Keith Elliott

Are there CTOs/VP of Engineering in Philadelphia who are interested in learning about Gen AI saving 30% of their team's time on top of CoPilot? Send me a DM. #cto #vpofengineering #generativeai #genai

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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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"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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2025 will be all about Physical AI. BrightAI is putting it to work to fix America’s pipes, power poles, and sewer drains, which are crumbling under the pressure of climate change. With 250,000 sensors deployed on infrastructure across the company, they are collecting data from the physical world to help blue-collar industries be proactive and spot infrastructure flaws before they become disasters. After bootstrapping to $80M in revenue, the company is just now taking on VC dollars to grow its Stateful OS platform. Full story on the platform launch https://lnkd.in/dccWfh7x CC: Alex Hawkinson Nancy Li Smith Erin Dudley Nick Kim

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Very proud moment as Parcl Labs Real-Time Rental Price Feeds are now available to trade on Parcl. 3 rental markets are now live on Parcl: 🌆 Chicago 🏔️ Denver 🗽 USA Why does this matter? It unlocks powerful new use cases: 🔓 if you rent: hedge against annual rent increases. Recent FBI raids 👀 highlight how powerless renters are to fight back against this dynamic. Not anymore. 🔓 If you charge rent: manage cash flow risk from vacancies, delinquencies, and unexpected price market changes in real-time. The foundation of all this is trusted, representative, and daily updated rental feeds. At Parcl Labs, we’ve built this using millions of daily data points, publishing a transparent methodology, and making them freely available to anyone in the world via our API. In addition to rental pricing, we provide sophisticated analytics like rental supply, gross yield, and competitive investor insights. These are the metrics that top rental operators have identified as critical for running their businesses. Now, anyone can access these powerful tools to gain a competitive edge and make informed decisions when trading, renting, or running a rental business. Real estate is for everyone. Real estate data is for everyone. Brick by Brick.

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Two weeks ago I posted a question "AI vs API vs UI?": https://lnkd.in/e3Z2cXd8 It asked whether the value of UIs will diminish in a world of AI Agents, a counter to Zdenek's question of whether we should still be building APIs at all? No doubt that there are a lot more UIs out there in the world than public APIs today. This reminds me of the discussion in the world of self-driving cars: many folks thought that we'd only achieve self-driving if we updated all the road infrastructure to be "machine-optimized". Many also think that we can only have self-driving when ALL cars are self-driving. Both seem ridiculous to me; the world is messy and the only scalable system is one that can deal with that. Now people are starting to see that we are on the path to self-driving which is mostly vision-based - like its human counterparts - and drive (more) safely alongside human drivers on existing infrastructure. The recent Claude announcement of AI computer use is a great demonstration of the capability of modern AI to deal with that messiness: https://lnkd.in/ePhEbz7w Arguably this strengthens the case for UI > API in the future. I think this might be true for legacy interfaces - why go to the effort of creating a nice API if you've already got a UI that AI can deal with? However, what about net new development? I still think the cost and difficulty of building decent UI vs good API means that focus will shift to API >> UI for agents. We're talking long-term here though, maybe 10 years. One interesting thing though, is how will businesses handle a shift to agents interacting on behalf of users? Such a strong disintermediation is going to create an even stronger deployment of 'aggregation theory' (https://lnkd.in/epMKwjdM) with winning agents having enormous power, more even than Google has today. What do you think?

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Science-fiction is inherently speculative. Author George #Orwell of ‘#1984’ predicted concepts such as doublethink, little earbuds and society’s obsession with the black mirror screens that dominate our lives — and according to the BBC, novelist and short story author JG Ballard, of “High-Rise” and “Crash” fame, has predicted the current tool and cultural fixation of #GenerativeAI in the modern society — only he didn’t just speculate them within a story, he is actually one of the first use-cases of a generative AI used in a creative context — Ballard was so fascinated with the growing technological advancement of computers that he used one to compose poems, essentially the precursor to what we have now with ChatGPT. Today, the debate about the creative use of generative AI and its value on creative industries is an extremely fraught and controversial topic, but if controlled in a space that doesn’t replace human labor or try to equate itself to traditional art forms, it is interesting to see what computers can do and how they are creatively limited in comparison. Ballard’s two poems, The Yellow Back Novels and Machine Gun City are generated, and both include notes on how Ballard achieved producing them. Regardless of quality (Ballard himself probably wouldn’t describe these two poems as his best work), it is interesting to see primitive versions of the technology that we utilize on the daily today, and how these moral conversations that concern them would continuously evolve.

