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Pratyaksh Sharma reposted thisPratyaksh Sharma reposted thisWe are humbled to announce South Park Commons Fund III: $275M to support exceptional founders from day -1. Since 2016, we’ve had a simple thesis: greatness is more likely to emerge when high talent density meets high curiosity. That's why we focus on -1 to 0. Great founders shouldn’t fear fundraising, they should fear local maxima. Too many brilliant people spend years scaling the wrong hill. We give them the time, space, and support to pick the right mountain. What started around Ruchi Sanghvi's and my kitchen table is now a global institution in SF, NYC, and Bangalore. Members explore what they want to dedicate their lives to together, in person, across these hubs every day. It’s an incredible privilege to be a part of their journeys. Applications to SPC exploded last year. We don’t expect the trend to slow down and we remain committed to our core thesis of talent density. Fund III will help us better support the small group of incredible people we admit each year as members. Fund I is in the top 5% of its vintage. Fund II is surpassing Fund I, and Fund III is outpacing both. This is a testament to the 1,000 members who made SPC what it is over the last decade. They embraced -1 and built ambitious companies like Render, Baseten, Luma AI, Gamma & Goodfire. Thank you to the founders who have supported us from the earliest days: Mark Zuckerberg, Dylan Field, Mike Krieger, Elad Gil, Nate Blecharczyk, Cal Henderson & many others. And thank you to our LPs—endowments, foundations, & global institutions—for their trust and conviction. We live in an incredible era of opportunity to improve the world with technology. If you’re both ambitious and curious, pre-idea, mid-exploration, or ready to find the right mountain, you are why we raised Fund III. You are why we built SPC. Join us so we can help you turn the illegible into the inevitable.
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Pratyaksh Sharma reposted thisPratyaksh Sharma reposted thisHubSpot didn’t just buy a tool (Dashworks) they bought a truth engine. Revenue teams on HubSpot, this is how your day just changed. 1️⃣ Ask → Answer. Dashworks brings Slack, Google Drive, Notion, Jira (you name it) apps straight into HubSpot, so users simply ask and get the answer—no more doc-hunting. 2️⃣ Context over clutter. Retrieval-augmented LLMs reason over live data and return complete answers with citations, not blue links. 3️⃣ Agent-ready. AI can draft QBRs, spot churn risk, or enrich HubSpot objects automatically because Dashworks becomes their real-time truth engine. 4️⃣ Secure & instant. Search runs inside HubSpot’s SOC-2 perimeter, while Dashworks maintains its own SOC-2 posture—so setup takes minutes and data never leaves the platform. We all know that phrase, but it’s worth saying it again “With AI, garbage in, garbage out” Ready to help AI help you? #HubSpot #AI #data #contextisking
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Pratyaksh Sharma reposted thisPratyaksh Sharma reposted this🚨 HubSpot is acquiring Dashworks, an AI-powered workplace search tool. This deal is all about making Breeze Copilot smarter—by connecting it to unstructured data and giving it natural language search capabilities. Why it matters: ✔️ Sales teams will get deeper context on accounts ✔️ Marketers can pull brand guidelines on the fly ✔️ CX reps can search support content instantly Dashworks' team will join HubSpot's AI group to make this vision a reality. #AI #HubSpot #Acquisition #WorkplaceAI #B2BTech #SaaS #SalesEnablement #MarketingTechnology #CustomerExperience #ProductivityTools
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Pratyaksh Sharma reposted thisPratyaksh Sharma reposted thismarketing team: "where was that campaign brief again?" sales team: "what did that prospect say in our last call?" service team: "what was our solution again for that issue?" Dashworks has entered the chat 👀 BIG NEWS: HubSpot's acquiring Dashworks. Soon, Breeze Copilot will level up to give your GTM teams insights from your *entire* digital universe 🧡 Link in comments for more info
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Pratyaksh Sharma reposted thisI know this news of HubSpot acquiring Dashworks seems like it could be a big deal. That's because it is. Imagine AI powered not just by the power of your customer data in HubSpot, but your other organizational data as well. All beautifully unified in one place. Succeeding in AI is all about capitalizing on contextual data. This takes us a big step closer. A warm welcome to the Dashworks team. Look forward to working with you.Pratyaksh Sharma reposted this🚀 Exciting news! HubSpot is acquiring Dashworks to accelerate our vision of giving every GTM professional a truly intelligent AI assistant. Your AI is only as good as the data that powers it – and Dashworks + HubSpot takes us one step closer to our vision of unified data and AI that truly transforms your work. I've been passionate about solving the four major data challenges that hold businesses back: siloed systems that don't talk to each other, bad data that can't be trusted, trapped insights in unstructured sources (80% of your business information!), and access problems that keep the right people from seeing what they need. With Dashworks joining us: ✨ Breeze will search across your entire business, not just what's in your CRM 🔍 Ask questions naturally, get immediate answers ⚡ Turn hours of searching into seconds of finding Welcome to HubSpot, Prasad Kawthekar, Pratyaksh Sharma and team! 🧡 Learn more: https://lnkd.in/gAV6dj48 #HubSpot #AI #UnifiedData #DataStrategyHubSpot to acquire Dashworks to accelerate vision of giving every GTM team member an AI assistantHubSpot to acquire Dashworks to accelerate vision of giving every GTM team member an AI assistant
