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Articles by Ashu
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You’re earlier than you think
You’re earlier than you think
Advice for those graduating into the world AI is shaping. It’s commencement season, and it’s a strange world to be…
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22 Comments -
Software, meet your new user: agentsMay 6, 2026
Software, meet your new user: agents
Reflecting on where we stand in AI, six months into the agentic age The difference between the models we had in…
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7 Comments -
Google's 20-year secret is now available to every enterpriseApr 2, 2026
Google's 20-year secret is now available to every enterprise
Why decision traces will reshape B2B the way behavioral data reshaped B2C Regular readers know that we've been building…
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12 Comments -
What it takes to win enterprise AI dealsMar 28, 2026
What it takes to win enterprise AI deals
I share what founders need to hear from the people who make the buying decisions. Where are we, really, in the…
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10 Comments -
Taking stock of the SaaSpocalypseFeb 28, 2026
Taking stock of the SaaSpocalypse
I unpack what this month’s software selloff is pricing in, and who stands to win as the seat model breaks. It seems…
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23 Comments -
Context graphs, one month inJan 31, 2026
Context graphs, one month in
What we've learned since the essay went viral A month ago, Jaya and I published our p.o.
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35 Comments -
Where AI is headed in 2026Dec 30, 2025
Where AI is headed in 2026
I grade my 2025 predictions and make 8 new ones. Last year, I published my first set of AI predictions.
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36 Comments -
AI’s trillion-dollar opportunity: Context graphsDec 24, 2025
AI’s trillion-dollar opportunity: Context graphs
Written by Jaya Gupta and Ashu Garg The last generation of enterprise software created a trillion-dollar ecosystem by…
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71 Comments -
Narrative power in the AI eraNov 26, 2025
Narrative power in the AI era
AI is both a technology and a story we’re telling I’ve been working in AI since 2006. Back then, we were using it to…
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What it takes to build AI agents that actually workNov 1, 2025
What it takes to build AI agents that actually work
Getting to 80% takes 20% of the effort. Getting to 99.
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23 Comments
Activity
43K followers
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Ashu Garg posted thisThe idea that you can map out your life's work at 22 is a fiction. When I left IIT, I had never met a tech entrepreneur, and I had no idea what venture capital was. I found my way to it by doing. The jobs you'll hold in 5 years, let alone 10 or 20, likely doesn’t exist yet. Even people in stable professions today will have to keep reinventing what they do. A helpful way to think about your career is as a prototype: something you build in order to learn from, rather than something you have to get right on the first try. The best teacher is contact with reality and the feedback only it can give you. Your prototype can start out small. You take a job, and you begin to notice things: what energizes you, what drains you, the skills you pick up faster than the people around you, and the projects you pour far more into than they demand, simply because you love the work. A sense of purpose rarely arrives as a grand, all-consuming mission, fully shaped and waiting for you at 22. More often it builds from small, repeated moves toward what interests you, the kind that add up over time into something far larger than you could have planned. Put otherwise, purpose is often a verb long before it becomes a noun. The best way to discover what you're meant to do is to start, and keep adjusting as you go.
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Ashu Garg posted thisMany founders start with a shiny new technology and go looking for a problem it can solve. The ones who build companies that last do the opposite. They start with a problem they understand deeply and reach for whatever tools, new and old, best solve it. This discipline matters more now than ever, because the leverage available to one person has never been higher. We're entering an era where 10 people can do what used to take 10,000. The more any one person can do, the more it matters what they choose to do with it. The obvious thing to do with your leverage is spend it on volume: ship more code, launch more features, send more outreach, generate more of everything. That confuses speed with velocity. Speed is pure motion. It's seductive because it's the easiest thing to measure and the easiest to mistake for progress. Velocity is motion with direction. It reveals itself over time, in whether your motion brought you closer to a goal worth reaching. A startup sprinting in 10 directions at once can feel enormously productive while going nowhere. To win the race, you have to run quickly *and* in the right direction. One of the biggest jobs of an early-stage founder is figuring out which direction that is. The best thing to spend your leverage on is ambition, on projects you would never have had the resources, expertise, or confidence to attempt before. But ambition needs a direction too. The right one is a problem that genuinely matters to you: one whose solution would mean something to a few people you know, and ideally to many. That’s where the best founders begin: with the problem itself, before any particular tool. The tools change by the week, but the problems worth solving change at human pace.
