Tutorials on Building Ai Applications

Learn about Building Ai Applications from fellow newline community members!

  • React
  • Angular
  • Vue
  • Svelte
  • NextJS
  • Redux
  • Apollo
  • Storybook
  • D3
  • Testing Library
  • JavaScript
  • TypeScript
  • Node.js
  • Deno
  • Rust
  • Python
  • GraphQL
  • React
  • Angular
  • Vue
  • Svelte
  • NextJS
  • Redux
  • Apollo
  • Storybook
  • D3
  • Testing Library
  • JavaScript
  • TypeScript
  • Node.js
  • Deno
  • Rust
  • Python
  • GraphQL
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Skip ML Theory: Ship AI Apps with One API Call and a Rollback Script

When you run an AI agent behind a production API, every millisecond you add compounds. Users don't care whether your agent consulted three models or made six tool calls. They want the result. Exposing that complexity at the API boundary leaves you with brittle clients and debugging sessions at 2…
Thumbnail Image of Tutorial Skip ML Theory: Ship AI Apps with One API Call and a Rollback Script

5 Open-Source Stacks for Building AI Applications: LlamaIndex+Qdrant to DSPy+SQLite, Debug by Failure Mode

Choosing an open-source stack stopped being optional the moment a local Qdrant + LlamaIndex setup stalled on first retrieval. When you're building AI applications, every cold-start second compounds across users. Two failure modes keep showing up. Poor chunking splits context so no single chunk can…
Thumbnail Image of Tutorial 5 Open-Source Stacks for Building AI Applications: LlamaIndex+Qdrant to DSPy+SQLite, Debug by Failure Mode

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Building AI applications: 5 moves from demo to deploy before wrong answers reach customers

Progressive rollout matters more than demo performance because AI systems fail quietly. You push a prompt change, tests pass, and a week later users report degraded answers with no exception thrown. Some research and analyst forecasts suggest many GenAI pilots and agentic AI projects stall before…
Thumbnail Image of Tutorial Building AI applications: 5 moves from demo to deploy before wrong answers reach customers

What MARL Gets Right About AI Agents: Coordination Patterns You Can Reuse in Real LLM Apps

Multi-agent systems can consume many tokens when agents share unrestricted context, leading to higher token usage than necessary. MARL's centralized training with decentralized execution pattern gives you the blueprint: design coordination with full visibility, then run each agent with local…
Thumbnail Image of Tutorial What MARL Gets Right About AI Agents: Coordination Patterns You Can Reuse in Real LLM Apps

Set clear scope for AI agents using Gemini context engineering

Scope stopped being optional the moment your agent could call tools, query databases, or keep memory across sessions. Scope is the set of information and operations your agent can access during a single task. Get it wrong and you watch your agent burn through tokens on irrelevant context, fire the…
Thumbnail Image of Tutorial Set clear scope for AI agents using Gemini context engineering

How to Test AI Agent Behavior Before Production: Three Strategies

Testing agent workflows stops being optional once you move past simple single-turn interactions. An agent can answer correctly in isolation and still lose a customer's order details mid-conversation, send the wrong arguments to a refund endpoint, or retrieve stale inventory data. These problems…
Thumbnail Image of Tutorial How to Test AI Agent Behavior Before Production: Three Strategies

Designing Full-Stack Autonomous AI Agent Systems

Autonomous AI agents are software systems that reason, plan, and execute multi-step tasks. They're moving into real business operations fast. For anyone aiming at a lead AI developer role, that shift changes what "senior" means. You won't just tune models. You'll design the systems around them.…
Thumbnail Image of Tutorial Designing Full-Stack Autonomous AI Agent Systems

AI Applications for Developers: Common Examples

The shift toward regulated industries has changed what "matters" actually means for developers building AI-powered systems. Shipping production systems in healthcare, finance, or government comes with a constraint your peers don't have: every decision the model makes must be explainable to an…
Thumbnail Image of Tutorial AI Applications for Developers: Common Examples