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Pratyaksh Sharma reposted thisPratyaksh Sharma reposted this🚀 Exciting news! HubSpot is acquiring Dashworks to accelerate our vision of giving every GTM professional a truly intelligent AI assistant. Your AI is only as good as the data that powers it – and Dashworks + HubSpot takes us one step closer to our vision of unified data and AI that truly transforms your work. I've been passionate about solving the four major data challenges that hold businesses back: siloed systems that don't talk to each other, bad data that can't be trusted, trapped insights in unstructured sources (80% of your business information!), and access problems that keep the right people from seeing what they need. With Dashworks joining us: ✨ Breeze will search across your entire business, not just what's in your CRM 🔍 Ask questions naturally, get immediate answers ⚡ Turn hours of searching into seconds of finding Welcome to HubSpot, Prasad Kawthekar, Pratyaksh Sharma and team! 🧡 Learn more: https://lnkd.in/gAV6dj48 #HubSpot #AI #UnifiedData #DataStrategyHubSpot to acquire Dashworks to accelerate vision of giving every GTM team member an AI assistantHubSpot to acquire Dashworks to accelerate vision of giving every GTM team member an AI assistant
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Pratyaksh Sharma reposted thisToday we announced that we've entered into an agreement to acquire Dashworks, accelerating our strategic vision to provide every go-to-market professional with an AI assistant. This acquisition strengthens our Breeze offering by adding deep search and reasoning capabilities across unstructured data sources, building on the success of Breeze Copilot. The Dashworks team brings valuable expertise that complements our AI product group as we continue enhancing our offerings for marketing, sales, and service professionals. We’re excited to welcome Prasad Kawthekar Pratyaksh Sharma, and the entire Dashworks team to HubSpot!Pratyaksh Sharma reposted thisEvery member of your go-to-market team will have an AI assistant in the future. This is our vision for Breeze Copilot, and HubSpot just entered into an agreement to acquire Dashworks to help us accelerate that vision. Prasad Kawthekar, Pratyaksh Sharma, and the Dashworks team have built an incredible AI semantic search system that connects to all of your data sources. They’ll join HubSpot to help us build this functionality right into Breeze. What’s most impressive about Dashworks is how simple it is to use. All the user has to do is ask the assistant a question, and it pulls from info scattered across documents, messages, tickets, KB articles, data from third-party apps…this used to take your teams hours of searching and Dashworks gives you the answer in seconds. When we talk about giving scaling businesses powerful AI that can help them transform, this is what we mean. We’re excited to welcome the Dashworks team to HubSpot soon and start building!
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Pratyaksh Sharma reposted thisPratyaksh Sharma reposted this📢 Big news: HubSpot announced an agreement to acquire Dashworks to accelerate our vision of building a truly intelligent AI assistant for every GTM professional. AI is only as powerful as the data it can access—and by combining Dashworks’ deep search and reasoning capabilities with HubSpot’s connected platform, we’re taking a huge leap toward unified data and AI that transforms how teams work. With this acquisition, we’re leveling up Breeze Copilot, Agents, and embedded AI features—unlocking insights across both structured and unstructured data sources. Here's what's coming: 🤖 Breeze will search across your entire business, not just your CRM 🗣️ Ask natural questions, get fast, accurate answers 💡 Turn hours of digging into seconds of discovery Breeze will pull context from across your tech stack and deliver clear, summarized answers—helping teams move faster, work smarter, and focus on what matters. Let’s build the future of intelligent GTM together 🚀 #HubSpot #HubSpotLife #AI #GTM #UnifiedData #DataStrategy #GrowBetterHubSpot to acquire Dashworks to accelerate vision of giving every GTM team member an AI assistantHubSpot to acquire Dashworks to accelerate vision of giving every GTM team member an AI assistant
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Pratyaksh Sharma reposted thisCongrats to my friends Prasad & Pratyaksh for successfully building up one of the most exciting AI companies and selling it to HubSpot. Proud to be an early investor and fan since the first round! 🙌🏻🚀Pratyaksh Sharma reposted thisThe exciting news: https://lnkd.in/gDCjpmgXDashworks Is Joining Forces With HubSpot | Dashworks AIDashworks Is Joining Forces With HubSpot | Dashworks AI
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Pratyaksh Sharma liked thisPratyaksh Sharma liked thisThrilled to be back on Stanford campus today kicking off MS&E 435: Economics of the AI Supercycle, a seminar unpacking the economics across every layer of the AI stack. Incredibly grateful to an amazing group of speakers dedicating their time for the community: Brad Gerstner (Altimeter) Ali Ghodsi (Databricks) Guillermo Rauch (Vercel) Sunny Madra (Groq / NVIDIA) Chase Lochmiller (Crusoe) Sachin Katti (OpenAI) Tuhin Srivastava (Baseten) Yash Patil (Applied Compute) Eric Kauderer-Abrams (Anthropic) 9 weeks. 9 speakers. 1 question: Where does value accrue in this new supercycle? Join us!
