Hunter Powers

There's now no shortage of options for open source model hosting providers. i.e., take your pick for Llama 3 (Groq, Together AI, Telnyx, etc.). Am I wrong in thinking the only real difference now between them is speed, cost and readability? If so, this space just became commoditized quickly and reminds me of the telco provider wars where its a race to the bottom on price. Which means the cost of running AI applications is approaching something close to zero... 🤯 #ai #cx

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Our Public Airtable Interface lets you search, sort, and filter through 8000+ #VC professionals. Discover essential details like firm names, websites, and social media links. https://lnkd.in/gyxeJQEm #startup #startups #venturecapital #funding #vc #privateequity

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Despite the disdain for "GPT-wrappers", LLM-driven apps is the layer where value is accruing. This is cuz evals are the differentiator & not easily replicable, unlike AI models. The reason why OpenAI pursues regulatory capture is cuz the moat is shallow, not cuz of existential risk of humanity. In Clay Christensen's lesser known "Law of Conservation of Modularity", he spells this out, summarized and outlined by Stratechery. https://lnkd.in/gt-2isr9 So if you don't want your lunch eaten. Get started on your evals today, and pick up a copy. https://lnkd.in/gxwnHVND

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Great podcast with Bret Taylor (CEO Sierra). Stellar communicator. Covers a lot of ground. One area he touches on are engineering changes instigated due to agentic frameworks. Resonates with a lot of the work we are doing at Doyen AI. A few nuggets - engineering moving from rule-based to goals & guidelines, increased focus on statistical approaches for managing non-deterministic systems, core agentic components are (a) knowledge of the business, (b) knowledge of business processes, and (c) knowledge of underlying systems for actioning. Worth a listen! #agents https://lnkd.in/ezdghk_v

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Sharing this ZDNET article about the new solution Blitzy On Wednesday, the Massachusetts-based company announced Blitzy Platform, an agentic system that "batch-builds up to 300,000 lines of code in a single 8-hour inference run, cutting end-to-end development timelines from months to days." The platform interprets product roadmaps, generates documentation, and identifies final steps for human developers, writing anywhere from 50% to 100% of the necessary code, depending on the project's complexity. " ___________ #AI #aiethics #automation #LLM #softwaredevelopment https://lnkd.in/gur-yFSh

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Hot take: Models and Infrastructure are rapidly becoming commodities. The true moats are UX, Distribution, and Data. I foresee the application layer as the nexus of innovation and impact. The application layer is where the real value will be created. While foundational, models and infrastructure are now standard fare. They are critical tools that can be used to build applications. 💡 That’s why prioritizing distribution and ecosystem creation is key. True differentiation lies in capturing and leading markets through dynamic strategies. #TechInnovation #FutureOfTech #UserExperience #DistributionStrategy #DataDriven #MarketLeadership #EcosystemBuilding #BusinessStrategy #artificialintelligence #aimodels #userexperience

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AI that can use computers like humans? Anthropic just made it a reality. Anthropic's latest announcement is reshaping the AI landscape: 1. Upgraded Claude 3.5 Sonnet with industry-leading software engineering skills 2. New Claude 3.5 Haiku model matching top-tier performance at lower cost and speed 3. Groundbreaking "computer use" capability allowing AI to navigate interfaces like humans 4. Significant improvements in coding, with Claude 3.5 Sonnet outperforming all publicly available models on SWE-bench Verified 5. Early adoption by tech giants for automating complex, multi-step processes This leap forward in AI capabilities opens up exciting possibilities for automation, software development, and open-ended research tasks. However, it also raises important questions about responsible deployment and potential risks. What are your thoughts on the implications of AI systems that can use computers like humans? How might this change the future of work and technology? #ArtificialIntelligence #TechInnovation #FutureOfWork #AIEthics #SoftwareEngineering Share your insights in the comments!

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I'm so excited about the problems "traditional" software companies are tackling right now. LLMs let software teams move faster, make sense of larger domains, and built much more complex tech than we were able to even five years ago. I believe the impacts in healthtech, govtech, and fintech will be large. Simultaneously, I'm amazed at all the ways one can lose money investing in AI companies - especially the foundational models. There's no part of this market that isn't trending toward commoditization. This has been true for a while but the release of llama3.1 makes it especially clear: Most workflows can retrain on this open-weight 405B model with no change to their budgets (AWS Bedrock charges *at max* the same as OpenAI) and then find ways to negotiate/optimize that cost way down while not sending their data away to a 3rd party. Even the B2C story is commoditized — what AI wrappers have believable ROI if I can run this one-liner and the hard part is accomplished for a fraction of a cent?: aws bedrock-runtime converse --model-id meta.llama3-1-405b-instruct-v1:0 --messages '[{"role":"user","content":[{"text":"Write me a post enumerating how investments in LLMs may yield no competitive advantage."}]}]' As I look past the flashy parts of AI I see: * Building one-off tools against novel APIs is now (and forever) instant * Analyzing requirements for most tasks and understanding new domains is an order of magnitude faster * GitHub Copilot yearns to write React for us * LLMs give the same boost as pair programming at zero cost I haven't been this excited to build complex products in a long time.

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OpenAI CEO, Sam Altman, is thinking about changing the company to a fully for-profit model 💰 If this happens, it could bring some big changes! First, it would mean that OpenAI’s current non-profit board would no longer make the decisions. This would make OpenAI more like its competitors, such as Elon Musk’s xAI and Amazon-backed Anthropic, which are both for-profit companies. However, these discussions are still in progress, and the final decision hasn't been made yet. So, nothing is certain at this point. What do you think will change? Will it be for better or for worse? It’s also interesting to wonder what Elon Musk thinks about all this. He might finally see OpenAI becoming "ClosedAI"! #artificialintelligence #ai #openai #xai

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Here's a quick video showing how to use Deckdrop as an AI research agent for #venturecapital and #privateequity initial diligence research, including competitor mapping.

