Ian W.

AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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HITRUST chose Vanta as the first pre-built HITRUST e1 solution, which means Vanta comes "out of the box" with the necessary controls, documents, and policies for an e1 assessment and eliminates the manual “do-it-yourself” approach that other platforms require. Up to 80% of requirements are automated by: *️⃣ Guidance around requirements: 44 new controls and 72 automated tests to ensure comprehensive and continuous compliance. *️⃣ Documents and policy templates: 80 new documents and 10 policy addendums to outline practices around managing sensitive data. *️⃣ Automated evidence collection: With over 300 integrations, Vanta automatically and continuously collects evidence from an organization’s technology stack. Our platform also applies overlapping, implemented controls from other supported frameworks, including up to 50% of SOC 2 and ISO 27001, to eliminate duplicative work across compliance programs. Coming soon, our partnership with HITRUST will expand into an integration with their audit portal, MyCSF, so customer evidence is transfered automatically from Vanta to MyCSF, further streamlining the HITRUST validation process Really excited for this collaboration!

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Venture Capital Contact Compilation. Explore a detailed list of 1,250+ VC firms, 7,400+ professionals in the VC space (including partners and principals), and 6,400 email contacts. https://payhip.com/b/cs7UQ #startup #startups #vc #venturecapital #investmentbanking #privateequity #managementconsulting #b2b #founders #entrepreneurs

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Apple’s contact-sharing crackdown Apple almost certainly implemented its contact-access changes in iOS 18 to dismantle these growth schemes. Given the use of a prompt mechanism, it’s tempting to view Apple’s contact-sharing crackdown as redolent of Apple’s App Tracking Transparency (ATT) privacy policy. But the comparison ends there. ATT served as a drag on the entire digital economy by constraining growth that would have otherwise been re-invested. Apple’s contact-sharing crackdown will simply abate the short-term windfalls that derive from successful implementations of contact harvesting growth tactics.

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The Stoke Space team in Seattle has built a rocket company faster than many people can build software. Morgan Beller breaks down how this is helping them capture a huge opportunity: https://lnkd.in/gc5jv2-c 

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Was chatting with another investor today on a call, and mentioned Tim Urban's Wait But Why Blog post, "The AI Revolution" and how prescient it was. Given that it was written in 2015, decided to flex AI's latest muscles to give it a new venue: a podcast. Podcast here: https://lnkd.in/gW8Dh89U Pretty easy to do: Step 1: Save the posts as PDFs. Step 2: Upload to NotebookLM, have it create a podcast. Step 3: Upload to Descript, give it captions. Step 4: Export, publish to Youtube. Total time to create the podcast: ~25 minutes. To generate this podcast and publish it even a few years ago would have taken >8 hours. Love the future (Original posts: https://lnkd.in/gW-PQXE7, https://lnkd.in/gg-nRvxz) #AIRevolution #GenerativeAI #WaitButWhy #TimUrban #NotebookLM #AIContentCreation #Podcasting #Descript #ArtificialIntelligence #ContentFlex #ProductivityHacks #FutureOfAI #AIinMinutes #TechEfficiency #AIandCreativity #YouTubePodcast #InnovationInContent #vibecap #vibecapital

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NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

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YC's "request for startups" - Winter 2025 - Government + Public Safety 1. Build LLMs to automate specific government tasks like form filling, application reviews, and document summaries 2. Create computer vision systems for license plate detection and crime prevention 3. Build software that reduces police paperwork time from hours to minutes 4. Create tools for emergency response coordination and dispatch 5. Build platforms that help communities and law enforcement communicate effectively - Manufacturing 1. Build ML-powered robotics systems that reduce labor costs in US manufacturing 2. Create automation tools specifically for American factories competing with overseas production 3. Build specialized industrial robots for factory inspection and maintenance 4. Develop systems that help manufacturers operate efficiently in US industrial hubs - Chips + Engineering 1. Build LLM tools specifically for FPGA design and optimization 2. Create Al systems for ASIC design that reduce development costs 3. Build tools that optimize specialized computation like crypto mining or data compression 4. Develop Al-powered CAD/CAM software that makes engineering tools more accessible - Stablecoins 1. Build platforms for businesses to hold and manage stablecoins 2. Create tools that make it easy for developers to integrate stablecoin payments 3. Build systems for banks to issue their own stablecoins 4. Develop infrastructure for cross-border stablecoin payments and remittances - New Jobs 1. Create tools that help people run local service businesses 2. Build platforms enabling people to earn income providing services online 3. Create systems that help service providers work more efficiently with Al assistance 4. Develop tools that help small businesses operate at the same level as large corporations #YC

