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Learn about the latest technologies from fellow newline community members!

  • React
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  • React
  • Angular
  • Vue
  • Svelte
  • NextJS
  • Redux
  • Apollo
  • Storybook
  • D3
  • Testing Library
  • JavaScript
  • TypeScript
  • Node.js
  • Deno
  • Rust
  • Python
  • GraphQL

Mastering Advanced RAG for Better AI Retrieval

Advanced RAG techniques turn prototype chatbots into production-grade retrieval systems. A basic RAG pipeline ingests documents, indexes them, retrieves top-k chunks, and generates answers. It works for low-stakes Q&A. It collapses under real-world pressure. Research shows that basic systems…
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What Is an AI Application? Examples, Patterns, and Use Cases

AI applications come in three broad shapes: single-call LLM solutions, workflow-based automations, and agentic systems. Each one balances cost, autonomy, and complexity in its own way. Pick the wrong one and the project stalls. In our experience, the model is rarely the culprit. Most enterprise LLM…
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What Is AI Inference and Why It Matters for Apps

AI inference is the moment a trained model turns data into a decision. That single step powers every smart feature in a modern app. Newline's AI bootcamps include hands-on labs covering the inference setups you'll actually see in production. Here's the shortlist of the five modes the labs walk…
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Building AI Applications with RAG and Tool Use

If you want to learn RAG fast, these five platforms balance ready-made data connectors, decent docs, and reasonable setup effort. *Time estimates assume a small team (2‑3 engineers) following a typical bootcamp curriculum and include basic testing. Group RAG builds into three buckets and ask how…
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What Is AWQ in LLM Quantization and How It Works

AWQ stands for activation-aware weight quantization. It scales the most influential weight channels based on offline activation statistics, then quantizes everything else to ultra-low bit widths. By protecting a small fraction of salient weights, it keeps quantization error down without…
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