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Pratyaksh Sharma reacted on thisPratyaksh Sharma reacted on thisMercury entered payroll with acquisition of Central. Here's why: I remember an ADP rep coming to my office with a literal binder to set up payroll. A binder. That was the best option available to us in 2009. I’ve been doing startups since 2006. Over the last 20 years, I’ve watched the tools founders use to build get completely reinvented. But the financial back office? Still frozen in time. Today, Mercury has acquired Central, an AI-native payroll, benefits, and compliance platform for startups. Central’s bet: don’t sell founders software and leave them to figure it out. Have AI agents and human experts do the work for them. Nearly 500 startup customers. $175M in payroll processed. Payroll has been a missing piece for Mercury. Central is how we close that gap. Welcome to the team, Josh Wymer, Pranav Kashyap, and Nilay Modi. Mercury customers can sign up for Central now: centralhq.com. We will integrate Mercury and Central fully over the next few months. Full blog in the comments.
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Pratyaksh Sharma reacted on thisPratyaksh Sharma reacted on thisBig news. Excited to finally announce a new chapter - Persana AI is joining forces with Rox, a Sequoia Capital & General Catalyst backed company building enterprise revenue agents. 🚀 This is the announcement I never quite knew how to picture when I left LinkedIn to chase a crazy idea - but it's the one I'm most proud to make. My cofounder Rush Shahani and I packed up and headed to Y Combinator with a clear mission: power modern go-to-market teams with intelligence from the best data in the world. What followed was some of the most challenging, rewarding years of my life. Sitting in that YC room surrounded by some of the smartest founders, mentors, and group partners I'd ever met set the bar for how we built - with speed, conviction, and relentless customer obsession. We shipped 100+ data integrations. Built data provider partnerships from scratch. Rewrote, relaunched, and kept pushing. And the results spoke for themselves — 10M+ workflows powered, 100M+ pipeline generated and influenced and over tens of thousands of teams trusting Persana to find and close their best customers. When a casual conversation with the team turned into something neither of us planned we realized within minutes it was clear - Rox and Persana had independently arrived at the exact same belief: the future of GTM isn't more tools. It's intelligent systems that unify data, decision-making, and execution. By combining Rox's platform with the Persana AI team’s agentic GTM intelligence knowledge, we're accelerating a shared vision - a fully autonomous, AI-native system for go-to-market. 🚀 To our customers, investors and supporters 🤝 you trusted us before agentic workflows were obvious, before signal-driven GTM was even a category. You pushed us to build better, faster, and smarter. To the Persana team 💜 what I'm most proud of isn't just what we built, but how we built it. With deep customer obsession, creativity, and an unwavering belief in the future we were shaping. This next chapter is an opportunity to take that impact even further. Thank you to all of our team, supporters, investors, advisors, friends, family, and many more - We wouldn't be here without you- Rush Shahani Y Combinator Aaron Epstein Race Capital Edith Yeung Stage 2 Capital Sean Po Dharmesh Shah Zane Homsi Aditya Jhaveri Sathish Pottavathini Rohan Pathak Muhammad Jahid Ruhan Khandakar Abiola Makinde Prathamesh Gunde Rusty von Waldburg Can Timağur Ishan Mukherjee Diogo Ribeiro Shriram Sridharan Joshua Perk 👻 Maddie Bell ⚡️🗓️ Albert Garcia GibertSaharsh Agrawal Amrutha Gujjar Deb Pratiher Jonathan Festejo Lauri J. Moore Prakash Maram Bryant Lee Akshay Chalana Akshit Khurana Evis Drenova Rishabh Panwar and many more!! We're just getting started. Let's go. 🚀🚀🚀 #startups #ai #gtm #persana #ycombinator
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Pratyaksh Sharma reacted on thisPratyaksh Sharma reacted on thisWhat an incredible GAM. I write this as I head back to San Francisco, extremely energized. Three things stood out: • On a personal level, this one meant a lot. 18 months ago we were just stepping into this space. It was incredible to see customers who believed in us early and helped shape Midship into what it is today. Seeing familiar faces in person after so many Zoom calls was special. • Our session on the future of AI-enabled audit was a highlight. The room was engaged, the questions were sharp, and it’s clear teams want real measurable results. Hearing from real Midship users about their results resonated with everyone. • The industry is ready. Leaders are moving from curiosity to execution. The focus is on impact, not experimentation for its own sake. The appetite to modernize audit is real. - The team is growing. Public companies and Fortune 500 audit teams use Midship today. They are running live testing, seeing real ROI, and we now have public case studies that show the impact. See you all next year.