Supabase for RAG Apps: Postgres, Vectors, and MCP Workflows in 2026

What does Supabase do for RAG apps? It's an open-source Postgres platform. It bundles authentication, instant APIs, edge functions, real-time subscriptions, storage, and vector embeddings. For anyone building Retrieval-Augmented Generation apps, that means your vector store, document permissions,…
Thumbnail Image of Tutorial Supabase for RAG Apps: Postgres, Vectors, and MCP Workflows in 2026

From Prompt Engineering to Context Engineering: Better Patterns for Real AI Applications

Most failures in a real AI application trace back to missing or messy context, not a weak model. When a production system "hallucinates," the prompt wording is rarely the culprit. It's what the model could and couldn't see at inference time. That changes how we teach lead developers. Prompt…
Thumbnail Image of Tutorial From Prompt Engineering to Context Engineering: Better Patterns for Real AI Applications

Hermes Agent vs Grok bot vs Openclaw

OpenClaw is designed to run locally—on machines ranging from Mac Minis to Raspberry Pis. It can connect to messaging platforms like WhatsApp, Telegram, Slack, Discord, and iMessage. The system is built to remember context from prior conversations, reducing the need to re-explain project setup.…
Thumbnail Image of Tutorial Hermes Agent vs Grok bot vs Openclaw

What Is AI Application and How Developers Use It

Developers waste months building AI features that solve the wrong problem. The failure isn't technical. It's treating AI as the solution before you've understood what you're solving. Before you write a line of code, answer this: does this problem genuinely benefit from machine learning, or would a…
Thumbnail Image of Tutorial What Is AI Application and How Developers Use It

What Is AI Applications and Common Examples for Developers

AI applications are software programs that use machine learning, natural language processing, and computer vision to handle tasks that once needed human judgment. They run in healthcare, finance, retail, and manufacturing right now. If you're a developer aiming at a lead role, the fastest path is a…
Thumbnail Image of Tutorial What Is AI Applications and Common Examples for Developers

How to Build AI Applications with RAG and Tool Use

Building AI applications with Retrieval‑Augmented Generation (RAG) follows a recognizable workflow. The table below summarises the typical questions of “what, how long, and how hard” for each major phase, along with tools that frequently appear in tutorials and open‑source projects. What does the…
Thumbnail Image of Tutorial How to Build AI Applications with RAG and Tool Use

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…
Thumbnail Image of Tutorial What Is an AI Application? Examples, Patterns, and Use Cases

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…
Thumbnail Image of Tutorial Building AI Applications with RAG and Tool Use

What Is an AI Application and How It Works

AI applications fall into five families: machine-learning prediction, natural-language processing, computer-vision analysis, robotics/automation, and expert-system reasoning. Each carries its own data needs and runtime behavior. *Difficulty is rated 1–5, where 1 is straightforward and 5 needs deep…
Thumbnail Image of Tutorial What Is an AI Application and How It Works

AI Application Examples for Developers

Based on feedback from Newline learners, GitHub Copilot usually delivers the biggest raw time savings. Its tight integration with GitHub repos means it can suggest whole functions that already match your project's style. Most people see the gains within a day. The speed is not the whole story.…
Thumbnail Image of Tutorial AI Application Examples for Developers

Supabase for AI Apps: A Developer's Guide to Realtime Data

Watch: How to subscribe to real-time changes on your database - SupabaseTips by Supabase Supabase is an open-source backend that hands your AI app a Postgres database, authentication, instant APIs, storage, and vector embeddings in one package. If you're building real-time AI features, the standout…
Thumbnail Image of Tutorial Supabase for AI Apps: A Developer's Guide to Realtime Data

Why Most AI Agents Fail in Production

Understanding why AI agents fail in production is critical because these failures cost businesses hundreds of thousands of dollars per project and erode customer trust. Industry data reveals 88% of AI agent projects fail before reaching production, with 61% of these failures tied to preventable…
Thumbnail Image of Tutorial Why Most AI Agents Fail in Production

Vector Databases vs Graph RAG: Picking the Right Memory for AI Agents

Watch: VectorDB vs GraphDB for Gen AI Agents | Databases for AI by AWS Events Use vector databases for semantic memory. Use Graph RAG for structured reasoning. Combine them when your agent needs both recall and explainability. That one line covers most decisions. The rest of this section unpacks…
Thumbnail Image of Tutorial Vector Databases vs Graph RAG: Picking the Right Memory for AI Agents

What is production ready AI engineering ?

