Shreyas Kumar

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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A common mistake early-stage VCs make is confusing good decisions with good outcomes. Good outcomes don’t always mean good decisions—luck often masks flawed reasoning. Focus on process over outcome bias. Invest in strong decision-making frameworks, not just duplicating “wins.”

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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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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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🚨🚨 🚨 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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Prompt engineering is breaking enterprises, and no one’s talking about it. When we first started building Future AGI, one thing was painfully clear: Enterprises are struggling to make LLMs work at scale. It’s not because they lack talent or resources. It’s because the process of prompt engineering is messy, unpredictable, and often downright inefficient. 1. A prompt that works beautifully for one data set fails miserably for the other. For enterprises juggling dozens of workflows, this is not just frustrating but also expensive. 2. Even when using the same prompt, AI outputs can fluctuate unexpectedly due to evolving datasets, model updates, and other changing variables, creating chaos for enterprises that need consistent, reliable results. 3. Every tweak, every experiment, every new model iteration comes with high compute costs and worse, lost time. It’s a vicious cycle of inefficiency that leaves little room for actual innovation. I spoke to 200+ AI experts in the past 1 year and realized this is a consistent problem. At Future AGI, we’re fixing this. We created a solution that simplifies every stage of experimentation: - Smarter prototyping: Test multiple prompts and models in one go. Compare outputs side by side to eliminate the guesswork. - Deep evaluation metrics: Get instant insights with 70+ built-in metrics or customize your own to measure exactly what matters. - Unified analysis dashboard: No more scattered spreadsheets. View all your data in one place for faster, more informed decisions. - Dynamic prompt optimization: Tailor prompts in real time to fit your dataset, ensuring consistent results across all use cases. For enterprises, the difference is night and day: Cut prompt experimentation time by 90%. Reduce compute costs dramatically. Ship products faster with better accuracy and reliability. At the end of the day, it’s about building confidence in your LLM workflows. If you’re tired of battling inconsistent outputs and inefficient processes, let’s talk. Drop me a message 💬 or check out the link in my comments to book a demo.

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During yesterday's LibreQoS APNIC webinar, I posed the question: won't more bandwidth solve these problems? As Herbert Wolverson said, yes, more bandwidth is good, but, still these problems remain if queueing is done incorrectly. Here's my take on why bandwidth alone is not as good as bandwidth + good queueing policies: Generally it was believed that 'data is important, so don't throw it away; hang on to it and send it later'. In practice, this has proven to be suboptimal as two issues may emerge: 1) latency increases for some flows due to heavy demand from other flows using the same bottleneck link, 2) even a single flow can have excessive latency due to aspects of typical TCP behavior (referred to bufferbloat) when the buffers grew large enough that the data being buffered was retransmitted anyway. It turns out that not all data is equally important. Active Queue Management is the art of deciding priorities, both in deciding what data to throw away, but also in allowing some later arriving data to be transmitted ahead of data in another connection that arrived before it. These problems have been studied, and good solutions have been found by using certain queueing policies in routers and switches, referred to as “fq_codel’ and “cake”. These track the different flows not by classifying the data, but by watching the behavior. Flows that send relatively little data (DNS lookups), or at a measured pace (video chat) have priority over flows that send a lot of data as quickly as possible (App and System updates, Video and ISO downloads).

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The newest issue of The Pragmatic Engineer found a surprise uptick in eng hiring in the typically slow summer season - I'm curious to see how the macro environment impacts this uptick as we head into the fall, but I remain hopeful. I asked Gemini to give me the tldr of the article to share with my team (sorry, Gergely Orosz) and found it missed one of the most interesting pieces of the article. Gemini's highlights - Unexpected surge: Tech recruitment has seen an unusual spike in June and July, contrary to typical trends. Increased demand: Startups, especially AI-focused ones, are driving the hiring uptick. Global phenomenon: This trend isn't just limited to the US; Europe is also experiencing increased recruitment activity. Reasons for hiring: Companies are backfilling positions, investing in growth due to improved financials, and responding to increased VC funding. Challenges remain: While the job market is improving, finding qualified candidates and offering competitive compensation remains difficult for many companies. What I found most interesting - The nature of the backfills: the article finds that a lot of new headcount are actually backfills of folks that were "over-slashed" during rounds of layoffs. Not entirely surprising with the "trim the fat" and "rebuild lean" mentality, but interesting nonetheless. Either way, the uptick is encouraging (and don't always rely on Gemini's tldrs). #hiring #startups #VCbacked #softwareengineering

