Grant Ingersoll

Some straight talk about why #platformengineering initiatives fail and how to make sure that doesn’t happen on your watch. Check out the latest from The New Stack and Jennifer Riggins. #DevEx #DevOps

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Anthropic's Claude can now use computers like we do - clicking, typing, and working with actual software. Not through APIs. Through the actual interface. For our manufacturing & retail clients asking about AI automation - this is HUGE. AI that can actually help with any existing software stack, not just integrate through complex APIs. • Uses standard software like a human (still learning but improving fast) • Crushing it at coding tasks 93.7% 0-shot (This is fantastic) • Faster, cheaper version (Haiku) coming this month While everyone's hyped about the tech, we're already mapping out practical applications for our clients. Real automation. #AI #Innovation #Manufacturing #Retail #DigitalTransformation https://lnkd.in/geFkjfr6

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Thanks to #genai360 for the shoutout here for Consilience AI and our AlphaIQ product. Davit Buniatyan Joseph Byrum, Ph.D., MBA

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"I don't have any strong words of wisdom about being a CTO. I'm stumbling through it just like everybody else." -- Damien Kan ChatGPT + Zoom Recipe; 1. Cloud record the zoom session 2. Download the transcripts 3. Prompt "in this transcript, can you find two good quotes from the person <Name>" 4. Wait for it... *apologies to Damien for the cloud transcript misspelling, this is a good time for the audience to notice the forest (the output) while accepting the tree (the spelling mistake)*

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Whether you're curious about AI or Springing into it, my good friend Craig Walls (cr-AI-g) has something to teach at dev2next Conference Check out the details at https://lnkd.in/eMD7pgtT

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As singularly focused as Senzing is on entity resolution developer tools —Enterprise Knowledge, LLC (EK) is singularly focused on consulting around semantic solutions. We’ve come to know EK and are quite impressed with their team and capabilities in this area.    With that, I’m excited to co-author this article with EK VP and Partner, Lulit Tesfaye. Entity Resolved Knowledge Graphs (ERKG) are going to prove to be an essential starting point for most projects centered around the semantic layer. Take a look…   #SenzingPartner #SemanticLayer #EntityResolvedKnowledgeGraph #EntityResolution #GraphAnalytics #GraphVisualization #KnowledgeGraph #GraphDatabase https://lnkd.in/drGacJVq

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Who is headed to SOF Week next week? I'd love to catch up. While I'm out there, I'll be looking for feedback on a couple of high-fidelity product prototypes; one is in the DevSecOps/C2 space, and the other one is focused on Sentiment Modeling - both are leveraging purpose-built and self-hosted Generative AI solutions. Let me know if you have a second to chat! #sofweek #innovation #defense

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Nice shoutout from Dotan Horovits 🇮🇱🎗and Charity Majors for #OpenTelemetry and the evolution of client-side telemetry (particularly the open source of Embrace) in 2024. Time for data to be portable, available, and not locked in. "F that sh$t. The only thing that deserves swearing is vendor lock in," screamed Charity Majors. Data should be available to anyone who can build on it, run analysis, do AI, etc. Stabilize, adopt, and run!

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Application-specific guardrails are necessary. As we closely track how the regulatory landscape evolves, it is imperative that we should be thinking about how to develop our own custom guardrails rather than relying on out-of-the-box metrics that aren’t effective at being reliable guardrails for our GenAI applications in production! Looking forward to keeping an open-source friendly world with the right set of safeguards for GenAI applications that can be constructive for innovation!!

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Excited to announce Lagrange Labs' $13.2M seed round, led by Founders Fund, with support from Archetype, 1kx, Maven 11, Fenbushi Capital, Volt Capital, CMT Digital, Mantle Network Ecosystem Fund, and various angels. Read More: https://lnkd.in/gvxFPH_B Lagrange was founded almost two years ago with the vision of using zero-knowledge proofs to expand the design space of applications that could be built onchain. We saw a simple problem: Developers could not securely use historical or cross-chain state. The name “Lagrange” was inspired by mathematician Joseph-Louis Lagrange, who discovered Lagrange Points, positions of equilibrium in space between two celestial body systems. These points of equilibrium were the perfect analogy for trustless interoperability in crypto. While Lagrange has moved away from being a purely cross-chain focused company, the name remains, as does our goal of bringing equilibrium and stability to onchain applications. The original vision of secure interoperability still lives on within our State Committee protocol. As we’ve progressed, we have crystallized our mission: to unlock new types of data-intensive and cross-chain applications by enabling verifiable computation over blockchain data at a ‘big data’ scale. The way we achieve this is through our hyper-parallel ZK Coprocessor. Over the past few months we’ve hit a number of major milestones as a company: • Launching Lagrange State Committees as the first ZK AVS on @eigenlayer mainnet. Hitting over $5 billion in restaked ETH after only 3 weeks. • The Lagrange State Committees have generated over 30,000 state proofs and have finalized 2,000,000 blocks. • Launching our first ZK Coprocessor testnet, ‘Euclid’. We’ve currently generated over 400,000 proofs at a growing rate of 127,000 proofs per week. • Launching our infrastructure as a decentralized network with 40+ node operators. • Our first paper, Reckle Trees, authored by Charalampos Papamanthou, Shravan Srinivasan, Nicolas Gailly and Andrus S. from our team was accepted into both CCS’2024 and SBC’2023. More to come soon 🤫 None of our progress would be possible if it wasn’t for the team behind Lagrange. A special thank you to the whole Lagrange family whose tireless work brought us to this point. Thank you to all of our friends, partners and investors who have supported Lagrange in getting here. We have an ambitious roadmap ahead of us this year and we look forward to achieving it all of you on our team.