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Ashu Garg shared thisMany graduates I talk to are asking some version of the same question: is the game over? It’s a fair fear. AI writes 75% of new code at Google and north of 90% at Anthropic. The on-ramps that used to look safe - first-year tracks in banking, consulting, law, and programming - no longer do. Computer science enrollment just posted its steepest drop on record. But the frontier always looks claimed to the people standing closest to it. Consider the class of 2002. The dot-com bust had just happened, and Yahoo, AOL, eBay, and Amazon looked like they had absorbed all the available oxygen. The smart move was to go to business school or get a “real job” in finance. Then came Facebook in 2004, YouTube in 2005, the iPhone in 2007, and Airbnb, Uber, Instagram, Stripe, and Databricks soon after. Most of the companies that defined the internet for two decades were built by people who, in 2002, were told they were too late. Twenty years from now, people will look back on this stretch of AI the way we look back on dial-up. You are earlier than you think. More on what that means for the class of 2026: https://lnkd.in/gdEixXjEYou’re earlier than you think - Foundation CapitalYou’re earlier than you think - Foundation Capital
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Ashu Garg reposted thisPlaying it safe is itself a bet, and usually a worse one than it looks. This matters most if you just graduated, because the math tilts hardest toward taking risks in the years right after school. Loss aversion is a powerful force, but over the course of a career, risk and return are tightly correlated. Consistently choosing the higher-upside path is one of the biggest determinants of how far you will go. What you want to look for is convexity: risks whose downside is capped and whose upside is unbounded. Joining a startup that folds often teaches you more than it costs. Sending a thoughtful email to someone you admire invites nothing worse than silence. That asymmetry will never tilt further in your favor than it does right after graduation, when you have the least to lose, the most time to recover, and the longest runway for good decisions to compound. Taking risks also improves your luck. Luck is a collision, an unplanned moment when a person, idea, or opportunity crosses your path. These collisions aren't completely random: you can engineer far more of them than you think. A few ways to do this: Work for people who attract talent, because their orbit will become yours. Share what you're learning in public to draw like-minded people toward you. Be useful before you're asked, so you build a reputation for taking initiative. And follow up after you've been ignored: a surprising number of connections happen on the second, third, or even fourth attempt. Each of these habits puts you in the path of more collisions. Over time, that widens the surface area of your luck.
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Ashu Garg posted thisPlaying it safe is itself a bet, and usually a worse one than it looks. This matters most if you just graduated, because the math tilts hardest toward taking risks in the years right after school. Loss aversion is a powerful force, but over the course of a career, risk and return are tightly correlated. Consistently choosing the higher-upside path is one of the biggest determinants of how far you will go. What you want to look for is convexity: risks whose downside is capped and whose upside is unbounded. Joining a startup that folds often teaches you more than it costs. Sending a thoughtful email to someone you admire invites nothing worse than silence. That asymmetry will never tilt further in your favor than it does right after graduation, when you have the least to lose, the most time to recover, and the longest runway for good decisions to compound. Taking risks also improves your luck. Luck is a collision, an unplanned moment when a person, idea, or opportunity crosses your path. These collisions aren't completely random: you can engineer far more of them than you think. A few ways to do this: Work for people who attract talent, because their orbit will become yours. Share what you're learning in public to draw like-minded people toward you. Be useful before you're asked, so you build a reputation for taking initiative. And follow up after you've been ignored: a surprising number of connections happen on the second, third, or even fourth attempt. Each of these habits puts you in the path of more collisions. Over time, that widens the surface area of your luck.
