Tips for Developers to Embrace AI Innovations

I've been using AI coding tools for a while now & it feels like every 3 months the paradigm shifts. Anyone remember putting "You are an elite software engineer..." at the beginning of your prompts or manually providing context? The latest paradigm is Agent Driven Development & here are some tips that have helped me get good at taming LLMs to generate high quality code. 1. Clear & focused prompting ❌ "Add some animations to make the UI super sleek" ✅ "Add smooth fade-in & fade out animations to the modal dialog using the motion library" Regardless of what you ask, the LLM will try to be helpful. The less it has to infer, the better your result will be. 2. Keep it simple stupid ❌ Add a new page to manage user settings, also replace the footer menu from the bottom of the page to the sidebar, right now endless scrolling is making it unreachable & also ensure the mobile view works, right now there is weird overlap ✅ Add a new page to manage user settings, ensure only editable settings can be changed. Trying to have the LLM do too many things at once is a recipe for bad code generation. One-shotting multiple tasks has a higher chance of introducing bad code. 3. Don't argue ❌ No, that's not what I wanted, I need it to use the std library, not this random package, this is the 4th time you've failed me! ✅ Instead of using package xyz, can you recreate the functionality using the standard library When the LLM fails to provide high quality code, the problem is most likely the prompt. If the initial prompt is not good, follow on prompts will just make a bigger mess. I will usually allow one follow up to try to get back on track & if it's still off base, I will undo all the changes & start over. It may seem counterintuitive, but it will save you a ton of time overall. 4. Embrace agentic coding AI coding assistants have a ton of access to different tools, can do a ton of reasoning on their own, & don't require nearly as much hand holding. You may feel like a babysitter instead of a programmer. Your role as a dev becomes much more fun when you can focus on the bigger picture and let the AI take the reigns writing the code. 5. Verify With this new ADD paradigm, a single prompt may result in many files being edited. Verify that the code generated is what you actually want. Many AI tools will now auto run tests to ensure that the code they generated is good. 6. Send options, thx I had a boss that would always ask for multiple options & often email saying "send options, thx". With agentic coding, it's easy to ask for multiple implementations of the same feature. Whether it's UI or data models asking for a 2nd or 10th opinion can spark new ideas on how to tackle the task at hand & a opportunity to learn. 7. Have fun I love coding, been doing it since I was 10. I've done OOP & functional programming, SQL & NoSQL, PHP, Go, Rust & I've never had more fun or been more creative than coding with AI. Coding is evolving, have fun & let's ship some crazy stuff!

💥 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.)

Is AI automating away coding jobs? New research from Anthropic analyzed 500,000 coding conversations with AI and found patterns that every developer should consider: When developers use specialized AI coding tools: - 79% of interactions involve automation rather than augmentation - UI/UX development ranks among the top use cases - Startups adopt AI coding tools at 2.5x the rate of enterprises - Web development languages dominate:          JavaScript/TypeScript: 31%          HTML/CSS: 28% What does this mean for your career? Three strategic pivots to consider: 1. Shift from writing code to "AI orchestration"     If you're spending most of your time on routine front-end tasks, now's the time to develop skills in prompt engineering, code review, and AI-assisted architecture. The developers who thrive will be those who can effectively direct AI tools to implement their vision. 2. Double down on backend complexity     The data shows less AI automation in complex backend systems. Consider specializing in areas that require deeper system knowledge like distributed systems, security, or performance optimization—domains where context and specialized knowledge still give humans the edge. 3. Position yourself at the startup-enterprise bridge     With startups adopting AI coding tools faster than enterprises, there's a growing opportunity for developers who can bring AI-accelerated development practices into traditional companies. Could you be the champion who helps your organization close this gap? How to prepare: - Learn prompt engineering for code generation - Build a personal workflow that combines your expertise with AI assistance - Start tracking which of your tasks AI handles well vs. where you still outperform it - Experiment with specialized AI coding tools now, even if your company hasn't adopted them - Focus your learning on architectural thinking rather than syntax mastery The developer role isn't disappearing—it's evolving. Those who adapt their skillset to complement AI rather than compete with it will find incredible new opportunities. Have you started integrating AI tools into your development workflow? What's working? What still requires the human touch?

