Benjamin Mears

Want a preview of what I'm going to be talking about in my keynote for RubyConf? You can catch a glimpse of it here!

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Come join us tomorrow in SF to learn more about the latest updates to run AI workloads on Google Cloud!

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🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

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I am excited to share the latest blog, co-authored by myself, Junhwi Kim and Vyas Sekar, detailing how we managed to successfully localized and improved LLM by combining Fine-Tuning and RAG. https://lnkd.in/gKDkgGPq

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Check out the latest in our "Debuggability" blog series! This blog is authored by Kevin Downey Jason Johl and me and talks about how we built a Debuggability Paved Road at Intuit using Ephemeral Containers and Argo Workflows. We'd love to hear your thoughts on the topic. Also, what tools do you build as-a-platform to help debug service, compute and network issues in Kubernetes at scale? #intuit #kubernetes #debug

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Since the YC deadline to hear back for W25 is coming up next week, here’s the story of how we went from 2 students coding in an MIT dorm room to being full-time YC founders building Delve in SF. For all you founders anxiously waiting to hear back, just keep building — you’ll be successful regardless of the decision. -- In Fall 2023, my co-founder Karun Kaushik and I were studying AI at MIT and spending all of our free time building an AI medical scribe to help doctors. We had vaguely heard about YC from a friend who had just gotten accepted but had no plans of applying whatsoever. However when the YC deadline rolled around, we spontaneously decided to put in an application on the very last day. We spent that whole day in Karun’s dorm room typing up the application and filming all the required videos. We finally hit submit the minute of the deadline! By the next day we had already moved on. We went back to the usual rhythm of coding in the dorm room, calling with doctors, attending conferences, etc. If anything, we were even more motivated now to get more traction and be successful regardless. Later that week, while we were eating in the dorm kitchen, I noticed I got an email saying we had a YC interview scheduled. My heart rate spiked. I jumped up out of my seat and immediately told Karun. I never seriously considered YC, but for the first time it hit me that it might actually be a possibility. When the day of the interview rolled around, we did mock interviews with former YC founders and focused mostly on calming our nerves. More important than the actual content of the interview, we wanted to show our excitement for building and hacking together as co-founders. Unfortunately the interview caught us by surprise. The group partners didn't seem to care as much about our actual AI medical scribe. Instead, they seemed more curious about a side idea we had listed in our application that we wrote up in a day. The idea was about streamlining HIPAA compliance (since we had to go through HIPAA for our AI medical scribe). By this point, we were already seriously considering pivoting into this and had made a lot of progress on the idea. The YC interview was our tipping point Not too long after, we got an ominous email titled “YC.” We were freaked out. I could not sleep the entire night. Then, in the middle of class the next day, I got a phone call from Karun with Surbhi on the other end of the line. My heart rate spiked. I ran outside of the classroom. That turned out to be our acceptance call:) -- For everyone out there still waiting to hear back, just keep grinding. Build as if you’ve been rejected and you might just be pleasantly surprised. I’m also always here to help with mock interviews/give advice! We’ve helped a handful of friends through the YC app process and are always here to support other founders!

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My cofounder Saumil goes on the Stack Overflow Podcast to deep dive on AI Agents! He goes into detail how we leverage AI agents to power automated workflows to accomplish the most complex tasks. Including agents firing off tasks to other agents to tackle the details. Need an agent to verify your work? No problem. Give it a listen and let us know what you think!

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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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AI & SaaS: A Tale of Two Futures What if Excel and other SaaS apps don’t vanish but evolve, blending seamlessly with AI? In a recent Interview (https://reurl.cc/eGz06j), Satya Nadella, Microsoft’s CEO, suggests the “agent era” will replace traditional applications. That’s Nadella’s vision: AI as a co-star, enhancing our favorite tools—think Excel with Python instead of Excel replaced by something else. But here’s the twist: 🔸 I see AI’s disruptive power surpassing any need for standalone software. 🔸 Why open an app when a smart agent can directly pull, manipulate, and display data on demand? Nadella believes AI + SaaS = Harmony. He sees Copilot orchestrating tasks, quietly boosting productivity. Developers still matter, refining user experiences to work with AI rather than against it. I foresee a more dramatic shift. If AI can generate everything from sales forecasts to text summaries instantly, maybe the spreadsheets and CRMs we rely on will fade away. Where do you stand? 📌 Collaboration or Disruption? 📌 AI enhancing SaaS or AI replacing it? No matter which path emerges, one thing’s certain: AI is rewriting the rules—and the next chapter of SaaS will be far from business as usual. Your thoughts? Let’s discuss. #AI #SaaS #FutureOfWork #Microsoft 