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Pratyaksh Sharma liked thisPratyaksh Sharma liked thisFulcrum 2x'd revenue in Q4 2025 and went from 8 to 25 people in 3 months. I don't have an elaborate onboarding handbook. I give every new hire the same 3 things, whether they're an SDR or a VP. 1. Ownership When someone joins Fulcrum, the first thing I give them is something real to own that is entirely theirs to figure out and deliver. Ownership changes how people show up. When something is completely your responsibility, you not only think about it differently, but you care about it differently. You wake up with ideas about it. You solve problems before anyone has to ask you to. It becomes a mission, not just a task. 2. Specific objectives Alongside ownership, I give very specific objectives. This basically eliminates confusion and ambiguity, which prevents someone from doing great work. They know exactly what they're working towards and exactly what success looks like. The more specific the target, the faster someone can move towards it. 3. Aggressive timelines The timeline is what makes everything real. At Fulcrum, timelines are aggressive by design. Urgency focuses people. It forces prioritization and brings out the best in smart people who already have the freedom to figure out how to hit a goal. Smart people with personal responsibility, clear goals and a deadline can move mountains.
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Pratyaksh Sharma liked thisPratyaksh Sharma liked thisLast week I attended Anthropic's inaugural Partner Summit. In classic AI fashion, Claude made my name tag… with the wrong title and some funky formatting 😵💫 I loved it. A perfect time capsule of where we are right now. Nobody, not the consultants, not even Anthropic, has everything fully figured out yet. We're all in the messy middle together. And with growth like this, it's no surprise: → Anthropic doubled their revenue in the first two months of 2026 → AI deals are closing at 2x the rate of traditional SaaS → Anthropic went from 12% to 40% enterprise market share in two years The pace is unlike anything I've seen before. And even so, not everything needs to be reinvented. Anthropic is making a big bet that like past platform shifts (Salesforce, Snowflake, Databricks etc.), Partners are the key to winning in Enterprise. The ecosystem, not just the technology, will determine who captures value — and the rate limiter is now strategy, not software. Turns out that even in a platform shift fundamentals still matter: relationships, trust and showing up in person for your partners.
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Tony Ndezwa
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Finding product-market fit isn’t a clean checkpoint — it’s a continuous, often chaotic process that can make or break a startup. At TechCrunch Disrupt 2025, leaders like Rajat Bhageria of Chef Robotics, Ann Bordetsky from NEA, and Murali Joshi of ICONIQ emphasize that it’s not about guessing what customers want, but about smart testing, real-time iteration, and deeply listening without getting overwhelmed by noise. The most striking takeaway is that product-market fit should be viewed as an ongoing journey rather than a milestone to tick off. This perspective shifts how founders approach growth: instead of waiting for a magic moment, they build, learn, adapt, and refine constantly. It also matters for investors who back startups navigating this messy terrain—recognizing signals of traction early while understanding the underlying effort required. Whether companies are prototyping or scaling, success hinges on solving real problems so customers can’t live without the product. How do you keep your teams grounded and focused through the unpredictable, often nonlinear path to product-market fit? What tactics or mindsets help you avoid noise and zero in on meaningful signals? #ProductMarketFit #StartupGrowth #TechCrunchDisrupt #FounderInsights
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Pamela Mishkin
OpenAI • 4K followers
I’ve been loving the new work coming out of Stanford’s Digital Economy Lab and The Budget Lab at Yale -- not because it shows dramatic change, but because it’s finally asking the right questions. The most honest conclusion we have right now about AI and labor is that we simply need more (and better) measurement and a readiness to support workers even before we have definitive answers. Going to spend the next few weeks sharing more here on my read on the work. Early shifts in AI-exposed jobs, especially at the entry level, are not proof that firms are “hiring AI instead of people.” Higher exposure predicts jobs where AI could plausibly reshape how firms think about junior work: 1/ jobs where most learning can happen off the job 2/ jobs where junior and senior work/tasks look pretty similar, with speed and polish as the main differences. Those are precisely the jobs where training data already exists -- where models can be trained if labs have access to the right data. So if those jobs are