George Sivulka

🚨🚨 🚨 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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CBO Interviews: Mladen M. Hoyss on Shaping the Future of Nothing OS | https://lnkd.in/eFyYx9n6 Getting the chance to talk with Mladen was a surreal opportunity, and I’m truly grateful to the Nothing community for choosing me to be their voice. It’s moments like these that bring my journey full circle. My history with Mladen’s work goes back to the early days of Blloc. While I couldn’t use the Zero18 phone, I’ve been following Blloc since the beginning. I got into the closed beta testing of Ratio, their minimalist launcher. At that time, I was heavily addicted to my smartphone. Ratio had a profound impact on my usage habits, significantly reducing my screen time. I was so impressed that I continued using it across three to four different phones, often proudly sharing screenshots of my reduced screen time on social media. When I heard about Mladen M Hoyss joining Nothing I thought, “This is a collaboration that was bound to happen.” The transition from Ratio to Nothing OS has been an evolution in my smartphone experience. It’s fascinating to see how Mladen’s philosophy has been integrated into a more comprehensive and fun environment at Nothing while still maintaining the core principles of mindful technology use. This interview has not only provided insights into Nothing’s design philosophy, but also allowed me to connect the dots in my personal journey with mindful technology use. I’m excited to see how Nothing continues to evolve and grateful for the opportunity to be part of this community-driven conversation about the future of technology. #NothingCommunity #NothingOS

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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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Today, I found a fascinating way to think about “Tokens” in LLMs. It came from a conversation between Jensen Huang and Patrick Collison at a recent Stripe event. When asked about the future of compute capacity in five years, Jensen smartly dodged that question a bit, but then gave an excellent explanation and analogy about why tokens are the new forces that will power the next decades of humanity. He explained that we're now producing something unprecedented (and at scale): floating point numbers that possess “value”, which we now call tokens. These tokens are valuable because they encapsulate “intelligence”. People are now taking these tokens and transforming them into English, French, images, videos, chemicals, proteins, robotic movements, etc. And many are working hard to expand the range of concepts and ideas we can create with these tokens. He then goes on to made a compelling comparison between tokens and electricity. In the past industrial revolution, we successfully found a way to convert “atoms” into “electrons” (by boiling water to power electricity turbines). And now, we've discovered a way to convert "electrons" into "tokens" (by using energy to power data centers that train and run LLMs). When electricity was first introduced, few people understood its value. Today, paying for kilowatts is routine. The same will happen with tokens. Right now, only early adopters and builders are paying tokens. Soon, everyone will be paying for tokens on a daily basis to supercharge productivity and power new products and services. Many new industries will be born from and built on top of tokens. When I first heard about this way of thinking, I got goosebumps. Perhaps it’s because Jensen has a good storytelling skill that helps him sell what he builds, but I have to admit this way of thinking gave me a brief surge of pride. I'm proud of humanity's collective effort and creativity, which have taken us from living under rocks to building machines that can "think." That's nothing short but a miracle.

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Ilya Sutskever confirmed at NeurIPS '24 pre-training scaling law will not be the same. Internet data, the AI fossil fuel, is running out. It is not the end of the AI hype, the rapid rise of foundational AI has outpaced our ability to fully grasp their long-term societal impact. AI agents and applications are poised to transform every facet of our lives, and we will see it happening in coming few years. There will still be improvements in model architecture, and synthetic data creation, so models will improve, just not at the lightning speed they did till now. Looking forward, the single most important milestone in AI is creating systems capable of advancing themselves—AI researchers that can innovate and improve foundational models autonomously. We need to assess how far are we from this stage.

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🎯Data was, is, and will be the only fuel. Analogy with the fossil fuel is a good one, since the data we have today is as dirty as oil and coal. What big tech companies tried is to put a lot of "garbage" data, and what they did get was fantastic. Yet, we are at the point of diminishing returns. Data is the beginning and the end of this cycle.” Great thread, particularly this comment 👆from Ilija Lazarevic

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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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Notion vs Glean reminds me a lot of Tesla vs Waymo... Just like I think Tesla is going to solve the 'software' problem of autonomous vehicles before Waywmo solves the 'hardware' problem... I think that Notion is going to solve 'enterprise knowledge search' before Glean solves the problem of document and knowledge creation... Why? A couple reasons: 1) Just like Tesla, Notion has a huge advantage when it comes to the amount of underlying data and documents to train on... They already have 100M+ users, and likely 10x that in terms of the number of documents, files, etc. in team's workspaces. 2) It's very hard to change people's behaviour re: where/how they create 'work'. It's why a lot of companies have come and gone in that category... As exciting as Glean's AI writing assistants might seem, most people are just fine (or even prefer) a good old fashioned editor/writer. I could be way off here, but this will be an intersting one to follow. Thoughts? Where am I getting it wrong? #ai #enterprise #knowledge #management #notion #glean

