Petpooja

Global expansion isn’t hard. Market choice is. Most founders fail early. They pick the wrong market. In 2026, growth is uneven. SaaS is 400B plus. APAC is growing at 20 percent plus. Opportunity exists. But not everywhere. Market scoring model 1️⃣ Speed over size Big TAM means nothing without fast deal cycles. Faster markets compound revenue quicker. 2️⃣ Regulation impacts cost Data laws and compliance increase CAC. Bad markets reduce margins silently. 3️⃣ Demand density wins Concentrated buyers scale faster. Scattered demand increases cost. The insight Expansion is not about more markets. It is about the right markets. 2026 rule Enter only if you can Acquire profitably Close faster Scale via partnerships If not clear, skip. Speed of scale beats size of opportunity. Save this. Follow Sarthak Maheshwari for more. #GlobalExpansion #SaaSGrowth #MarketStrategy

Every project leader I speak with says the same thing, in some form: "Our project is different. We can't standardize the way a factory does." And they're right — at the level they mean it. The site is different. The client is different. The scope is different. The risk profile is different. No two projects look the same from the top. But one layer down, the machinery underneath is remarkably consistent. Three activities run every project, regardless of sector — hydro, shipbuilding, refinery turnaround, data center, EPC, infrastructure. And each one carries a tax the organization has quietly accepted as the cost of doing business. → Updates: A site engineer at the end of the day, hunched over a laptop, entering the day's progress into Excel. Then again into the DPR. Then again into ERP tomorrow morning. Then again into the client's portal on Friday. Your most expensive people — engineers, supervisors, site leads — spend two to three hours a day feeding systems instead of running work. And by the time the data is in, the moment to act on it has passed. The update describes yesterday. → Approvals: An RFI raised on Monday. Technical review on Wednesday. Commercial input on Friday. Client confirmation the following Tuesday. Eight working days for a decision that could have closed in two. The approver isn't the bottleneck. The context is — sitting in three different inboxes, attached to four different threads, waiting to be assembled before anyone can decide. → Iterations: A sequencing call. A vendor choice. A delay absorption versus claim decision. Most project managers evaluate two options when five would have served the project better. Not because they don't know the other three exist. Because evaluating five by hand is exhausting — and the deadline to decide is today. Three different taxes. Paid in capacity, cycle time, and decision quality - every project, every sector, every year. This is where AI shifts the operation. Not a tool bolted on top. Part of how the work gets done. → Progress captured as it happens, from the person closest to it. → Context assembled around a decision before it reaches the approver. → Five alternatives evaluated in the time it used to take to build two. The manager chooses between options, not between exhaustion and guesswork. The uniqueness argument is honest at the top. But it has quietly protected a layer underneath where the work is almost identical across companies — and where the leverage has been sitting unused for a decade. The first move in operational AI for projects isn't about the project. It's about the machinery that runs every project. Of the three — updates, approvals, iterations — which one silently costs your project the most? #ai #projectmanagement #ledaership

A close friend of mine is building something serious across fintech, lending, SaaS, and more — and they’re hiring across multiple roles. I’ve seen how they operate. It’s fast, high ownership, and definitely not for everyone. The work is largely aligned to US markets, so being comfortable with US hours is important. 📍 Vadodara is preferred 🌍 Remote works for truly strong candidates If you’re someone who likes taking charge, figuring things out, and actually wants to grow — this could be worth exploring. Before you apply, I’m happy to speak with a few strong candidates and help you position your application better. If interested: → Comment “Interested” → Or DM me with the role you’re looking at + a quick intro video. I’ll take a look and guide you from there. #Hiring #StartupJobs #Fintech #SaaS #TechJobs #Vadodara #IndiaJobs #opportunity (Sharing the original post in the comments 👇)

Is coding dead? I have been seeing this question everywhere lately. Tech influencers and leaders keep debating it, so I wanted to share my perspective. Earlier, I used to believe that coding was the job. If you were a strong coder, you could build a great career. But with the rise of AI, my perspective has changed. A coding was never the job. The real job is engineering, problem-solving, understanding requirements, designing systems, and figuring out the best way to approach a problem. Today, AI can generate code faster and better than us. But building software is not as simple as saying, "Hey, build this app." Software development is an iterative process where you need to: 1. Define the problem(modules or sub-modules) 2. Design the solution 3. Provide business context 4. Choose the tech stack, cloud services, and database 5. Review outputs critically 6. Refine continuously These are decisions engineers make not AI and this is where an important gap appears. In traditional development, mistakes can be caught during reviews. You understand why something is wrong. But in vibe coding, you might not even realize that the AI made a mistake. A good software engineer doesn’t just write code they Identify issues, Understand errors, Guide the solution. When you are good at breaking down problems and debugging, you don’t just write better code — you give AI direction, making it easier for AI to fix errors and bugs. That’s why I call this approach AI-assisted coding, not just vibe coding. And here’s my take: Vibe coding alone rarely creates production-grade systems AI-assisted coding, guided by strong engineering thinking, can So, are software engineering jobs going away? I don’t think so. But the role is evolving. Where earlier a team might need 10 engineers, today it may need 1–2 highly skilled software engineers who can Think critically, Design systems, Guide AI effectively