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Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs

part of the generation brick of Enterprise Document Intelligence, a series that builds an enterprise RAG system from four bricks: document parsing, question parsing, retrieval, and generation. Article 8A (the answer contract) declared the typed schema family and the ANSWER_REGISTRY that maps each answer shape to its schema. This part builds the call that fills …

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Getting Started with the Claude API in Python

  Contents# Introduction# Prerequisites and Installation# Making Your First API Call# Understanding the Response Object# Using System Prompts# Streaming Responses# Next Steps # Introduction  You want to add Claude to a Python application. Creating an account and making your first API call is straightforward. The official documentation can get you from zero to a working request in a few minutes. The next questions …

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Google DeepMind and A24 launch research partnership

Today, Google DeepMind and A24 are announcing a first-of-its-kind partnership focused on research. The collaboration pairs a world-leading research lab with the industry’s most filmmaker-forward studio to help artists develop new workflows and techniques. This ensures the tools of the future are shaped by the creators who use them. This partnership creates a deep research …

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Tokenminning: How to Get More from Your Chatbot for Less

virus spreading through big tech. Engineers are being judged, directly or indirectly, by how much AI they can consume. More tokens, more output, more compute. Some companies even had leaderboards. It’s the 2026 version of ranking engineers by lines of code. ContentsLess is moreThe Cost of Tokenmaxxing1. Financial Cost2. Inference Speed3. Quality🛠️ Real strategies for …

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5 AI Coding Platforms to Build Apps Without the Headache

  Contents# Introduction# 1. Lovable# 2. v0 by Vercel# 3. Replit# 4. OpenAI Codex# 5. MiniMax Code# Final Thoughts # Introduction  Have you ever thought, “If I had programming skills, I could launch my own startup or build the app idea I have been thinking about”? For many non-technical founders, creators, and professionals, the hardest part is not coming up with ideas. It …

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Start building with Nano Banana 2 Lite and Gemini Omni Flash

Limitations: Omni offers 10-second video generations currently, with longer durations coming soon. Uploading audio references and scene extension is not yet supported in the Gemini API for this model. Video references up to 3 seconds in duration are accepted by the API schema but are not correctly processed by the model at this time. Character …

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How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification

is a good way to test NLP models because it focuses not only on what a sentence says, but also on how it is written. Kaggle’s Spooky Author Identification competition is a compact version of this challenge: given a single sentence from gothic or horror fiction, the model has to predict whether it was written by Edgar Allan Poe (EAP), Mary Wollstonecraft Shelley …

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5 Agentic Workflows to Automate Your Data Science Pipeline

  Contents# Introduction# Prerequisites# Workflow 1: Automated Exploratory Data Analysis Agent# Workflow 2: Agentic Feature Engineering and Selection# Workflow 3: Agentic Hyperparameter Optimization# Workflow 4: Automated Model Monitoring and Drift Detection Agent# Workflow 5: Agentic Pipeline Orchestration and Self-Healing# Wrapping Up # Introduction  The average data scientist spends roughly 45% of their working time on data preparation and cleaning, not on modeling, not on …

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We Built a Routing Layer to Cut Our AI Costs. It Broke the Product.

cut their AI inference bill by more than half last quarter. Eight weeks of clean engineering work. It was the win the engineering team had been chasing all year. It was also the wrong optimization. Three months later, customer satisfaction was dropping, churn was ticking up, and the cost savings were structurally tied to the …

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Fine-tuning Language Models on Apple Silicon with MLX

  Contents# Fine-Tuning Language Models on Apple Silicon with MLX# Understanding Why MLX Suits Apple Silicon# Setting Up Your Environment# Preparing Your Dataset# Training Your First LoRA Adapter# Choosing a Base Model and Adapter Settings# Reducing Memory Use with Quantization# Testing and Generating with Your Adapter# Fusing and Serving the Model# Wrapping Up # Fine-Tuning Language Models on Apple Silicon with MLX  Fine-tuning a language model …

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