Mokshith Voodarla

Excited to share our 0-1 #DevGTM Playbook for early-stage technical founders building developer tools and infrastructure! We at Speciale Invest had the privilege of working closely with many brilliant technical founders and evaluating many Devtools/Infra Cos. During our in-depth discussions, we discovered a recurring obstacle that many of these technical founders face: despite identifying problem statements and developing cutting-edge technology but crafting a clear and effective GTM strategy often proves to be a significant challenge. Recognizing this gap, we set out to create a comprehensive playbook tailored for early-stage technical builder/founders in their 0-1 journey. My colleague Aryan Pareek and I have spoken to over 50+ #DevTool founders, GTM experts, and #developers from companies like Amnic, Appsmith, DhiWise, Dyte, Esper,Hashnode,Hivel,Keploy 🐰, LambdaTest, Neverinstall, Portkey, Reo.Dev, Requestly (YC W22), ToolJet, Tailcall, Typo, Unstract , videosdk.live, Zenduty and many more. And we consolidated the best examples and learnings into our playbook. We dive deep into various aspects of #developer GTM, including defining your ICP, buyer journeys, messaging for developers, #SEO, content strategy, and more - all tailored for dev founders. Check out the playbook here: https://lnkd.in/g425vuqy Huge thanks to all the #devfounders and #GTMExperts who shared their invaluable insights and experiences with us. Special shoutout to Dev founders and all the amazing developers and GTM experts who gave us their valuable time. 🙏 📣 Special Creds to the devfounders who helped us with this: Sachin Jain Achintya Gupta Navaneeth Padanna Kalathil Vishal Virani Anirudh Murali Shiv Sundar Kshitij Mohan Sudheer Bandaru Venkatesh Radhakrishnan Harini Janakiraman Arjun Kava Narendran Hariparanthaman Sidhant Goyal Madhukar Kumar and many more.... (lol - I've exceeded the maximum mention limit) Special thanks to Freshboost for assisting us with SEO strategies. *Please tag technical founders/builders who would find this useful, and feel free to share your feedback with us over DM* P.S.: It's still a work in progress, and we'd love to hear from more technical founders about your experiences and challenges about your GTM journey. Your feedback will be invaluable in making this resource even better for the broader dev ecosystem. Arjun Rao Vishesh Rajaram #TheDeepTechVC — Build from India for the World

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LinkedIn team (Juan Pablo Bottaro Karthik Ramgopal) shared a helpful deep dive into their deployment of an LLM-powered job search assistant. Highly recommend this read! Few highlights:  💡 Relatively easy to setup RAG-based pipeline. Achieved 80% of the basic experience in 1 month, then 4 more months to reach 95% mark. 💡 Evaluating the quality of application outputs is hard. I hear this from many enterprises developing LLM applications. (check out Okareo by Matthew Wyman Boris Selitser) 💡 Balancing latency with quality is important. While certain techniques like Chain of Thought could improve accuracy and reduce hallucinations, it will increase response times, which can affect user experience. https://lnkd.in/gtGGwf5P #largelanguagemodels #RAG #EBR #LLM #ml #ai #hallucinations #evaluations #

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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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Little can be added to those statements. Hardware can be one of your best competitive advantages, if done right.

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Watching YC's Fall '24 batch, we're seeing a fundamental transformation in how companies scale. A few key observations: Development cycles are compressing. In F24, a developer tool reached $102K ARR within three weeks of launch. This isn't an outlier either, in the batch we also saw examples of... $655K ARR, growing >10% week-over-week $1.6M ARR, growing 23% month-over-month $670K ARR in 2 months Capital Efficiency will be the new normal as the traditional startup playbook is being rewritten: Zapier (YC S12): Reached hundreds of millions in ARR after only seed funding Baubap (YC S21): Achieved $100M+ revenue and profitability with single seed round SoloSuit (YC W21): Showing similar capital-efficient growth trajectory 🔄 Why This Matters... - Seed rounds might become the dominant form of venture capital (as Chamath recently noted on All-In) - The 18-24 month seed-to-Series A timeline is compressing - Companies can reach $1M+ ARR with small teams and minimal capital The new challenge is... Speed to defensibility > Speed to market With lower barriers to entry, competitive advantages must be built faster. The winners aren't just those who get there first, but those who build lasting moats quickly.

