Testing AI prototyping tools at Saltmine: the good, the bad, and the future

🚀 Replit Launches Two Major Updates for Developers! 🔹 1. Plan Mode: Safer & Smarter Planning ✅ Safe exploration with no code changes ✅ Collaborate with Replit Agent for strategic guidance ✅ Create task lists and define project features ✅ AI-powered brainstorming and feature suggestions ✅ Seamlessly transition to Build Mode when ready 🛡️ Plan without risk. Execute with confidence! https://lnkd.in/dtH8a6GB 🔹 2. Replit Agent: Now Supports Any Framework ✅ Supports any language and framework ✅ Import and work on existing projects ✅ Build custom terminal apps, games, and more ✅ Seamlessly integrate with databases (e.g., MongoDB) ✅ No more limits on app types—create your own stack 🔧 Build with total flexibility and power! https://lnkd.in/d_KJW2r6 These updates make Replit the ultimate tool for both planning and building your next big project. Safe, flexible, and powerful. #Replit #SoftwareDevelopment #AI #DevTools #TechInnovation #Productivity #Programming

🚀 The new wave of AI tools is changing how we build products. I came across Rocket AI, which lets PMs create MVPs in minutes. Just describe your concept (or import a Figma design), and it builds the frontend, backend, and deployment for you handling things like authentication too. This goes beyond the basic “drag-and-drop” UI builders we are used to. But Rocket isn’t alone. There’s a whole ecosystem of AI tools redefining product building: 🔹 Vercel’s v0 – turns text prompts into React components and full UIs instantly. 🔹 Replit AI – helps you go from an idea to production code with smart code generation. 🔹 Uizard – converts sketches or screenshots into design prototypes. 🔹 Claude / GPT-4 – assists in creating PRDs, user stories, and even initial code setup. Each one solves a different piece of the product puzzle: ✨ Uizard = ideation & design ✨ v0 / Replit = coding & iteration ✨ Rocket AI = end-to-end MVPs (closer to production-ready) Curious to hear: 👉 Have you tried any of these AI product builders? 👉 Which one do you think gets closest to replacing the traditional MVP process? #ProductManagement #AItools #FutureOfWork #DigitalInnovation #StartupTools #ProductDesign #AIforBusiness

𝗔𝗜 𝗷𝘂𝘀𝘁 𝘀𝗮𝘃𝗲𝗱 𝗺𝗲 𝟮𝟬𝟬+ 𝗵𝗼𝘂𝗿𝘀 𝗼𝗻 𝗺𝘆 𝗰𝗹𝗶𝗲𝗻𝘁’𝘀 𝗠𝗩𝗣! First-time product development usually feels like a maze — gathering scope, creating wireframes, aligning designs, and getting everyone on the same page. But this time, AI changed the game. ✅ I just entered my client’s concept in 𝗯𝘂𝗹𝗹𝗲𝘁 𝗽𝗼𝗶𝗻𝘁𝘀 ✅ AI instantly converted it into a 𝗰𝗹𝗶𝗰𝗸𝗮𝗯𝗹𝗲 𝘄𝗲𝗯 + 𝗺𝗼𝗯𝗶𝗹𝗲 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 ✅ It gave us 𝗮 𝗰𝗹𝗲𝗮𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 before even writing backend code This meant: No wasted time on endless iterations Faster client approval A head start on backend development 200+ hours saved! Now, instead of struggling to visualize the product, founders can 𝘀𝗲𝗲 𝘁𝗵𝗲𝗶𝗿 𝗶𝗱𝗲𝗮 𝗶𝗻 𝗮𝗰𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝗗𝗮𝘆 𝟭. 👉 If you’re thinking of starting an MVP but don’t know where to begin, AI prototyping is the fastest way to move forward. Would you like me to share how you can get started right away?

