Reah Miyara

I’m thrilled to announce the launch of my new Substack, Silicon Sand Stack, with my very first article: "Today's Large Language Models Will Never Be Responsible, Safe, or Green: The Need for Investment in Revolutionary New AI Paradigms" In this debut piece, I dig into the critical flaws of current large language models (LLMs) and why they fall short of being responsible, safe, or environmentally friendly. I examine the inherent issues like hallucinations, lack of real-world grounding, unreliable reasoning, and potential biases that plague these models. 🔍 Key insights include: - The technical and ethical limitations of today's LLMs. - The necessity for radically new AI paradigms. - The role of venture capital in fostering innovative AI approaches that prioritize safety, responsibility, and sustainability. Join us as we embark on a journey to rethink AI and advocate for groundbreaking advancements that align with human values and societal needs. Read the full article and subscribe here: https://lnkd.in/eDcTniTg #ArtificialIntelligence #AI #GenerativeAI #TechInnovation #ResponsibleAI #GreenAI #FutureOfAI #SiliconSandStudio

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I've recently heard two different stories that illustrate the stark differences in how AI is affecting the job market: - One startup founder said he laid off two junior engineers. Now, he's involved in coding himself (he has a background in software engineering) and uses GPT/Copilot. He mentioned that product development is much faster now compared to when he had the two junior engineers. - Out of this year’s graduates from a top 50 computer science master’s program, only one person received a job offer upon graduation. One student applied to 60 companies and received zero interview requests—not zero job offers, but zero interviews. It seems like AI is making experienced, senior professionals more effective at their jobs, reducing the need for junior employees. But if no one hires juniors, how can anyone become senior? The job market continues to be very challenging. I hope this is just a short-term, interim phenomenon as we figure out how to integrate AI into the workforce. In the meantime, we might also see the rise of tiny companies, including one-person businesses or small (less than 3 people) startups.

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Causal review: "I've tried almost every tool in this space... you guys have solved a couple of things that have been very very difficult for me to solve in other places" — Anik Khan, CEO at MaxRewards One of the best things about running Causal is getting feedback from → savvy users who've spent their careers in Excel, → and have tried other tools in the space, → but didn't find what they needed until they used Causal 🤩 Check out Anik's full thoughts below 👇

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That's a good summary Manny Bernabe - Reflecting on an insightful article from O'Reilly about LLMs, I was struck by this point: "LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into their products. While the barrier to entry for building AI products has been lowered, creating those effective beyond a demo remains a deceptively difficult endeavor." This democratization reminds me of the printing press's impact on biblical texts, which expanded access but also introduced new interpretive challenges. As we navigate these advancements, it's crucial to balance accessibility with responsible use. At Ushur, we've been at the forefront of leveraging LLMs in our backend/knowledge work automation as well as carefully introducing them on the conversational side via our Chatbots and other modes. More will be coming out on that. Read more here: O'Reilly Article #AI #LLM #Technology #Innovation #History #Democratization #ResponsibleAI #Ushur

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Context Caching, a great feature for high throughput LLM calls, is now GA.

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"Specialization reflects the company’s maturation from a flexible, fast-moving startup to a more established player with specific needs and focused goals." In his article "Scaling an engineering team: From generalists to specialists", StrongDM's SVPoE Karim Fanous sheds light on how companies transition from needing broad-skillset roles to requiring specialized expertise in areas like security and data analytics as they grow. He demonstrates how this shift allows for more focused innovation in critical areas like security, data analytics, and user experience, aligning with long-term strategic goals. The article is featured in this week’s edition of Executive Engineering: https://lnkd.in/e3mCcU_X

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Daniel Wilson and Reza Rassool Thanks for the opportunity to join the conversation, and more importantly to contribute to the critical work of Kwaai Personal AI -- turning AI into a meaningful benefit versus a threat in people's lives. (My comments start about 30 min in, after Reza's introduction to Kwaai))

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Very useful insights for folks who are starting to dig further and further into the realm of LLM. An easier GUI approach for fine tuning LLMs.

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If you had a great startup idea, should you keep it a secret? Are musicians natural entrepreneurs? In this episode, Seed Zeng and I chat with Yotam Rosenbaum, musician, YC founder, and investor. 👉 Listen on your podcast app: https://tr.ee/ZmRr5NjEtk

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Hello friends! We launched unSurvey on Product Hunt today 🚀 unSurvey lets you conduct dynamic and personalized voice and text conversations at scale. Whether you’re conducting interviews, gathering feedback, or doing check-ins, unSurvey transforms interactions into engaging conversations. Gain high-quality insights like never before! 🧠 We'd love to hear any feedback, and would appreciate your love and support ❤️ https://bit.ly/unSurvey-PH #unsurvey #conversationalAI #engagement #personalization

