Patrick Kelly

From the scrappy pre-seed, stealth-mode team of 2 through to fast-scaling Series C/D companies with many, many millions in the bank, each startup brings its own excitement and challenges. Of course, hiring strong technical talent is *always* important, and that’s a challenge in itself. But there’s also a certain nuance around the “people fit,” especially in the earliest stages. And I’m not necessarily talking about culture add/fit. The main thing that comes to mind with this, and one of the biggest things I drill down on with candidates, is motivation to join a company at the beginning stage. It’s absolutely not for everyone, and that’s okay!! The second thing that comes to mind is being radically transparent with candidates about what’s under the hood at the startup they’re considering. There’s no point in sugar-coating or selling a dream that doesn’t exist. Even in the most innovative and exciting startup there’s bound to be some cons to joining. It’s the nature of the beast that is startup land. Give the full picture, the right candidate will be excited regardless. Getting as much as you can out in the open in your initial screens and interviews, and re-checking on how folks are feeling throughout the process, is one of the things I feel is not spoken about as often yet is so important! (+ a good way to minimize cold feet/drop outs!)

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Are you in town for Generative AI World 2024 in Boston? Say hi at Woodstock - AI Woodstock, that is. Link below #ai #genai

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Dev agencies, if you don’t want to be treated as a commodity, stop acting like one. - Learn the business context for engineering needs - Stop squeezing your clients every opportunity you get - Prioritize transparency, communication, and accountability In short, position yourself as a partner that uses tech to solve business problems. You really can’t complain about being seen as a commodity if you treat everything as a transaction.

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LLMs are trained on internet-scale data. But to make AI work for work, models need to be grounded in your enterprise data -- a much harder problem than meets the eye! How do you connect to all your data sources and ingest them frequently enough while managing compute/storage costs? How do you make sure that you only show users results from documents they have access to? How do you rank results without internet-scale click and link data? How do you process the content of documents that are often hidden in PDFs and presentations? These are tricky problems, and it's why enterprise search didn't work for nearly a decade, until Glean came along. Turns out it's the exact same set of challenges needed to give LLMs the right context to work their magic on your enterprise content. Glean spent 5 years building the ingestion, search, and ranking infra needed to solve this problem well. This has given them the right to run the "Work AI" race and delight users at work. CEO Arvind Jain shares more on the latest episode of Training Data. Listen here, or wherever you get your podcasts: YouTube: https://seq.vc/ytd Apple: https://seq.vc/pit Spotify: https://seq.vc/jnz 

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Just sent out the latency newsletter for April. Noticing some really interesting trends. 🏆 Claude Instant regains the top spot after losing it for only a single month 🐢 Claude Sonnet + Haiku both saw a major increase in latency (~35% on average). This pattern of a slow down in performance the month after launch happened to GPT-4-Turbo as well. Could be related to increased adoption as people had more time to test and implement. 🐇 A model that I really like but doesn't get much attention, gpt-3.5-instruct, saw the biggest gains ( >50%) Plus a whole bunch more Get access to all the data via the link below. One email, once a month, packed with actionable and data-backed recommendations.

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There's a major misconception out there that LLMs solve all your problems when implementing conversational AI in the enterprise 🚩 I hear it often that all you need to do is throw an LLM at the problem and like magic... problem solved 🔮 This is causing massive confusion both on the buying and selling side 🤔 Buyers are told vendors can do everything, everywhere, all at once because of LLMs 😅 Vendors are told the competition can do it better, faster and cheaper even when the honest vendors are setting real expectations 😦 And it sets everyone up for failure 👎 What happened to integrity and honesty in business? 🧐 A word of caution, if anyone is telling you something that seems too good to be true, it probably is 💀 At that point, pause and please find someone who will actually be honest with you and walk you through the process in a way you understand 🤗

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McKinsey Says that Product Marketing Managers (PMMs) are the Quiet Force Behind Software Success A new McKinsey study (link in the comments, https://lnkd.in/gE9vyDxx) has put PMMs in the spotlight, emphasizing their growing importance in the software industry. Its findings reinforce the ideas I've shared in my previous posts on the evolving role of PMMs in the AI era (https://lnkd.in/ggjfSawh) and their pivotal role in driving personalization and efficient growth (https://lnkd.in/erV6Nipd). Here are some key insights from the study: ► Instrumental in Product Launches - PMMs are essential in launching successful products in today's competitive market. ► Revenue Growth - Companies in the top quartile in revenue growth have a 25-30% higher ratio of PMMs to PMs compared to bottom quartile companies, reinforcing the value of well-resourced PMM teams. ► Bridging Gaps - PMMs align product value with market needs, bridge gaps between teams, and orchestrate launch processes. As Goksu Nebol-Perlman, VP of Business Product Marketing at Meta, said, "Product marketing is the glue that keeps the product, the market, and the customer together." Why are PMMs critical more than ever? ► Market Differentiation - With changing market dynamics and increased competition, PMMs help products stand out and succeed. ► AI-Powered Analytics - As data and AI-powered analytics become essential for decision-making, PMMs use these tools to guide strategies and gain deep customer insights. ► Efficient Growth - AI helps PMMs with analytics to make smart decisions and target efficiently for sustained growth. ► Cross-Functional Collaboration - In a world where teamwork is key, PMMs are at the center of collaboration to create better customer experiences. By leveraging AI to understand customers deeply, make data-driven decisions, and deliver personalized experiences at scale, PMMs are driving efficient growth and success in the software industry. Visionary companies that recognize and invest in empowering their PMMs will be better positioned to thrive in the AI era. Your thoughts? ► How is the role of PMMs evolving in your organization? Are they being recognized as more than just the Masters of the One-Pager and Battle Card Wizards? ► Have you empowered your PMMs to drive strategic initiatives? ► How are they using AI to understand the market, analyze product-market fit, prioritize top segments, craft defensible positioning and messaging, develop personas and buyer's journeys, launch products, enable sales, and align cross-functional teams? Interested in collaborating and learning how to inspire PMMs with what's possible with AI? Please feel free to DM me. #ProductMarketing #PMM #StrategicMarketing #AIInMarketing #AI Michael Conn Product Marketing Alliance Rowan Noronha Product Marketing Community GrowthPath Partners