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Attached is NVDA's price chart for the last year - up c.175% (up 2500%+ in L5Y period!!) 🚀 I nonetheless have conviction that it currently remains undervalued... Among other things, due to strong competitive positioning as a GPU-maker, AI-driven demand fuelled by investor and consumer interest, and moats it has built. This is mirrored by equity analysts – GS’ latest price target was $800, BofA’s $1,500. I do believe the growth is capped, and driven in equal measure by under-supply and bubble-like demand as it is by fundamentals. Even so, Nvidia remains an attractive medium term investment. ⚖️ Demand – Supply Dynamics: AI is a generational innovation, and agents /apps are already upending SaaS, or at least so think VCs who deployed $64.6B in AI start-ups in 2023 alone. As early movers and now the industry standard (c.80% market share), Nvidia's A100 and H100 chips are not only the most efficient option, but also crucial for large scale AI training and inference (chips are the biggest cost head for LLM companies). With unprecedented interest in AI (ChatGPT acquired 1M users in 5 days of launch, OpenAI reported $3.4B ARR in Apr’24, AI agents are widely recognised as the tech of the future), Nvidia stands to capture a large portion of the $250-$500B data centre conversion market over the next 5 years​. This is proven by the $22.6B in data centre revenue Nvidia brought in during Q2’24, up 23% from the previous quarter and up 427% from a year ago 🥇Competitive positioning: While Intel, AMD, Arm have been active, the only credible competitor, AMD, has a relatively smaller market share in the high-performance AI GPU segment, and Nvidia’s consistent technological advancements keep it further ahead, e.g., Nvidia’s Rubin which will succeed Blackwell in 2026 is orders of magnitude better than anything on-market. 🤝Moats via ecosystem building: Collaborations with hyperscaler cloud providers like AWS and Microsoft Azure have expanded Nvidia’s reach. Moreover, acquisitions like Mellanox have strengthened its position in high-performance networking, critical to AI infrastructure. Coreweave, a prominent cloud services partner / distribution channel for chip capacity is an NVIDIA investee and is built almost entirely on Nvidia chips. Finally, they continue investing in networking, software tools, data centre solutions but also purpose-built AI PC and smartphones to create an ecosystem that not only locks in customers and enhances switching costs, but pivots more of their revenue to more predictable recurring contracts. 📈 Financial underpinnings: Nvidia’s expected FCF for next 2 years stands at $100B, providing huge ammo for R&D, strategic investments and M&A (only $30 to $35B earmarked for buybacks) and in turn, growth. Even so, their shares trade at a P/E ratio of c.20x  NTM earnings, far below historical median of 35x+, indicating the market expects them to grow in the medium term. KEEN TO HEAR THOUGHTS! #Nvidia #AI #Compute #Chips #Tech #Growth

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Many assumed that the industry would be well on its way to AGI or superintelligence before AI model builders’ efforts to take advantage of scaling laws might hit a wall. Recent headlines are chipping away at that assumption. One question I've been getting in my inbox: is this the era of diminishing returns for AI? We don't think so. Even if an avalanche of new clusters and synthetic data fail to deliver significantly better model performance, there are good reasons to believe that AI is still going to become orders of magnitude more powerful in the coming years — powering a new generation of startups and ultimately productivity growth and positive benefits for mankind. In fact, with agents and reasoning just beginning to scale and thousands of industry-specific models coming online, it's about to get really exciting. Read our latest blog post on the topic: https://lnkd.in/gFhniNX8

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"Probably the worst decision that I made as CEO of Loudcloud was to take ona. 10-year lease with a $30M letter of credit. We leased the space right before the dot-com crash. Rents dropped 10-fold in Menlo Park... worse yet, due to the recession, we couldn't hire so the space we already had was sufficient and we never ended up moving into the new building. So I basically lost $30M for nothing." This is from Ben Horowitz's recent interview with Big Think. Even a quarter century ago the typical office lease construct is misaligned with how a company needs to operate. Our industry could get away with this when office spaces had a monopoly on how and where work got done, but those days are dead. As global markets are crashing, we'll be looking at another disruptive cycle for businesses which will create more cost cutting pressure and the first thing to go when they roll will be inflexible leases. CRE needs to develop new offerings and fast. https://lnkd.in/ePuZgzkU