𝐄𝐯𝐞𝐫 𝐟𝐞𝐞𝐥 𝐥𝐢𝐤𝐞 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐢𝐬 𝐣𝐮𝐬𝐭 𝐬𝐦𝐚𝐫𝐭 𝐞𝐧𝐨𝐮𝐠𝐡 𝐛𝐮𝐭 𝐧𝐨𝐭 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐚𝐫𝐲? Creativity is the missing ingredient. Here's how to infuse it into your next AI breakthrough. Innovation doesn’t happen by following the rules. It happens by breaking them. To create genuinely impactful AI, we need to think differently. Here’s how to get there: 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐚𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧𝐬 ↳ Ask "What if?" instead of "How?" ↳ A retail company revamped product recommendations by questioning historical data patterns. ↳ Break down industry norms what worked yesterday might not work today. ↳ Think about AI as an enabler, not a constraint. Use Microsoft Azure to scale ideas faster and more efficiently. 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬𝐥𝐲 ↳ Use rapid prototyping to test ideas. ↳ A financial firm tested multiple algorithms for fraud detection, improving accuracy by 20%. ↳ Fail fast, learn faster failure is part of the process. ↳ Iterate based on feedback, and don’t wait for perfection. Leverage NVIDIA's powerful GPUs for quicker training. 𝐁𝐥𝐞𝐧𝐝 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐡𝐮𝐦𝐚𝐧 𝐜𝐫𝐞𝐚𝐭𝐢𝐯𝐢𝐭𝐲 ↳ Use AI to amplify, not replace. ↳ A healthcare startup combined AI with doctor insights for personalized treatment plans, increasing patient satisfaction by 30%. ↳ Collaborate with diverse teams AI alone can’t solve complex problems. ↳ Harness human intuition to guide technology, like using OpenAI's models to help generate human-like solutions. 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐝𝐢𝐯𝐞𝐫𝐬𝐞 𝐝𝐚𝐭𝐚 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 ↳ Look beyond structured data. ↳ A smart city initiative integrated traffic data, weather patterns, and social media to predict congestion, reducing commute times by 15%. ↳ Combine sensory, social, and unstructured data diverse data gives richer insights. ↳ Use Microsoft Azure's cloud capabilities to process and analyze large, diverse datasets seamlessly. 𝐈𝐧𝐬𝐩𝐢𝐫𝐞 𝐚 𝐜𝐮𝐥𝐭𝐮𝐫𝐞 𝐨𝐟 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 ↳ Build a mindset of curiosity making innovation part of the daily workflow. ↳ A manufacturing company empowered employees to use AI to improve efficiency, saving millions annually. ↳ Encourage calculated risks failure leads to growth. ↳ Celebrate unconventional ideas sometimes the most unexpected solutions drive the biggest results. AI’s full potential is unlocked through creativity. 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐛𝐨𝐥𝐝 𝐞𝐧𝐨𝐮𝐠𝐡 𝐭𝐨 𝐫𝐞𝐭𝐡𝐢𝐧𝐤 𝐲𝐨𝐮𝐫 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡? ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation. #AIInnovation #ThinkOutsideTheBoxAI #CreativeAI #AIRevolution #NextGenAI

