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13 posts· Last used 2d

RakeshPalani @RakeshPalani@social.vivaldi.net
· 2d ago

🚀 Building Stoic — an AI Search & Answer Platform

Stoic is an AI-powered search and answer platform I’m building to turn documents, web content, images, and other information into useful, grounded answers. It uses RAG, embeddings, semantic search, and vector databases to retrieve relevant context before generating an answer. The backend is built with Python and FastAPI, with Supabase + pgvector for vector storage and similarity search. I’m also experimenting with LLM model routing, streaming responses, multimodal processing, and LLM-as-a-judge evaluation. The goal is to make AI search more reliable by focusing not only on generation, but also on retrieval quality and evaluation. I’m building Stoic as a hands-on learning project and continuously improving the architecture, performance, and user experience. If you're interested in AI engineering, RAG, LLMs, semantic search, or building AI products, I’d love to connect and hear your thoughts. 🤝

🔗 Try Stoic: https://stoic-app.vercel.app 💻 GitHub: https://github.com/Rakesh051204/cloud9-frontend

#AI #GenerativeAI #LLM #RAG #MachineLearning #AIEngineering #Python #BuildInPublic

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anoncheg @anoncheg@mastodontech.de
· 3w ago

Title: P1: Main principles of programming and AI Engineering.

  • Outcome-Oriented Principle - not model benchmarks, real impact.
  • Clarity & Structure Principle - reliability
  • Iterative Decomposition Principle - step-by-step for error reduction and modularity.
  • Grounding-First Principle - Base answers on trusted external or retrieved sources
  • Data Hygiene & Provenance - one bad source poisons results long-term #dailyreport #aiengineering #principles #aiprinciples #programming
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Oliver Zehentleitner @oliverzehentleitner@burningboard.net
· 1mo ago
Someone “cleaned up” the workaround. The code looked simpler. Then the production bug came back. That strange delay was not just ugly code. It carried knowledge: The upstream API could replay the same order unless the request was handled carefully. But the reason existed only in someone’s head. Keep the Why is an agent skill that captures this kind of rationale as a natural by-product of development and stores it as versioned Markdown inside the project. So before the next developer or agent removes the workaround, they can see: • why it exists • what was already tried • what could break • what must change before it can safely be removed Legacy does not begin when code gets old. It begins when the reason disappears. Free and open source: https://keepthewhy.com/ What “ugly” workaround in your codebase is actually carrying important knowledge? #OpenSource #AIEngineering #DeveloperTools #SoftwareEngineering #CodingAgents #ContextEngineering #TechnicalDebt #Documentation
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Oliver Zehentleitner @oliverzehentleitner@burningboard.net
· 1mo ago
I thought AI coding agents had a memory problem. The repository had a memory problem! A new agent session, another developer, or a different machine can all open the same codebase. They get the code, tests, and Git history. But they do not automatically inherit the conversations, rejected approaches, incident learnings, or constraints that shaped the implementation. Keep the Why is an agent skill that captures this reasoning as a natural by-product of development. It stores the relevant context as versioned Markdown inside the project, making it directly available to every developer and agent working with the same repository. So the understanding travels with the code - across sessions, developers, agents, and systems. Free and open source. One install command. About two minutes to set up. No runtime dependencies. No MCP server required. https://keepthewhy.com Do you consider a project portable if the code can be cloned, but its reasoning cannot? #OpenSource #AIEngineering
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