changing -- in number, scope, or quality -- it could reflect many forces. It might be delayed hiring, re-sequencing of skill development, or a rethinking of what early career work even is. It does not automatically follow that senior workers have suddenly become vastly more productive thanks to AI. This (correlation != causation) matters because it has real implications for how we design policy responses. If the symptom is that early career workers are struggling, then the right interventions should be tied to those workers -- new pathways, better training models, wage protections, smoother transitions -- rather than tied narrowly to AI -- subsidizing adoption, enforcing impact assessments, or chasing productivity metrics. If we assume the wrong mechanism, we will build the wrong response. The goal isn’t to respond to "AI." It’s to respond to what’s happening to workers. Bharat Chandar, Erik Brynjolfsson, Ruyu Chen, Martha Gimbel, Molly Kinder, Joshua Kendall, Madeline Lee
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Lakshmi Shankar
Together • 3K followers
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Luis Woldu
Hands • 262 followers
87% of YC companies are enterprise. Now it makes complete sense that AI is starting in enterprise. The models are expensive to run, and enterprise lets you automate workflows and vertically integrate. But to reach billions of people in their daily lives. The real prize is still consumer. A lot of founders are scared of consumer right now. At any point, OpenAI or Google can vertically integrate and kill your company. But you can make a similar argument for enterprise applications. The other part is that consumer is genuinely hard. Distribution is more mature, the App Store feels saturated, and there aren’t obvious new channels. At the same time, we are in the midst of a revolution. Interfacing with the world will be more than just a handful of chatbots. If you pay attention to daily life, to your parents, your kids, anyone close to you, you’ll notice how many small, annoying things still exist. Yes, the App Store is mature. But the top apps right now are AI apps. If something is truly great, innovative, or fun, people will go out of their way to find it. Menlo Ventures analyzed routine tasks of US adults. The Takeaway: Fewer than one in five people actually use AI for their day-to-day tasks. Most people stick with whatever system they already have, even if it’s annoying. At Hands, we're building with these 3 rules in mind: 1. Meet people where they already are 2. Make it easy to try. 3. Prove the product's value almost immediately
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Iftach Orr
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Mary Antony
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Building with LLMs has completely changed how we think about product velocity. A few years ago, shipping a production-ready model could take an entire quarter. Today, teams can do it in days. Sometimes even hours. Our head of AI, Ankit Arya shares what it’s been like leading AI at Inscope through this shift. The boost in engineering productivity, the power of new tools, and the sheer speed at which we can now build - it’s unlike anything we’ve seen before 🚀
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Aakash Bhatnagar
Second Axis • 2K followers
We took PMs in AI to New York for an evening of quick demos on the AI tools and workflows that are making PMs more effective. Nikita Kabra (PM at Walmart) showed how she uses Replit to prototype ideas and put something clickable in front of stakeholders instead of a PRD. Her takeaway: the conversation completely changes when people can see and interact with what you're proposing. Hashim Syed (AI GTM Lead Google) demoed Gemini Enterprise as a single workspace for everything a PM touches — presentations, research, strategy docs — all with AI built in. The common thread: AI isn't replacing PM judgment. It's removing the friction between having a good idea and getting your org to act on it. Thanks to everyone who came out. More events coming soon — drop a comment or DM if you want to stay in the loop. read more: https://lnkd.in/eFBer2JJ next event: https://luma.com/klwqbe4t Second Axis Kabir J. Jinal Thakkar #productmanagment
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Will Stewart
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Northflank is partnering again with Cerebral Valley, PyTorch, CoreWeave (and more) for the OpenEnv Hackathon in San Francisco, March 7–8. Participants will be building RL environments and post-training base models across 5 themes, solving the most pressing problems in RL and agentic orchestration. We'll be supporting teams in deploying their models through the Northflank platform with compute from our wonderful partners, CoreWeave (Matthew Lu, Jacob Feldman). $100K+ prize pool and and a stacked lineup of judges and mentors from Meta, Hugging Face, University of California, Berkeley, Cursor, Scale AI, and more. You can submit an application at the link in comments.