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Ashu Garg shared thisIt’s commencement season, and it’s a strange world to be graduating into. A few thoughts on what it means to graduate into the world AI is making, and whether there’s room in it for you. The short answer: you are not too late. I know it doesn’t always feel that way. The on-ramps that used to count as "safe" for new graduates - first-year tracks in programming, banking, consulting, and law - no longer do. AI is changing knowledge work faster than the institutions preparing students for that work can adapt. But every frontier looks claimed to the people standing near it. It looked that way after the dot-com bust, and in 2026 with AI. My advice: Start before you feel ready. Experiment with the tools. Treat your career as a prototype, not a test you have to ace on the first try. Take asymmetric risks while your downside is still low. Guard your attention fiercely. Build around your strengths. Find specific people with specific problems, and be useful to them. You don’t need to know your life’s work at 22. Most people don’t. You find it by making contact with reality, noticing what gives you energy, and continuing to move in a direction that becomes increasingly your own. The world is changing quickly, but it's full of opportunities. You’re earlier than you think.
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Ashu Garg shared thisAshu Garg shared thisCongratulations to our General Partner Steve Vassallo on being named to this year’s Forbes Midas List! This recognition reflects the extraordinary founders Steve has had the privilege to back and build alongside. We’re proud to celebrate him, and even prouder to stand beside the founders who make Foundation Capital possible. https://lnkd.in/eGQM5EpV
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Ashu Garg shared thisSecurity and governance concerns are a big reason why AI pilots stall. You need to have visibility into what the hundreds of agents at your organization are doing, and what they might unintentionally have access to. This is compounded by the fact that every legacy enterprise application was designed around a human interface. When agents replace that interface, the architecture of enterprise software has to be rethought from the ground up—permissions, security, even UX. I’m excited to see where that leads us. More thoughts in my newsletter: https://lnkd.in/g8u8FphS
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Ashu Garg shared this“A procurement decision about a software is actually a group decision,” shares Sumit Johar, CIO at BlackLine. When selling software, get familiar with a company’s procurement process so you know which stakeholders need to get on board. It might be more people than you think. See what other CIOs—including Karthik Chakkarapani (Zuora) and Saket Srivastava (Asana)—have to say about the selling tactics that actually move the needle: https://lnkd.in/guUnbfeK
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Ashu Garg liked thisAshu Garg liked thisSuper excited to share that I've joined Salesforce as EVP & GM of Data Management. It’s an incredible opportunity to take on this role at such a time of change in the industry driven by AI. Software and applications will change in a few years in ways we can't fully imagine right now. But two things feel certain. There will always be a need for an AI infrastructure layer, and there will always be a need for the data layer. AI ready data will drive practical, positive change in the world, at scale. Combine that with the prospect to help build on one of the most iconic brands and platforms in the world, alongside some of the brightest minds in the industry - it became a once-in-a-lifetime chance to influence at the heart of the AI transformation. Deep gratitude to the leaders who placed their trust in me: Rahul Auradkar, Muralidhar Krishnaprasad (MK), Miguel Milano, Srini Tallapragada, Marc Benioff, and Steve F.. Thank you for the opportunity. And to the mentors who shaped how I lead along the way — Mark Barrenechea, Rory Read, and so many others — thank you. I carry your lessons into this next chapter. Energized for what we build next. Informatica, Salesforce
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Ashu Garg liked thisSuch a cool blog! This describes the essence of what we’re doing Unconventional AI…using the physical world to do computing. Computing is not an artificial thing; nature has discovered this concept over and over again. We’re building something that can harness nature to compute with the lowest possible energyAshu Garg liked thisMost real-world systems are dynamic. So why do we still treat computation as static? Our latest blog by Sara Achour and Matthew Bull explores computation through motion using gyroscopes, rods, springs, and ordinary differential equations to perform handwritten digit classification. A deep dive into: • dynamical systems as compute • differentiable ODE solvers • physics-inspired machine learning • emergent computation through interaction Read here: https://lnkd.in/gWPrdmbp
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Ashu Garg liked thisAshu Garg liked thisI'm hiring a Director of Sales, EMEA at Arize AI! We're the leading AI engineering and evaluation platform, trusted by 150+ enterprises like Booking.com, Uber, and PepsiCo. EMEA is our biggest growth opportunity, and I'm looking for someone to help build it — building the team, the playbook, and the market across Europe and the Middle East! More details here: https://lnkd.in/ejTxFtM2 Know someone great? Tag them or send them my way.
