Jeff Eddings

It’s eye-opening to see how AI model businesses are dealing with tight margins despite big investments. 🤯 Aidan Gomez’s comments about the “zero margin business” highlight the fierce competition and price cuts in the industry. 💸 With giants like OpenAI and Anthropic lowering prices to stay ahead, it’s a tough time for startups. The future of AI might depend on finding new ways to make these technologies profitable. 🚀 #AI #MachineLearning #TechTrends #Startups https://lnkd.in/ejkDu_wT

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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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Want to tease something my team has been working on over at Buffer! The attached image isn't a mockup, but a screenshot of a live prototype of RSS feeds directly in Buffer! It's only available internally and still pretty rough at this point, but we'll have it available for user testing soon. With this enhancement, we hope to help Buffer users easily find new and engaging content to share with their audiences. What do you think? Will RSS feeds in Buffer help you create more content?

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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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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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Thank you so much, Jeremy Kahn for the great conversation this week at Fortune #BrainstormAI on the future of Google Search and how we’re expanding what’s possible with AI. One of the most exciting parts of working on Google Search is the immense opportunity we have with AI to be even more helpful – the truth is, there aren’t just billions of questions that people ask Google every day, there are billions of questions that people don’t ask at all – because it’s too hard to formulate the question, or they aren’t sure whether they’ll get a useful answer. In this next era of Search, we really want to  make it truly effortless to ask any question, make sense of any type of information, across all modalities – whether that’s video, image, or text. That includes asking your question in any way that’s most natural to you - think about Google Lens, where you can search what you see, and get an AI Overview with context and links to better understand it. There’s an even greater opportunity as our AI models continue to improve. Just this week, the Google DeepMind team showed off a range of new capabilities with Gemini 2.0 (https://bit.ly/3ZU3JVe), which we’re already testing in AI Overviews so people can tackle even harder questions. And the Android team shared a glimpse into how we’ll help people better understand and interact with the world around with the new XR operating system (https://bit.ly/3ZRx9TR). What’s most exciting to me is how AI is unlocking entirely new ways to make information useful, so regardless of what language you speak, or your skill level in a particular domain, we can help you understand information effortlessly. I can’t wait for the year ahead for Search – there’s so much more to come! You can check out our full conversation at Fortune Brainstorm AI here: https://bit.ly/3DmPdMA 

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Y'alls know accessibility is important to me, and that comes in a lot of forms! I wanted to share out two things I've been working on recently that follow the a11y theme, with Global Accessibility Awareness Day (GAAD) right around the corner: First, plugging the work we've been doing at Ro around building technology to give patients more access to healthcare so they can achieve their health goals via Patient Intake. Intake isn't "just a form" - it's the key way for patients to communicate their health history, goals, and preferences to their providers in a way that feels human. Behind the scenes, we're making sure every piece of information a patient provides is structured to make sure every point is seen and heard so that our providers can deliver the best possible personalized care. More info on the platform here: https://lnkd.in/gC4ru5ip Second, the lovely Elliot Kipling and I caught up on the Enginears podcast last month around healthcare accessibility and digital accessibility in general. The episode dropped today, check it out here: https://lnkd.in/gbGcBvsB

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Check out this solution for building user authentication features into generative AI RAG applications!

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My bets where AI won't replace humans: 1) Human connection: Like the one that many of us just had at Config. 2) Shared human experience: When you look at another person's eye and you know you've been through a similar joyful and painful journey. 3) Real stories behind art and culture: I listen to music, and I like paintings not just because they feel good, but because there's a real human story behind it that speaks back to 2. 4) Ingenious net new creative fields: AI can use past things to create something new by mixing them, but it's mostly always can't really create something completely new and unseen. 5) What's your bet?

