Kevin T.

AI is going to reduce the cost of code generation dramatically. Left unchecked, this will cause an exponential increase in cost of observability and root cause analysis. We believe that AI will come to the rescue, and Agent SREs will be a $100B category. The wrinkle is that there are already 50+ start-ups vying for the Agent SRE crown. If you have ideas on what will define the category winner, do ping me and/or Jaya Gupta https://lnkd.in/gyAnWarK

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Large language models are wonderful at ingesting large amounts of content & summarizing. Uploading an academic paper and I can pester it with an infinite list of questions & it will respond with equally infinite patience. In comparing the two most recent Microsoft earnings calls, Claude highlighted: - faster than expected Azure growth (29% vs 27%) - AI contributing 8% of Azure revenue up from 7% - higher CapEx spending & greater capacity constraints for data centers - much better commercial bookings growth : 17% vs 13% Excellent analysis. Benn Stancil described LLMs as great averagers of information. But what if I don’t want the average? If I seek the data point two standard deviations out? If I’m channeling my inner Anthony Bourdain & I’m seeking the fermented shark or cobra heart within my query? At Google, we ranked web pages & ads with many signals. An engineer taught me that of them found rarity of a word across a set of documents. For example, if there’s a document containing 10 instances of the word ghotiacross a collection of documents, where the average document has 0 ghotis, that document is likely the best to answer a search about fish. I haven’t found a way to goad an LLM to produce the rare result. Why did one user decide to use a particular piece of software when 10 others did not? Maybe I haven’t learned how to prompt an LLM well. Ideogram launched a feature called Magic Prompt that expands a basic prompt into richer instructions that elicit better results, marrying the language of a user & a computer. A user might write : A boy with a dog in a park. Magic Prompt replies : A heartwarming scene of a young boy playing with his happy, wagging dog in a lush, green park. The boy has a playful smile on his face while he tosses a bright yellow ball for his furry friend to fetch. The park is filled with beautiful flowers and trees, creating a serene atmosphere for this bonding moment between boy and dog. Magic Prompt surprised me in three ways. First, unlike Google search where terse queries tend to work very well, LLMs relish verbosity. Less Hemingway & more Faulkner. Second, maybe there’s a prompt for rarity. If you know what it is, please share with the rarity shibboleth. Third, I wonder what other prompt techniques are out there that have yet to be discovered to wrest the best from an LLM. This technique is called TF-IDF. It means Term Frequency Inverse Document Frequency.

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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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Nice highlight on Utah's tech cluster, aka Silicon Slopes. Not much silicon -- a lot of software, but a fine example of what can be done with persistent efforts, in this case beginning decades ago that I am aware of. My first business trip to SLC was in the 1980s and the potential was obvious. Interestingly, my first trip to NM was even earlier, and the potential was obvious here as well, but UT has done a much better job in diversification. In UT the LDS was a dominant feature, in some respects like the state in NM -- only game in town with deep pockets. UT was just much more business oriented and business friendly in their culture. The results speak for themselves. We covered UT in our VC firm during the 2000s (NM as well), during which time they were gaining momentum. Interestingly, UT is still under-served by local VCs when compared to the largest markets, though much healthier than NM. I hope UT never becomes like SV -- pricing locals out of the market, creating bubbles that require extreme predatory tactics to keep bubbles inflated, monopolistic behavior in tech giants, etc. Much better to learn from WA and CA's mistakes, and maintain a sustainable, diversified cluster with ongoing creative destruction and generational change in tech leadership.

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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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Some investors are still dismissing most AI startups as "GPT wrappers" -- i.e. they think companies that are using OpenAI or Anthropic will quickly get replaced or have no moat, since every company has access to these models. This is a flawed mental model. You can easily see the flaw if you think of OpenAI and Anthropic as providing intelligence, just as humans provide intelligence. You would never dismiss "a company that hires people" as "a human wrapper" and claim there's no moat because anyone can hire people. Instead, you have to think more deeply about accumulating advantages, network effects, product, expansion opportunities, etc. There are almost no YC startups that are literally just building a thin wrapper on OpenAI or Anthropic. They are all building real products, understanding customer problems, developing features that anchor them deeper into workflows, and solving problems that can't just be solved by using ChatGPT. A better general way of thinking about AI startups is The Black Hole Model, which I've talked about here before.

