Prasad Shinde

🧠 How JFrog ML Is Transforming the AI Software Supply Chain AI is becoming core to software delivery, but most ML pipelines remain fragmented, insecure and hard to govern. JFrog ML extends JFrog’s Software Supply Chain Platform to unify DevOps, DevSecOps and MLOps all in one secure enterprise-grade ecosystem. JFrog ML delivers:  ✅ End-to-end model lifecycle management ✅ Built-in versioning, metadata and promotion workflows ✅ Continuous security scanning of AI models and dependencies ✅ Seamless CI/CD integration for ML artifacts ✅ Multi-cloud and hybrid support with full traceability As AI scales, so do the risks. JFrog ML helps teams manage models with the same trust, traceability and automation as software, at an enterprise scale. 👉 Read the full blog here: https://lnkd.in/gkPGEfnP #MLOps #DevSecOps #SoftwareSupplyChain #AI #JFrog

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When you join a new org or switch teams in the same, it is quite an unsettling and borderline anxious experience. Here are a few things that I did to ramp up faster. 1. I consciously made an effort to remain unblocked 2. I read a ton of code and its history, sometimes even unrelated 3. I asked questions about the past, the present, and the future 4. I took up relatively mundane tasks - cross-team work, tests, and docs 5. I extended a helping hand in whatever capacity I could 6. I proactively met skip-level leaders and managers to understand the vision Some companies do have a culture of pairing up with someone existing in the team, but even if you do not get a mentor, it is important that you still navigate the situation and ramp up as quickly as you can. The above list is not exhaustive, by any means, and hence you can always add things that you find helpful in your context. but the lowest common denominator is to show extreme intent and interest to get output and drive outcomes. Hope this helps.

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As a founder who want to be in the US for building my company, this is what it meant there is insane panic over the h1b issue, here are the updated facts you should know about - this only applies to the new applications, so if you already in h1b , you are good to go and your renewal will not be changed to 100k, it is the old pricing - if you are already a student studing with f1 visa with otp, you are under old pricing -this fee increase is not annual but is charged per approved petition. - for people who change jobs with transfer, you get the previous pricing, even if you re enter the us , there is no possible change - but if you are coming to study after september 21, aka today, when your employer applies h1b, you are in the new slab , paying 100k usd - same with people coming over directly via h1b and starting a new h1b in the usa from a new employer tldr: people already in the us, either in job or student , there is nothing to worry about, as you are in the old slab for new ones, it is hard as it gets what about founders and startups who want to be in SF for building in AI bootstraping - i want to self fund my h1b , but as a new application, we need to pay the 100k - the better one is O1, which is hard to get, and need to show abilities and funding for the startup - L1, but there is significant infra and process needed for it - or give a 800k - million dollars for EB 5 the only option i see here is get biz upto a million or more, and self sponser this might work for established players, but for startups , if there is a better option, that will help us to build

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$100K H-1B visa fee: A challenge or an opportunity for India? With the recent order imposing a $100,000 fee on H-1B visas, many predict tough times for Indian tech talent in the US. But here’s another way to see it — this could accelerate a shift we were already witnessing: global companies investing more in India. We already have a strong talent pipeline and competitive salaries. The next step is clear — we need to create the right environment and culture to innovate. That means: - Expanding beyond metros into Tier-2 cities. - Building ecosystems where engineers don’t just deliver, but invent. - Solving infrastructure and workplace challenges that hold back creativity. - Taking the best from Western corporate culture (clarity, meritocracy, ownership) and blending it with India’s sense of belonging, community, and “individual first → team first” mindset. This is not just about support work moving here. It’s about India stepping into a leadership role in global innovation. I truly believe — this century belongs to India. 💡 What do you think — is this policy a setback, or a catalyst for India to lead? 🔗 Source: https://lnkd.in/d6DFUQut #H1B #TechInIndia #Innovation #Bharat

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This $100,000 H-1B fee isn’t just an immigration hurdle, it’s an economic blackhole. For the U.S., it risks slowing down innovation, raising hiring costs, and weakening its position as the global magnet for talent. For India, it could flip the long-running brain drain into a brain gain, as more skilled professionals stay back or return to build here. In the long run, Canada, the UK, and Europe may benefit most.

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Treating an Internal Developer Platform (IDP) like a real product is the key to scaling developer experience🔑 Join Ninad Desai & Ruturaj Kadikar at #KubeCon to learn how to design IDPs that scale with your teams without slowing them down👇 https://lnkd.in/g8UprYSR #KCIN25

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The FAANG → startup gap is widening. When I was at Uber, we could still reliably hire great engineers out of FAANG. The velocity difference existed, but it was bridgeable. Now? For a seed or Series A team, the ramp is often too steep: ambiguity everywhere no guardrails shipping is the strategy “ownership” means end-to-end, not a slice Here’s what I think happens next as big tech layoffs continue: If you’re hiring: expect more noise. A larger pool of “available” talent won’t automatically translate to startup-ready talent. Your filter gets harder: slope > pedigree. If you’re a FAANG engineer: if you want risk, speed, and real ownership, jump earlier. After ~3+ years in a highly structured environment, the pivot gets meaningfully harder. Not because you’re not capable, but because the operating system is different. And I’m seeing a pattern: A lot of FAANG engineers want to move faster… But many early-stage startups aren’t prioritizing candidates who’ve spent 5+ years in big tech. Not a knock. Just a market reality. Curious if others are seeing the same thing: What’s your “threshold” where the FAANG → seed jump gets tough? 2 years? 4 years? More?