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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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We moved one Member of our team of GTM Agents to "prod" this past weekend, all in a span of few hours. LLM Model Costs are really a race to the bottom. Millions of tokens processed, and tons of Alpha generated from Signals & Interactions across the internet at the cost of a handful of dollars. The tooling has come such a long way too in such a short time. Within hours, we moved our prompts to a managed platform, got our logs traced with users and sessions, all with Langfuse (YC W23). While we are busy talking to our users, our Agents self improve, log their behavior, evaluate themselves - generating more and more value with every passing day. If you can identify problems, then you can build - it is a more leveled playing field than it has ever been. It just keeps getting easier and easier to make things people want - as long as you can figure out what people want :)

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Some things are best understood by actually seeing it. 👀 Especially our custom object lifecycle (aka funnel ) reporting. We get a lot of folks coming to us who are interested in getting this unified reporting model. So I thought it would be best to just show off what the data looks like. Don't get it twisted - you still need a great foundation to support a lot of the data  and process for getting a correct view and accurate reporting. We help with all of that too. You gotta do the basics first but then you will be ready to report like this. Props to Alison Rouse  for developing this amazing solution and Christie Montague Fusco for the visuals 💥 #revenuwgrowtharchitecture #gtmops #revops #funnel #lifecycle #analytics

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Got an email "GCP's Apache Kafka service is now live" at noon. After dinner, spent half an hour confirming it works well with Timeplus. I don't think you can make the bootstrap server publicly accessible. Has to setup a bastion host or run your app within the same project/VPC. Setting up the SASL username/pwd was not easy. Follow the guide carefully https://lnkd.in/gcKEVh3g and you may need to disable the iam.disableServiceAccountKeyCreation Anyway, if your workload are in GCP already, having such Kafka service in GCP is nice way to burn your cloud credits. Yet another Kafka vendor, great.

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Interesting answer from AlphaSense CEO Jaakko (Jack) Kokko on why they acquired Tegus - all about access to proprietary data, in this case the transcript library on private companies. You recently acquired a company called Tegus. Was this about having more proprietary data to feed into your models? First and foremost, it was about providing our customers with the best possible intelligence on the market, especially on private companies. A couple of years ago, we acquired a company called Stream, the No. 2 player in what you might think of as a library of interview transcripts between investors and experts on a company — their former executives, competitors, customers, partners. It allowed us to produce information that you can’t access anywhere else, and our customers loved it. Tegus is the market leader in that sector and was the natural next step for us. They have the world’s best library of private company content, and we immediately wrapped our search capabilities, our generative AI, our monitoring capabilities around their content and made it much more valuable. We now have the broadest coverage of insights on both public and private companies, allowing our AI to provide even better answers to our customers.

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My latest for TechCrunch: Another Black Exit! Open source compliance and security platform FOSSA has acquired developer community platform StackShare, the company confirmed to TechCrunch. Founded by Yonas Beshawred, StackShare is one of the more popular platforms for developers to discuss, track, and share the tools they use to build applications. This encompasses everything from which front-end JavaScript framework to use to which cloud provider to use for specific tasks. Read more to learn about the company and its acquisition!

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In the future, all efforts will on data curation and engineering. All model training and Fine-tuning will be standardized. That doesn’t leave much to do for AI engineers :)

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Planning to expand your dev team in NYC? These 4 shocking stats about NYC tech talent trends could save you time and money.. Here's a sneak peek: - 70% of developers want hybrid work. - Only 40% of tech companies offer it. If you're a hybrid company, put that near the top of your job description to attract better talent! #nycjobs #techhiring #staffingreport #hybridwork #techtalent #nyctech

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New awesome #NoPriorsPod episode drop w/ Howie Liu founder Airtable. Topics: *cobuilder launch, integrating AI into an existing successful product *app layer for AI *can you start a company as a platform vs. a killer app? *airtable's transition to enterprise *no code in the age of AI codegen? like, listen, subscribe! links in first comment

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SQLMesh's incremental modeling is the most powerful framework in data engineering for building efficient and scalable data pipelines. Check out Sung's amazing guide that walks through the look and feel of developing incremental models with SQLMesh.

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