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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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$2 H100s: How the GPU Bubble Burst Last year, H100s were $8/hr if you could get them. Today, there's 7 different resale markets selling them under $2. What happened? The market has flipped from shortage ($8/hr) to oversupplied ($2/hr), because of reserved compute resales, open model finetuning, and decline in new foundation model co’s. Rent instead. https://lnkd.in/gpQEidmC

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Observations on the SF tech community after meeting >100 people the past few days: • Prominent hacker house energy. Met multiple founding teams who live/work/sleep in the same office. Work-life integration to the extreme. You simply don’t see this in NYC. Ppl care too much about things like their living space, social life, personal boundaries, etc. • Obsession trumps all. Talked to a few early-stage startups that had full-time volunteers, often passionate new grads or dropouts who were obsessed with the space and wanted to learn. • The hard pitch. I was pitched >10 times a day at my events. Hard ones too. One lady wouldn’t leave without trying to guarantee a spot on my calendar. I said no. Perhaps founders are taught to be more aggressive and text here? • Dropout culture and early starts. Met a TON of impressive young people (20-24) who had accomplished 10x more than I have, and could speak more eloquently about me on many topics. Fewer “generalists” and more up-and-coming experts who mastered a niche (e.g. Gen Z consumer social, short-form video production, viral social, etc). • Hybrid founder/investor persona. Also encountered a persona that you don’t see often in NYC: the venture-backed founder who is simultaneously running a VC fund, and seeking to succeed at it. • No small talk. Lot of ppl skip the small talk at events and get straight to the point. “How are you?” is met with a 3-min pitch on their AI startup. Can get exhausting but I get why people do it. • Founder communities. I can only name a few established founder communities in NYC. There seems to be one on every corner in SF. Places for founders to learn, gather, and work. Often free—and very accessible. We need more of this. • Building vs selling. Met with many technical builders in SF who convinced me of the opportunity but only after multiple conversations. Great product/vision but poor at communicating it. Call this underselling. The opposite is often true in NYC: I often meet ppl who are incredible salespeople who haven’t built the underlying product yet. Both are required to be successful but perhaps these communities can learn from each other. • Everyone is building AI agents. I was in a room with a half dozen founders who were building AI agents to fix software bugs. The obvious question is how well they can execute and build distribution. • Veteran talent. Many founders and CEOs of the world’s largest companies live here. Google, OpenAI, Airbnb, LinkedIn, etc. They’re accessible and actively involved with speaking, mentoring, and teaching. We even had the co-founder of Lyft speak at our event. Generally less “veteran talent” in NYC, and the folks here are less active in the tech ecosystem. More of them live further away and have chosen to reduce their participation post-exit. Different culture here.

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The reality of what generative AI is doing is opening up access to really capable AI and challenging companies on how they can leverage that. It’s saving people a huge amount of time compared to something they’ve been doing manually. And in my opinion, any time you can save this kind of time and effort, all of society lifts up. Think about this analogy: planting crops by hand to plowing by horseback to plowing by tractor… allowing for scale is a game-changer. Now that this access is being democratized, it’s making individuals a lot more impactful and scalable to have more output and throughput. But I think in time more will be expected of people. Right now the applications that AI is being implemented into are the lower hanging fruit, and now people can focus on the harder problems. 6 months from now… a year from now… it will be interesting to see the change Gen AI will have on every functional role.

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The $10,000 Mistake Most Founders Make! (Before Building Their Product) I've seen it happen time and time again: Passionate founders pouring their life savings into building a product... only to realize no one wants it. This $10,000+ mistake (often much more) is heartbreaking, but entirely avoidable. So what's this costly error? Failing to validate your idea before building. Here's why this happens and how to avoid it: 1. The Enthusiasm Trap Founders fall in love with their idea and assume everyone else will too. Solution: Detach emotionally. Your idea is not your baby; it's a hypothesis to be tested. 2. The "Build It and They Will Come" Myth Many believe a great product will automatically attract users. Reality Check: The market is crowded. Even great products can fail without proper validation and marketing. 3. Overestimating Market Need Just because you think it's a good idea doesn't mean there's a market for it. Action Step: Conduct thorough market research. • Use surveys, • interviews, • and competitor analysis. 4. Ignoring the MVP Approach Trying to build a perfect, feature-rich product from the start. Better Way: Start with a Minimum Viable Product. Test core features with real users before scaling. 5. Neglecting Customer Discovery Building based on assumptions rather than actual user feedback. Fix: Talk to potential customers BEFORE writing a single line of code. How to Validate Without Breaking the Bank: • Create a landing page to check the interest • Use no-code tools to build a prototype • Run small-scale ads to test messaging • Conduct pre-sales or crowdfunding campaigns • Offer consulting services in your niche to understand pain points    It's cheaper to find out your idea won't work before you build it than after. Even the most brilliant ideas can fail if there's no market demand. --- Want to build your MVP or dive deeper into any of these steps? 👉 DM me word "MVP". --- Follow Mikail Bayram for actionable tips, real-world examples, and lessons learned from building 15+ products from scratch.

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It’s still early to predict which companies will rise as the new giants of the AI application layer. Significant breakthroughs are likely still ahead at the foundational model level, and companies operating in the application layer will need to stay highly agile. Any major advancement in foundational models will disrupt their business models.

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The right model combined with domain specific fine tuning (Marqtune) equals massive improvements to relevance.