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When Steve Huynh was a principal engineer at Amazon, meetings began with a "study hall." Amazon had a "reading culture" even among engineers, Huynh recently told the Pragmatic Engineer podcast, speaking of his time at the tech giant. Employees frequently drafted six-page memos, which they shared with the company to update progress and demonstrate new projects. Huynh, who said the company's embrace of writing and reading the memos was part of its "secret sauce," said employees' writing was often constrained to the format, whether it was a business strategy or press release. Huynh started at Amazon in 2006, only a few years after the company turned its first profit and while Jeff Bezos was at the helm. Read more about how Bezos instilled this culture of memo-writing from the top down on Business Insider: https://lnkd.in/gBqbEf_j (Credit: Daniel Berman) #amazon #reading #jeffbezos

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If you're a Senior Engineer looking to move up, the next role will likely be a Staff Engineer. And in this guide, Shruti shares tips from her own experience of getting promoted to Staff Engineer at PayPal and Slack. She talks about what Staff Engineers do (and how it's different from Seniors), why you might not be getting promoted, and how to take that next step. https://lnkd.in/g58dnFEG

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Random thought from living in Europe Self driving cars are a solution for cities being built poorly built for people -- it's like adding a proxy on a system full of tech debt to patch a problem but ignore the root cause. The interesting part here isn't that much that US cities are built for cars and not people, but more that as a software engineer, you should be aware of decisions you can't easily reverse -- and carefully weight the pros and cons of the different solutions

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GenAI, Transformers & Their Real-World Relevance Transformers, initially crafted for language translation, have silently evolved into the foundation of today's AI advancements. From decision making to content creation, their presence is ubiquitous. However, the true significance lies in the thoughtful application of this transformative power. GenAI has transcended beyond mere demonstrations and research. It is reshaping: - Customer experiences through personalized interactions - Operational strategies via intelligent automation - Knowledge tasks with tools that enhance human cognition scalability The crux lies in harnessing intelligence for tangible outcomes - amplifying intuition, streamlining decision-making, and hastening insights. I'm intrigued to learn: What recent GenAI application has resonated with you the most? Secondly, How many technologies do you know that ended up revolutionizing far beyond their original purpose? #GenAI #Transformers #AppliedAI #EngineeringLeadership #Innovation #ThinkBig #HVSays

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I’ve interviewed to 500+ candidates combined in the last 7 years of my journey at Amazon, Cars24, and now Nielsen…(150+ in the last 1 year alone) This is the best guide I can give you on how to pass technical interviews and what 90% of candidates often forget while interviewing. (Btw, I also used this to switch jobs 3 times in my career) [1] Don’t rush to code, talk through the problem first Many candidates fail because they panic and try to impress with fast coding but the right move is to slow down and talk out loud. Break the problem into conditions, use a whiteboard or pseudocode if needed. Even a simple question becomes easy once you write the logic in plain English. [2] Think before you type Take your time to understand the requirements before you touch the keyboard. Rushing leads to bugs. Thoughtful implementation shows maturity. Even if you know the answer, show structured thinking and clear decision-making. [3] Always explain your choices, even if they’re wrong Interviews are less about the correct answer and more about your thought process. Prefer saying something logical than staying silent. For example: “I’m using a regular for loop here because it gives me better control.” [4] Practice talking while solving Most people freeze not because they lack skill  but because they don’t rehearse speaking + thinking. Get used to explaining your logic out loud while coding. Follow the rule: ABC – Always Be Chatting (not just coding). [5] Don’t sit in silence when you're stuck Silence kills momentum. If you get stuck, ask questions: “Should I print this line-by-line or return a result array?” Showing that you can stay calm under pressure matters more than getting it perfect. [6] Prefer readable, scalable solutions over clever tricks Avoid language-specific shortcuts (e.g., JS type coercion). Use modulo instead of overcomplicating divisibility logic. Interviewers prefer clean, maintainable code that works across languages. [7] Optimize code for simplicity and future changes If new conditions are added (like printing something for multiples of 7), your code should be adaptable. Use a variable-based solution instead of nested `if-else` trees. Think like an engineer, not just a coder. [8] Know your basics (like Big-O) Be ready to talk about time complexity. Even if the problem is trivial, say something like: “We’re iterating 100 times, so technically O(n), but since it's a fixed 100, it simplifies to O(1).” Technical interviews are not just a test of memory or speed. They’re also a test of clarity under pressure, structured thinking, and communication.

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