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Have you ever thought about how compliance laws are a lot like design systems? I recently spoke with Joe Toscano, founder of DataGrade, and he made a fascinating comparison. Joe explained how Brad Frost's atomic design methodology took an engineering approach to design: → Creating standardized elements → Templatizing them → Scaling them across a system This led to the rise of human interface guidelines (HIGs) from tech giants like Google, Apple, and IBM. But here's where it gets really interesting: Joe argued that privacy laws and regulations are like the ultimate HIG for product design. Just like how HIGs provide rules and constraints for design systems... Compliance laws set guardrails for what we can and can't do with user data. If we design something that isn't compliant, it doesn't matter how sleek the UI is. Legal requirements become the top-level framework we have to design within. That's what got Joe into studying laws - to understand this new "design system" for building products. So next time you're cursing a lengthy legal doc or trying to make sense of GDPR... Remember, it's all just part of the atomic design of compliance. — Do you agree with the analogy of compliance laws as the ultimate design system? Why or why not? Hear more of Joe's unique perspectives on the intersection of product, privacy, and law on the latest episode of The Minimum Viable Podcast.

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Remember this scene in the Matrix: "Woah, I know Kung Fu." The 2024 equivalent is: "Woah, I have the right context files / datasets when collaborating with Claude." ~~ This weekend's hackathon was to complete a project I started: learning how do deploy different types of serverless apps on DigitalOcean's proprietary App Platform. After 250 failed deployment attempts, I decided to start from scratch with more research. I spent about an hour converting this page https://lnkd.in/gwRiCgHd into an exceptionally robust markdown file representation, maintaining data structures and hierarchy. Using this new knowledge reference (@mentioning it in Cursor) and starting a project from scratch, we found the solution in under 30 minutes. ~~ The lesson is context is king. You can solve a ton of problems if you just bring the right data and ask the right way, you'll be shocked how fast you'll get to a solution. Anyone else going to start creating their own private library of context files and datasets?

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Changelog: Push Action items to Linear Multi captures action items during sessions. Now, track them with a single click. We'll clean up the title for you, then you flesh out the details in Linear's streamlined UI. This is actually quite different from how most issue tracker integrations look. Not to name names, but most of the time integrations: - are just HTML forms inferior to the destination's first class UX - require auth Thankfully, Linear supports `/new` deeplinks on desktop and web, which let us quickly take the user to the right place, without auth. I wish more tools supported that!

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I learned a lot from Guillermo Rauch (Vercel CEO). Both from personal conversations and from watching him build Vercel. Here are the top 4 lessons I’m implementing while building Grafbase: 1) Focus on the developer experience Back in the early 2000s, we spent a lot of time building commodity infrastructure in-house. Then in 2008, Heroku came around and everyone was excited about the new possibilities. A few years later, the “Frontend Revolution” happened and companies like Netlify and Vercel did to frontend what Heroku did for infrastructure. But no one had started that revolution for the API layer yet. And that’s where my initial inspiration for Grafbase in 2020 came from. So, you could say that Grafbase is my 20 year-old dream finally coming true. 2)  Listen to your customers and iterate quickly with them As a customer, I’ve always appreciated companies that listened to us and acted fast on our requests. This is not unique to Vercel, but we’ve made this a core value of ours. One customer of ours requested Redis support for our rate limiting feature and the ability to “hot reload” their configuration. We listened to that feedback on a Friday, and they were using it in production by next Wednesday. I genuinely believe that listening to our customers and iterating FAST is how we win. 3) Simplify everything Back in 2020, Vercel used to be called ZEIT. And they didn’t just have a different name, they also had support for Docker files (which was kind of controversial back then). They quickly removed that feature as a way to remove one more barrier to entry. We took inspiration from that and always tried to find ways to further simplify Grafbase and make it accessible for more people. 4) Listen to your idols, but also go your own way when it makes sense We used to have a fully managed, serverless database as part of Grafbase. But last year, we dropped support for it and instead chose to go all-in on GraphQL Federation. The decision was somewhat controversial, but it had to be done. GraphQL Federation is what our customers wanted, and SaaS is not the only way. Now, we’re dealing with a new set of problems as we need to re-architect parts of our platform to enable complete self-hosting of Grafbase. Our investors are exceptional founders, but I learned that we sometimes need to go our own way, too. – What’s one of your biggest lessons?

