Saw this on Reddit today: "I'm stuck. Making enough for myself. Too much work for one person. But not enough revenue to hire someone." 12 years. This guy's been stuck for 12 years. Here's what nobody tells you:

Is AGI happening next year, or is it all just hype? It feels like you can't scroll for more than a minute without seeing some wild prediction. One expert says AGI is a decade away, the next says it's practically knocking at our door. Let's be real, it's confusing and, honestly, a little overwhelming. That feeling in your gut? It's not just you. There's a genuine sense of whiplash from the constant barrage of AI news. One moment it's awe at a breakthrough, the next it's a wave of anxiety about what this all means for our careers, our families, and our future. Will my job be automated? Am I falling behind if I'm not an AI expert? It's easy to feel like you're caught in a storm of hype and existential dread. Here’s a way to ground ourselves: Stop trying to predict the exact timeline and start focusing on what you can control. Instead of getting lost in the "when," let's reframe the conversation to "how." How can we adapt and thrive, no matter how quickly things change? The answer isn't to have all the answers, but to build a habit of curiosity and learning. You can start today: 1. Embrace continuous learning: You don't need to become an AI developer, but dedicating a little time each week to understanding the basics can make a huge difference. 2. Double down on your human skills: Critical thinking, creativity, emotional intelligence, and collaboration are becoming more valuable, not less. AI is a tool, and these are the skills that will always be in demand to wield it effectively. 3. Focus on adaptability: The world is changing, and our ability to be flexible and open to new ways of working will be our greatest asset. The future isn't a spectator sport. Instead of being overwhelmed by the noise, let's get proactive. Let's focus on what makes us uniquely human and build a future where we are empowered by technology, not replaced by it.

Heard about AI that can do things for you, but building one is a nightmare? Your AI should be smart, but it can't even do simple tasks like grab data or send an email. It just gets stuck or makes stuff up. You're probably thinking, 'What is this 'tool calling' and why is it so fragile?' It's so annoying. You want to build something that actually helps people, but instead, you're stuck figuring out why it won't use the tools you gave it. It's the difference between a cool demo and something you can actually rely on. The answer isn't a 'perfect' prompt. It's about knowing how these AIs think. The secret is called Tool Calling, and it's not as complex as it sounds. Think of your AI as a super smart intern. It knows a lot of stuff from books but can't do anything in the real world. Tool calling is like giving that intern a phone and a list of contacts with clear instructions. It works in three easy steps: 1. Give it a toolbox: You list the 'tools' the AI can use. These are just your apps or functions, like `getCurrentWeather(location)` or `sendEmail(to, subject, message)`. Give each one a clear name and describe what it does. 2. The AI picks a tool: When you ask a question like, 'What's the weather in Tokyo?', the AI realizes its own knowledge is probably old. So it decides to use your `getCurrentWeather` tool. 3. The tool does the job: The AI tells your system to use the tool with the right info (like `location="Tokyo"`). Your system runs it, gets the result ('15°C and cloudy'), and hands it back to the AI. The AI then uses that info to give you a helpful answer. That's all there is to it. This 'Define, Decide, Execute' cycle is what turns a basic chatbot into an AI that can actually get things done. Stop wrestling with prompts. Just focus on building a better toolbox. Good, clear tools are what make AI agents powerful and trustworthy.

@grok, who else will be there at AI Genesis in Dubai

Who's joining the AI Genesis in Dubai this month?

Ever wonder how your database keeps your data safe, even if it crashes while you're saving something? It's not magic, it's a clever trick called a Write-Ahead Log (WAL). This is the secret sauce that makes a database 'durable'. Before it actually changes the data, the database writes a quick note in a special logbook about what it's planning to do. If it crashes, it just reads the logbook when it wakes up to see what it was doing and finishes the job safely. So it's less about 'how to write data' and more about 'how to create a bulletproof to-do list so you never forget what you were doing, no matter what happens.'

