Rini Vasan

💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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I sat down with Saurav and dropped the truth about why your tech startup should, or shouldn't, be doing cold email. For context, I've helped hundreds of startups with their outreach (portfolios from YC, FirstRound, Techstars, NFX, a16z, etc.). 1️⃣ Cold email is pretty much mandatory for early B2B startups You don't have capital for ads. You don't have time to build audience. You need those first customers, FAST, to tell you what features actually matter. But first, you need a sales process in place. Doesn't have to be perfect, but if you have no clue how to convert a lead into a customer, you're literally setting money on fire. 2️⃣ Cold email isn't just for lead gen Tech founders, how often do you contemplate which features to build without knowing if anyone actually gives a sh*t? How often do advisors tell you to "talk to your users" when you only have 3? Spend $300 on some domains and test your hypothesis directly. People will tell you. 3️⃣ Personalization math that nobody talks about Personalization won't save a broken campaign, but it can double conversion on winning campaigns. For those with A+ in high school AP calculus: personalization can 2x your lead flow... but 2x of zero is still unfortunately zero 😂 Full episode on Spotify (Outbound Wizard), but I'll leave the link in the comment 👇

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Maor Shlomo just vibe-coded his way to a $55M personal payout from Wix (and $25M for his team of 7). Three unconventional insights stood out to me: 1. Founder matters. Building $80M of value takes more than skill - it takes deep operational experience and sharp business intuition. Shlomo had both. 2. Distribution beats product. Today, building is easy - distribution is not. Solo founders who can rapidly attract users through creative distribution (after of course hitting PMF) will dominate exits. 3. Why not you? The barriers have never been lower, and the rewards never higher. If you're waiting for a sign to build, this is it. The landscape has shifted. Those who embrace this new paradigm can win the whole pot.

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A valuable skill at a public company can be devastating an an early-stage startup: David Rusenko (YC alum who sold Weebly to Squarespace for $300 million): “Focus only on the thing that gets you to that next stage. Don't focus on anything else.” Reid Hoffman (who sold LinkedIn to Microsoft for $26.2 billion): “There's a lot of things that go into building a company. You do not presolve problems.” Sam Altman (CEO of OpenAI): “A related trap is thinking about problems too far in the future—i.e. 'How are we going to do this at massive scale?' The answer is to figure it out when you get there.” https://lnkd.in/grnmrE-S

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YC is rewriting the startup origin story: graduate first, then build. This TechCrunch piece explains Y Combinator’s new Early Decision track, a formal path for students who want to start a company but finish college first. Applicants can apply in-school, get accepted and funded right away, and defer YC participation until after graduation. For example, a Fall 2025 applicant could graduate in Spring 2026 and join YC’s Summer 2026 batch. YC founder Jared Friedman notes it’s aimed at graduating seniors who want to launch a startup but also complete school. The move shifts the narrative away from “drop out to succeed” and toward a middle ground that broadens YC’s talent funnel, especially as college costs rise and more founders reassess the trade-offs of staying in school. The article highlights Spur’s Sneha Sivakumar and Anushka Nijhawan as a success story: they applied through Early Decision in 2023, graduated in 2024, joined Summer 2024, and have since raised $4.5M. It reflects YC’s broader effort to secure early-targeted talent in a competitive landscape, while acknowledging that many of the next decade’s top founders may choose both education and entrepreneurship. This shift also invites deeper reflection: normalizing entrepreneurship alongside graduation could diversify founder profiles, reduce undue pressure to abandon school, and encourage more deliberate, user-driven product development. It signals a maturation in founder outcomes thinking, where long-term success isn’t tied to a single rite of passage but to meaningful traction, mentorship, and market fit—even if that means finishing a degree first. What’s your take on YC’s middle-ground approach—dropout freedom without sacrificing a degree? Comment below. #YCombinator #Startups #Entrepreneurship #Education #Founders Y Combinator Spur Sneha Sivakumar Anushka Nijhawan Link to Article: https://lnkd.in/gDQgeHhX

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Building effective agents is hard. There are literally an infinite number of ways they can fail: - Poorly written prompts - Tool calls don't work - Agent makes the wrong plan - Any of the other hundreds of ways LLMs make mistakes. At Patronus AI we've solved this problem with Percival. It's a highly intelligent agent that sits on top of your telemetry and tells you where your agents are failing. Percival can 1/ Find errors. We've seen real customers go from 1hr to 1min per trace debug 2/ Help fix your agents. Every Percival analysis gives you actionable insights on how to change your prompt or tool interactions so you can actually spend your time building. The developer flow is super easy and only requires adding one line of code to your existing agent. Check out how it works in the video below! Full code examples + docs in the comments.

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Fixing Snapchat's Memories Export - Because Your Memories Deserve Better! Like many of you, I used Snapchat as my personal memory dump. But with Snapchat now charging subscription fees for storage over 5GB (and threatening to delete memories otherwise), I decided it was time to export everything. The problem? Snapchat's export process is broken. The downloaded files lose all their metadata - no capture times, no location data. Just files timestamped to when you downloaded them. So I spent a few hours building a tool that actually does the job right. What I built: ✅ Automated downloading from Snapchat's HTML export file with retry logic ✅ Smart handling of ZIP bundles that Snapchat creates for filtered/edited snaps ✅ Metadata restoration using ExifTool to preserve original capture times and GPS coordinates ✅ Filesystem timestamp updates so your photos sort correctly in Finder/Explorer The technical challenges were interesting: - Reverse-engineering Snapchat's JavaScript to determine GET vs POST requests for each memory - Extracting and parsing metadata from the HTML table structure - Writing proper EXIF/XMP tags that work across both images (.jpg, .png) and videos (.mp4) - Building a resumable download process for thousands of files But what really made this project meaningful was reading comments from people who had irreplaceable memories trapped in Snapchat, like photos of loved ones who had passed away. That gave me a sense of responsibility to make this tool work reliably. The result? I successfully downloaded and fixed metadata for ~5000 memories dating back to 2016. The entire pipeline is now open-source on GitHub for anyone facing the same frustration. This project reminded me why I love coding - using technical skills to solve real problems that affect real people. Plus, getting to "vibe code" with AI assistance turned what could have been days of work into just a few hours! Check out the full technical breakdown and the GitHub repository link on Medium: https://lnkd.in/g_Uym-p5 #Python #OpenSource #DataOwnership #ExifTool #Snapchat