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India is not just catching up in the AI race; we're innovating and leading in ways that are uniquely ours. The Economic Times recently highlighted how our approach to Generative AI goes beyond just Large Language Models (LLMs), addressing the specific challenges and opportunities within our diverse market. Rahul Agarwalla, Managing Partner at SenseAI Ventures, shared his invaluable insights into this AI revolution. His leadership and vision are truly driving innovation in the field, and it's an honour to be part of this journey with him. For anyone interested in the developments in AI and how India is making its mark on the global stage, this is a must-read.

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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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I first met Mike Maples, Jr in 2010, when Founders Network was just an idea. Over the years, Mike and his team at Floodgate (special shoutout to Arjun Chopra) have been great mentors, sharing their insights and guiding our founders. Most recently, their advice played a pivotal role in helping us launch Founders Network Fund. Today, Mike shared the key ideas with FN'ers from his new book Pattern Breakers (https://a.co/d/buKBd5O ), which explores the insights he’s gained from backing companies like Twitter, Twitch, and Okta -- and which put him on the Forbes Midas List 8x. This is the most important book on startups in the last ten years, according to Steve Blank. My notes on the key concepts of his talk: 1) Inflections – "not a trend but a change event that represents a turning point in our ability to provide empowerment." e.g. The GPS chip in iPhone 4s 2) Insights – "a non-consensus and right insight about the future that's powerful and non obvious and leverages inflections." e.g. Lyft = GPS + strangers driving you 3) Founder future fit – "Living in the future before other people live in the future and then try to build what's missing in the future." If you're a founder, check out the book. Grateful for the impact Mike and Arjun continue to have on both me and the Founders Network community!

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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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Kim Burgaard Rachel Perez Jay Leverett David Bellotti -- I'll be listening to this one for sure! Sergio H. Serge Madenian Episode Summary: The episode features an in-depth interview with Mike Krieger, the new chief product officer at AI company Anthropic, discussing his background, the decision to join Anthropic, the company's focus on responsible AI development, the challenges and opportunities in building AI products, and his vision for the future of AI technology and its applications. https://lnkd.in/guWW8cku

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An excerpt from our weekly newsletter: (a link to subscribe is in the comments) Enterprise software malaise. The biggest news in tech this week seems to be a widening sense of capitulation around enterprise software valuations. Jamin Ball of Altimer Capital wrote that software sentiment had crumbled. There are concerns around AI, but the main issue seems to be a slowdown in growth in general. As Jamin writes, “growth hasn’t rebounded - there was no v shaped recovery. In fact, forward estimates are starting to go down, not up. Sure, the strong headwinds have abated, but the headwinds haven’t been replaced with tailwinds. Everyone is still waiting for growth to “re-accelerate,” and a number of companies are implying this will come in the back half of the year, but will it? Feels like investors are broadly more comfortable waiting for the data to show up. What’s more likely is the current state we find ourselves in is likely to be the equilibrium.” Anita Ramaswamy, writing in The Information, argues that the selloff and the collapse of sentiment is overdone: “The market’s panicked reaction may be overdone. It’s far too early to judge how much companies will shift their spending from existing enterprise software firms to new AI services, and when. Given questions about large language models’ accuracy and cost, which have caused many businesses to hold off spending on AI-powered services so far, the near-term impact on software spending may be minimal. And, of course, software companies such as Salesforce are adding AI tech to their existing products in an effort to cash in on the AI boom. That complicates the issue, as some software believers think AI can help drive growth for older companies, while others think it will kill them. Whatever happens, the shift is likely to take years.” Jared Sleeper pointed out that software is just naturally growing slower as it goes logarithmic. “As bad as all of that sounds, there is nothing to fret about here per se over the long run. It isn’t surprising that an adjustment away from behaviors learned over two decades. It is actually an altogether more benign explanation for the current malaise than some of the others circulating. In this scenario, SaaS still has a healthy decade of well-above-GDP growth ahead of it (without considering AI’s impact). There are still plenty of exciting startups which for one reason or another are less mature than the sector overall and merit investment levels that harken back to headier days. We're experiencing a normal adjustment period of a disruptive, exponentially growing technology- not an existential crisis.” Tomasz Tunguz also addressed these concerns, pointing out that “In the last 12 months, public software companies will grow on average 17%, which will add $100b in revenue across these businesses.”