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Patrick Ellis
AI2 Incubator • 2K followers
Below are my 4 most impactful takeaways from a *really* fun panel I was on at Foundations as a part of Seattle Tech Week. Alongside Diego Oppenheimer, Yinhan Liu, and Kevin Coleman. We spoke to “Building and Scaling a GenAI Startup” with Aviel Ginzburg and Jacob Nibley bringing the fire (as always) as hosts! Very thankful to have been part of the fantastic discussions! Here are my main insights, for anyone scaling a GenAI startup: 1️⃣ The New “T-Shaped" Engineer: AI-Native and Truly Full-Stack. Engineers, PMs and Designers can now competently work across adjacent domains. With AI, a single person can take an idea end-to-end, leveraging AI agents to bridge their knowledge gaps. For example, using Deep Research to distill a checklist of SEO best practices, Claude Code + Playwright MCP to audit UI/UX design, and Claude Code + GitHub Actions to code review, auditing security and architecture. This end-to-end ownership eliminates meetings and costly communication overhead. It's the key to building a hyper-lean, high-impact team. 2️⃣ AI-Native Startups Are Building Agent Orchestration Frameworks. Context is everything. AI models, from CodeGen to business strategy, need context about your project/business/task. They need tools (e.g. MCP’s) to access data, take action, and run longer agentic loops. And they need clear validation frameworks to verify if their output is good. Having an effective orchestration layer allows anyone (and any agent) on your team build effective (and safe) AI-leveraged work. 80/20 action item: create a directory with markdown files encapsulating critical info (e.g. strategy docs, PRDs, system architecture, style guides, etc). And try a few new MCP’s (Playwright, Firecrawl, GitHub, and Notion are couple of my favorites). 3️⃣ Don’t Throw Away Your Prompts. As AI generates more of our output (code, marketing, strategy), the value shifts to the input - our prompts, context engineering, and orchestration layer. But yet, we treat them as temporary queries. Prompts are the new source code, and our source code is the new (ephemeral) binary, to borrow an analogy from Sean Grove from OpenAI. Just as we’d never throw out our source code, we should not throw out our prompts. Truly AI-native startups are investing heavily in an orchestration layer that allows the work put into crafting prompts, context, validators, and tooling to compound. Not starting from scratch each time. 4️⃣ Use "Vibe Coding" for Rapid Prototyping. The design-to-engineering handoff, with high fidelity design mocks, is obsolete. Empower your non-technical team to build quick prototypes using tools like v0, Bolt, or even Claude Code. This lets designers ‘iterate in the canvas of code,’ discovering real-world constraints without needing a single engineering meeting. And even more critically, to get feedback from key customers/partners before Engineering writes a line of code. Thanks for reading! I spoke further to these ideas in the YouTube video below 🙂
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Cuong Chi Le
The University of Texas at… • 1K followers
Excited to share that we’ve had another paper accepted to ICSE 2026: “TestWeaver: Execution-aware, Feedback-driven Regression Testing Generation with Large Language Models” 🎉 One big challenge with LLM-based test generation is the coverage plateau: after a few rounds, the model keeps producing “new” tests that still follow the same execution paths, because it can’t reliably reason about what actually happened at runtime. TestWeaver breaks that loop by giving the LLM the right execution context (not more prompt fluff): + Backward slicing to focus only on what truly influences an uncovered line + Retrieving the closest existing test (near-miss paths) to guide the next attempt + Inline execution annotations (key variable values along the executed path) so the LLM can make grounded changes that actually flip branches, instead of guessing Across 35 real-world Python projects (~100K LOC), TestWeaver avoids stagnation and improves coverage, reaching up to 68% line coverage and 62% combined line+branch coverage, faster and at lower cost than prior state-of-the-art LLM-based approaches. Read more about our paper here: https://lnkd.in/gJyaG-qC See you guys in Brazil 🇧🇷 #LLMs #TestGeneration #AutomatedSoftwareEngineering #ICSE2026
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Nils Matteson
AfterQuery Experts • 1K followers
Presented at the ML+X community meeting at UW-Madison today on deploying a retrieval-augmented generation (RAG) system on AWS Bedrock and evaluating LLM performance at scale. Our team took KohakuRAG, the #1 solution from the 2025 WSDM WattBot Challenge, and deployed it as a fully serverless pipeline on AWS Bedrock. I built the Bedrock integration, evaluation framework, and cost tracking infrastructure, then benchmarked 9 LLMs and 3 ensemble strategies across 282 questions to answer a practical question: which model should you actually deploy in production? A few results that stood out... - Ensemble majority voting (0.840) outperformed every individual model. - Llama 4 Maverick delivered 98% of the top score at a fraction of the cost and latency. - Model behavior matters as much as raw accuracy; our highest-citation model finished last overall due to aggressive refusal behavior. - Text-only embeddings create a ceiling on figure-based questions that no LLM can overcome. The full recording will be available on ML+X Nexus: (https://lnkd.in/gSTe_EdW) soon. I would definitely recommend watching the whole thing if you are interested in deploying or learning more about RAG applications! Update: Now live at https://lnkd.in/gv7_jHkv Thank you to Christopher Endemann for the mentorship and for bringing me onto this project; I learned more about applied ML infrastructure than I have in any course in just a few months. Thank you to Blaise Manga Enuh, Ph.D. for the collaboration on the local deployment pipeline.