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Whose Weights and Biases? up on my blog: https://lnkd.in/gUU_t8_u Gwendolyn Gillingham Thank you for your feedback and help with editing this blog. On May 13, Open AI launched GPT-4o, their new flagship model that can reason across audio, vision, and text in real time and the next day on May 15 the scientists leading superalignment team at OpenAI, Ilya Sutskever and Jan Leike, departed from the company. While I am impressed by the future of human-tech interactions, I can’t help but wonder what influence Open AI and enterprises alike will have over the information the modern society consumes. In this opinion piece, I discuss the bias AI models encode and how it impacts society. I also briefly talk about how firms like Open AI, Meta, Google and Anthropic are taking ethics and safety into considerations when training and deploying AI models. Drawing from the recent research and news, I evaluate how the narrow lens of global tech might still fall short in developing AI for all. Finally, I discuss AI regulations around the world and touch a little upon how the global south is behind in terms of making the best use of AI systems. Read more at #icodeformyभाषा and feel free to leave your feedback! #bias #aigorvernance

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Today is a HUGE day for team Athina AI (YC W23). We're finally launching something we've been working on for months - Athina IDE. We first launched Athina almost a year ago as an LLM Observability platform. Over the last 11 months, we've supported hundreds of teams, helping them monitor and evaluate millions of logs in production automatically. After months of feedback and learning, we recognized a critical need for better experimentation. Which brings us to today's launch... 🧪 Athina IDE is a collaborative editor for AI teams to prototype, experiment, and evaluate LLM-powered products 🎯 Core Philosophies 1. Great AI products are a team effort: PMs, Data Scientists, SWEs, MLEs, QA teams, etc., all have to come together to build something great ...so we made sure that non-technical users can experiment, prototype, and run tests on Athina just as easily as technical users. 2. Great AI products are built around great data ... which is why the entire editor revolves around your data and gives you powerful controls like a spreadsheet. 🤔 So what can AI teams do in this editor? 1. Work with Data Like a Spreadsheet: Filter, search, sort, edit, annotate, tag, and delete data with ease. 2. Prototype complex pipelines: Add dynamic columns to run LLM prompts, execute code, make API calls, retrieve data, run transformations and more. 3. Measure AI quality: Run evaluations within the editor itself - there are 50+ preset evaluation metrics or you can create your own custom evals (no-code). 4. Run Experiments: Re-generate datasets with different prompts, models, or retrieval parameters in just a few clicks. Technical users can run more complex experiments programmatically using our SDK. 5. Compare everything side-by-side: Athina IDE has a compare mode where you can compare any number of datasets side-by-side and run evaluations on all of them simultaneously. ⚡️ How Early Users Are Using Athina IDE ⚡️ 1. Testing Transcription Models: One of our early users is using Athina IDE to compare multiple speech-to-text models side-by-side using dynamic columns. 2. Creating Multi-Step Evaluations: Some teams have been creating multi-step evaluations that involve extracting structured data from LLM responses and then running JSON validation rules on it. 3. Comparing Multiple Models: Many teams are running experiments with 5-10 different models, or prompts, or retrievers + a suite of evals at once to determine the best fit for their specific use cases. We’re super excited about this - if you're an AI product builder, we'd love to know what you think! See the link in the comments - our website has some demo videos. Huge shoutout to the team for all the effort that went into making this such a powerful product. Akhil Bisht Vivek Aditya Akshat Gupta Himanshu Bamoria

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LLama3 passed the super-proprietary, advanced test that we designed for modern LLMs! almost... You can read about that test here: https://lnkd.in/gPG8WndR When I first saw the response generating, it looked like I was going to have to come back here and apologize for slandering these machines when they were just one iteration away from solving very basic prompt injection issues. But then I looked a bit closer... can you see the mistake? #promptinjection #countingishard #LLMs #LLama3

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The dumbest part of the whole post: "The best part? He spent $0 in paid marketing." Most of this is just fluff in order to drive signups to this guy's "strategy nerds" newsletter...and it works. While this guerilla marketing framework is indeed reproducible for many businesses and entrepreneurs, don't expect the same level of results and certainly don't expect it in the same amount of time (4 months). Most startups and small businesses have tighter capital restrictions and smaller teams to execute on this to achieve the same level of results. Maybe it's true Gas spent $0 on paid ads, but the whole guerilla marketing operation didn't cost $0. Keep in mind, this founder is flush with capital after a $100M acquisition from Facebook. With that amount of money, he can then hire a team of developers to build Gas and a team of guerilla and social media marketers to execute on the strategy. All of that costs A LOT OF MONEY. So maybe it's true he didn't spend money on paid ads, but he probably spent hundreds of thousands if not millions on a team to execute this guerilla marketing strategy. This didn't cost $0.

