Nikhil Kumar Chandra

H1B Visa fees 100,000 Dollar. Time to awake indians. No other way then to get out of delusion of development. We should focus more on economy then cultural activities going on now a days. This visa row is indication of following 1. AI is really on the way to destruct and change IT sector forever. 2. Jobs placements will be lower then expected. 3. Even if this visa fees is reduced then also there will be an uncertainty on the AI changing the world order. 4. Narratives build that now india will be great again, is false i think. 5. Only way to avoid heavy losses is to truly invest in AI research.

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A $100k fee on new H-1B visas. AI squeezing billables. Private equity rolling up agencies. The “big consulting” landscape is being reshaped in real time. For many, this means thinner margins, shifting talent strategies, and pressure to consolidate. For us at Teifi, it’s another signal that our approach is the right one. Bootstrapped from day one, no outside capital dictating our moves. Competing daily with the biggest firms, while staying focused on innovation and excellence. Solving unified commerce at scale on Shopify, where adaptability matters more than size. The real question isn’t whether the age of big consulting is over. It’s who can deliver value when the old playbooks no longer work. We’re clear on our answer.

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Rcently I wrote about how AI agents are fundamentally different from LLMs in their capabilities and what they are purpose-built for. Here is the most dramatic proof of that shift happening right now. A developer I know with experience of building Java and frontend bagged a project of complete solution deployment (think of an end to end tool) from an SME by having a conversation in simple english in Cursor and iterating on the output within a day. Prototyping that would have taken more people and more time with different areas of expertise happened in a day by just one person with one subject area of expertise Total spend: $60 in API costs and subscription to bag a project of much bigger scale Andrej Karpathy coined a term for this: vibe coding. You describe the what, the agent handles the how. The code is a byproduct you never look at. You only care about outcomes. This works because tools like Cursor and Claude Code are not LLMs suggesting the next line. They are agents that understand your entire project, make decisions about file structure, write across multiple files, and test their own work. The distinction I wrote about in my last post, in action. A car built using the same engines that you are using in your ChatGPT conversations but for the specific purpose of Coding and Software Development Now here comes the controversial part - vibe coding makes us lless smart, kills our problem solving capability, and mostly generates garbage output. My Opinion - Vibe coding does not kill the engineering role. It redefines it. The skill moves from writing code to orchestrating agents that write code. Just like a Tennis Player although being athelete will not be able to hit sixes when made to hold a cricket bat right? Its the field of sports, it requires being true athlete and somehow similar also in the skills required (atleast compared to Football or curling maybe) But still it needs different set of practice and different skill development. We still need the expertise to make use of these tools, but the way we get to outcomes are not gonna be the same any more. One senior engineer with these tools now produces what used to need a team of three or four juniors. The hiring playbook has not caught up to this yet. But it will. Any comments from the Hiring Managers? What trends do they see? #VibeCoding #AIAgents #AITools #ProductManagement #StartupLife

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Pro tip to get a Referral for your dream job through LinkedIn 👇 NEVER Send a cold DM to the person who can refer you, asking them to "Let YOU know in case they have a relevant opening based on YOUR profile." I repeat NEVER You are searching for a job - YOU need to find relevant positions and let the person KNOW where they should refer you This is the #1 mistake that people make #linkedin #job #jobsearch #tip

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Quantization in Large Language Models (LLMs) As LLMs scale, intelligence is no longer the bottleneck — memory, latency, and cost are. Quantization turns out to be one of the most powerful (and misunderstood) techniques enabling real-world deployment of large models. In this blog, I break down: • Why LLMs are surprisingly quantization-friendly • INT8 vs INT4 vs NF4 — what actually works in practice • How modern architectures absorb low-bit noise • Where quantization fails and how to design around it Quantization is no longer an optimization step — it’s foundational infrastructure for production LLM systems. Link: https://lnkd.in/gqGje75G #LLMs #AIEngineering #Quantization #MachineLearning #GenAI #MLOps

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How I Made Decisions at Amazon In 5.5 years at Amazon, I made thousands of decisions. Some were small. Scope tweaks. Deadline shifts. Meeting structure changes. Some were big. Launch calls. Escalations. Resource tradeoffs. Risk acceptance. And here’s what I learned: Indecision is usually more expensive than a wrong decision. Amazon forced me to build a decision framework…not rely on instinct alone. Here’s how I approached it: 1/ I asked: Is this a one-way door or a two-way door? ↳ A one-way door decision is hard to reverse ↳ Long-term architectural changes ↳ Big resourcing bets ↳ Major customer commitments These required deep dive, multiple perspectives, and slower motion. ↳ A two-way door decision is reversible ↳ Pilot launches ↳ Process changes ↳ Messaging experiments These required speed. Most people treat two-way doors like one-way doors. That’s how velocity dies. 2/ I defined the real risk before acting ↳ What’s the worst-case outcome? ↳ How recoverable is it? ↳ What’s the cost of waiting? Often the cost of delay was greater than the cost of being imperfect 3/ I gathered just enough signal…not perfect clarity ↳ 70% confidence was often enough ↳ The remaining 30% came from moving ↳ Over-analysis was a hidden tax on momentum 4/ I wrote my thinking down ↳ Framing the decision in writing exposed weak assumptions ↳ It also made it easier for others to pressure test it ↳ Writing sharpened judgment 5/ I owned the outcome…even when it missed ↳ If a decision didn’t land, I didn’t blame the inputs ↳ I asked: What did I miss? What pattern did I ignore? ↳ Decisions compound…so does accountability The biggest shift Amazon gave me was this: Decisiveness is a leadership trait. But structured decisiveness is a competitive advantage. 📬 I write weekly about clarity, ownership, and high-stakes execution in The Weekly Sync: 👉 https://lnkd.in/e6qAwEFc What’s one decision you’ve been overthinking that might actually be a two-way door?

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