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“Ashu has been an incredible investor, mentor and partner for me. He always makes time to connect and his ability to provide the right feedback at critical moments has been very helpful on our company journey. Ashu is generally not prescriptive in his feedback, which I prefer, but rather asks the right questions and provides insights from his investing experience that help us think through the challenges and decisions we face. However, when asked, Ashu has been very helpful in constructing strategy and giving specific opinions. He's also been a strong partner with introductions and funding strategy. We wouldn't have the opportunity we have without his support and I couldn't give Ashu a higher recommendation.”
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Peter Hsing
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Early stage venture capital firm investing in strong technology startup teams in Silicon Valley by providing capital (seed, Series A + follow-on), helping set strategy, tuning business models and go-to-market plans, developing/testing pricing models, advising on major operational issues, recruiting key talent, and assisting in business development, capital raising, and M&A.<br><br>Portfolio includes: AiSight, ALICE Technologies, AllClear ID, Alucid (acq'ed by Magic Leap), Amplitude, Apptimize (Airship), Arch Systems, Aromyx, Authentic8, Censia, Chai Labs (Facebook), Corona Labs (Fuse Powered), Fixed (Lawgix), FundRocket, Fuse Powered (Upsight), Gas Pos, Good Counsel, Indio (Applied Systems), IO Turbine (Fusion-io), Iterable, Kamcord (Lyft), Magic Leap, Modern Life, Modewalk (Moda Operandi), Moesif, Natero (Freshworks), NextRoll (fka AdRoll), Omny, Origin, Outward (Williams-Sonoma), Parabola, Periscope (Sisense), Perzo (Symphony Communication), Relimetrics, Runa (Staples), Safi Analytics, SensorTower (Riverwood), Shelterluv, Silexica, Sisense, Spellbrush, Splashtop, Symphony Communication, tag.bio, Talage, Tarsier, TerrAvion, Womply, WorkPatterns, Xcalar.<br><br>Angel investments: Ayla Networks, Bitesize, find bioscience, FuelX, ITapp (ServiceNow), Kwant.ai, KYC Hospitality, Shopline (JOYY).<br><br>Tags: Enterprise SaaS, marketplaces, payments, API / APIs / API-first, augmented reality (AR), virtual reality (VR), machine learning (ML), artificial intelligence (AI), deep learning, FinTech, InsurTech / InsureTech, PropTech, CRETech, bioinformatics, computational biology, IOT, industrial IOT (IIOT), image processing, video processing, machine vision, computer vision, big data, predictive analytics, data science, data warehouse / data warehousing.
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KonnectHouse - Procurement insights
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🚀 Levelpath Raises $55M Series B Led by Battery Ventures to Advance AI-Native Procurement Platform Levelpath has announced $55+ million in Series B funding led by Battery Ventures, bringing total funding to $100 million to accelerate its mission to reinvent enterprise procurement through autonomous AI agents and its proprietary Hyperbridge reasoning engine. ✨ The round includes participation from Redpoint Ventures, Benchmark , 01 Advisors, NewView Capital, and WiL (World Innovation Lab). Battery Ventures General Partner Neeraj Agrawal, who previously led the firm's investment in Coupa Software (acquired for ~$8 billion in 2023), joins Levelpath's board and has made multiple appearances on the Forbes Midas List. "Levelpath represents the future of enterprise procurement: intelligent, automated, and strategically aligned with business objectives," said Agrawal. "The team has built remarkable technology that delivers demonstrable value to some of the world's largest companies." Alex Yakubovich, Co-founder and CEO of Levelpath, adds: "Procurement touches every part of a business, yet it's been held back by tools that fight against you instead of working with you. We didn't retrofit old systems with AI. We reimagined what procurement could be when intelligence is built into every interaction." Stan Garber, Co-founder and President, comments: "We're not just building procurement tools, we're building the intelligent infrastructure that turns procurement into a competitive edge that will radically accelerate productivity at scale." The funding will accelerate product development of AI Agents that autonomously handle sourcing events, supplier onboarding, and risk assessments while scaling go-to-market efforts and ecosystem partnerships. Read the full announcement here: https://lnkd.in/e3ATgcNw #AINativeProcurement #ProcurementAI #SeriesBFunding #AutonomousAI #ProcurementTransformation