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Are LLMs better at Deductive (start with a general formula/relationship and compute outputs for different inputs) or Inductive (start with examples of inputs and outputs and figure out the generalized relationship) reasoning.... The answer is they are better at Inductive reasoning... Based on this what kind of applications are they better suited for???? More details in this amazing study....https://lnkd.in/gjKYUraW

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Generative AI: miracle worker or sophisticated guesser? While AI can now write, code, and create, it's essentially still a prediction machine. This revelation challenges how we view and utilize AI in business. What does this mean for your AI strategy? Let's discuss. #ArtificialIntelligence #BusinessStrategy #TechInnovation #AILimitations #FutureOfWork

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Unpopular opinion: AI won't revolutionize design. It will make good designers great. And great designers unstoppable. Here's why: • AI accelerates ideation • Humans guide the process • AI optimizes for engagement • Humans ensure mission alignment • AI analyzes user data • Humans translate insights into impact At our social impact design studio, we use AI tools to complement human-centered design strategy. For us, it's not about the tech. It's about the mission and the impact. But don't get me wrong, AI is a powerful tool that allows you to produce more work in less time. However, the real magic in design happens when we create with empathy, compassion, creativity, and a deep understanding of who and what we're designing for and how to drive real change. That's still 100% human. What's your take on AI's role in design? I'd love to hear your thoughts. --- Follow me for more insights and stories on how to leverage design for positive change. #AI #SocialImpact #UXDesign #WebDesign #NonprofitTech #AIForGood

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2 pieces of advice for integrating GenAI and LLMs into investment and due diligence workflows: Over the past year, we've collaborated with our clients to integrate Generative AI (GenAI) and Large Language Models (LLMs) into their workflows. I'd like to share a couple of key learnings about integrating these technologies for optimal results and a user-friendly experience. To design effective AI interactions, consider users' jobs to be done, and anticipate their expectations. Provide simple, clear examples to help users understand how the AI-enabled workflows work and what to expect. Effective user onboarding is crucial, as it helps users quickly grasp the AI's capabilities and build confidence in using it. Smooth and easy onboarding can significantly enhance the initial user experience, setting the stage for successful long-term engagement with the AI at your firm. Integrating AI directly into the user journey, rather than having it as a separate tool (e.g., chat, bot, or assistant), improves the overall experience. AI features should fit naturally into the main workflow, making them readily available and reducing interruptions. This ensures that AI support is both timely and useful, boosting productivity and satisfaction. Keep on experimenting!

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Oops, Took a LOT Longer... But We Are There Now! Back in 2017, I was designing my first agentic agent I wrote an article titled "2017: The Year AI Grew Up", where I celebrated the promise of artificial intelligence and its potential to change our world. https://lnkd.in/g5wK7nS Looking back, I now see that while AI did indeed "grow up" in 2017, it was only beginning its true journey into maturity. In hindsight, it feels like we were all excited about AI taking its first steps, but we underestimated just how much development was still ahead. Well, five years later... it took a bit longer than I thought, but we're finally there! Today, AI is not just a headline or a concept; it's an embedded part of our everyday lives and professional practices. Back in 2017, we could see the promise, but it wasn't always clear how AI could bridge the gap between hype and meaningful utility. Now, it’s different. AI has crossed that gap, and we find ourselves standing on the doorstep of a vast landscape of new opportunities. This isn't just about assisting; it’s about transforming entire industries. Let’s look at healthcare—a space that I'm passionate about, especially mental health. In 2017, I saw how agentic AI agents would work, and built our first demo. https://lnkd.in/g-fAdtst BTW we don't "demo" Casey as we did Tony originally we introduce and let you have a conversation. But without LLM technology we couldn't yet get there. Today, I'm thrilled to share how tools like Clinician Assist are making therapy better by offering hyper-personalized, context-aware AI that works hand-in-hand with therapists. We’ve built Casey, a therapist assistant that’s not replacing clinicians but supercharging them—giving them the capacity to support clients in ways that I was imaging back in 2017. The journey took longer, and the hurdles were greater than expected, but these setbacks paved the way for deeper learning and refinement. Today, we're in an era where Vertical AI can be 10X bigger than SaaS, thanks to its ability to precisely meet industry-specific needs—from assisting mental health professionals to automating tedious tasks in creative fields. I couldn't be more excited about this moment. We now have mature AI tools that work with deep contextual awareness, understanding, and personalization—not as generalists, but as specialists that support people in unique and impactful ways. If you’re reading this, you know that 2017 was a big year for AI, but 2024? This is the year where AI finally does what it promised: making human endeavors more impactful and freeing up time for people to focus on what matters most. To everyone still pushing and believing in the power of AI—the time has come, welcome to the future!

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Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

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I wrote two blog posts digging into the BM25 full text search algorithm. It has a really clever origin story and actually isn't too hard to understand! https://lnkd.in/eBWNNSi9 The second one is Comparing full text search algorithms: BM25, TF-IDF, and Postgres: https://lnkd.in/e3zhbwhK

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"If you're relying on AI not becoming 100X better, you're about to get steamrolled." -- Jason Warner, ex-CTO of Github and CEO of poolside How we code and build software is being rapidly transformed with AI. Sitting down with Jason the future looks clearer. Over the next 5 years: 🌏 All 7 billion people can code 🏗️ Search bars will spit out software 🔋 AI will generate its way out of data scarcity 🌩️ Foundation models to become cloud companies I unpack the 5 year frontier of software development in Future of Code: AI-led Software Development https://lnkd.in/gze2QSbu #AI #Software #Code

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