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To all the founders about to quit and dreaming of a FAANG job or equivalent in pay & prestige, listen up: 1. What you're doing now-- trying to build & grow something new-- is the only "job" that AI and robots won't be taking. 2. The job you are dreaming about is already gone. Push through to fight another day. Be relentlessly resourceful as PG puts it. Your problem needs to be solved and no one is going to solve it for you. If you want help/advice on cutting expenses, sharing upside with community in exchange for build & grow collaboration, and raising capital from your users-- DMs open. There is no greener grass than the type that grows on PMF. Using Slyk IO to launch, grow, and raise fast puts you on the fast track to finding out. Your community can help you get there. I know because I'm doing this with Cadoo . #founders #startups #community

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What I learned from Jerry Yang & Larry Page TL;DR: Technology companies launching disruptive products should religiously focus on end-users above everything else, including partners. Solve end-user problems and everything else follows– adoption, revenue, and partners. Yahoo! & Jerry Yang (2006) In 2006, phone calls were expensive and based on time & distance pricing. Skype changed all that with VoIP, which bypassed telco networks and made super cheap calls using the Internet. They already amassed 34 million users and were growing at insane rates. At the same time, Yahoo Messenger had over +110 million users worldwide. We could leverage this userbase and create a VOIP product to challenge Skype. Yahoo had all the advantages on paper: the brand, the users, and the technology. So, with the leadership of Brad Garlinghouse, we built a competing product with a very disruptive pricing plan – 1 cent/min; Skype's pricing ranged from 2-3 cents/min. However, a week before the launch, Jerry Yang paused the project at a partner’s request. ATT-SBC was concerned our pricing would cannibalize its own voice business. Yang did not want to upset that relationship. Ultimately, Yahoo missed its opportunity, and Skype was off to dominate the VoIP market. Google & Larry Page (2008) Before the iPhone, companies had to negotiate with mobile carriers to get their apps on phones. Google was struggling to secure deals with Verizon for Google Maps and a new phone concept, Android. Around the same time, the FCC planned to auction 3G spectrum. Google pushed for open application and open device requirements on the carriers who won the auction. This infuriated the carriers. They argued that such conditions would decrease the spectrum's value below the $4.8 billion auction floor set by the US government. The FCC was leaning towards the carriers. The agency did not want to risk the auction not meeting the government's financial goals. Google's only option was to participate in the auction to ensure the minimum bid. Inside Google, the decision to bid was intensely debated. Android founder Andy Rubin argued the carriers would hate us and never partner with Android; Chris Sacca argued that bidding was the only way to guarantee Google's future in mobile. Page sided with Sacca. Page’s rationale: “. . . the carriers already hate us. If we don’t bid, they would hate us and not respect us.”  We bid, the openness provisions were triggered, and we secured a path for Google apps on 3G. Sixteen months later, Andy got his deal Verizon –  Verizon Droid! Page was willing to sacrifice mobile carrier deals to ensure Google’s products were universally accessible to its users. He played spectrum poker with $4.8 billion to achieve that result. (cont. below)

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There's now no shortage of options for open source model hosting providers. i.e., take your pick for Llama 3 (Groq, Together AI, Telnyx, etc.). Am I wrong in thinking the only real difference now between them is speed, cost and readability? If so, this space just became commoditized quickly and reminds me of the telco provider wars where its a race to the bottom on price. Which means the cost of running AI applications is approaching something close to zero... 🤯 #ai #cx

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After a year in stealth, today is the day… Andrew, Josh, and I are beyond thrilled to share that Miriel AI is officially live! 🎉 👉 Get Early Access to the Beta: https://miriel.ai What is Miriel? It’s the fastest way to build personalized AI applications or integrate AI into your existing products. With less than 10 lines of code, you can weave any data source into powerful applications like: Customer engagement agents that understand your product and every individual customer Voice-controlled personal assistants that seamlessly access your email, calendar, and any relevant files Intelligent video monitoring systems that alert you to custom threats in real-time Code assistants that understand your development environment, codebase, and any relevant API docs 🚫 No expensive training. No complicated setup. ⏱ You’ll be up and running in minutes. Why does this matter? AI is at an inflection point. Miriel puts the power of AI into your hands. We’re making it possible to build applications we could only dream of before. With the ability to rapidly prototype, iterate, and scale, the future of AI will move faster than ever before. We’re already collaborating with a number of incredible companies on game-changing projects. And this is just the beginning. We can’t wait to see what you build with Miriel. The possibilities are endless. Join us and weave the future: https://miriel.ai #artificialintelligence #startups #miriel

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Edge AI is the future! Are we prepared for the data privacy tornado that brings? Every device will have AI compute power and this will open up a new horizon for consumer (and business) applications. But who can reliably own the data? Businesses should build their own models now. #AIOwnership #DataPrivacy #EdgeAI

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