How to Drive Hypergrowth With AI-Powered Developer Tools

💥 From C-suite back to VS Code: After years as a Chief Product & Technology Officer I decided to build an AI-native product completely solo—not a proof-of-concept, a real ship-ready product. Vibe coding, but for real. The first weeks were humbling. My muscle memory was rusty, and the LLMs sometimes added confusion. Then the rhythm clicked—and everything changed. Here’s what I learned. 👇 1. AI teammates have personalities. 𝗚𝗿𝗼𝗸 𝗮𝘀 𝘁𝗵𝗲 𝗖𝗵𝗶𝗲𝗳 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁. Keeps the big picture and debates architecture. Maintains the best context and reasoning for the entire scope of the product. 𝗚𝗲𝗺𝗶𝗻𝗶 𝗮𝘀 𝘁𝗵𝗲 𝗦𝘁𝗮𝗳𝗳 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. Reliable day-to-day pair-programmer. The workhorse day in and day out that consistently keeps velocity up. 𝗼𝟯 𝗮𝘀 𝗧𝗵𝗲 𝗙𝗶𝘅𝗲𝗿. Slower & pricier, but reasons through the toughest problems and most intractable bugs. 2. A new cadence for AI augmented development: “Flow cycles” beat 2-week sprints. 1️⃣ Grok + ChatGPT map the next micro-milestone. 2️⃣ Cursor + Gemini hammer out code. 3️⃣ Grok sanity-checks alignment → next cycle. Velocity feels 10× my old agile playbook. 3. Pay for genius when cheaper brains stall. When you’re stuck, the tokens you spend on a deeper-reasoning model are the cheapest hours you’ll ever buy. Conceptually no different than assigning a distinguished engineer to a team that is struggling, but much faster. Takeaways for tech leaders ➡️ AI development will reshape the industry, rewrite job roles, accelerate startup growth and fell incumbents. You need to embrace the disruption. ➡️ Treat LLMs like team members with strengths and quirks. ➡️ The 2-week sprint is dead; it just doesn’t know it yet. ➡️ True AI-augmented devs become orchestrators of a 24/7 digital crew. The bottleneck shifts from coding → human cognition. 🔥 Moving this fast is exhilarating and exhausting—the hardest part isn’t writing code, it’s keeping up with the pace of insight. Question: Where are you on this journey? Are your teams embracing AI tools, have you rearchitected your software with agentic flows, are your teams using more dynamic processes—or are you still running pure human sprints and classic dev tools? 🧐 (𝘍𝘰𝘭𝘭𝘰𝘸/𝘊𝘰𝘯𝘯𝘦𝘤𝘵 𝘧𝘰𝘳 𝘣𝘶𝘪𝘭𝘥-𝘪𝘯-𝘱𝘶𝘣𝘭𝘪𝘤 𝘭𝘦𝘴𝘴𝘰𝘯𝘴 𝘢𝘴 𝘵𝘩𝘪𝘴 𝘫𝘰𝘶𝘳𝘯𝘦𝘺 𝘤𝘰𝘯𝘵𝘪𝘯𝘶𝘦𝘴.)

Most developers treat AI coding agents like magical refactoring engines, but few have a system, and that's wrong. Without structure, coding with tools like Cursor, Windsurf, and Claude Code often leads to files rearranged beyond recognition, subtle bugs, and endless debugging. In my new post, I share the frameworks and tactics I developed to move from chaotic vibe coding sessions to consistently building better, faster, and more securely with AI. Three key shifts I cover: -> Planning like a PM – starting every project with a PRD and modular project-docs folder radically improves AI output quality -> Choosing the right models – using reasoning-heavy models like Claude 3.7 Sonnet or o3 for planning, and faster models like Gemini 2.5 Pro for focused implementation -> Breaking work into atomic components – isolating tasks improves quality, speeds up debugging, and minimizes context drift Plus, I share under-the-radar tactics like: (1) Using .cursor/rules to programmatically guide your agent’s behavior (2) Quickly spinning up an MCP server for any Mintlify-powered API (3) Building a security-first mindset into your AI-assisted workflows This is the first post in my new AI Coding Series. Future posts will dive deeper into building secure apps with AI IDEs like Cursor and Windsurf, advanced rules engineering, and real-world examples from my projects. Post + NotebookLM-powered podcast https://lnkd.in/gTydCV9b