Watch: OpenAI + @Temporalio: Building Durable, Production Ready Agents - Cornelia Davis, Temporal by AI Engineer Production-ready AI engineering is what happens after the demo works. It's the work of deploying models that survive contact with real users, real traffic, and real failure modes. The…
Thumbnail Image of Tutorial What is production ready AI engineering ?

Fine-Tune a Learning Agent in Artificial Intelligence

Your dataset decides whether the fine-tune works or burns your budget. A small set of clean, consistent input-output pairs beats a giant noisy dump almost every time. FireAct is the proof point: fine-tuning Llama-2-7B on just 500 GPT-4 trajectories improved HotpotQA performance by 77%. High-signal…
Thumbnail Image of Tutorial Fine-Tune a Learning Agent in Artificial Intelligence

How to Keep AI Project Costs Low

AI costs come down to tokens, calls, and compute. The model you pick matters less than people think. The fastest way to spend less is to stop paying for work you don't need: bloated prompts, repeated calls, oversized context windows, and the retries that pile up quietly while you're not looking.…
Thumbnail Image of Tutorial How to Keep AI Project Costs Low

Prompt Engineering is Dead, Skills Are Alive

Prompt engineering stopped being the dividing line between strong and weak AI work. The skill set that replaced it, problem framing, context design, evaluation, retrieval, tool use, and workflow orchestration, is what actually ships reliable products. Clever wording still helps. It's just a small…
Thumbnail Image of Tutorial Prompt Engineering is Dead, Skills Are Alive

RAG-Token vs DoRA for Learning Agents

These two methods solve different problems that have held back learning agents for years. RAG-Token keeps answers factual by pulling fresh information at the token level. DoRA adapts large models for a fraction of the usual compute. Run them together and you get an agent that updates fast and…
Thumbnail Image of Tutorial RAG-Token vs DoRA for Learning Agents

Turning AI Prompting into Production-Ready Agents

Watch: Stanford CS230 | Autumn 2025 | Lecture 8: Agents, Prompts, and RAG by Stanford Online Production-ready AI agents are no longer a futuristic concept-they’re a critical asset for businesses and industries striving for efficiency, compliance, and innovation. Unlike experimental prototypes,…
Thumbnail Image of Tutorial Turning AI Prompting into Production-Ready Agents

Why 80% of US AI Startups Switched to Chinese Models

Watch: Chinese AI startups see progress amid U.S. AI trade concerns by CNBC Television The shift of 80% of U.S. AI startups to Chinese models reshapes the AI market, driven by cost efficiency, performance, and strategic advantages. Chinese open-source models like Alibaba’s Qwen and DeepSeek’s R1…
Thumbnail Image of Tutorial Why 80% of US AI Startups Switched to Chinese Models

Why Forward Deployed Engineers Are In High Demand

Watch: Forward Deployed Engineer: The Role Up 800% (And How to Get It) by Beyond Coding Forward-deployed engineers (FDEs) have become a cornerstone of modern AI adoption, driven by explosive demand across industries. Job listings for FDEs surged by 800–1,165% in 2025, with major players like…
Thumbnail Image of Tutorial Why Forward Deployed Engineers Are In High Demand

Sergey Levine Approach to Fine Tuning LLMs

Fine-tuning large language models (LLMs) transforms their capabilities from general knowledge repositories into specialized tools for complex decision-making. By adapting models to specific tasks, industries achieve performance gains that pre-trained models alone cannot match. For example, a…
Thumbnail Image of Tutorial Sergey Levine Approach to Fine Tuning LLMs

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