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Matthew Montañez
Fullsend Technology • 724 followers
Last week, YC's Startup School posted Andrew Ng's presentation about how to build faster with AI. The conversation online about his talk tends to focus on the existential debate around artificial intelligence. But the most powerful insight in his presentation has nothing to do with AI. Here's the insight: Concrete ideas buy you speed. Why? Abstract ideas are almost never wrong; they don't have enough detail to be proven right or wrong and they are impractical/impossible to test. Concrete ideas are much easier to experiment on and validate. Here's an example that Andrew shares: ❌ Not concrete: AI to optimize healthcare assets ✅ Concrete: Software for hospitals to let patients book MRI machine slots online to optimize usage The first idea is ambiguous and can lead teams to work in many different directions without any guidance on whether or not they are building a good, useful technology. The second idea is specific and therefore can be built, tested, and validated quickly. This insight might sound obvious but in practice it's very difficult to implement for three reasons: 1️⃣ Big, abstract ideas tend to be the ones that sound most exciting, get the most attention, and are helpful for raising capital. 2️⃣ When you're super-specific about a startup idea, it can feel like you're sacrificing all of the potential of a bigger idea, a bigger market to serve, and the biggest impact you might have. 3️⃣ Being proven wrong is uncomfortable, and continuously testing concrete ideas creates many more opportunities to be humbled. I've found in practice that startup founders need both skills: (1) the ability create a vision of the future that motivates themselves and others to put in the work and sacrifice to actually produce a big impact, and (2) the ability to be super specific about each step forward so that they can execute effectively towards that vision. https://lnkd.in/dakNMePR
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Nithik Yekollu
Rovr (YC W26) • 4K followers
Over the last year, I made the decision to turn down a return offer at Google, a full ride to an Ivy League program, and my role at Mercor, one of the fastest growing AI startups, to focus entirely on building Rovr (YC W26). Working as a Forward Deployed Engineer showed me a simple truth. AI does not fail because the models are weak. It fails because the delivery around the models is slow, fragmented, and operationally heavy. The companies winning in AI today resemble the early days of Salesforce, ServiceNow, and Workday. Success comes from strong integrations, hands-on workflows, and engineering teams embedded directly with customers. These teams carry the real weight of AI transformation. Forward Deployed Engineers, solutions engineers, onboarding leads, and implementation specialists are overloaded with context switching, redundant operational work, and alignment issues that slow deployments down and limit impact. Rovr (YC W26) is built to address this. We are creating an intelligence layer that supports the services-led side of AI by improving discovery, cleaning up handoffs, strengthening alignment, and reducing the operational burden that holds teams back. If your team works in forward-deployed engineering, applied AI, solutions engineering, enterprise onboarding, or implementation, I would be happy to connect and exchange insights. Calendar link: https://lnkd.in/gND-u48y
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Saurabh Prabhuzantye
Kloop AI • 7K followers
This is both a very interesting and a worrying take for early stage startups. As someone who is building in the AI GTM space, I have been concerned about smarter GTM teams building agents for their use cases rather than buying from startups like us with what Hemant calls internally deployed engineers. Over a 10 year period, this could become a real possibility but the reality today is very different and probably much less exciting. 1. The current organizational incentive system encourages engineers to work on core product problems rather than internal problem statements. 2. Hacking a new system for an internal GTM team may sound exciting but maintaining that code and ensuring those agents work well at scale, over a period of time is a different ball game altogether. 3. Can you design an evolving and improving system with best in class features with internal deployed engineers? Will the same engineers add new features? I almost feel like this would work for a really small percentage of companies, teams and engineers unless there is a systemic change across the board. Having said that, I definitely understand Hemant Mohapatra's pov that this has been the best ROI for AI in a short time frame with adoption.