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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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At the end of the Day, it’s best to use AI in the near term for what it is best at, REGURGITATION! … I.e. Data Aggregation Summary (to reduce) Then, in the long term, use it for what it can be best at, REGENERATION! … I.e. Data Augmentation Summation (to add) Watch out for what we are building!

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pro tip: i made the "blazing fast" AI dev stack for y'all! ⌘ Langbase is supa fast!! Deploy your AI agent pipes today. Excited to see what y'all will ship with composable AI. Here's an entirely free and open-source chatbot. ⌘ Open Pipe: https://lnkd.in/g-wU_ZyF 📖 Open-source Chatbot: https://lnkd.in/gT5_wFkc Let's go!

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Back in 2010 or so I was obsessed with game engines like Unity and Unreal. Sure, they had been around for a while before that, but for me, at that time, these engines unlocked the magical world of game development by taking away the pain of graphics programming, user input, platform deployment (Android Studio, looking at you) and so much more. In 2012 I think I made one game a month for 12 months. Yes, they were all terrible but that's not the point. The point is that it was fun and easy to make games. Remember the mantra, "make games not engines"? The same logic applies to building CAD applications. Developing an application that lets users edit geometry in a robust, easy, and scalable way is very difficult. If you come from game dev, you might think meshes are the way to go. That will seem like great idea until your users want to boolean two meshes that came from who-knows-where. ❌ If you've got lots of extra dollars to spare and you come from the world of engineering, you might consider licensing one of the desktop CAD kernels. Good luck to you! Have fun learning the math behind BREPs for the next couple years let alone shipping that application to paying customers to recover your upfront costs. ❌ At Metafold 3D our vision is to change this. In this talk, our CEO Elissa Ross demonstrates how we developed a personalized medicine application that any clinician could use in less than a week. ✅ Get started today with our #opensource SDKs: https://lnkd.in/gkETXB5k

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Gemini's context window is underrated. Can fit entire PDFs, books, 30k+ lines of code. Easily becoming my go to for quick complex tasks which no one has built RAG pipelines for - highly recommend for ambiguous tasks where you want to throw everything at the model

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You can manage uncertainty in a way that creates a fulfilling and enriching developer experience. My friend Patrick Sheridan, CEO of Modus Create, dropped this nugget of wisdom on the Technocratic podcast. The episode is posted at https://lnkd.in/eVzKR-qw You can also sign up for the podcast and the newsletter at https://lnkd.in/gqygAfzy In the meantime, enjoy this clip. #softwaredevelopment #business #ModusCreate

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A really solid discussion to understand where the landscape of software and AI technology is going and how the tech can be used by companies. https://lnkd.in/ghNNUd9i

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Vishal Sachdev highlights the strategic integration of open source and proprietary tech in architecting tech stacks, developer ecosystems and resulting business models. The world class companies effectively balance value commoditization in open source and value capture in proprietary tech. This is the strategic challenge for companies operating in #blockchain and #AI. I believe those who operate at the intersection of blockchain *and* AI stand to win this strategic battle...

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