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Production AI Institute
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Google's Gemini 3.5 launch changes the vendor conversation, not the deployment conversation. Model release headlines matter less than the controls around them. Enterprise teams still need to test prompt injection resistance, data handling, and how the model behaves inside governed workflows. Our Gemini 1.5 Pro PSF assessment shows how we evaluate that. https://lnkd.in/gv6A62TV #ProductionAI #AIGovernance #MLOps
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The Daily AI Brief
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The "API Wrapper" Era is Officially Dead If your AI strategy is just sending proprietary enterprise data to a massive cloud model, you don't have a strategy. You have a security risk and a bloated compute bill. The major takeaway from this week's NVIDIA GTC 2026 is the rapid shift toward Domain-Specific AI. Fractal Analytics just accelerated this transition with the launch of LLM Studio. The Paradigm Shift: > The Old Way: Slapping a RAG pipeline onto a generic, one-size-fits-all model. It’s slow, expensive, and completely lacks deep, industry-specific context. > The New Way: Using enterprise platforms (powered by NVIDIA NeMo and NIM microservices) to design, build, and govern smaller, hyper-specialized models built exclusively on internal data. Why this changes the game for ML Teams: 1. Data Sovereignty: You own the model and the weights. Sensitive enterprise data never leaves your infrastructure. 2. Automated LLMOps: Modules like AutoLLM streamline the entire lifecycle from fine-tuning to deployment, removing the friction between data science and production. 3. Cracking the Unit Economics: Stop paying the inference cost for 100-billion parameters when a finely tuned 8B model handles your specific document triage faster and cheaper. The competitive moat in 2026 isn't having access to an LLM. It's owning a model that understands your specific business better than anyone else's. Are you building custom intelligence, or are you just renting it? #DataScience #MachineLearning #LLMOps #NvidiaGTC #EnterpriseAI #TheDailyAIBrief
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Scanner, a San Francisco-based developer of a security data platform, has raised $22 million in a Series A round led by Sequoia Capital, with participation from Mantis VC and strategic angel investors, including the CEO of Vanta and the former GM of Splunk Security. Led by CEO Cliff Crosland, the company plans to use the funding to scale its engineering team, accelerate development of its AI-native search index, and expand its platform to support agentic threat hunting across massive log datasets. Scanner’s specialized security data lake enables sub-second queries across petabytes of historical logs, offering SIEM-level speed at a fraction of the cost, and is already used by high-growth enterprises like Notion, Ramp, BeyondTrust, and Lemonade to detect and investigate complex threats. FOUNDER: Cliff Crosland INVESTORS: Sequoia Capital & Mantis VC ROUND: Series A AMOUNT: $22,000,000 HQ: San Francisco, California #VentureCapital #Scanner #CliffCrosland #TradedVC
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MinusOneDB
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We made the LUMA Partners AI Lumascape under Data Infrastructure/Data Infra Refineries. The interesting part isn't the recognition. It's what it reveals - the backend infrastructure that got us to this point isn't built for the requirements of AI. Everyone's excited about foundation models. OpenAI raises billions. Anthropic raises billions. But when enterprises actually deploy AI, they discover their data warehouse can't afford to have agents running thousands of exploratory queries. The math: 1,000 queries on a 10TB dataset costs ~$100k on traditional infrastructure. That's not a typo. So that AI assistant that's supposed to revolutionize your business? It either bankrupts your IT budget or gets throttled down to uselessness. Data warehouses were built for scheduled reports and pre-computed dashboards. They charge by the query because that's their business model and their architecture. MinusOneDB exists because we saw this problem coming. Millions of queries at flat-rate pricing. Rapid responses at petabyte scale. Infrastructure where the marginal cost of curiosity is zero. AI's potential isn't limited by model capabilities anymore. It's limited by infrastructure that punishes exploration. That's why we belong on the AI Lumascape. Not as another AI company, but as the infrastructure that makes AI in the enterprise actually possible. #AI #DataInfrastructure #EnterpriseAI #DataWarehouse