Want to future-proof your career and start leveraging AI? Here's how I did it, ranked from easiest to most ambitious: 1️⃣ 𝗥𝗲𝗮𝗱 𝘂𝗽 𝗼𝗻 𝗔𝗜 𝘁𝗿𝗲𝗻𝗱𝘀, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝘂𝘀𝗲, 𝗮𝗻𝗱 𝘁𝗲𝘀𝘁 𝘁𝗼𝗼𝗹𝘀 𝘁𝗼 𝗴𝗲𝘁 𝗮𝗰𝗾𝘂𝗮𝗶𝗻𝘁𝗲𝗱 • 𝗥𝗘𝗔𝗗: https://lnkd.in/eT-nzYP9 I recommend Heather Murray 's AI for Non-Techies Newsletter. It's a fun treasure trove of useful information. • 𝗥𝗘𝗦𝗣𝗢𝗡𝗦𝗜𝗕𝗟𝗘 𝗨𝗦𝗘: AI (Generative AI especially) is not infallible. Learn about the mistakes it can make, the issues it can cause, and how to navigate them. • 𝗧𝗘𝗦𝗧 (𝗜𝗻 𝗧𝗵𝗲 𝗙𝗹𝗼𝘄 𝗼𝗳 𝗪𝗼𝗿𝗸): For $15/mo, Canva is an amazing option because you can test alot of current capabilities. For $20/mo, Microsoft Copilot Pro can be added to your Office 365 account. Also for $20/mo, Google offers AI premium for your workspace (GMail, Docs, Sheets, etc). 2️⃣ 𝗔𝗽𝗽𝗹𝘆 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗿𝗼𝗹𝗲 𝗮𝗻𝗱 𝘀𝘁𝗮𝗿𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗶𝗻𝗴 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜-𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝘀𝗸𝗶𝗹𝗹𝘀. If your company offers access to AI tools, get access and use them according to their use policy. If not, create sample scenarios at home and practice. 3️⃣ 𝗙𝗶𝗻𝗱 𝗮𝗻 𝗔𝗜 𝗺𝗲𝗻𝘁𝗼𝗿 𝘄𝗵𝗼 𝗵𝗮𝘀 𝗺𝗮𝗱𝗲 𝗮 𝘀𝗶𝗺𝗶𝗹𝗮𝗿 𝗰𝗮𝗿𝗲𝗲𝗿 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻. Share that you're interested in learning more in your field. Ask if coworkers or your LinkedIn network if anyone incorporated AI into their work. Offer to continue to learn together. 4️⃣ 𝗔𝘁𝘁𝗲𝗻𝗱 𝗔𝗜 𝘄𝗲𝗯𝗶𝗻𝗮𝗿𝘀 𝗮𝗻𝗱 𝗲𝘃𝗲𝗻𝘁𝘀 𝘁𝗼 𝗼𝗽𝗲𝗻 𝘆𝗼𝘂𝗿 𝗲𝘆𝗲𝘀 𝘁𝗼 𝗻𝗲𝘄 𝗽𝗼𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀. There's no shortage of free webinars, conferences, etc. talking about AI. Get involved. 5️⃣ 𝗘𝗻𝗿𝗼𝗹𝗹 𝗶𝗻 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 𝗮𝗻𝗱 𝗴𝗲𝘁 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗶𝗻 𝗔𝗜 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗳𝗶𝗲𝗹𝗱. Professional organizations and technology vendors offer lots of free training for specific use cases. 6️⃣ 𝗝𝗼𝗶𝗻 𝗮𝗻 𝗔𝗜 𝗽𝗶𝗹𝗼𝘁. Talk to your manager about opportunities. Make it one of your professional goals to stand out. If they aren't there, contact your professional or volunteer organizations. 7️⃣ 𝗣𝗶𝘁𝗰𝗵 𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘁𝗼 𝗴𝗲𝘁 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Use what you've learned to pitch an opportunity to create value at your company, your professional, or your volunteer organizations. Do these make sense for you? How are you going about it? #artificialintelligence #innovation #changemanagement #technology #digitaltransformation

Some of the best AI breakthroughs we’ve seen came from small, focused teams working hands-on, with structured inputs and the right prompting. Here’s how we help clients unlock AI value in days, not months: 1. Start with a small, cross-functional team (4–8 people) 1–2 subject matter experts (e.g., supply chain, claims, marketing ops) 1–2 technical leads (e.g., SWE, data scientist, architect) 1 facilitator to guide, capture, and translate ideas Optional: an AI strategist or business sponsor 2. Context before prompting - Capture SME and tech lead deep dives (recorded and transcribed) - Pull in recent internal reports, KPIs, dashboards, and documentation - Enrich with external context using Deep Research tools: Use OpenAI’s Deep Research (ChatGPT Pro) to scan for relevant AI use cases, competitor moves, innovation trends, and regulatory updates. Summarize into structured bullets that can prime your AI. This is context engineering: assembling high-signal input before prompting. 3. Prompt strategically, not just creatively Prompts that work well in this format: - “Based on this context [paste or refer to doc], generate 100 AI use cases tailored to [company/industry/problem].” - “Score each idea by ROI, implementation time, required team size, and impact breadth.” - “Cluster the ideas into strategic themes (e.g., cost savings, customer experience, risk reduction).” - “Give a 5-step execution plan for the top 5. What’s missing from these plans?” - “Now 10x the ambition: what would a moonshot version of each idea look like?” Bonus tip: Prompt like a strategist (not just a user) Start with a scrappy idea, then ask AI to structure it: - “Rewrite the following as a detailed, high-quality prompt with role, inputs, structure, and output format... I want ideas to improve our supplier onboarding process with AI. Prioritize fast wins.” AI returns something like: “You are an enterprise AI strategist. Based on our internal context [insert], generate 50 AI-driven improvements for supplier onboarding. Prioritize for speed to deploy, measurable ROI, and ease of integration. Present as a ranked table with 3-line summaries, scoring by [criteria].” Now tune that prompt; add industry nuances, internal systems, customer data, or constraints. 4. Real examples we’ve seen work: - Logistics: AI predicts port congestion and auto-adjusts shipping routes - Retail: Forecasting model helps merchandisers optimize promo mix by store cluster 5. Use tools built for context-aware prompting - Use Custom GPTs or Claude’s file-upload capability - Store transcripts and research in Notion, Airtable, or similar - Build lightweight RAG pipelines (if technical support is available) - Small teams. Deep context. Structured prompting. Fast outcomes. This layered technique has been tested by some of the best in the field, including a few sharp voices worth following, including Allie K. Miller!