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⌛ The deadline for YC’s S24 batch is April 22! I get a ton of requests for application feedback, so I’ve consolidated my advice here: 𝐓𝐞𝐚𝐦 > 𝐢𝐝𝐞𝐚 + 𝐩𝐫𝐨𝐠𝐫𝐞𝐬𝐬 Your idea will change; that’s what it means to be pre-product-market fit. A bet on an early-stage company is a bet on the team to iterate rapidly and figure it out; think of your idea and your progress as useful evidence. Don’t be discouraged if you feel like you haven’t made enough progress, because the amount of progress you’ve made is not nearly as important as how fast you made it. In other words: “slope, not intercept”. 𝐁𝐞 𝐬𝐢𝐦𝐩𝐥𝐞. 𝐁𝐞 𝐝𝐢𝐫𝐞𝐜𝐭. Explain your idea clearly in 1-2 sentences: who are your users and what problem do you solve? Avoid jargon, marketing-speak, technical intricacies, or an exhaustive list of features. Tacking on “...with AI” or “...with blockchain” is not any more helpful than “...with computers”. Bad: “We are transforming the global software development industry with a revolutionary AI platform that streamlines the SDLC for tremendous efficiency gains using cutting-edge LLMs.” Better: “We’re a developer tool that automatically reviews code and fixes bugs.” 𝐊𝐧𝐨𝐰 𝐲𝐨𝐮𝐫 𝐮𝐬𝐞𝐫𝐬 You should feel like an expert on your users, and have some unique/contrarian insights about them. You’ve hopefully identified a “hair-on-fire” pain they have, and you’re building a product to fix their problem. It’s hard to spend too much time talking to your users. Interviewing 50-100 people seems like a good place to start. If you’ve talked to less than, say, 10-20, it might be hard to know you’re solving a real problem. 𝐊𝐧𝐨𝐰 𝐡𝐨𝐰 𝐲𝐨𝐮’𝐫𝐞 𝐠𝐨𝐢𝐧𝐠 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐦𝐨𝐧𝐞𝐲 What’s your path to $1B in revenue? Keep it simple. You don’t need a 5-tier pricing strategy and a multi-year product roadmap, you need to know roughly how many potential customers you have and how much they’re likely to pay (bottom-up analysis), and how big the overall market is (top-down). Reasonable estimates are OK. Remember, the VC model is based on a power law of startup returns, where one huge exit makes up for all other investments going to zero. This makes a tiny chance at becoming a $10B company quite valuable, and a high chance at becoming a $50M company surprisingly useless. 𝐒𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐚𝐩𝐩𝐥𝐲? Emphatic yes! YC was an awesome experience for us and every YC alumnus I’ve asked so far - it changed the trajectory of our startup. Even if you don’t get in, we found the application process itself was valuable and forced us to think deeply. If you’d like some eyes on your application, send me a message - I’m happy to help!

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13 years ago @marc andreessen of A16Z wrote a piece titled "Software Is Eating The World" (link in bio!) - arguing that the next 20 years would see the proliferation of software throughout every enterprise/workflow across the world 🥣 🌎 30 days ago, Emergence Capital wrote a piece titled "Service-As-A-Software" (link also in bio!), arguing that with new advances in AI, you could flip the economics of traditional service driven business models and now can offer that service in the form of a low cost, scalable, software offerings - which was impossible before the recent LLM evolution 🤖🚀 We Factored Quality are biased, but I deeply believe the biggest distribution opportunity of Service-As-A-Software is via fundamental platforms/vendors in vertical applications like us, that sit in key enterprise workflows & have strong trust with end users. Over the next few months, Factored Quality is going to release a number of FREE tools for VP's of Supply Chain, Operations, and Dir's of Quality and Regulatory that should automate 95% of what you've historically had to pay consultants to do. We're starting today with our free AI Powered GAP Analysis - a tool that let's consumer product brands / importers / retailers enter in a few questions - and in <30 seconds receive a detailed product regulatory analysis outlining what product safety and testing certifications you need to hit. Check it out and give it a spin below, and then reach out to Andrew Hoagland John Reynolds and Jenson Tuomi to help you run an expert certified analysis + get onboarded to the FQ platform to run audits, inspections, or testing across 28 countries 🔥 Link Here: https://lnkd.in/dEMVk9R7 + More details in bio!

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Three lessons from building and launching a P2P payments app without doing the proper customer validation first (despite knowing better): 1) Building something you need is a great starting point - but not enough to validate an idea. As someone drawn to building solutions, I’m an early tech adopter. This led me to overestimate consumers’ willingness to onboard, especially with a P2P payments app that requires KYC/KYB compliance and relies on a network effect to be useful. 2) Manual solutions are often best to start with It’s good business to be in business - transitioning from ideating to operating as early as possible is the BEST way to learn effectively. While the move fast and break things mindset has attracted well-deserved skepticism, getting stuck in a multi-year build trap distracted me from critical real-world validation. 3) Consumer appetite shifts quickly In my case, the proliferation of free and instant P2P payment options like Zelle added additional pricing pressure that I couldn’t compete with. This disincentivized the consumer event/payment collection market, turning my bottom-up go-to-market strategy upside down. Pitch (https://www.pitch.fun) is currently on the back burner as I’ve begun building custom solutions in various industries to sharpen my skills, solve specific problems, gain a hands-on understanding of operations, pain points, and workflows, and build a network of experts to lean on as I keep my eyes peeled for high-impact opportunities.