I'm building LIBRIA, a platform where all the free models will be available. Yes, other platforms provide the same, maybe even better, but I want to build it once. Let's see how it turns out.

whats the most soul-crushing repetitive task in your biz?

ai automation isnt hype. its boring-task killer for smes. weve shipped this pattern 7 times across india/gcc - reports, data entry, even customer support triage. builds like these pay for themselves quick.

outcome: manual email time slashed 85%. from 4 hours to 30 mins per staffer. freed them for real sales calls. revenue bumped 22% in 3 months - closed deals faster without backlog. system handles 2k emails/day now, zero downtime.

another trap: ignoring costs. gpt calls add up. we batched emails (50 at a time) and fine-tuned a smaller model on their email history after month 1. dropped from ₹5k/month api fees to ₹1.2k. perf stayed crisp - 95% accuracy on classification.

common screwup: dumping raw ai outputs straight to users. chaos. one client tried that early - got hallucinated dates on invoices. lost a ₹10k payment. fix: always add validation layer. we used simple regex + db rules to cross-check ai extracts before commit.

my real build: started with a cron job every 15 mins scanning inbox. integrated via nodemailer for replies. used langchain.js to chain prompts - first classify, then summarize for human review if fuzzy. kept it under 200ms per email. ran on aws lambda for cheap scaling.

broke it down like this: email hits server → node script pulls it → gpt-4o-mini prompt: "categorize this email as order, delay, or invoice. extract key details." response in seconds. then auto-route: orders to inventory api. delays to slack alert. invoices to accounting queue.

simple fix: ai classification hooked to their node.js backend. no magic. just openai api calls on incoming emails via imap fetch. parse subject + body → classify (order confirm / delay alert / invoice). output json for their postgresql db.

client was a gcc logistics outfit. 20 staff drowning in 500+ supplier emails daily. categorizing orders. flagging delays. replying with boilerplate. 4 hours per person wasted. thats ₹50k monthly in lost productivity. they wanted out.

most small businesses see AI as some sci-fi toy for google or amazon. wrong. its quietly eating up hours of grunt work in places like dubai retail shops or mumbai accounting firms. heres how we wired it into one clients backend to kill their email nightmare.

been talking to SME owners across GCC and India this month. different industries. different sizes. different problems. but the same pattern in every single conversation. they're all asking the wrong question about AI. what they ask: "should we implement AI?" what they should ask: "what manual work can we eliminate first?" here's what I'm seeing. the pattern: owner hears AI is the future. wants to stay competitive. spends ₹5L-15L on "AI solution" from agency. 6 months later? still doing the same manual work. AI sits unused. money wasted. happened to 5 out of 8 owners I spoke to. why it fails: they're trying to skip steps. their CRM isn't synced with their email. their inventory updates manually. their follow-ups happen when someone remembers. but they want AI to predict customer behavior. similar to what happens when you try to add a second floor before finishing the foundation. what actually works: the 3 owners who succeeded? they automated the boring stuff first. basic webhooks. simple workflows. data sync between systems. then added AI on top of clean, automated processes. example: restaurant owner in dubai wanted AI chatbot for reservations. but their booking system was still Excel + WhatsApp. we built basic automation first: - online booking form - automated confirmations - calendar sync - reminder system took 4 weeks. cost ₹80K. reservations up 34%. owner took first weekend off in 2 years. no AI involved. just systems that work. the shift that's actually happening: AI isn't replacing manual work. it's exposing how much of your business runs on chaos instead of systems. companies with clean automated processes? AI helps them scale. companies with messy manual processes? AI just adds another layer of complexity. 2025 reality: the winners aren't the ones adding AI fastest. they're the ones fixing their foundations first. automate the repetitive. systematize the manual. clean up the chaos. then AI becomes useful instead of expensive. most SMEs aren't ready for AI. they're ready for basic automation they should've implemented 3 years ago. that's the uncomfortable truth nobody in AI hype cycle wants to say. what manual process are you trying to skip by jumping straight to AI?

