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USA
Joined February 2015
Explore Shivay Lamba's interactive Edge Inference walkthrough: inside-edge-inference.vercel… Full episode coming soon on CodingCat.dev: codingcat.dev/podcasts
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Offline On-Device AI Agents with Gemma & Local Tool Calls (feat. @howdevelop) How do you run an agentic healthcare workflow without sending private patient data to the cloud? Run local Speech-to-Text + a 12B Gemma model on-device to execute pharmacy tool calls 100% offline.
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Replying to @pablomanjarress
brutal, but true!
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Podcast S1E17 with Dan Ribbens (@DanielRibbens): Payload CMS We chat with Dan Ribbens about Payload CMS a self-hosted, headless JavaScript CMS.
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Podcast S1E16 with Sam Bhagwat (@calcsam): Delivering a Content Mesh On this podcast we talk with Sam Bhagwat, Cofounder & Chief Strategy Officer at Gatsby. We dive into Gatsby 3.0 and why Jamstack is allowing us
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Running AI agents in production comes down to unit economics and governance. Combining a lightweight `initial_context` outline with scheduled build diffs keeps token costs low and catches source drift early. Production notes in the reply 👇
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Podcast S1E15 with Shawn Wang (swyx) (@swyx): What's up with Svelte On this podcast we talk with Shawn Wang also known as swyx. We dive into what is happening with Svelte, the Svelte Society, and Svelte Radio.
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CodingCatDev Deep Dive: Use Firestore to Build Hugo Content Use a frontend app (Angular), to update a Firestore Backend, trigger Firebase Function, while maintaining git commits, and buld/deploy your Hugo site.
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Podcast S1E14 with Patrick Martin (@pux0r3): Firestore for Unity and C++ We talk with Patrick Martin from Firebase all about game development and how Firebase's Firestore is now available in Beta.
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Podcast S1E13 with Scott Tolinski (@stolinski): Creating a Learning Platform We talk with Scott Tolinkski of LevelUpTutorials all about his past and why he started his Learning Platform.
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3/ Think you are diversified in VOO, SPY, QQQ, or VTI? 37% of the S&P 500 and 48% of the Nasdaq-100 sit in these 8 interconnected AI balance sheets (plus TSMC, ASML, and SK Hynix in VXUS). Full breakdown + Architecture Diagrams: codingcat.dev/post/anthropic…
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2/ In public, Meta, Google, Apple, Microsoft, xAI, and Anthropic are rivals. Under the hood: • Meta trains Llama on 1M+ NVIDIA GPUs while using Google Cloud & Gemini Search grounding. • Apple routes Apple Intelligence to Gemini, OpenAI, and Claude. • All chips -> TSMC.
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1/ Look at how "AI round-tripping" works in the S-1: • Google commits up to $40B -> Anthropic signs $114.5B in Google Cloud TPU commitments through 2033. • Amazon commits up to $33B -> Anthropic signs $110B in AWS Trainium2 commitments. • Plus ~16% marketplace fees!
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Anthropic just filed its Form S-1 for a Nasdaq IPO ($ANT), and the footnotes read like the ultimate Big Tech infinite money glitch. $16.3B H1 2026 revenue. -$80.7B H1 GAAP net loss. $518B in take-or-pay cloud commitments. How 7 CEOs built a circular AI economy: 🧵👇
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