📋 I Treated My AI Coding Tools Like Interns—It Changed Everything When I shifted from "co-coding" with AI to managing AI tools as if they were junior developers, things clicked. I wrote specs. I chunked tasks. I made documentation a priority. Suddenly, AI-generated code was better aligned, easier to debug, and didn’t go rogue. It was like having a virtual dev team, but only if I acted like their Tech Lead. Now, the tooling to scale that model is arriving. Companies like Auctor, Cloobot, and Ressl AI are tackling requirements and architecture. Cirra AI automates Salesforce changes. TestZeus and Testsigma eliminate QA bottlenecks. SRE.ai (YC F24), Copado, Opsera, Hubbl Technologies, and Elements.cloud are handling deployments and DevOps at scale—with LLMs under the hood. 💡 The next wave of SDLC tooling is agentic. Are you ready for the agent era of software development? 👉🔗https://lnkd.in/g8fAtCDs #AIEngineering #CognitiveDevOps #AIAgents #SDLC #SoftwareDevelopment #LLMTools #AgentSwarm #DevOpsAutomation #SalesforceDevOps #GenAI #TechLeadership #AIinSoftware #VirtualDevelopers #AITooling #AIProjectManagement #AgentEconomy #FutureOfDevOps #AIProductivity 

$0 → $10M ARR in 8 months. 25 people. Most AI tools overpromise and underdeliver. Fyxer AI is the opposite — it's one of the few I used daily that actually works. It completely replaced my previous EA/VA — and it's no surprised that it just joined the Lean AI Leaderboard. Here’s how they’re scaling like crazy using AI across growth, marketing, and product ops 🧠👇 🛠️ AI Tooling Stack • Chatwise – AI assistant (with MCP support) • Convergence – OpenAI operator for automated growth ops • Claude + ChatGPT – Content & Writing with custom knowledge • Greptile – Code reviewer + auto-committer • Linear MCP – AI ticket generation + experiment tracking • GitHub Copilot (Agent Mode) – Context-aware feature development • Getdot.ai – Internal AI growth analyst • Granola – Meeting transcription + Claude integration • Arcads AI , ReelFarm, ElevenLabs , HeyGen – Full-stack AI video marketing • Motion (Creative Analytics) – Creative performance insights 🔬 The Growth Loop: A/B Everything “When 25% of A/B tests win, it becomes a volume game — if quality holds.” With AI, one person can now ship like an entire growth engineering team. Fyxer launches 1–2 experiments per day, per IC. 🦊 Dot — The 24/7 Growth Analyst → Full context of company DB and table relations → Creates charts, analyzes trends, compiles customer behavior reports → Like having a senior analyst in Slack 24/7 ☑️ Linear MCP + Result Writer → Draft prompt to create an experiment template → AI fleshes out the ticket, fixes hypothesis, chooses from a selection of metrics, adds background data, etc. 👩🎨 CRO Designer Agent → Analyses submitted designs for CRO weak spots → Suggests changes to the UI, copy, and layout → Uses GPT-4o image creator to handle all asset creation → Iterates until optimal performance is achieved 🧑💻 Copilot Agent Mode → Pulls in the entire context of codebase → Automates 50% of new features/experiments → Automates 90% of the cleanup/deployment of experiments 🛣️ Convergence → Creates feature flags in Posthog → Launches tests across environments → Sends daily performance reports via email 🎯 AI-First Marketing Stack → ChatGPT with uploaded consumer insights → Claude for copywriting → Granola + Claude for brainstorming to briefs → Arcads AI , ReelFarm, ElevenLabs , HeyGen – Full-stack AI video marketing → Motion - AI-integrated creative performance reviews and intelligent insight agents Fyxer’s edge? Relentless execution + full AI adoption across every function. They’re not just building an AI tool — they’re running the company as an AI-native system. Congrats to Fyxer AI — and welcome to the Lean AI Leaderboard. See you at $100M ARR. 🚀 --------------------- 💡 Know an AI-native startup punching above its weight? Tag them or DM me — I’m adding new companies to the Lean AI Leaderboard weekly and backing the next generation of AI-native founders. (ps, I'm not affiliated with Fyxer whatsoever)