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Search Party Video-Podcast
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2025 in Review: Pro Tips From Top ETA Searchers Throughout 2025, the Search Party video-podcast asked successful Entrepreneurs-Through-Acquisition (ETAs) to share their stories and best practices. Here are highlights: • Curt Black, CEO of Aproove, a workflow SaaS company, explains how he tried to stand out from other ETA searchers by offering sellers ideas on how to scale and solve specific operational challenges. • Chandos Mahon, an exited searcher and former CEO of Castle Tire advises CEOs of lower-middle-market companies to stay on good footing with their lending partners by calling more often with good news than bad. • Diego Cuenca Cuenca, an exited searcher and former CEO of TICKETECH - Parking Technology Solutions, shares how he felt during the darkest days of Covid, when he thought his business would collapse and he would become known as having led one of the "worst search fund outcomes in history." Asked how he persevered through fear of failure, Cuenca says he did what his high school wrestling coach told him to do when being defeated - to simply focus on scoring the next point. • Bob Boniface, Co-CEO of trucker training company TransTech, shares the importance of quality-of-earnings due diligence, and how he learned from his own QoE how an investment in sales would accelerate growth, allowing him to set the most important priorities for the first 90 days of post-acquisition value-add. • Henrique de Freitas, the ETA-backed CEO of Reliable Premium Management Inc., explains that young aspirational entrepreneurs who "need structure or a very clear path to success" are not going to thrive in an unpredictable ETA search journey. More important is grit, says de Freitas: "That's what sets the best searchers aside, is you keep going." Follow Search Party Video-Podcast on LinkedIn Search Party Video-Podcast Lead Sponsor: Next Coast Legacy Search Party Video-Podcast Sponsors: Avidbank Boulay Oberle Risk Strategies Plexus Capital, LLC #searchparty #searchfund #eta #entrepreneur
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Ranger AI Emerges Out of Stealth to Transform Business Operations for Industrial Engineering. Ranger, the agentic revenue operations platform for industrial tendering, led by James Zhan, Sari Saadi, and Kyle Jordan, has emerged from stealth with $8.4 Million in seed funding led by Bonfire Ventures, along with 25madison, Inovia Capital, and Panache Ventures. Read More At: https://lnkd.in/gBwEFwXs X: https://lnkd.in/gUuSEMwV Source: Press Release
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Pendoah
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Manual validation was killing compliance velocity. We built an AI assistant for our client, Steve Mattos. Built: NLP and ML for authenticity at scale Minimal UI with full audit trail Real-time dashboards for throughput and accuracy Results: 65% faster validation cycles 40% fewer review loops 30% lower compliance costs 3x consultant capacity 5 minutes to 45 seconds per document 10,000 plus docs per month with 95% month-one adoption Why it worked: We shaped the workflow, not just the model We mapped edge cases before launch We made reporting effortless DM “PROVAL” for the case study and a quick walkthrough. #enterpriseai #agenticai #pendoah #digitaltransformation #automation #mlops