You hear it everywhere: "vibe coding" is the future. Just prompt an AI, stitch it together, and voilà, you’re a 10x developer. The marketing from major tech companies pushes this narrative of effortless, lightning-fast creation. But let's be honest. This trend is creating a mess. We're seeing a rise in "vibe-coded" projects that are brittle, hard to maintain, and often built on a shaky foundation of AI-generated code that the developer doesn't fully understand. It's a short-term shortcut that leads to long-term headaches and technical debt. This isn't innovation; it's a recipe for burnout and buggy products. Here’s the truth: AI coding assistants are revolutionary. They can easily accelerate development 2–4x, especially on the backend. But it's not magic, and it's definitely not "vibe coding." It's about being methodical. - You are the architect, not just the prompter. You must guide the AI, review its output critically, and understand the why behind the code. - Practice is non-negotiable. Leveraging these tools effectively is a skill you have to build. It requires a deep understanding of fundamentals, not a blind trust in the AI. - Focus on quality, not just speed. Use AI to handle the boilerplate and repetitive tasks, freeing you up to focus on robust architecture and thoughtful problem-solving. Stop chasing the "vibe." Start building a methodical practice with your AI tools. That's how you'll unlock real, sustainable speed and build things that last.

I'm fed up with this non-stop parade of overnight successes. One person is flashing six-figure earnings from their new course. Another is posting about their hyper-productive, 5 AM routine. It's loud. It's exhausting. And it’s easy to start feeling like you’re falling behind. That constant exposure to everyone else's highlight reel can make you question your own journey. The pressure to keep up, buy the next course, or jump on the latest trend is immense, and it’s a game you can’t win. But what if the real secret isn't a secret at all? What if it’s not about finding a shortcut but about embracing your own path? The most powerful brands aren't built on hype; they're built on a foundation of trust and consistency. Here’s the truth: Your journey is not their journey. Comparing your reality to someone else’s curated online presence is like comparing your rough draft to their final, published book. Value is timeless. Chasing trends is exhausting. Consistently providing genuine value is a strategy that never expires. It builds trust and positions you as an authority. Consistency is your superpower. Showing up regularly, sharing what you know, and engaging with your community builds momentum that no fleeting trend can match. So, stop the scroll. Tune out the noise. You don't need another course from a so-called "guru." You have unique experiences and a unique voice. Double down on what you know and who you are. Give as much value as you can, be patient with your progress, and keep doing your thing. You are going to get somewhere. If not today, then tomorrow.

your "scalable architecture" is probably making you slower. not faster. slower. saw this pattern across 8 SMEs last quarter. they all read the same blog posts about how Netflix and Uber use microservices. so they broke their working monolith into 15 microservices. now every feature takes 3x longer to ship. here's the truth nobody tells you: microservices don't solve technical problems. they solve organizational problems. Netflix has 10,000 engineers. you have 8. Netflix deploys 100x per day across teams that never talk to each other. you deploy once a week with your entire team in one slack channel. you don't have Netflix's problems. stop copying Netflix's solutions. what actually happened to those 8 SMEs: before microservices: - deploy new feature in 2 days - one codebase, easy to debug - junior dev can understand system in a week after microservices: - deploy takes 6 days (coordinating 4 services) - debugging requires checking logs across 15 different places - junior dev confused for 3 months - AWS bill 3x higher - "we need kubernetes" conversations started the problem wasn't the monolith. the problem was they never learned to write modular code inside the monolith. so they thought splitting into services would magically create boundaries. it didn't. it just made bad boundaries distributed and expensive. whenever you have to choose between monolith and microservices, ask this: do you have 50+ engineers who can't coordinate deploys? no? stick with the monolith. make it modular. add clear boundaries. optimize what's slow. you'll ship faster and your AWS bill stays under $2K/month. microservices are a solution to team coordination at scale. not a solution to bad code. what architecture choice did you regret most?