Stop building products inside #AI app‑builders or “vibe” coding platforms. They’re fast — until you need control, audit logs, tests, or predictable costs. ⚠️ If you’re a front‑end engineer, build in the environment you already own: your IDE (#VSCode) + #Copilot in agent/chat mode. You get versioned code, real CI/CD, reliable instrumentation, and full control over data and cost — while still shipping faster than hand‑coding everything. Here’s the workflow I use to ship real SaaS (fast, safe, repeatable): 🎯 Pick one clear user job (e.g., “answer contract clauses” or “generate listing text”). 🧪 Prototype #RAG on 3–5 real docs to catch chunking/parsing issues early. 🎨 Build the #UX first (single input, visible source cards, thumbs/feedback). 🤖 Use #Copilot as an IDE agent (give it precise instructions/steps, not vague prompts). 🧩 Run the #Convex Agent + RAG stack from the repo (message store, vectors, tools) — fewer moving parts than stitching multiple cloud services. 💸 Add #observability & per‑user caps from day one so pilots don’t become expensive production problems. I’m packaging a starter repo: #Nextjs + #TypeScript + #MUI front end, Convex Agents + RAG component backend, prompt library, and a Copilot instruction template you can paste into your agent session. It’s built so your Copilot agent can scaffold features, create branches, propose tests, and generate clean commits — but you remain in control. If you want to know when the repo is ready for preview, comment “REPO” or DM “COPILOT”. #frontend #Copilot #LLM #Convex #RAG #SaaS #webdev #vibecoding

Stop wasting valuable engineering and design talent on manual "pixel-perfect" checks. Watch our AI-powered Vision Agent automate the entire design review in this demo. It generates a full QA report in seconds, catching everything from visual bugs to a hidden $1 pricing error between the Figma design and the code. Free your team to focus on what matters: innovation.   What's the biggest bottleneck in your workflow? 👇 #Automation #AI #SoftwareDevelopment #UIDesign #Productivity

We cut handoff time by 30%. The missing step wasn’t obvious. After 10+ AI products, designs still broke in code. Details slipped. Design and dev used different words. Specs varied. QA kept catching drift. Here is the 3 step fix. 1. Map the drop points • Trace one feature from Figma → tickets → code • Note where info vanishes • Log each gap in plain words 2. Single source of truth • Share design tokens for type, color, spacing • Auto-add redlines to key screens • Use one naming style across files 3. Handoff gate • Add a short PR checklist • Check design ↔ code before merge • Confirm each acceptance point The result? • 30% faster handoffs in two sprints • Zero UI surprises in QA • Calmer designer-developer flow Action Items: • Trace one feature this week. List three drop points. • Name tokens before a screen ships. Attach redlines to the ticket. • One mandatory gate beats five optional docs. Hi, I’m Ali Abid 🧩 I post weekly about design, leadership, AI, and lessons from building real products. I help AI and SaaS founders design products their users love

Instead of spending the weekend battling CSS and pixel-perfect layouts, I let AI do the heavy lifting. I knew the UI layer would be the most time-consuming part (you know the drill: endless CSS tweaks, pixel-perfect alignment battles, state management headaches… 😅), so I tried Lovable to speed up the process Of course, there was a credit limit. To work around that, I connected my GitHub repo and started a hybrid workflow: Major new UI additions → Generated with Lovable.dev Small fixes, architectural tweaks, refinements → Done locally This allowed me to leverage AI for initial scaffolding while maintaining control over the critical details that matter for long-term success. The result: a functionally sound, scalable UI delivered in a fraction of the usual time—without falling into the trap of "vibe coding." This experience really highlights how AI tools are changing the way we build software: 👉 Faster iteration 👉 Higher-quality prototypes 👉 Developers stay in control of architecture & reliability I’m excited about what this means for the future of building products at speed without sacrificing maintainability. #AI #FrontendDevelopment #Prototyping #DeveloperTools #Productivity #Startups #WebDevelopment

I had an AI redesign a homepage for me. The result was great, but it cleverly created a "View All Insights" link that led to a 404 page. Normally, that's the start of a half-day of work: scope the feature, design a UI, write a ticket, build the backend, build the frontend... Instead, I ran a single command. It kicked off a team of AI sub-agents (product-manager, ux-designer, sr-engineer) who planned the entire feature in parallel and generated a comprehensive ticket. Then, another command kicked off the implementation. The whole process ran in the background while I moved on to other tasks. This is what an asynchronous, parallel development workflow looks like. It's about shifting from being a coder to being an orchestrator. I wrote a detailed post breaking down the exact patterns, agent definitions, and commands I use to make this work. What's the most tedious part of your workflow you'd want to automate away?