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Optimize for 🚀 Bio Cores First, Silicon 🤖 Cores Second David Heinemeier Hansson makes a compelling case: prioritize human programmers (bio cores) over computer processing power (silicon cores). Here’s why it matters: 👉 Investing in programmer efficiency is far more valuable than solely optimizing for computer speed. 👉 Human labor costs far exceed computing costs. Boosting human productivity should be the main focus. 👉 In the age of AI and high-level programming languages, the bio cores drive your real gains. Bottom line: Make your programmers more productive, and watch your team (and tech) thrive. Read the full article here: https://lnkd.in/gK8HEGtk P.S. : The image below is brilliantly summarized using Napkin.ai for a quick visual breakdown.

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Pathways to Tech Week #2 at  Headstarter: Being surrounded by people who are building amazing software and companies is a truly inspiring experience. My favorite speakers were: • Tariq Ahmed: First Engineer at AppLovin ($30Billion IPO) • Julian Alvarez 🚀: CEO of Wisdolia • Hamza Ali: CEO of Olostep  Here's what I have learned from them: 𝟭- "𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗮𝗻𝗱 𝗽𝗶𝘃𝗼𝘁" Tariq, one of the first engineers at AppLovin, shared that AppLovin tried several things in their early stages that failed. However, the company had incredible success when it pivoted to mobile advertising; They didn't have expertise in the field. Today if you've experienced in-app advertising, AppLovin was likely behind it. 𝟮- "𝗦𝘁𝘂𝗱𝘆 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲𝗿𝘀, 𝘆𝗼𝘂'𝗹𝗹 𝗯𝗲 𝘀𝗵𝗼𝗰𝗸𝗲𝗱 𝗮𝘁 𝗵𝗼𝘄 𝗺𝘂𝗰𝗵 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗹𝗲𝗮𝗿𝗻." Julian runs Wisdolia a startup aimed at helping college students actively study. By simply asking what types of students they were. His team found that medical students were the ones the ones who converted the most into paying customers. It seemed evident because they were the most overwhelmed by the amount of information they had cram. But the evidence in their face was a lightbulb moment for Wisdolia. 𝟯- "𝗦𝗰𝗿𝗮𝗽𝗽𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲 𝘄𝗲'𝗹𝗹 𝗴𝗲𝘁 𝘆𝗼𝘂 𝗳𝗮𝗿." Hamza, CEO of Olostep, describes his company as "a company that enables AI agents to browse the web just like humans". I was fascinated when I heard that, and as the Olostep team acquires more customers they value scrappy and people that initiative on board. Those qualities set engineers apart when building successful products.

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This will unlock a lot of possibilities, and comes with some really nice advantages like: - More efficient chatbots - Easier builiding proces - Better customer experience - Cheaper prices for chatbots Now's the best time to start integrating customer support chatbots in your business to deal with customer while you focus on selling 💯

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Here's a project I did this weekend analyzing the type of state-enforced bias and censorship on Chinese LLMs. This is probably going to be more important moving forward as more of these are not only quite strong (Qwen2 7B Instruct, which I tested is competitive with Llama 3 8B) but more importantly, being liberally licensed - Yi 1.5 and Qwen2 are both largely Apache 2.0 licensed. These are also multi-lingual models, which means it's probably going to end up being used for all kinds of things without downstream users' knowledge. Anyway, in for a penny, in for a pound, so figure I might as well share this widely, since I haven't seen a similar writeup/exploration on this yet and IMO the results are quite interesting: https://lnkd.in/g_WKscA6 Besides the writeup I also applied the recent "abliteration" (orthogonalized refusal vector removal) technique to create a model that largely (~80%) removed refusals without significantly affecting performance and a "deccp" dataset as a starting point for those looking to play around with testing Chinese state censorship alignment of their models. Model, dataset, and all related code are Apache 2.0 licensed and can be found linked from the article.

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New iterative reasoning technique could improve RAG app accuracy by 50% (vs. Naive RAG)! Another solid example of the rapid innovation cycle on RAG research. BTW, you can quickly find out answers to question like "Why did combining RAG with Fine-Tuning and Iterative Reasoning, such as OODA, improve accuracy by 50 percent?" in Epsilla (YC S23)'s "RAG Research Paper" smart search app at: https://lnkd.in/gVgzpbue , with number of research papers included exceeding 360. #genai #rag #smartsearch #innoation