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Erik Miehling
IBM • 1K followers
We’re happy to announce our workshop, Foundations of Agentic Systems Theory (FAST), as a part of AAAI 2026. We aim to bring together researchers from a variety of fields (notably beyond engineering and computer science) to build our understanding of *why* agentic AI systems work (or do not work). We will explore how existing mechanisms of emergent behavior carry over to systems of LLM-based agents, the properties of agents that facilitate this emergence, and the degree to which we can control/induce desirable system-wide outcomes. If this sounds relevant to your current research, we invite you to submit your work: https://lnkd.in/eQeBkHzJ Chenchen Ye, Atoosa Kasirzadeh, Djallel Bouneffouf, Anne Arzberger
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Ahmed Abdelhadi Metwally
Google • 13K followers
Excited to share a new blog post on our "Scalable Framework for Evaluating Health LLMs" 🚀 (https://lnkd.in/gzhuvTfN) Daniel McDuff and I summarize how this novel framework addresses key challenges in evaluating AI models to health queries, especially when user query includes data from wearables, blood biomarkers, and/or user-provided historical health data. This framework is designed to: (1) Significantly reduce evaluation time. (2) Improve inter-rater reliability. (3) Precisely identify quality gaps in LLM responses. A huge thanks to our incredible team, and a special shout-out to my former intern, Neil Mallinar, who was the main driving force behind this project. Great contributions from: Ali Heydari, PhD, Xin Liu, Tony Faranesh, Brent Winslow, Nova H., Benjamin Graef, Cathy Speed, Mark Malhotra, Shwetak Patel, and Xavi Prieto. #AI #LLM #evaluation #health #wearables #biomarkers
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Abraham A.
BookSwapNorway • 1K followers
🧠 AI Rights Historical Wrongs Researchers at Stanford and Princeton have fine-tuned a large language model to uncover racially discriminatory housing clauses buried in millions of historical property deeds — accelerating a process that could have taken a decade by hand. 📍 The problem: Many property deeds in Northern California — especially in Santa Clara County — still contain illegal racial covenants from as far back as the 1850s, banning people of color from owning or living in certain homes. 🔎 The solution: The team used OCR to digitize 5.2 million deed pages, then fine-tuned Mistral-7B via LoRA on a hand-labeled dataset of discriminatory phrases. The model learned to identify harmful language in context, avoiding false positives like the surnames "Black" or "White." 📊 Results: Detected 24,500 lots with racial clauses (~25% of homes in 1950) Found that 10 developers were responsible for 1/3 of all discriminatory clauses Model reviewed all pages in 6 days at a cost of $258 Equivalent manual effort: 10 years, $1.4M 💥 This work doesn't just use LLMs to build the future — it uses them to understand and repair the past. The open-sourced model now enables other U.S. counties to follow suit. #AI #MachineLearning #LLM #ResponsibleAI #Stanford #Princeton #HistoryMeetsAI #TechForGood #EthicalAI #SocialImpact #PropertyTech https://lnkd.in/dtec-zbx
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Ameer Haj Ali, PhD
UniversalAGI • 8K followers
🎯 Just fired my conversation with Ion Stoica - the legendary Berkeley professor and co-founder of $62B Databricks and Anyscale. After working with Ion as my PhD advisor, manager, and company advisor, I finally got him to share the patterns behind his success and what's coming next in AI. Key insights that stood out: 🔸 "Execution is everything" - Even the best ideas fail without proper execution. The only way to know if your idea is good is to execute it well first. 🔸 The card game analogy - "You're dealt cards you can't change. Everyone gets unlucky sometimes. Focus on playing your hand optimally vs complaining about luck." This mindset shift changed how I approach every challenge. 🔸 Why China might win the AI race - They have the talent, data, and increasingly the infrastructure. Plus better collaboration between academia and industry. 🔸 The next big bet - Vertical integration across the AI stack. Just like a Formula 1 car, optimizes everything together, AI systems will need tight integration from hardware to application. 🔸 Building reliable AI - "There's no silver bullet. You need precise specifications and verification - just like good management." 🔸 How AI wrappers can differentiate - "It's like early internet days - everyone's building apps. Winners will be determined by business model innovation, not just tech. Find better alignment between your costs and customer value." 🔸 Building a $62B company - Databricks succeeded by: betting on cloud (versus on-premise), focusing on data scientists when few existed, execution, and timing major secular trends perfectly. For young AI founders: Ion's advice is to bet on vertical integration, reliability, and business model innovation. What resonated most with you? Timestamps in comments ⬇️ Full conversation: https://lnkd.in/gK3fQRgb #AI #Startups #TechLeadership #ArtificialIntelligence #Berkeley #Databricks
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