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☕ Azul receives strategic investment from Thoma Bravo to accelerate Java innovation Azul, a Sunnyvale, CA-based company powering the Java platform for cloud enterprises, has received a strategic investment from Thoma Bravo. Existing investors Vitruvian Partners and Lead Edge Capital also participated. The financial terms of the deal were not disclosed. Led by CEO Scott Sellers, Azul enables Java developers, devices, and businesses to deliver applications with enhanced capabilities, performance, security, and value. Its platform is trusted by: 36% of the Fortune 100 50% of Forbes Top 10 World’s Most Valuable Brands Global banks and leading companies including Avaya, BMW, Deutsche Telekom, LG, Mastercard, Priceline, Salesforce, and Workday The investment will help Azul expand operations and accelerate development, continuing its mission to empower enterprises with high-performance, secure, and reliable Java solutions. Team- Gil Tene CTO & Co-Founder Peter Maloney CFO & COO Ian Whiting Chief Revenue Officer Andrew Savitz CMO Martin Van Ryswyk Chief Product Officer Leonid Leniashin Senior Vice President, Engineering @George W. Gould Senior Vice President, Corporate Development & Partner Alliances Jennifer Wang Vice President of Finance #Java #Cloud #EnterpriseSoftware #StrategicInvestment #Azul #Innovation #ThomaBravo #SoftwareDevelopment
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Enterprise automation is going AI-native, but today's platforms are fragmented. Automation engines lack deep AI observability. Observability platforms can't run workflows. Our deal engine surfaced this combination - UnifyApps + Fiddler AI to create this unified automation+observability platform. Every agent decision, LLM output, and workflow step becomes observable, explainable and compliant, all by design. And what is more, the timing is ripe - ✅ AI agents are entering core workflows ✅ Regulation demands explainable automated decisions ✅ Enterprises want fewer vendors, not more ✅ No incumbent offers integration + automation + AI observability in one stack Watch the video for the full breakdown #AI #MachineLearning #EnterpriseAI #Automation #AIAssurance #LLMObservability #GenAI #AICompliance #AgenticAI #RPA #MarTech #TheIndiaPortfolio #CorpDev #Mergers #Acquisitions #VentureCapital
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Asteris AI
397 followers
💻 CODER RAISES $90M SERIES C LED BY KKR FOR ENTERPRISE AI DEVELOPMENT INFRASTRUCTURE Coder, the Austin-based platform for secure cloud-based developer environments, raised a $90 million Series C led by KKR, with participation from Qube Research and Technologies, Uncork Capital, and existing investors. The round marks an unusual moment: KKR is both lead investor and reference customer. The firm deployed Coder across more than 500 engineers roughly a year ago. Since then, more than half of all code commits at KKR occur inside Coder-managed environments - up from zero AI-assisted development. KKR's CIO said the core value was the ability to integrate AI coding tools in a governed and controlled way. Coder's platform sits between the developer and the AI coding agent, providing standardized cloud workspaces that let enterprises bring in agentic coding tools without giving them unconstrained access to production systems. More than 80% of enterprise developers now use or plan to use coding agents daily - creating a governance problem that centralized environments are designed to solve. The broader implication: AI coding tools have moved from experiment to infrastructure in enterprise settings. The companies now winning are not necessarily the model developers but the infrastructure providers that let enterprises use those models safely and at scale. Funding will go toward platform innovation focused on enterprise AI workflows and expansion in Europe and Asia. #AIDevelopment #EnterpriseAI #CodeInfrastructure #Coder
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GTMnow
41K followers
Today’s edition breaks down how to build CLAUDE.md, the “brain” Claude Code loads at the start of every session, and walks you through how to build this system that includes: - Context: ICP, positioning, competitive intel, signal library - Skills: reusable workflows like account research, sequences, scoring - Workflows: how your team actually operates Read the full breakdown: https://lnkd.in/dKgjvg7H
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IgniteTech
305K followers
Live today at 3 PM CDT | IgniteTech CEO Eric Vaughan joins BDEX's Deconstructing Data with co-hosts David Finkelstein and Jessie Lizak to share how AI drives profitable restructuring, faster integrations, and future-ready stacks. Key Topics: ☑ AI restructuring playbook: turning red to profitable ☑ M&A at scale: integration with AI ☑ Architecting products with AI at the core ☑ Tech stack Join live: LinkedIn: https://hubs.li/Q03GZjS-0 YouTube: https://hubs.li/Q03GZCKM0 #IgniteTech #AIImplementation #DigitalTransformation
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Menlo Times
2K followers