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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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US is far ahead in the AI race: - 71% of all funding - 43% of all AI companies vs 4% in Canada Full report in 2025 Tech Trends: https://lnkd.in/eq96hqvJ

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As the year winds down, I’ve been reflecting on the ideas that left the deepest impression on me, and two books stand out as absolute highlights of 2024: 📖 𝗧𝗵𝗲 𝗪𝗼𝗿𝗹𝗱𝘀 𝗜 𝗦𝗲𝗲 by Fei-Fei Li — https://a.co/d/7XqP5J8 📖 𝗔 𝗕𝗿𝗶𝗲𝗳 𝗛𝗶𝘀𝘁𝗼𝗿𝘆 𝗼𝗳 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 by Max Bennett — https://a.co/d/8UleBZ7 Both are deeply thought-provoking works that I can’t recommend enough! Together, they explore the similarities, differences, and fascinating interplay between biological and artificial intelligence that has reshaped how I think about the future of both. Here are a few similarities that stood out: 🔹 Uniform Neural Structure The brain’s neocortex is built from identical neurons arranged into repeating microcircuits, yet it handles everything from face recognition to poetry. DNNs borrowed this idea: uniform layers of nodes that specialize through training. Another proof that simplicity scales! 🔹 Reinforcement Learning with Curiosity Evolution gifted humans with curiosity, inspiring us to explore the world, take risks, and learn from our experiences. Early ML algorithms lacked this drive, sticking to safe paths with predictable rewards. But in 2017, DeepMind changed that with AlphaZero by explicitly modeling exploration and creating an AI that defeated the world champion of Go! 🔹 Imitation Learning: Understanding the Why Primates revolutionized learning by imitating intent instead of blindly copying actions. Similarly, early autonomous driving systems mimicked human actions based on sensory data and often failed. Andrew Ng’s team solved this in 2003 by training AI to infer the intended trajectories of the experts, advancing robotics and autonomous driving. And here are key differences: 🔹 Backpropagation vs Hebbian Learning AI relies on backpropagation, popularized by Geoffrey Hinton in the 1980s, while the brain uses Hebbian learning, often summarized as “neurons that fire together wire together”. 🔹 Catastrophic Forgetting AI suffers from catastrophic forgetting, losing old knowledge when learning new tasks, which requires freezing models after training. In contrast, human brains replay and integrate memories through the hippocampus, enabling continuous learning without overwriting prior knowledge. 🔹 Simulation and Imagination The brain excels at creating mental models of the world and imagining future possibilities. This “simulation engine” interprets sensory information by assuming it originates from a 3D world that unfolds over time. 🤔 Interestingly, both authors allude to Spatial Intelligence as the next AI frontier. And recently, Fei-Fei Li founded World Labs, a company building Large World Models to perceive, generate, and interact with the 3D world — how cool is that! What an exciting time we live in! I’m eager to see what 2025 will bring. What ideas or books shaped your thinking this year? I’d love to hear your recommendations! 🎄 Wishing everyone a Merry Christmas and a fantastic New Year! 🙌

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Can probabilistic models, like LLMs and Nate Silver's election forecast model, ever truly be perfect, with no errors? According to Nate Silver: "We don’t talk about it much, but there’s a small amount of error introduced into the model because it’s probabilistic rather than deterministic. The margin of error on 40,000 simulations is about ± 0.5 points of win probability for either candidate; after 80,000, it declines, but only to ± 0.35." Sounds like a familiar refrain around Infactory as we discuss the limitations of LLMs... P.S. Vote!

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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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Releasing quantized Llama models. Faster, lower memory footprint, designed to run on small devices. Details here: https://lnkd.in/gdmjfByi

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🎉 Big congratulations to our incredible team (Haoyu Chen, Nick Greenquist,Yuxing Wang, Sridevi Maharaj, Ruiyang Wu and many more) for delivering several quarters-long impactful features on the Play Store! 🚀 We’re thrilled to have launched key updates like Search Home, Sports Moment, and WearOS recommendations, all designed to enhance personalization and help billions of users worldwide search and discover content they love. 🌍✨ Check out the Play Store Q3 updates: https://lnkd.in/dn5wx8Mi #GooglePlay #Personalization #Recommendation

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