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Keep Learning New Things... Indeed, the path to efficiency requires discipline, consistency, and sacrifice. I can't believe that it is already the 3rd week of the Headstarter Fellowship! To be honest, I felt quite lazy throughout this week, with low dopamine levels. However, I'm getting back on track and am ready for the next week of the fellowship. During the previous week, I had the opportunity to work on some exciting projects: - The Pantry App Project: I focused on the Firebase backend, learning how to integrate features such as authentication, analytics, and database CRUD operations. Live: https://lnkd.in/dCGFCiH5 Github: https://lnkd.in/dWpHgc4y Pathway to Tech Event 2: Here's My Key Takeaways: Dennis Crowley: Founder of Foursquare ($150M Revenue) When asked about the ideal tech stack, He emphasized the importance of being "willing to fill in gaps." It's not just about knowing specific technologies but being able to and ready to learn. His experience on failures "People quit on themselves or quit on their startup.", reminding me to stay resilient and never quit Tariq Afaq: First Engineer at AppLovin ($30Bn IPO) Tariq's talk was packed with valuable information. when he mentioned that this field is more than just coding, like being an engineer we can solve problems under pressure and get things back on track He also mentioned that you don't need to be super intelligent to succeed. Instead, focus on creating in-depth products, being quick and responsive, and working hard. Julian Alvarez: Ex-Meta, CEO of Wisdolia ($20K+ MRR) The most energetic speaker at this event! , His thoughts on how startup founders stick to their ideas rather than believing in their team's capabilities become the reason for their failure something exceptional and new for me to learn. His startup Wisdolia— is truly something. It generates flashcards for you from any YouTube video, blog, article, or PDF. I can already see it becoming a vital part of my learning routine! Hamza Ali - CEO of Olostep I was deeply inspired by Hamza Ali's vision for Olostep, which is enabling APIs to interact with the web as humans do. It's truly a brilliant idea that showcases his commitment to shaping the future of tech. 🔗 Here's the link to the recording: https://lnkd.in/dzX6Saaz

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In March, I left Strac (YC W22) to build something new (more on this soon). Here are my key takeaways from taking Strac from zero to one: While at Strac, my favorite part of the role was always taking an engineering-first approach to our go-to-market motion. I loved tinkering, and frankly there's never been a more exciting time to do so as AI rapidly changes how effective sales teams function. Some of my favorite projects at Strac included writing a script to set up emails optimized for outbound deliverability, building a web scraper to check if a company was SOC2 compliant, and integrating our sales tech stack for seamless retargeting and outreach. The most impactful lessons: 1. Sales is a Numbers Game: Five years ago, sales teams relied heavily on traditional methods like cold calling and emailing purchased conference lists. However, nowadays, mass calling and emailing without the prospect showing any intent is ineffective. Today, with the explosion of data available online, intent signals—such as web activity, social media interactions, and content engagement—are critical for identifying leads. The challenge now is not just gathering this data but integrating and analyzing it to make your outbound effective at scale with the right messaging. 2. Get Hacky with Finding Leads & Continuously Experiment: You should run dozens of experiments on different lead lists and iterate until you find message-market fit. During the first month at Strac, we ran over 50 distribution experiments to find where our product had the most obvious signs of product-market fit and iterated on our outreach approach until we found message-market fit. When building out your ETL for sales, it’s important to build a framework that’s conducive to this. If you build a slow-moving, rigid outreach framework, you will limit how many experiments you can run, and your business will die. With startups, every single day you are just slowly dying—iterate fast. 3. Show Immediate Value to Your Customers: Strac was one of the most customer-obsessed companies I’ve ever seen. The most important thing is to fundamentally understand what problem your customer is trying to solve, as there is likely someone else also trying to solve that problem if it's a real problem. Translating this to structuring outreach: if you just make an ask, it's unlikely you'll get a response—you’re not solving any problem your customer faces and instead creating a problem for them. However, if you show someone how easily they can gain something from talking with you, you'll have a prospect excited to have a conversation. As I transition back to the founder role, I'm excited to build tools that address these challenges and help companies accelerate their go-to-market. I will continue to work closely with Strac as they go from one to ten, and I'm excited to have them use our solutions to get there faster 🚀

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After Apple’s research saying LLMs can’t think, it’s funny that now all I’m seeing is research on teaching them to think – something most humans still struggle with. Papers: Looking Inward: Language Models Can Learn About Themselves by Introspection https://lnkd.in/gf6rgnUP On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalisability https://lnkd.in/gNsPtDbi Thinking LLMs: General Instruction Following with Thought Generation https://lnkd.in/guQtRnsD #AIResearch #MachineLearning #ArticificalIntelligence

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Just published the first issue of my new Substack - The Vertical AI Report. The future of AI is Vertical, and I'm excited to share more about how this foundational trend is evolving, hopefully from a little different perspective. As an engineer, and a founder, of a Vertical AI company I'm incredibly passionate about the opportunity that Vertical AI is creating for the world. 🔗 Issue #1 is now live, link in the comment section below. #VerticalAI

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