. Check out the code here: https://lnkd.in/eiQDzBuX

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If your audience can't do something useful with your product in less than 10 mins, you are at a significant disadvantage. No amount of DevRel will fix this.

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Most program registration systems weren’t really designed - they just evolved. WordPress. Google Forms. Spreadsheets. Payment links. Zero visibility. There’s no real user flow. No feedback. No trust. Meanwhile, every other part of life - banking, grocery delivery, travel - is fast, clear, and mobile-first. So we asked: What would registration look like if it were built like a modern product? We built SwiftEnroll to answer that: ✅ Streamlined signup ✅ Real-time enrollment dashboards ✅ Mobile-friendly and admin-friendly ✅ Built-in fairness - not first-come chaos We’re not just simplifying logistics - we’re rethinking what registration should feel like, delivering a system that saves time, reduces stress, and actually works. 👉 www.swiftenroll.com #SwiftEnroll #UXMatters #EdTech #ProgramManagement #ProductDesign #RegistrationReimagined #ModernSoftware

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