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Very excited that VitalStage Ventures is a small part of Groq’s new round of funding. We are all-in on AI infrastructure as the picks and shovels enabling this new generation of innovation. With respect to Groq, we particularly love their new hardware + software approach to accelerating AI inference. Just try out Groq.com and you can sample what the fuss is about. Having specialized Language Processing Units (LPUs) for inference enables dramatic performance gains at higher efficiency with less thermal impact. And now by hosting models via GroqCloud, Groq lowers the barriers for teams to be able to leverage many models without having to set up and deploy their own AI infrastructure. #ai #groq #ainative https://lnkd.in/gcEcXfme

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Y Combinator（YC）のRequest for Startupsについてまとめた記事の第1弾をnoteに投稿しました。アクセラレーターとして多くのスタートアップを育ててきたYCが、応募してくるスタートアップに対して立ち上げてほしいアイデアを募集している要件をまとめています。Y CombinatorのRequest for Startupsは、今後注目される領域や技術・サービスが現在不足している点を考える上で、一つの指標として非常に参考になる内容と思います。 ブログ記事はこちら（Here is the blog post）↓ https://lnkd.in/gMicj2Jp *Although the articles are in Japanese, please feel free to contact me in English if you are interested in the content. I have posted the first article summarizing Y Combinator's (YC) Request for Startups on note. As an accelerator that has supported many startups, YC has outlined the ideas they want incoming startups to develop. Y Combinator's Request for Startups serves as a valuable indicator for understanding the areas, technologies, and services that are currently lacking and will be in the spotlight in the future.

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Solid advice to founders from the co-founder and CEO of Databricks, Ali Ghodsi 👇 Target other startups first. Ali draws on Geoffrey Moore’s book Crossing the Chasm, which identifies five key groups in the tech adoption cycle: 1️⃣ Innovators 2️⃣ Early adopters 3️⃣ Early majority 4️⃣ Late majority 5️⃣ Laggards Ali’s advice is especially relevant for AI-focused founders, given the myriad of risks (security, privacy, explainability, and so on) that come with putting generative AI models into production. It is difficult to target established, traditional companies—the laggards—as an initial market. These large enterprises come with a labyrinth of security requirements, bureaucratic red tape, and drawn-out sales cycles. Beginning with other startups increases the likelihood that a product will be adopted and provides a powerful way to collect early user feedback. Establishing this data flywheel can help an AI startup differentiate their model and iterate their way to product-market fit. Salesforce and Dropbox used this particular playbook. Once you've proven your model with startups, then you can set your sights on enterprise 🚀

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🚀 RAG systems can feel complex, but here’s a way to simplify: build your RAG solution using a pipeline as an API. This approach makes it: ✅ Streamlined: The entire process flows smoothly in one organized pipeline. ✅ Transparent: Unlike black-box APIs, you gain full visibility into data flow, understanding each step along the way. ✅ Customizable: Tailor each stage of the pipeline to fit your specific needs. By leveraging a pipeline-based API, you not only make RAG more accessible but also give yourself control over the process. Simplify, see more, and stay adaptable. For more insights, check out George Strong Ph.D post! #AI #LLM #RAG

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The major areas of AI innovation automate white-collar work. Reviewing the BLS’ data on employment for white collar work, I aggregated the data to these categories. It’s striking that most of them already have a significant number of AI startups pursuing their ambitions to change workflows. Software engineers were the first to benefit with Copilot. Today, there’s a panoply of different kinds of AI software for developers, including test generation, code refactoring, code generation, & security analysis. Within education, The promise of computer adaptive testing and an individualized tutor and the style of Alexander the Great & Aristotle is possible. Automated document ingestion & reconciliation for closing books is changing accounting. AI powered financial due diligence rips through public filings, private placement memoranda, & compliance automation. Legaltech has surged automating demand letters for personal injury, writing briefs for attorneys, & chomping away paralegal work. We could keep iterating through the list. But if a founder wanted a list of jobs to automate, the BLS’ white collar jobs boards is a wonderful place to start. AI will change these workflows & capture a meaningful fraction of the labor spend. It’s a roadmap for the White Collar Revolution.

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My latest episode is about what you need to know about AI!