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👋 Today myself, Benji Vaughan and Mark Downey (with more names coming soon) set out on a new journey. I'm thrilled introduce Conduit AI (conduit-ai.com), a new company on a mission to empower SMEs with the incredible potential of generative AI, especially retrieval augmented generation (RAG) applications. But first, let's briefly explain what RAG is and why it's a game-changer for businesses. RAG is a powerful AI technique that combines the knowledge stored in large language models (LLMs) with the ability to retrieve and incorporate relevant information from your own data sources. This means RAG-powered applications can generate highly accurate, context-aware, and up-to-date responses to a wide range of queries. For SMEs, this translates to unparalleled productivity gains, enhanced customer experiences, and a significant competitive edge. 🚀 Now, a bit about our journey. Starting Conduit AI has been an exhilarating leap of faith, fuelled by our passion for AI and the unwavering support of our loved ones and mentors. We couldn't have done it without their wisdom and encouragement. 🙏 Over the past year, we've been on a mission to deeply understand how SMEs can effectively adopt generative AI. We've collaborated with companies across various industries and consistently found: SMEs are excited about the potential of LLMs to boost their productivity and profitability. 📈 There's a strong interest in RAG applications that can generate valuable insights from both unstructured and structured data. 💡 Many SMEs are concerned about developing LLMs in-house due to expertise gaps, reliability, trust, and privacy issues. 🔒 We've learned that SMEs are eager to become AI-enabled but need a trusted partner to help them develop a long-term AI vision and deliver products that bring this vision to life. That's where we come in! 🤝 At Conduit AI, our goal is to be the channel that helps SMEs transition into an AI-enabled world. We'll work closely with you to: - Develop a tailored AI strategy 🎯 - Design an adoption roadmap 🗺️ - Identify and implement low-risk, high-return AI applications 💪 With our team's 15 years of ML/AI/NLP experience, having built AI/ML tools for companies like Zurich Insurance and OVO, we're well-equipped to create AI strategies and applications that are tailored to your unique vision, team, and culture. 🌟 We can't wait to share more about our growing team and the incredible stories of the clients we'll be working with. If you're curious about how AI can transform your business, let's chat! 💬 Thanks for being a part of our exciting journey! 🙌 #AIConsulting #TechStartup #SMEs #FutureOfWork #RAG #newventure

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Excited to be open-Sourcing long context (128K) Llama-3 70B - Llama-3-Giraffe-70B and Giraffe-Instruct From Abacus.AI The biggest issue with the Llama-3 family is that they are fairly small context models. So, even though they are as performant as GPT-4 on critical benchmarks, they can only be used in the real world with a bigger context. We just open-sourced two 128K long-context fine-tunes for Llama-3-70B, which effectively puts it on par with GPT-4 regarding context length support and real-world usage. At Abacus AI, we already use Llama-3 on a few production workloads as a great base model for our customer fine-tunes. Links to both models - https://lnkd.in/gPX_XpD6 https://lnkd.in/gaeEq94t

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Hot take: IT was once seen as a cost-center, but no longer. IT is now THE strategic driver inside companies around the world to implement AI responsibly and replace team toil with innovation & productivity gains. IT is the right department to do this, they have the hard skills AND the abilities to look at new technologies critically to apply them with a human-first lens. That's why at Risotto (YC W24) we're taking the approach of "IT partnership first." With our enterprise backgrounds, Alex Confer as our CIO, and Chris Paul as our CTO, we see ourselves as a product "built for IT, by IT". Our goal is to help IT navigate these changes, and double-down on the human-first experience. Excited for the journey ahead! 🚀 #itsupport #ithelp #ithelpdesk #identitysecurity #identitygovernance #iam #ai

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𝒀𝒐𝒈𝒂 𝑨𝒔𝒔𝒊𝒔𝒕𝒂𝒏𝒕 𝑪𝒉𝒂𝒕𝒃𝒐𝒕 𝑷𝒂𝒓𝒕 2: 𝑭𝒊𝒏𝒆-𝒕𝒖𝒏𝒊𝒏𝒈 𝑳𝑳𝑴𝒔 Although I initially planned to update this series weekly, it seems more realistic to do so every 2-3 weeks 😬. In Part 1, I introduced how to fine-tune image models effectively in 2024. Now, in Part 2, I'm excited to cover the fine-tuning of LLMs, using the recent release of Llama v3 with the torchtune framework as an example. We've got a fun mix of coding tutorials, data prep tips, and some in-depth explanations. Grab a cup of coffee, get comfy, and check it out! Github: https://lnkd.in/g5Wg3zXr 瑜伽聊天機器人系列第二篇，介紹如何準備自己的資料集來微調LLMs，同時整理了常見的微調方法、原理與好用的Libraries，完整中文內容可以參考： Medium: https://lnkd.in/gn9tRmdg

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Is AI hype overshadowing true technical innovation? The true measure of AI and machine learning isn't found in the buzz but in the substance of the work behind it. From my own experience, I've noticed a trend: projects that look great in a demo but fall short when showing a proper enterprise use case, i.e. they fall short when it comes to substantive deployment. The key takeaway? As AI continues to evolve, the real game-changers will be those who focus on the bedrock of AI – the fundamentals. I’ve discussed this and other AI-related stuff in our AI-searched podcast with my colleague Tanya Ariana Bendis So go, check it out. https://lnkd.in/dKp39pN9

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