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DEMYSTIFYING WEB COMPONENTS SERIES Sorry for yelling. Web Components are powerful, but often misunderstood (lack of SSR, styling woes, blah, blah...). At Southleft, we're diving deep into their potential. We're kicking off a blog series on Web Components, starting with SLOTS. Why? They're a game-changer for component flexibility. Our "Understanding Slots" four-part series covers: → Intro to Slots → Advanced Techniques → Best Practices & Styling → Advanced Styling of Slotted Elements If you've been sleeping on slots, it's time to wake up! They're reshaping how we build component architecture. Check it out starting with Part 1: https://lnkd.in/gmFeXvVP What's your take on Web Components? Interested in learning more and possibly using them in your next design system project? Or maybe a refactor of your existing design system? Let's talk! #WebDev #FrontEnd #WebComponents #DesignSystems

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Developer productivity is one of the four key themes in Gartner's Hype Cycle for Emerging Technologies: https://lnkd.in/e9uvGPtD Client inquiries about developer productivity have skyrocketed in recent years, driven by the impact of generative AI of course. We've recently published research on AI Agents in Software Engineering, as well as the Magic Quadrant for AI Code Assistants, which both aim to improve productivity. At the same time, we are asked about the right metrics to measure developer productivity, including DORA metrics. It is not unusual to speak with organizations who have developed their own alternatives to DORA metrics, to measure their developers. Akis Sklavounakis's report comparing DORA, SPACE, DevEx, and other metrics is one of our most widely-read reports for software engineering leaders. Kudos to Arun Chandrasekaran, Jason Wong, and Ankita Khilare for producing this year's Hype Cycle.

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When building RAG and LLM apps, the devil is in the documents. One of the hardest and least appreciated pieces of any RAG pipeline is turning complex visual documents into LLM-ready data. I'm talking about financial, health, insurance, gov docs and more. If they have tables, charts, diagrams and so on, it's a recipe for downstream hallucination. So how you fix it? This episode of RAG Masters explains the theory and then looks at PyPDF, Tesseract, LlamaParse, Unstructured and EyeLevel's X-Ray. See who comes out on top. https://lnkd.in/eTJw4hgX

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There's a lot to keep up with in the world of LLMs. But understanding a few key foundational elements can get you pretty far. One framework I find useful is relating them back to the greatest hits of Creed — the 90s/00s commercial rock band that we all know and love. "Higher" - Think of how LLMs learn to reach increasingly higher levels of understanding: - They start with basic pattern recognition - Progress to more complex language understanding - Eventually reach sophisticated reasoning capabilities - Each training iteration aims to achieve better performance "My Own Prison" - This relates to the concept of LLM hallucinations: - Like being trapped in a self-created reality - LLMs can sometimes generate convincing but incorrect information - They're confined by their training data - Need careful prompting to stay accurate "What's This Life For" - This connects to the purpose and goals of LLMs: - Designed to understand and generate human language - Assist with various tasks and problems - Help make information more accessible - Serve as tools for human enhancement, not replacement "With Arms Wide Open" - This represents how LLMs embrace and process information: - Accept diverse inputs and prompts - Process multiple languages - Handle various types of queries - Maintain an open architecture for different use cases "One Last Breath" - This relates to token limitations: - LLMs have finite context windows - Must make the most of limited tokens - Need to be efficient with information - Sometimes have to truncate responses Each concept builds on the previous one, just like Creed's discography evolved over time. Just remember that LLMs, like any tool, have their strengths and limitations - they're meant to uplift and assist, not to replace human judgment. Hope you find this as useful as I do.

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Deciding whether to develop your own #GenAI technology or invest in existing solutions? Our Head of Field Engineering Justin Hayes breaks down the pros and cons of each option. Check out our blog for a build vs buy guide ➡️ https://lnkd.in/gZfn-Q3m?

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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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Quick question for the LinkedIn Community: Does anyone have good data on or insight into how many on-prem software companies there are in the US, as well as how many are currently transitioning to private cloud or broader cloud offerings? I’m curious about the trends and challenges involved in this shift.

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