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In retrospect, 2024 was a pretty amazing year for innovation out of University of California, Berkeley. My Top 10 highlights below. Any I missed? 1. Databricks secured the largest VC round ever—a $ 10B funding round, led by multiple Berkeley founders. 2. OpenAI raised the 2nd largest VC funding round ever - $6.5B. Co-founded by Berkeley alum John Schulman. 3. Since launching Built By Berkeley (a newsletter that tracks Berkeley founded companies funding) in July 2024: 229 companies raised 27.4B. ~$1.1B per week raised by Cal founders! Wild! 4. Out of the 13 2024 Nobel Prize laureates - 4 had Berkeley connections. 5. Record-breaking Berkeley Olympic performance: 5 Gold, 10 Silver, 8 Bronze medals. More than many countries!  6. Berkeley-founded Perplexity emerged as a genuine Google Search competitor, reaching a $9B valuation (9x increase in a year) with 15M+ active users (probably already much higher than this). Along with some unbelievable execution speed from Aravind Srinivas and team. 7. Chatbot Arena, founded at Berkeley, became the industry standard for AI model benchmarking. 8. Rich Lyons became the new Berkeley chancellor. Rich will be a huge innovation catalyst for Berkeley. 9. Berkeley beats Stanford University 21-24 in the Big Game…not innovation but fun nevertheless.  10. Berkeley leads the PitchBook rankings with 1,642 VC-backed undergrad alumni-founded companies. 245 more than second-placed Stanford. Berkeley is shaping up to be ground zero of the coming AI wave from a company formation and research perspective. Looking forward to a bigger and better 2025.

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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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Understanding Hallucinations and Fabrications in LLMs 🤖💭 Inspired by 'LLMs Will Always Hallucinate, and We Need to Live With This' - Sourav Banerjee et al. Large Language Models (LLMs) are powerful tools, but they sometimes produce errors known as hallucinations or fabrications. These errors are not just occasional—they are inherent features of these systems. Therefore, we need to take the time to train people on how to use LLMs effectively to navigate their limitations and raise awareness. 💡 Hallucinations and Fabrications - Let's break down the difference: Hallucinations occur when LLMs provide inaccurate or incomplete information, usually based on existing knowledge but applied incorrectly. Examples include: 1. Factual Incorrectness: Providing incorrect facts—e.g., stating a blood sugar level is 150 mg/dL when it is actually 120 mg/dL. ⚠️ 2. Misinterpretation: - Corpus Misinterpretation: Misunderstanding the context or meaning within the data, leading to incorrect conclusions. ⚠️ - Prompt Misinterpretation: Misinterpreting a user's question due to ambiguity, such as confusing "lead" (the chemical element) with "lead" (leadership). ⚠️ 3. Needle in a Haystack: When LLMs struggle to find the correct details: - Missed Key Data Points: Leaving out crucial information, like only mentioning one cause of World War I. ⚠️ - Partial Incorrectness: Mixing correct and incorrect facts—e.g., stating Neil Armstrong walked on the moon in 1959 instead of 1969. ⚠️ Fabrications are different—they involve creating entirely false information with no basis in the training data. Examples include inventing a non-existent scientific study or making up a fake quote from a historical figure (this happens a lot!). ⚠️ Summary: Hallucinations and fabrications in LLMs can lead to issues such as misinformation, legal and ethical risks, erosion of trust, and amplification of biases. Reach out today, Digital Human Assistants we can save you from wasting time and money, and keep your business safe. 🔒🤝https://lnkd.in/gRPDET6x Paul Edginton Ricky Sydney

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