AISDLC: Structured AI Pipeline for Merged PRs

Design-driven development is about solving problems before writing code. Instead of building first and fixing later, teams validate ideas through prototypes to reduce risk and cost. It is faster and cheaper to refine design early than to rework a live product. This guide explains what design-driven development really means and how to apply it. ➡️ https://lnkd.in/dd_Rh3GZ

Test-driven development in practice isn’t about writing more tests — it’s about writing better software with faster feedback. A few TDD patterns that consistently work in real teams: • Start with the smallest failing test Not the whole feature. Just the next behavior. Small tests keep momentum high and design decisions clear. • Use Red → Green → Refactor strictly Write a failing test. Make it pass with the simplest code. Then improve the design. Skipping the refactor step is where mess starts to accumulate. • Test behavior, not implementation Good TDD tests describe what the system should do, not how it does it. That makes refactoring safer and tests less brittle. • Triangulate when design is unclear If you’re unsure about the right abstraction, write another test case. Patterns often emerge after the second or third example. • Let tests drive API design If a test feels awkward to write, the API may be awkward to use. TDD is often a design tool as much as a testing technique. • Keep the feedback loop fast TDD breaks down when tests are slow or flaky. Prioritize fast unit tests, clear boundaries, and reliable execution. • Use higher-level tests sparingly Not everything needs to be driven through the UI or integration layer. Most behavior is cheaper and easier to shape lower in the stack. • Refactor test code too Duplicate setup, noisy assertions, and unclear naming make test suites hard to trust. Clean tests improve maintainability. In practice, TDD works best when it’s lightweight, pragmatic, and focused on design feedback — not dogma. The biggest shift is this: You stop asking “How do I implement this?” and start asking “What should this do next?” That change alone improves code quality. #TDD #SoftwareEngineering #Testing #CleanCode #DeveloperExperience #Programming #SoftwareEngineering #CodingLife #TechLeadership

📍PMs & Designers Ship Code Key points • PM shipped a feature in a day. • Designers fix UI directly with AI. • Coding barrier reduced. Takeaways • Idea → execution is instant. • Non-engineers now build. 📍Bottleneck Shift Key points • Build time: weeks → hours. • Engineering no longer limiting. Takeaways • Decisions > coding speed. • Product clarity is critical. 📍Build > Coordinate Key points • Tickets slower than building. • More ideas become viable. Takeaways • Less bureaucracy. • More rapid experiments. 📍Role Collapse Key points • PMs & designers now execute. • Boundaries blur. Takeaways • Rise of hybrid builders. • Ownership over specialization. 📍Engineers Evolve Key points • Focus: architecture, validation. • AI handles routine coding. Takeaways • Higher leverage role. • Define correctness, not just code. 📍Process Shrinks Key points • Fewer tickets, handoffs. Takeaways • Faster, flatter teams. • Less communication overhead. 📍New Model Key points • Idea → build → validate. Takeaways • Continuous iteration. • Real products = testing. 📌Final Insight • Build cost ↓, speed ↑ • Bottleneck = decisions 📌Bottom line: Winning teams minimize coordination and maximize direct execution.

