Param Harrison
@learnwithparam
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โก Helping engineers go from AI demos โ production. Master Agentic AI apps in 6 weeks ๐ https://t.co/CfvI1P4zVp
Tallinn, Estonia
Joined March 2011
Which coding agent and IDE is good for creating Jupyter notebooks?
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You Already Know RAG โ But Thatโs Not Enough for Production AI https://t.co/IwbLiAqZz9 via @YouTube
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How is your Claude code or cursor workflow to biol multiple branches in parallel?
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AI Engineering Bootcamp in partnership with MasteringBackend https://t.co/BgNR0pmwJ5 via @YouTube
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@lemonsqueezy Your web form for contact keeps on popping "Something went wrong", even when managed to submit one, it looks like contact us form and not create any ticket for a week already. How to follow up?
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If youโre tired of building basic wrappers and want to learn the systems engineering behind real AI agents, this account is for you
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Designing Memory Systems for AI Agents is the biggest challenge, I have tried to compress some of the patterns here along with a detailed article here, https://t.co/MN16To2bQt
learnwithparam.com
Learn how to architect production-ready memory systems for AI agents. Deep dive into short-term buffers, semantic retrieval, episodic learning, and planning memory. Build agents that remember, learn,...
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Scaling Autonomous AI Agents demands architecture, not just model calls. The simple chain is fragile in production. Get the Proven 3-Layer Blueprint to move your high-throughput agent from prototype to reliable, cost-effective scale. Read the series:
learnwithparam.com
Learn how to architect a production-ready AI code review agent. Deep dive into the ingestion layer, filtering patterns, GraphRAG for code context, and model cascading for cost optimization.
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I use AI a lot. But the first thing Iโm doing when this app makes money? Hiring people. Doing everything yourself still sucks โ AI doesnโt change that.
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Ofcourse, it is a dev tool with smaller surface area so this is possible. Not every business can operate that way. But the point is, make your brain (engineers) closer to the end user
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Cursor is the fastest company to reach $1B ARR in history. And they have 0 full-time PM. Probably the best tool built by sitting closer to the user directly from engineers.
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Copyrighting for open source code need to evolve for AI generation. Now a code can be inspired yet completely different from original source to avoid any copyright implications easily with latest AI like gemini 3.0 pro.
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Used antigravity to create a similar web app and it opens the browsers to read the dom. Google seems to be going truly all-in with antigravity, I believe, even with current version that they can able to to take their google workspace customers with them.
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My money is on Google winning the AI race. They have more high-skilled manpower, and the multimoda data to get there.
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Learn ๐บ๐ฎ๐๐ต before neural networks Learn ๐๐ผ ๐ฐ๐ผ๐ฑ๐ฒ before vibe coding products Learn ๐ต๐ผ๐ ๐๐ต๐ถ๐ป๐ด๐ ๐ฏ๐ฟ๐ฒ๐ฎ๐ธ before trying to scale them
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Learn ๐๐๐๐๐ฒ๐บ ๐ฑ๐ฒ๐๐ถ๐ด๐ป before Agentic AI Learn ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ before RAG Learn ๐ต๐ผ๐ ๐๐ฃ๐๐ ๐๐ผ๐ฟ๐ธ before MCP Learn ๐ก๐๐ฃ before prompt engineering Learn ๐ฑ๐ฎ๐๐ฎ ๐ฝ๐ฟ๐ฒ๐ฝ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด before fine-tuning Learn ๐ฐ๐น๐ฎ๐๐๐ถ๐ฐ ๐ ๐ before LLMs
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Google is the nuclear arsenal. They invented the TPU that OpenAI uses; they've been working on AGI via DeepMind for 15 years. They have a cloud datacenter and have started charging rent to OpenAI. They're the only global B2B compliance-capable solution for Enterprise.
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