ClickHouse Raises $400 Million Series D led by Dragoneer to Accelerate Expansion Across Analytics and AI Infrastructure. ClickHouse, a leader in real-time analytics, data warehousing, observability, and AI/ML, led by Aaron Katz, Alexey Milovidov, and Yury Izrailevsky, has announced the close of its Series D financing, raising $400 million, led by Dragoneer Investment Group, with participation from Bessemer Venture Partners, GIC, Index Ventures, Khosla Ventures, Lightspeed Venture Partners, accounts advised by T. Rowe Price Associates, Inc., and WCM Investment Management. Read More At: https://lnkd.in/gNeq4k-V X: https://lnkd.in/gKQcA53K Source: https://lnkd.in/g5qajHPY
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The Top Voices
4K followers
Data versioning startup lakeFS, co-founded by Oz Katz and Dr. Einat Orr, has raised $20M in growth capital to accelerate product development and expand enterprise partnerships. lakeFS, launched in 2020 by Treeverse, brings Git-like version control to massive datasets, helping teams manage and reproduce AI and ML pipelines with confidence. Its solution is already used by organizations like NASA, Lockheed Martin, Volvo, Microsoft, and the U.S. Department of Energy. The round was led by Maor Investments, with continued support from Dell Technologies Capital, Norwest, and Zeev Ventures. The funding brings lakeFS’s total raised to $43M and will support scaling engineering and go-to-market teams, as well as deepening enterprise integrations. lakeFS is positioning itself as a foundational layer of the modern AI stack — bringing control, safety, and reproducibility to the data powering today’s most ambitious AI projects. Yoni Braun Yair Snir Ronda Scott Ido Meroz Ilai Pines Noémie Oiknine Eric Elalouf Ofek Peretz Guez Philippe oren zeev Michael Bunning Dror Nahumi Cathy Azuelos Messas Noémie Oiknine Harry Brody Iddo Avneri Peter Guagenti Phillip Berlin Jas Bajwa Asaf Fybish Yoav Yetinson Gottfried Sehringer Peter Guagenti Idan Novogroder Yahli Ramberg Tal Sofer Offir Cohen Philip DeLeo Tami Mor Tal Sofer Itai Admi Nir Ozeri
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Ontra
32K followers
AI is transforming private markets – but it’s also raising new questions around data privacy, model training, and information exposure. On August 19, join experts from Vanta and Ontra as they break down what non-technical leaders need to know to evaluate AI tools with confidence. Featuring: 🎙️ Chase Lee, VP of Product, Vanta 🎙️ Michael A., Director of Security Operations, Ontra 🎙️ Troy Friedlander, Director of Product Management, Ontra Learn how to adopt AI without compromising sensitive data. Save your spot: https://lnkd.in/ggRmaUBY
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Techitup ME
8K followers
Confluent, a leading data streaming platform provider, has launched Sell With Confluent, a new reseller program designed to help system integrators accelerate growth and capture opportunity in the rapidly expanding data streaming market. As real time data becomes the backbone of modern enterprises, Confluent is simplifying how partners sell, close deals, and scale their practices. Key Highlights: • 86% of IT leaders consider data streaming a strategic priority, according to the 2025 Data Streaming Report. • Sell With Confluent removes traditional reseller bottlenecks such as slow approvals, complex processes, and unclear pricing structures. • New self service quoting tools enable automated quotes with instant approvals and programmatic discounts. • A restructured rewards framework provides competitive double digit discounts for partners sourcing and closing deals. • Deal protection safeguards partner sourced opportunities and pipeline investments. • The Business Investment Fund supports co marketing initiatives including workshops, regional events, and customer engagement programs. • Partner Nexus serves as a centralized hub for more than 1,400 global partners with access to tools, program details, and growth resources. Kamal Brar Vice President, Partners and Technology Group at Confluent, said partners see daily demand for data streaming platforms and that Sell With Confluent removes slow approvals and complicated processes so they can capitalize on the opportunity in front of them. Discover how Confluent is enabling partners to sell faster, earn more, and lead in the growing data streaming platform market. #DataStreaming #ChannelPartners #EnterpriseGrowth https://lnkd.in/dbfsFK8Q
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