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I'm really intrigued by this idea (not mine) that some bitcoin mining companies (Iris, Cleanspark, etc) have successfully built low-cost power shells that can now be leveraged by AI compute needs -- where power itself is becoming the gating factor in the continuing growth of AI. I think this will become a real 2025 story -- as the gating factor of "chip shortage" lessens, and the gating factor of "cheap power" rises in significance. There's not really a venture capital angle here, but there is a public market one. Perhaps more importantly, it becomes a touchpoint between "crypto" and AI. Something to ponder and keep an eye on (full disc: I own a bunch of bitcoin miner stocks, including those mentioned above).

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🚨🚨 🚨 Unlocking Innovation: The Power of the Adjacent Possible Innovation programs can stall when the team tries to ideate way beyond their collective perspective and frame of reference. Like a software engineer brainstorming genetic modification of mycelia, or a senior executive ideating process improvements which they haven't been participated in for decades. It's challenging, exhausting, and ultimately self-defeating. Enter the concept of the "Adjacent Possible" - a game-changer for innovation. What is it? The Adjacent Possible, coined by Stuart Kauffman, represents the realm of new ideas within reach based on our current knowledge, resources, and capabilities. Why it matters: ➡️ Realistic Innovation: it focuses on achievable advancements, not far-fetched concepts. ➡️ Incremental Progress: each innovation opens doors to new possibilities. How to leverage it: • Constantly explore the boundaries of your adjacent possible. What's in your line of sight is what you know best. • Combine existing ideas in novel ways. Even small perturbations from status quo can generate large innovation leaps. • Stay curious about developments in adjacent industries. Cross-domain patterns create fresh sources of ideas, while also expanding the adjacent possible. Most of all, it takes the 'genius' pressure off the team. No one knows everything, but each one of us knows a lot about something, and we can all drive plenty of innovation potential by focusing on the spaces we know best. Are you exploring your Adjacent Possible? Share your thoughts below! 👇 #Innovation #innovationsystems #AdjacentPossible

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I like this idea of "technical taste". It gets into where software engineering is as much creative art as it is science. Four great takeaways (IMHO) for software devs in this era in particular: 1. Aspiring engineers should cultivate a sense of curiosity, experiment with different tools and technologies, and embrace a mindset of continuous learning. 2. AI has the potential to streamline processes and enhance productivity for engineers, but it may also lead to disruptions in traditional software development workflows. 3. Developing technical taste and judgment is essential for making informed decisions about which technologies and approaches to pursue. 4. Collaboration and open-mindedness are key to leveraging the full potential of AI and staying ahead of technological advancements. I also really liked these two quotes from Sam Schillace: 1. "The right time to do something is when you have that feeling in the pit of your stomach that's like, 'oh, this is a great idea and it's going to suck to build because nothing's ready yet.'" 2. "Technical taste is like, 'how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things?'" 3. "It may be the case that very small teams can do very large projects, or like we were talking about before, it may be the case we're just going to get really ambitious about what we try to do with the same size of teams, which is kind of where I would put my money." Great job on continuing to put out great episodes, Brett Berson and team First Round Capital! #ai #engineering #softwaredevelopment

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Chat with Miles Clements about building Linear For me companies are like design problems: you have context, constraints & goals unique to you. Then you look for a solution that has a good fit to your unique problems. The company problem also evolve over times the solution Directly applying what others have done doesn’t work that well, yet there is lot emphasis on playbooks, pattern matching and playing copying what other companies are doing in industry. https://lnkd.in/ghxjJwYU

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~40% of Frec’s customers are engineers working in tech, many of whom expect frequent and large capital gains. They also prefer to self-manage their money, which makes Frec an ideal platform. Meet Evan: an engineering leader who upgraded from investing in ETFs to direct indexing. Direct indexing appealed to Evan because it offers comparable returns to his ETFs while saving him money on advisory and ETF fees. “ETFs are true and tested, and giving folks a different way of investing in the market while saving more money is really appealing.” He’s used many other robo-advisors and other fintech tools, but “Frec seems to maximize tax loss harvesting opportunities better than any other product that I’ve used.” He also likes Frec's transparent fee structure.  “I feel like with a lot of other companies, that’s muddled in with the cost of investments,” he told us, “and you don’t really know how much you’re paying or how much you’re not saving as a result. I think Frec does a really good job of laying all of that out and giving you a true understanding of the cost of these investment products.” Read more about his Frec journey here ⬇

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