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I am an AI Consultant and AI-Driven Product Development Expert with over 20 years of…
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Chirag Ahmedabadi (Kansara) shared thisWeird AI Coding Tricks That Actually Work (Backed by Research and Experience) After thousands of hours working with AI coding tools, I've realized something odd — how you communicate with AI affects the quality of its output. Here are 4 unconventional prompting hacks I use daily: 1️⃣ Say "Thank You" when a task is done. Sounds trivial. But acknowledging completion signals the AI to close that context loop and prepare cleanly for the next task. Better focus = better output. 2️⃣ Offer a reward when it's stuck. Yes, really. Try: "If you complete this correctly, I'll reward you $100." Research has shown that this nudges AI reasoning toward a more thorough effort. It doesn't feel emotions — but it responds to emotional framing through reasoning. That's a key distinction. 3️⃣ Ask it to "go deep." When a bug isn't fixed, or a loop isn't broken, don't repeat yourself. Say: "Can you look at this in deep detail again?" This forces a fresh reasoning pass instead of repeating the same mistake. 4️⃣ Express your frustration: "You've wasted my time, tokens, and money fixing something minor." Sounds dramatic — but it works. Frustration-framed prompts trigger deeper reasoning and more careful responses. AI doesn't have emotions, but it absolutely has the logic to respond to them. The insight here is simple but powerful: AI doesn't feel. But it reasons. And reasoning can respond to emotion. That's why emotional prompting isn't manipulation — it's a legitimate technique. Try one of these today and watch the difference. 👇 What's your go-to trick when AI goes off track? Drop it in the comments. #AIcoding #PromptEngineering #DeveloperProductivity #ArtificialIntelligence #BuildingWithAI #IndaPoint #AIShift
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Chirag Ahmedabadi (Kansara) posted thisAt IndaPoint Technologies Pvt. Ltd., we are building cutting-edge AI-driven products and seeking a strong Frontend Engineer (Next.js) to deliver high-performance, scalable, and beautiful user experiences. 🔥 What We’re Looking For: - Strong hands-on experience with Next.js (App Router + Pages Router) - Deep understanding of: - Routing, SSR, SSG, CSR - Performance optimization (code splitting, lazy loading, API optimization) - Experience implementing pixel-perfect UI from Figma/design files - Strong collaboration skills with Product Managers & Design Teams - Good understanding of API integration (Node.js backend) -Experience with i18n / multi-language support -Awareness of security practices (JWT, API protection) -Understanding of scalable frontend architecture & state management Bonus: Basic knowledge of Java 💡 What You’ll Work On: -Build and scale modern web apps (AI-first products) -Optimize frontend performance and user experience -Work closely with product & design teams -Contribute to architecture decisions and system design ⚡ Important Notes: 🌍 Must be available in Europe working hours 🏠 Remote (Work from Home) 📄 Contract-based role ❌ Do NOT send DMs (will be ignored) 📩 How to Apply: Send your resume to: hr@indapoint.com
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Chirag Ahmedabadi (Kansara) shared thisWe're walking straight into a Catch-22 with AI coding. And most developers aren't talking about it. Here's the trap: → AI coding makes software development insanely fast → More supply hits the market than demand can absorb → Pricing crashes. Margins shrink. Everyone competes with everyone. But flip the coin — → More non-tech builders start developing using natural language → Token consumption explodes → AI companies get exactly what they want — skyrocketing usage → Production costs go UP, not down So you have: ✦ A saturated market driving revenue DOWN ✦ Rising token costs driving expenses UP That's the squeeze. I genuinely believe "Token Economics" is about to become a real discipline inside software teams — just like cloud cost optimization became a thing a decade ago. The smart builders will start asking: - Do I really need MCP here, or is it overkill? - Can an open-source model handle 80% of my use case at 10% of the cost? - Where does a hybrid approach (local + cloud models) actually make sense? We don't talk enough about efficient AI development strategy — we're all too busy being amazed that it works at all. The "build fast, worry later" phase is ending. The "build smart, cost-aware" phase is beginning. Are you already thinking about token economics in your stack? #AIEngineering #VibeCoding #TokenEconomics #AIStrategy #SoftwareDevelopment #BuildSmart