𝗪𝗵𝘆 𝗖𝗹𝗮𝘂𝗱𝗲 𝗔𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀 + 𝗖𝗹𝗮𝘂𝗱𝗲 𝗔𝗣𝗜 𝗰𝗵𝗮𝗻𝗴𝗲 𝘁𝗵𝗲 𝗴𝗮𝗺𝗲: they turn ideas into 𝗹𝗶𝘃𝗲, 𝘀𝗵𝗮𝗿𝗲𝗮𝗯𝗹𝗲 𝗺𝗶𝗻𝗶-𝗮𝗽𝗽𝘀 you can click, tweak, and ship. 𝗙𝗮𝘃𝗼𝗿𝗶𝘁𝗲𝘀 𝗜 𝘁𝗿𝗶𝗲𝗱: 𝟭. 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗗𝗿𝘂𝗺 𝗠𝗮𝗰𝗵𝗶𝗻𝗲: [https://lnkd.in/eGcDGwQR)  Playable micro-tool. Great for 𝗿𝗮𝗽𝗶𝗱 𝗨𝗫 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴. 𝟮. 𝗦𝘁𝗼𝗿𝗶𝗲𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗦𝗸𝘆: [https://lnkd.in/eQHQKhMz)  Beautiful 𝘃𝗶𝘀𝘂𝗮𝗹 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗿𝘀 that blend learning and narrative. 𝟯. 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗦𝗩𝗚 𝗔𝗺𝗽𝗵𝗶𝘁𝗵𝗲𝗮𝘁𝗲𝗿: [https://lnkd.in/eqM3sCKb)  Crisp 𝗦𝗩𝗚 𝘃𝗶𝘀𝘂𝗮𝗹𝘀 meet code and history. 𝟰. 𝗜𝗱𝗲𝗮 𝗦𝗽𝗮𝗿𝗸: [https://lnkd.in/eJmsY_Af)  From 𝗻𝗼𝘁𝗲𝘀 𝘁𝗼 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 in minutes. 𝗪𝗵𝗮𝘁’𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁? - 𝗟𝗶𝘃𝗲 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁: interact, don’t just read. - 𝗦𝗶𝗻𝗴𝗹𝗲 𝗹𝗼𝗼𝗽: prompt → generate → test → iterate. - 𝗘𝗮𝘀𝘆 𝗵𝗮𝗻𝗱𝗼𝗳𝗳: share a link, get feedback, improve fast. 𝗛𝗼𝘄 𝘁𝗵𝗲 𝗖𝗹𝗮𝘂𝗱𝗲 𝗔𝗣𝗜 𝗵𝗲𝗹𝗽𝘀? - 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗮𝘁𝗶𝗰 𝗔𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀: request, store files, render in your app. - 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻: send update prompts and version outputs. - 𝗙𝗶𝘁𝘀 𝘆𝗼𝘂𝗿 𝘀𝘁𝗮𝗰𝗸: log prompts, add analytics, ship previews. 𝗤𝘂𝗶𝗰𝗸 𝘀𝘁𝗮𝗿𝘁𝗲𝗿 𝟭. 𝗗𝗲𝘀𝗰𝗿𝗶𝗯𝗲 the outcome and constraints 𝟮. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 an Artifact 𝟯. 𝗙𝗲𝘁𝗰𝗵/𝗵𝗼𝘀𝘁 via the API 𝟰. 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 with small, testable changes If you build, teach, or PM, this compresses the path from 𝗶𝗱𝗲𝗮 → 𝗱𝗲𝗺𝗼 → 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻.

👩💻 PMs, let’s be real. We’re constantly caught between two extremes: Stakeholders want prototypes that look real Teams want them yesterday But how do you balance speed vs. design fidelity without drowning in code? Here’s what’s working in the PM community right now 1. Cursor + Claude Code → Import your design system, generate HTML/CSS/JS, run locally. Great for semi-technical PMs who need lifelike recreations. 2. Lightweight Flow → Draft a PRD with ChatGPT → add mock JSON → ask Claude to implement front-end. Clickable, fast, no backend required. 3. Figma + AI Plugins (Magic Patterns, Uizard) → Non-technical PMs can spin UI from natural language. Perfect if your design system lives in Figma. Pro Tip: Don’t make it too polished. A greyscale prototype signals “draft,” prevents stakeholders from thinking it’s production-ready. Key takeaway: Treat AI like your junior implementer. The clearer your instructions, the better your prototype. AI won’t replace design/engineering. But it will get your idea from notion → validation in hours, not weeks. - Anuhya Kantamraju #ProductManagement #AI #Prototyping #Innovation