Spec-driven development and hypothesis-driven development are both legitimate engineering strategies, and the teams I work with across Fortune 50 enterprises are getting crushed because they cannot tell the difference between when to use each one. Spec-driven development starts with a defined outcome. You know the feature, you know the acceptance criteria, you know the integration points, and you know what done looks like. The spec becomes the contract between product, engineering, and now AI. In practice, the best teams I am seeing are writing specs that double as structured prompts, feeding them directly into AI agents that generate code, produce unit and integration tests, scaffold API documentation, and flag architectural conflicts before a single engineer opens their IDE. The spec is no longer a document that sits in Confluence. It is the executable input for the entire dev lifecycle. Hypothesis-driven development starts with an assumption. You do not know if the feature should exist, if the user wants it, or if the architecture can support it at scale. The goal is not delivery, it is learning. The best teams using this approach right now are spinning up AI-generated prototypes in hours, deploying them behind feature flags, collecting real usage data, and killing or promoting the experiment within days instead of sprints. The hypothesis is not a story in Jira. It is a time-boxed bet with a clear kill criteria. Here is where engineering leaders are making expensive mistakes. They run hypothesis-driven workflows on production features that already have clear requirements, which means AI is generating code against vague prompts, the team is iterating in circles, and what should have been a two-week delivery becomes two months of "we are still learning." The spec existed in someone's head but never made it into a format that AI or the team could execute against. And they run spec-driven workflows on exploratory features that should be cheap and disposable, which means a product manager spends three weeks writing a PRD for something that should have been a 48-hour prototype. By the time the spec is approved, the market assumption it was built on is already stale. We have built frameworks across hundreds of enterprise engagements to diagnose exactly this problem. The question is never "should we use specs or hypotheses." The question is "do we have clarity or are we buying clarity?" If you have it, spec it tight and let AI execute. If you do not have it, design a cheap experiment with a kill date and let AI build the throwaway version while your engineers focus on the systems that actually need their judgment. The methodology is never the bottleneck. The bottleneck is engineering leaders who default to one mode because it is familiar instead of choosing the right tool for the actual problem in front of them. #SpecDrivenDevelopment #HypothesisDrivenDevelopment #AIEngineering #AgileIsDead? #BuildWithAI

Engineering leaders are seeing a new pattern with vibe coding: - Fast starts. - Unstable architecture. - Context drift across iterations. - Code that works… but doesn’t scale. - Velocity goes up — predictability goes down. That’s where BMAD (Build • More • Architect • Dream) is proving useful — especially for teams, not just individuals. It introduces lightweight structure to AI-assisted development without slowing delivery: • Build in small, verifiable increments • More shared context (PRDs, schemas, contracts) • Architect before expanding generated code • Dream big — but scale with guardrails What this looks like in practice: • Architecture-first prompting (modules, boundaries, interfaces) • Persistent context instead of ephemeral chat history • Spec-driven generation (PRD → design → implementation) • Built-in evaluation (tests, assertions, lint, guardrails) • Versioned prompt + output cycles for reproducibility • Iterative expansion instead of uncontrolled generation Simple mental model: BUILD → MORE → ARCHITECT → DREAM ↺ iterate with controlled context > Vibe coding increases developer velocity. > BMAD makes that velocity team-safe and production-ready. https://lnkd.in/gw3F8w5y

Claude Code is having a moment right now. A lot of the conversation is around “vibe coding.” But the real advantage isn’t vibes, it’s skills. Skills are what turn Claude from a chat interface into something that actually does work. Load the right ones, and suddenly you’re not just prompting Just plug in a skill and start. Here’s a curated list 👇 1. Launch Planner (MVP Builder) https://lnkd.in/launch-planner-example Takes an idea and turns it into a clear MVP plan, scope, and execution steps. 2. Code Reviewer / PR Reviewer https://lnkd.in/code-review-example Analyzes code for bugs, performance issues, and best practices. 3. UI / UX Critic https://lnkd.in/uiux-critic-example Reviews your interface and gives actionable feedback on usability, layout, and accessibility. 4. Full Agile Workflow Skill Set https://lnkd.in/e5yTuiUf Brings planning, tickets, and delivery workflows into one place. 5. Research + Writing Assistant https://lnkd.in/e7jHZDQw Helps you go from scattered ideas to well-structured content. 6. Interview / Meeting Insights https://lnkd.in/eG7BuMnp Turns messy conversations into summaries, decisions, and action items. 7. Scientific / Data / Analysis Skills https://lnkd.in/eusfSUjH For research-heavy, analytical, and technical workflows. 8. File / Content Organizer https://lnkd.in/eC-4RF89 Clean up folders, docs, and unstructured data. 9. Skills Search https://lnkd.in/e8UCXiNf Discover and manage skills like plugins. 10. Claude Code Workflow Collection https://lnkd.in/e3Pq7EM6 A large library of workflows and resources to explore. 11. Skills Integration Templates https://lnkd.in/efpmdAME Make skills work across different tools and environments. 12. Skill Creator https://lnkd.in/eaczJDFw Create your own Claude skills from scratch. Skills are where Claude Code really becomes powerful. They allow AI to: ✅ Follow structured workflows ✅ Apply real-world frameworks ✅ Act like a specialized teammate ✅ Automate multi-step work All inside a single chat. And the real shift? It’s not about using one skill. It’s about combining them—so Claude stops being a tool and starts behaving like a system.