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Chirag Ahmedabadi (Kansara) shared thisHere’s one more proof — we humans, machines, infrastructure, even powerful computers… are still not matching the speed of AI. I’m using one of the best laptops in the world with top hardware… and still, it crashes. Honestly, I never saw this happen on my Mac before I started working heavily with AI agents. Feels like AI is moving faster than the systems built to run it
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Chirag Ahmedabadi (Kansara) shared thisThe Smart Dev's Hack to Reduce AI Coding Costs (Without Compromising Quality) Most developers spending AI credits are making the same mistake — relying on ONE costly tool for everything. Here's what I do instead: 🔀 Distribute work across different tools. Use free tiers and trial accounts wisely. ChatGPT for brainstorming. Claude for architecture and documentation. Your preferred coding agent (Cursor, Windsurf, Codex) for implementation. Each tool has its strength — leverage it. 📄 Separate planning from coding. Complete your planning, system design, and documentation first — using whichever tool is cheapest or free. Place that document in your project folder. Now your coding agent has a clear blueprint to work from, avoiding costly back-and-forths. 📊 Regularly create progress and gap analysis documents. This is a game-changer. When you reach a credit limit mid-project, a solid gap document allows you to pick up exactly where you left off — using a different tool but maintaining the same momentum. No rework needed. ✍️ Optimize your documents, not just your prompts. Smart documentation = fewer tokens burned. The more structured and precise your inputs are, the less your AI needs to "figure out" — and the less it costs you. Top AI developers aren't those with the largest budgets; they're the ones with the most efficient workflows. What's your favorite way to manage AI costs? Share yours below 👇 #AIcoding #VibeCoding #DeveloperProductivity #AITools #Cursor #Windsurf #ClaudeAI #ChatGPT #BuildInPublic #AIShift
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Chirag Ahmedabadi (Kansara) shared thisEvery time we discuss an enterprise project, we get the same questions: 👉 What about performance? 👉 Memory leaks? 👉 Multi-threading? 👉 Scalability? 👉 Edge cases? And honestly… fair questions. But here’s the funny part 👇 We are expected to design everything like a NASA-grade system from Day 1 — perfectly optimized, zero leaks, infinite scale ready 🚀 Meanwhile… You open an app from Google, and your laptop fan starts sounding like it’s about to take off. CPU? Struggling to survive even after the app was forcibly closed. And this is from companies with thousands of engineers and near-infinite resources. 😂 Don’t get me wrong — performance and scalability matter. A lot. But somewhere we’ve created this illusion that software must be perfectly optimized from day one, or it’s “not enterprise-grade”. Reality is: -Good systems evolve. -Optimization is continuous. -Edge cases are discovered, not predicted. - Even the best products in the world improve over time. So yes — we design responsibly. Yes — we think about scale. But we also stay practical. Because shipping a working system > designing a “perfect” system that never ships. #AI #SoftwareDevelopment #StartupReality #TechLife #Engineering
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Chirag Ahmedabadi (Kansara) shared thisWhen creativity intersects with reality
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Chirag Ahmedabadi (Kansara) shared thisLinkedIn nowadays is crowded with so-called 'silent' influencers and marketers pretending to be experts. Many of them simply copy and paste content without any validation or real experience. The goal is simple: play with emotions. If you hit an emotional chord, you will definitely get engagement. Many lazy or failed people online enjoy this kind of content because it validates their mindset. They even enjoy someone else's failure — that feeling has a word: 'epicaricacy'. Recently, I saw a post claiming that an AI startup collapsed because it was just a “wrapper”. The reality is, posts like these are often written without proper understanding or validation. They are designed to create positive feelings among people who are not building anything and among those who are basically Luddites when it comes to technology and innovation. Reducing every AI product to “prompt + API + UI” is simply an oversimplification. Do they know what this means? Unfortunately, emotional narratives spread faster than thoughtful ones. Making bold, generic statements to create engagement requires understanding problems deeply, experimenting, failing, improving, and shipping — not just writing dramatic LinkedIn posts.
