firstfall they start the intro to the code on jvm **
They Successfully completed five meetup ,iam registered the sixth event, exploring the meetup through the payilagam
** One of the college student to explain their project **
It's mostly related on javascript to the projects and node js,stripi.
*Why Healess cms *
It's used to modify the digital content website
Stripi
It's headless cms server side rendering
Better performance=better SEO
Next.js
Using a colour plugin and chatgpt plugin
**Saas company data engineer explain the data pipeline
Data warehouse
It's used to store the structured data
Data lake
Data lake concept mostly used on the developer and engineer side .
another one backend developer explain the olama LLM
What Is Ollama?
Ollama is an open‑source local runtime and package manager for large language models (LLMs)—ideal for macOS, Linux, and Windows. It allows you to pull, quantize, and run models locally, keeping everything offline, private, and under your control
arxiv.org
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codefarm0.medium.com
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analyticsvidhya.com
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Core Capabilities
Easy setup & CLI: Install via Homebrew on Mac (brew install ollama) or use an OS-independent installer to get started with a couple of commands: ollama pull llama3.1 && ollama run llama3.1
analyticsvidhya.com
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codefarm0.medium.com
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blog.langformers.com
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Model registry: Supports popular open‑source models—LLaMA, LLaVA, Mistral, Gemma, Qwen, CodeLlama, Dolphin, etc.—automatically quantized for efficient use on CPUs or GPUs
kodeco.com
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codefarm0.medium.com
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blog.cordatus.ai
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Quantization support: Uses GGML/GGUF formats to shrink models (4‑bit, 8‑bit), enabling them to run even on modest hardware
kodeco.com
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blog.cordatus.ai
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codefarm0.medium.com
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OpenAI-style API: Exposes endpoints like /v1/chat/completions, so you can seamlessly swap OpenAI for Ollama in your existing apps
blog.langformers.com
Advanced Features
Tool & function calling: With models like Llama 3.1, Ollama supports external function/tool calls. You declare functions (e.g., “get_current_weather”) and the model can invoke them via JSON calls during chat
kodeco.com
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ollama.com
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ollama.com
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Modelfiles & adapters: Customize models through lightweight Modelfiles or LoRA adapters—no need for full fine‑tuning.
Cross-platform UI & API: Use via CLI, desktop app, Docker, or REST API—compatible with Python, JavaScript, LangChain, VS Code, etc.
arxiv.org
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blog.cordatus.ai
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codefarm0.medium.com
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Benefits vs Trade‑offs
Benefits:
Privacy-first: Your data stays local .
Offline and latency‑free: No internet required, instant inference
analyticsvidhya.com
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kodeco.com
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platodata.ai
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Cost‑effective: Avoid cloud usage fees.
Developer-friendly: Works like Docker/Homebrew for AI
platodata.ai
blog.cordatus.ai
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codefarm0.medium.com
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analyticsvidhya.com
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Drawbacks:
Requires decent hardware: 8 GB+ RAM, ideally GPU with 12 GB VRAM for larger models
arxiv.org
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kodeco.com
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platodata.ai
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Model sizes demand disk space.
You manage updates, security layers, and service integrity yourself .
How It Works: Under the Hood
Pull & store: Downloads quantized model files (GGUF/GGML) into ~/.ollama
medium.com
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blog.cordatus.ai
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codefarm0.medium.com
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Run locally: CLI or server uses llama.cpp engine in Go to serve inference
blog.cordatus.ai
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codefarm0.medium.com
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Interact: Input text → tokenized → inferenced → streamed output. Keeps context across turns
kodeco.com
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shekhar14.medium.com
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blog.cordatus.ai
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Real‑World Use Cases
Private chatbots, coding assistants, or multimodal AI running offline.
Embedding LLM inference into IDEs via REST or plugin integration.
Edge applications or document processing tools where data privacy is critical.
Research/back‑end environments where cloud isn't an option.
TL;DR
Ollama is like Homebrew/Docker for LLMs—simple to install, privacy-first, cross‑platform, and OpenAI‑compatible. It empowers you to run top-tier models on your own machine with secure, offline, fast results. Just ensure your hardware is up to the task.
Would you like help installing it, pulling your first model, or setting it up with tool calling or LoRA? Happy to walk you through any step!
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