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We launched structured data retrieval in Bedrock Knowledge Bases last week at re:Invent. While embeddings-based semantic search has worked wonders for unstructured data, we felt we need a new approach for structured data. We needed a way to translate customer natural language queries to an expressive query language that traditional databases already support. https://lnkd.in/eTPStNif #rag #text2sql #aws #bedrock

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You want to do Machine Learning on large scale Marketing solutions ? Check below 🚀

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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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Your ability to adapt to changing conditions will be your super power. Being overly adapted to existing conditions makes survival questionable.

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Thats the exact explanation I gave to a group of investors last week. (… it’s just Precision/Recall, attributes vectorization as “embedding”, K-Nearest Neighbor, User Search Intent, DAG, ML algorithms, etc all over again …). LOL and they asked what’s new this time around. And I was like not much, ML is still the backbone, and AI is the inference and actions based on the model. It works better when there is a feedback loop in a closed system. But the current state of the art like ChatGPT is based on scraping the Internet (garbage in, garbage out). Kinda like early search engines. What is a better analogy is that NVIDIA/Snowflake etc are selling tools, aka pickaxes to prospectors. The gold is up to you to find. And the opportunity right now is to monetize industry specific (healthcare, travel, financial services) clean data to build models with (aka a treasure map for the prospectors). From an enterprise perspective, what’s out there today aka generative AI isn’t very useful. Too much hype. To build useful engines will require an effort similar to search revolution 2 decades ago. From

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How you can make your LLM applications better!

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Fantastic insights from Jesse Zhang of Decagon on building AI agents for customer support. Highly recommend for all builders in AI for customer engagement. Some snippets below: I think everyone believes that there's going to be a lot of AI agents that come up. And so for us an interesting thing would be what would the humans that work around AI agents do? Like what tooling would they have? What sort of control or visibility would they have into the agents that they're working with or managing? You can kind of think of [agents] almost like it's a web of LLM calls or API calls or like other logic that all works together to produce a better experience. I think the biggest differentiator for something working or not is actually the shape of the use case. Two properties I would say are very important for something to evolve past the demo. The first is that the use case you're solving, the ROI has to be like very quantifiable. The second piece is that the use case has to be incremental. On one end of the spectrum you have people that really try to personify their agents. There's a human avatar, a human name. On the other end of the spectrum, it's like it calls itself an AI. We've talked to many teams where they've kind of done some experiments themselves and like built the initial version. And then they're like, okay, yeah, it's pretty clear, like, we don't want to fix this long tail [of issues] and constantly building new logic CX team. People are scared about LLMs because they're not deterministic. But you can put sensitive and complex stuff behind a deterministic wall... We've seen a lot of big customers, their security teams will basically come in and like red team our product essentially, because that's one of like the best ways to actually gain confidence in does this work or not. The type of intelligence that we care a lot about for us is, I would describe it more as instruction following. It seems like a lot of the research recently has been around more reasoning type intelligence, getting better at coding, getting better at math. That's helpful for us too, but it's not as helpful as the first type. It's much easier to get an AI agent adopted if you are automating a very specific flow that is either clearly revenue generating or was a bottleneck in the business before to get new demand or it was like a major cost center that scaled linearly with customer growth or revenue growth or something like that. I think people do care about hallucinations, but they care a lot more about the value that can be provided. It's what percentage of conversations can you resolve? How happy are my customers? And then hallucinations might kind of be lumped into the third category, which is what's the accuracy? I think one fun learning for us has been that most people have opted into the per conversation [pricing] model. The reason is that per resolution, the main benefit is you're paying for what the AI is doing. But then, what is the resolution?

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Also #intuit confirms that #bigid is the #dspm with the best #datadiscovery capabilities! BigID has more classifiers (including AI\ML based classifiers), and more connectors (including PaaS, SaaS, on-premise and even Mainframe!), than any other DSPM! #datasecurity #dataprivacy #datagovernance #cybersecurity

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The challenges of scaling and recovering AWS MSK clusters are now effectively mitigated, representing a significant advancement in cluster management.

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