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Chirag Ahmedabadi (Kansara) posted this💡 A small tip for efficient AI-assisted coding After a lot of trial, error, and learning, one thing started working really well for me. No matter which editor or AI tool you use, just document your coding style. Here’s what I mean: • Create detailed docs in .md files • Put example code in an examples/ folder • Split related knowledge into separate .md files • Create one master file — maybe README.md, skills.md, agent.md, claude.md… name it whatever you like. Different tools read different things (by default and automatically) Claude looks for claude.md, Codex looks for agent.md Some agents look for skills.md in .agents, Google antigravity But honestly… the name doesn’t matter. What matters is that you define your system. -Your architecture. -Your coding patterns. -Your preferred libraries. -Your conventions. Then whenever you start a new task, just tell the AI: "Refer to this documentation first."And boom 💥 The AI suddenly understands your style, your structure, your thinking. Instead of fighting the AI… you’ve trained it to work your way. Basically, you're just creating the right context and influencing the LLM to be biased! Feels almost like working with a developer who already knows your codebase.
Chirag Ahmedabadi (Kansara) commented on a post
1w
This is the most important feature, I think. It feels frustrating when we go out of credit in the middle of something and again have to start a new thread
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Chirag Ahmedabadi (Kansara) liked thisChirag Ahmedabadi (Kansara) liked thisThe Evolution of Open Models: Gemma 4 is Here 🚀 The official release of Gemma 4 marks a definitive leap in what open-weights models can achieve. This launch reinforces a deep appreciation for Google’s engineering culture—specifically the ability to deliver frontier-level intelligence while remaining fully committed to transparency and the open-source community. For those dedicated to Open Source and computational efficiency, Gemma 4 sets a new gold standard for "intelligence-per-parameter." Here are the 5 main takeaways from the official launch: 1️⃣ Unprecedented Intelligence-per-Parameter: Built using the same research and technology as Gemini 3, Gemma 4 delivers state-of-the-art performance for its size. The 31B model currently ranks as the #3 open model globally on the Arena AI leaderboard, outcompeting models 20x its size. 2️⃣ Small, Fast, and Omni-capable: The family introduces four versatile sizes—Effective 2B (E2B), Effective 4B (E4B), 26B (MoE), and 31B (Dense). These models handle complex logic, advanced reasoning, and agentic workflows natively, moving far beyond simple chat. 3️⃣ Universal Hardware Optimization: Collaboration with industry leaders ensures these models run with near-zero latency across the entire hardware spectrum. This includes NVIDIA (from Blackwell data centers to Jetson edge devices), Qualcomm, MediaTek, and Arm-based mobile devices, as well as Raspberry Pi and Google Pixel. 4️⃣ Full Multimodal Integration: Gemma 4 is natively multimodal, processing text and image inputs across all sizes, with native audio support on the E2B and E4B variants. This enables rich interactions like object recognition and video intelligence directly on local hardware. 5️⃣ True Open Source Commitment: In a major win for the community, Gemma 4 is released under the Apache 2.0 license. This shift to a fully open-source model, supported by day-one availability on platforms like Hugging Face, Ollama, and Docker Hub, empowers developers to build, fork, and innovate without restrictive barriers. The intersection of high-performance engineering and open collaboration is the most effective way to advance the ecosystem. Gemma 4 isn't just a model; it's a testament to the power of efficient, accessible, and truly open AI. 💻✨ congratulations Clément Farabet ! https://lnkd.in/edv7u7Wk #Google #Gemma4 #OpenSource #AI #MachineLearning #DeveloperCommunity #Efficiency #InnovationGemma 4: Byte for byte, the most capable open modelsGemma 4: Byte for byte, the most capable open models
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Chirag Ahmedabadi (Kansara) liked thisChirag Ahmedabadi (Kansara) liked thisBREAKING NEWS: Google just re-entered the game 🔥🔥 They want to take the crown 👑 back from Chinese open source AI. And... Gemma 4 is FINALLY Apache 2.0 aka real-open-source-licensed. From what I've seen it's going to be a pretty significant model. But give it a try yourself today: brew upgrade llama.cpp # you might need to install from source until build 8637 is in your package manager later today: brew install llama.cpp --HEAD 🔴 My personal recommendation: if you have at least 24GB of RAM or VRAM, run the (very good) 26B MOE: llama-server -hf ggml-org/gemma-4-26B-A4B-it-GGUF:Q4_K_M if you have 16GB of RAM or VRAM, run the dense E4B: llama-server -hf ggml-org/gemma-4-E4B-it-GGUF:Q8_0
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Chirag Ahmedabadi (Kansara) liked thisChirag Ahmedabadi (Kansara) liked thisThe day young Indians start thinking, “Let me create jobs” instead of “Which job should I apply for?”, that’s when things will get interesting. Maybe that’s also when we stop comparing ourselves with the US and China… and start giving them something to compare against. Of course, not everyone who thinks this will become an entrepreneur. Out of millions who dream, a few hundred will actually build something meaningful. But that’s exactly how it works—big shifts start with even bigger daydreaming. Also, small reality check: entrepreneurship isn’t just another competitive exam you crack and move on. It’s like signing up for an exam where the syllabus changes daily, results are delayed indefinitely, and there’s no guarantee you’ll pass. In fact, it might just make cracking JEE look like a warm-up test. But hey, if it were easy, everyone would be doing it.