I've never considered spec driven development as part of my development workflow. Tried it, but found it didn't fit well with my rapid iteration style. Sure do planning, but predefining specs seemed too low level, only a little higher than hand writing the code. I'm now doing what I'm calling story driven development. I'm sure this already existed in some form before, I'm just doing it with agents. This pattern has survived for 3 months now (so signs are it will survive unless another model upgrade pushes me to rethink everything again). Here's the basic flow: 1. Story-writers write a story 2. Human reviews the story (optional) 3. Planners convert story into plans/specs 4. Devs implement from specs 5. Test writers write tests 6. Testers verify and execute tests 7. UAT testers test as per the stories Stories are much faster to review then specs and you can be more selective on which bits you expand with more detailed specifications and which bits you leave to the agents to decide. If the story is wrong (and it's pretty much always is on the first iteration) you can just update the story. I find Opus is for the most part smart enough to figure out which parts of your code base need to be rewritten and if not giving a story to testing agents in my experience enables them resolve the issue much more reliably then just telling your agents to "fix this bug". I've even started to move away from markdown files to json so I have a structure that can hard link stories to tests and code. I'm using this as a foundation for experimenting with a CICD style of verification. It challenging to get the right mix of deterministic code and agent handoff but the results have so far been quite promising.

WHY "SHIFT LEFT" IS THE NEW SDLC STANDARD 🚀 In my 14 years in Enterprise Security, I’ve seen how one "small" logic gap in a requirements can lead to weeks of rework and critical production bugs. By leveraging an AI stack of Claude (for logic and functional builds) along with Figma and Lovable (for UX and interactive prototyping), I’m moving "Quality" to the very start of the Product Discovery phase. Instead of waiting for a dev build or staging environment, I’m building interactive prototypes to: 🔹 VALIDATE THE USER FLOW: If I can find a navigation bottleneck in a clickable prototype, I’ve just saved the engineering team a massive refactoring effort. 🔹 VERIFY REQUIREMENTS: Stakeholders can "test drive" the app early. This eliminates the "I thought you meant X" conversations during UAT. 🔹 VISUAL AND FUNCTIONAL REFERENCE: For Devs and QA, having an interactive source of truth is worth 100 pages of static documentation. It clarifies edge cases before a single line of production code is written. AI isn't just for writing code—it’s for ensuring we build the RIGHT PRODUCT, the RIGHT WAY, the FIRST TIME. #ShiftLeft #Productmanagement #SDET #SDLC #SoftwareProcess #QualityAssurance #AI #ClaudeAI #Lovable #AgileDevelopment

Mixed feelings here. I 100% agree about not being an entry-level engineer. I agree that making a thing tells you so much more than writing about a thing. But is this the PM’s job? I thought it was watching markets, tracking competitors, figuring out pricing,gtm, aligning teams, learning from stakeholders… and a lot more. PM does the scary thing: they are responsible for outcomes. UX design (interaction design, information architecture, visual design) should be leading the discovery of the right form of the product. They are the ones who have had (hopefully) formal training in design and HCI. I know vibe coding is fast, I do it. It still takes hours to find the right form, never mind the research work that should accompany it. Disagree?

Have you ever seen a project fail… not because of coding… but because requirements were wrong? Imagine spending 6 months building a product… and the client says: “This is not what I wanted!” 😳 In this article, I’ll show you how to write perfect user stories that avoid this disaster… with real examples. #acceptancecriteriaexamples #agileuserstories #businessanalystuserstories #howtowriteuserstories #INVESTcriteriauserstories #userstoryformat