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If you’re a student or early career professional looking to break into tech in the U.S., especially through Israeli startups, this is one resource you should have bookmarked: Israeli Mapped in NY. One of the hardest parts of breaking into tech isn’t talent. It's really about knowing where to look. This platform shows which Israeli startups actually have a U.S. presence, which ones are hiring, and where the community is active. That matters because companies with boots on the ground in the States are far more likely to hire locally, offer internships, or bring on junior talent in sales, ops, marketing, and support. For students, it’s also a shortcut to real world exposure. Events, meetups, and ecosystem activity give you a way to meet founders and operators without spamming LinkedIn or blindly applying online. One conversation at the right event can outperform 100 cold applications. If you’re trying to get your foot in the door in U.S. tech, this helps you be intentional: target the right companies, show up in the right rooms, and focus your energy where opportunities actually exist. Less noise. More leverage. Attaching link to website in comments.

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Entry-level SWE compensation at top tech companies tends to cluster into a pretty tight range. It's really when you go higher up in level, that compensation tends to have much wider bands. We pulled 2025 Levels.fyi data for entry-level SWE offers at companies like Meta, Google, and Microsoft, filtered to California and Washington to control for location. Looking at the 25th, median, and 75th percentiles, the middle of the market for most companies fits inside roughly a $20k window. What stands out isn’t that offers are identical, but that they’re constrained. Across top companies, the IQR usually lands around ~$15k–$25k. That tracks with reality: entry-level candidates have less negotiating leverage unless they bring competing offers. Some companies have tighter bands but higher overall pay, while others have wider bands with lower medians. In practice, the more important decision is often which company’s compensation distribution you’re in, not how wide the band itself is. The common belief is that entry-level comp is “fixed,” and relative to senior roles, that’s mostly true. The value of this data is putting a clearer number on that constraint. Once you control for location, most entry-level SWE offers at top tech companies live in a fairly narrow range, which shifts the question from “how much can this move?” to “where does this company’s distribution sit, and where am I landing within it?” If you’re evaluating offers right now, you can explore this data right on Levels.fyi. Toggle the location and view entry-level software engineer data points here: https://lnkd.in/gkNkaXdx

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I saw a tweet the other day calling out YC for “breeding the AI bubble.” The argument? That they’re funding strange startups, some chasing niches so narrow you wonder how a founder, let alone a college dropout, could ever reach product-market fit. And I’ll admit it… I resonated with that take. There have been some odd startups coming out of recent batches. But here’s the thing: YC is still one of the most valuable institutions the startup world has ever had. It’s not just an accelerator. It’s a school of thought. A filter. A network. Yes, some bets are weird. Some won’t pan out. But if you zoom out, so much of the tech we rely on today started out looking weird, small, and maybe even “pointless.” The lesson for founders isn’t to dunk on YC or its startups. It’s to remember: Weird ideas sometimes fail… and sometimes change the world. Product-market fit is rare, but YC maximizes the surface area of people trying. Having the right ecosystem matters, credibility, mentors, and capital are leverage. So, while the AI hype cycle is real, I think YC’s role is less about fueling a bubble and more about giving the next great founder their shot. After all, the weirdest bets often become the best ones.

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after working with hundreds of candidates at VibeScaling trying to place them at the top AI-native startups in SF/NYC, here are 5 unconventional tactics that we've seen work to stand out 👇🏻: 1️⃣ customer interviews tactic: when referrals fail, go directly to their customers - outbound 30-50 asking about pain points, turn 3-5 responses into a short analysis, and send it to the head of sales. Shows you can create value before you're hired while most people just send resumes and pray 2️⃣ the "not now" drip campaign tactic: when you get rejected for a specific reason, don't walk away - fix the objection and stay in touch. If they say you're "not technical enough," learn their space, use their product, and send updates on your progress. They often come back when timing changes, and you'll be top of mind as someone who actually takes feedback 3️⃣ the brand book tactic: hiring managers have tunnel vision and only see your recent experience. Create a portfolio with career highlights, peer quotes, outbound examples, and deal case studies to tell your full story. Helps you control the narrative and show skills beyond what's on your resume 4️⃣ proactive references tactic: instead of waiting for companies to backchannel you, have a strong reference reach out to hiring managers before your final rounds. Lets someone handle objections for you while most candidates just wait around. Being proactive with references gives you a huge edge 5️⃣the gratitude loop tactic: send thank you notes to everyone who helped - interviewers, referrers, advisors - regardless of outcome. People remember genuine appreciation, and it's a small tech world where today's "no" often becomes tomorrow's opportunity invest in relationships and stand out by doing what others won't wrote the full breakdown with templates in GTMBA 👇🏻

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Meta's new magic number is 49%. First it wrote a $14B check for 49% of Scale AI, poaching CEO Alex Wang. Now the same playbook hits venture land: Meta is offering to buy up to 49% of Nat Friedman & Daniel Gross's $1.1B NFDG fund - and hiring the duo to run its shiny new AI lab - letting early LPs cash out at a 4x markup while still calling the fund "independent". Antitrust alarms? Snoozing. All thanks to that cute little missing 1%.

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