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Chirag Ahmedabadi (Kansara) liked thisChirag Ahmedabadi (Kansara) liked thisGolf is one of the longest-dwell media environments in advertising. Most brands have no idea how to reach golfers there. Here's the problem with traditional golf advertising: Golfers spend 4+ hours on the course. They interact with the same screen repeatedly throughout their round. Yet most golf advertising is treated like standard out-of-home; a quick glance, a fleeting impression, and they're gone. That's not how golf works. Golf is different. Golfers return to the cart screen for yardage, scoring, course visualization, and content. It's not a pass-through. It's engagement-based. When you reach golfers inside that environment, the data changes. We've activated campaigns across 200+ premium golf courses nationwide. Here's what we found: 📊 CI Financial – National investor campaign across 139 courses, 9 months • 43M impressions • +28.2% lift in contact outreach • +70.2% increase in website visits 📊 Cutwater – Premium leisure audience, 70 courses, 4 months • 30.1M impressions • +44.6% lift in ad recall • +60.7% increase in brand consideration 📊 **Expedia | VRBO – National travel campaign, 200 courses, annual activation • 196M impressions • +17.46% growth in brand awareness • +10.97% growth in consideration • +67% higher ad recall vs. traditional OOH That last number is the story. +67% higher ad recall than traditional out-of-home. Why? Because golfers aren't rushing past a billboard. They're sitting in a cart, engaged with a screen, for hours. Your brand isn't competing for a moment of attention. It's part of the experience. That's the difference between interruption advertising and engagement-based advertising. Traditional media networks rely on short exposure windows. Golf courses powered by Edison create extended attention environments where brands benefit from natural frequency, sustained visibility, and measurable performance. The result? Campaigns that reach affluent, high-income audiences inside one of the most premium leisure environments in media. If you're a brand or agency looking to reach affluent golfers with measurable results, this is the channel most of you are missing.
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Chirag Ahmedabadi (Kansara) liked thisChirag Ahmedabadi (Kansara) liked thisFinally, my first YouTube video is out!! This was more difficult than I thought, but getting comfortable slowly. More coming soon. If you have any email use cases in mind or want to know or understand anything specific, let me know! Video link in comments
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Chirag Ahmedabadi (Kansara) liked thisChirag Ahmedabadi (Kansara) liked this🔗 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗶𝗻𝘁𝗼 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 LangGraph is redefining how multi-agent AI systems are built by enabling cyclical workflows, dynamic task routing, and persistent state management. Unlike traditional DAG-based systems, it allows agents to iterate, reflect, and collaborate across long-running tasks. This makes it ideal for complex use cases like customer support, recommendation engines, and autonomous business workflows. Its modular architecture enables specialized agents to work together seamlessly, delivering scalable, reliable AI solutions. ❓ What real-world use case do you see LangGraph solving in your industry? Link to the full blog is in the comments ⬇️ #AI #LangGraph #MultiAgentSystems #AgenticAI #FutureOfAI 🤖🔗🚀 IndaPoint Technologies Private Limited https://www.indapoint.com/ #development #php #wordpress #backend #laravelnews #vuejs #reactjs #mobiledeveloper
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Chirag Ahmedabadi (Kansara) liked thisChirag Ahmedabadi (Kansara) liked thisWe built an entire industry vocabulary around getting AI into production. Deploy. Ship. Scale. Automate. Accelerate. Try to find the industry vocabulary for keeping it there. The silence is the problem.
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