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 🔥 "How many engineers does it take to replicate Docusign as a software product in 2026?" Sarah Guo dropped this thought-provoking question at last week’s panel. Answers from the eng/product leaders at top companies were eye-opening: Scott Densmore: 10 engineers Romain Huet: 5 engineers Ainsley Escorce-Jones: Just 1 Lee Robinson: 1-2 engineers Say hi to a future where rewriting major software might be simpler than you think. Reach out to me if you are planning to do that! 🙋🏻‍♂️ PS: Take a look at what Gil Arditi is working on at Morph.ai 💡 #software #sdlc #softwaredevelopment #llm #ai #ml #largelanguagemodel #copilot #gpt4

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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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Truly inspiring to listen to Dax Dasilva tell the story of Lightspeed Commerce on 20VC. With $900M in ARR I was thrilled to learn that Dax bootstrapped the company for 7 years to get to $10M in ARR. I hope it doesn't take us 7 years to get Liquid360 there but it makes me feel better about the 3.5 years we've put in to date. For me these stories are deeply encouraging. When you're building something very big it can take time to get to launch. A couple of weeks ago we met with a global player in the VC world. They were looking to invest $5M - $10M and we said we weren't there yet. We'll get there but it wouldn't be the right move today. They then jumped to another conclusion. The wrong one. I get it. They have a multi-billion dollar fund and capital to deploy. We have a company to grow and too much cash will not help us do it right. We've made good decisions being lean. Our customers are starting at $500K. This is an enterprise/gov't product. We have to satisfy a very discerning customer. No bugs, no glitches. A dependable and reliable product. We are excited by the feedback. We are making the world safer. Harry Stebbings suggested that Lightspeed is one of the most misunderstood companies. It's market cap is $2.6B which seems really low relative to the ARR. Very interesting episode.

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Andrej Karthpathy, one of the world's foremost AI researchers announced his new AI ed-tech company, Eureka Labs. "This Teacher + AI symbiosis could run an entire curriculum of courses on a common platform. If we are successful, it will be easy for anyone to learn anything, expanding education in both reach (a large number of people learning something) and extent (any one person learning a large amount of subjects, beyond what may be possible today unassisted)." #edtech #frontiertechnology #startups https://lnkd.in/gSwGaBUB

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I completely agree about the hype in robotics; do you? I'm not sure why but I keep seeing new startups with ideas that have already failed multiple times secure millions in funding. Yes, there is a lot of potential and there are some companies that have real-world pilots. Yet I almost never see these 1-2 company pilots turn into a stable and sustainable business model. While one on hand I'm happy for them and robotics in general, it's not great to see so many companies go out of business each year. The "successful" robotics companies are judged by how much funding they raise, not by how much profit they make over their series VC raise. While there is a lot of potential with funding, I personally think it usually leads to more expensive robots. Expensive robots lead to fewer sales, which leads to cash burn out. Do you know of any robotics companies actually profiting from businesses more than investors? This would be closed-loop solutions that have publicly proved true product market fit.

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Many thanks to the Google Cloud team and Kiran for putting this event together! I'm excited to share our observations on enterprise AI, both generative and conventional machine learning, at the upcoming SaaS AI day. Founders building AI products might find it interesting and I hope to meet you there. Please use the link below to sign up. #ai #founders Thomvest

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🚨 How does it feel when your portfolio startup is acquired by NVIDIA? Awesome! 🚀 It can now be disclosed that NVIDIA is acquiring our portfolio artificial intelligence startup Deci AI! 👏 Congratulations to the founders Yonatan G., Jonathan Elial, and Ran El-Yaniv, as well as fellow investors Insight Partners, Square Peg, Emerge Ventures, Jibe Ventures, Vintage Investment Partners, ICON - Israel Collaboration Network etc. 👉 Read more on Calcalist: https://lnkd.in/diSPt4Jc A few reflection points: 1/ Nvidia is very active in Israel, having previously acquired Run:ai earlier in 2024, Excelero in 2022, and started with the $6.9B acquisition of Mellanox Technologies in 2019. Now there are more than 4,000 Nvidia engineers in Israel. 2/ Deci AI was founded in 2019 and has since raised $55M in financing. We participated in the seed round when Sharin Fisher first brought the deal, then doubled down during Series A with Sharin as the lead and Victor Orlovski's active involvement. We were honored to serve on Deci AI's BoD as well. 3/ Nvidia commented on the deal 🗣️: "NVIDIA's expertise in AI will be complemented by Deci's capabilities in NAS, which automates the design of deep learning models to improve performance. Deci's engineering team has knowledge in NAS, Foundational Models, Inference, and in developing complex algorithms." This is a great appreciation of Deci AI's mission to enable deep learning models to run efficiently on any hardware in real-time. 4/ I hope that soon AI models will be built, optimized, and operated in various other environments, such as cloud or even mobile, of course with the help of Deci AI as part of Nvidia. I'm really looking forward to it! 🙏 Congratulations to my fellow colleagues at R136 Ventures, especially Victor Orlovski and Evgeny Pestryakov as well as Tom Dennedy, Ratan M. and again, to the founders and Deci's team – it was a pleasure for us to support such great entrepreneurs, engineers, and operators!

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