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Jul 18, 2026
kimi-k3
Kimi K3 vs Claude Fable 5: Benchmarks, Pricing, and Which Model to Use
Moonshot's Kimi K3 challenges Claude Fable 5 on agentic and frontend coding benchmarks at a third of the price. Where each model...
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Jul 11, 2026
gpt-5-6
GPT-5.6 Explained: Sol vs Terra vs Luna, Pricing, and How It Compares to Claude Sonnet 5
OpenAI's GPT-5.6 series splits into Sol, Terra, and Luna tiers. What each model is for, what it costs, and how the family...
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Jul 4, 2026
claude-sonnet-5
Claude Sonnet 5 Explained: What's New, Pricing, and Whether You Should Switch
Claude Sonnet 5 is Anthropic's new default model for Free and Pro users. Here is what actually changed, what it costs, and...
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Jun 18, 2026
ai-education
AI in Education: Personalized Learning, Tutoring Bots, and What Changes for Students
AI tutors adapt to every learner's pace. Learn how Khan Academy and Duolingo use AI in education and where human teachers remain...
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Jun 16, 2026
transformer
Attention Mechanism and Transformers: The Math Behind Every LLM
Every LLM runs on the Transformer attention mechanism. Learn scaled dot-product attention, multi-head attention, and positional encoding from scratch.
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Jun 15, 2026
ai-finance
AI in Finance: ML for Trading, Risk, and Fraud Detection
Machine learning powers fraud detection, credit scoring, and algorithmic trading. Learn how AI is transforming finance, what works, and what the limits...
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Jun 14, 2026
decision-tree
Decision Trees: A Complete Guide with Hand-Worked Examples
Decision trees split data by finding the best question at each node. Learn how information gain and Gini impurity work, with a...
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Jun 13, 2026
knowledge-distillation
Knowledge Distillation: How Small Models Learn from Big Ones
Knowledge distillation trains a small student model to learn from a large teacher, retaining most capability at a fraction of the cost....
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Jun 11, 2026
llm
LLM as Judge: How to Evaluate AI Models Automatically at Scale
Human evaluation of LLM outputs is slow and expensive. LLM-as-judge uses a capable model to score and critique another model's outputs automatically....
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Jun 10, 2026
edge-ai
Edge AI: Running LLMs on Your Phone Without the Cloud
LLMs no longer require a data center. Phi-3, Gemma, and Apple Intelligence run directly on device, with no data leaving your phone....
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Jun 9, 2026
ai-coding
AI Coding Assistants in 2026: Cursor, GitHub Copilot, and the Future of Software Development
AI coding assistants have moved well beyond tab-completion. Cursor edits across files, GitHub Copilot reviews pull requests, and Devin claims to handle...
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Jun 8, 2026
llm
Context Engineering: The New Skill That Is Replacing Prompt Engineering
Prompt engineering is giving way to something deeper: context engineering. How you structure what goes into the context window, what you include,...
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Jun 7, 2026
vision-language-models
Vision Language Models (VLMs): How GPT-4o, Claude, and LLaVA Understand Images
Vision Language Models bridge the gap between pixels and language. This post covers how CLIP encodes images, how visual tokens are projected...
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Jun 6, 2026
mixture-of-experts
Mixture of Experts (MoE): The Architecture Behind GPT-4, Mixtral, and Grok
Mixture of Experts scales model capacity without scaling compute. Instead of activating all parameters for every token, MoE routes each token to...
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Jun 5, 2026
diffusion-models
Diffusion Models Explained: The Math-Free Guide to How Stable Diffusion and DALL-E Work
Diffusion models generate images by learning to reverse a noise process. No math required. Here is the intuition behind Stable Diffusion, DALL-E,...
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Jun 4, 2026
ai-agents
AI Agent Memory: Short-Term Context, Long-Term Storage, and Episodic Recall
Stateless LLMs forget everything when the context window closes. Building agents that actually remember requires understanding four distinct memory types and when...
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Jun 3, 2026
llm
Reasoning Models Explained: How o1, o3, and DeepSeek R1 Think Before They Answer
Reasoning models like o1, o3, and DeepSeek R1 don't just predict the next token. They think through problems step by step before...
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Jun 2, 2026
lora
LoRA and QLoRA: Fine-Tuning LLMs on a Single GPU
Full fine-tuning a 7B parameter model requires 112 GB of VRAM and costs thousands of dollars. LoRA reduces trainable parameters by 99%...
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Jun 1, 2026
rag
Graph RAG: When Knowledge Graphs Beat Vector Search
Standard vector RAG fails on multi-hop questions that require connecting information across multiple documents. Graph RAG solves this by building a knowledge...
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May 31, 2026
llm
LLM Evaluation Pipelines: How to Test What Your Model Actually Does
Standard unit tests cannot evaluate LLM outputs because the same question can have dozens of correct answers. This post covers LLM-as-judge scoring,...
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May 30, 2026
llm
Semantic Caching for LLMs: Cut Your API Bill by 60%
Exact-match caching fails for LLMs because users never ask the same question twice in identical words. Semantic caching solves this by comparing...
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May 29, 2026
openai
OpenAI Agents SDK vs Anthropic SDK: A Technical Comparison
OpenAI and Anthropic both now ship production-ready agent frameworks. This post compares them side by side: how each models an agent, handles...
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May 28, 2026
xgboost
XGBoost: A Complete Beginner's Guide
XGBoost dominates every structured-data leaderboard. This guide explains exactly why: gradient boosting, second-order Taylor objectives, regularisation, optimal leaf weights, and split scoring,...
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May 26, 2026
mcp
Model Context Protocol (MCP): A Complete Beginner's Guide
MCP is the USB-C port for AI applications, one protocol that connects any LLM host to any external tool or data source....
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May 25, 2026
codex
OpenAI Codex Explained: How LLMs Learn to Write Code
OpenAI Codex powers GitHub Copilot and sparked the AI coding revolution. This post explains exactly how it works, the 54M-repo training corpus,...
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May 24, 2026
random-forest
Random Forest: A Complete Beginner's Guide
Random Forest builds hundreds of deliberately different decision trees and takes a vote. This guide explains exactly why that works, covering bootstrap...
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May 20, 2026
synthetic-data
Synthetic Data: How AI Trains Itself on AI-Generated Data
Real-world data is expensive, biased, and often private. Synthetic data lets AI systems generate their own training sets, and it is becoming...
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May 19, 2026
AI-drug-discovery
AI for Drug Discovery: How AlphaFold Reinvented Biology
Developing a new drug used to take 10 to 15 years (commonly cited as approximately 12 years on average) and cost over...
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May 12, 2026
neuromorphic-computing
Neuromorphic Chips: The Brain-Inspired Hardware That Could Replace GPUs
GPUs brute-force intelligence with matrix math. Neuromorphic chips take the opposite approach, mimicking how biological neurons fire and wire to achieve AI...
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Mar 27, 2026
multi-agent
Multi-Agent Systems: Orchestration, Communication, and Collaborative AI
A single agent hits context and capability limits fast. Multi-agent systems distribute work across specialized roles with structured communication protocols. Orchestration patterns...
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Mar 25, 2026
inference
LLM Inference Optimization: Quantization, KV Cache, and Serving at Scale
Serving a 70B model cheaply requires quantization, KV cache tuning, continuous batching, and the right serving stack. A systems-level breakdown of vLLM,...
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Mar 22, 2026
embeddings
Embedding Models: Training, Fine-Tuning, and Optimization for Retrieval
Embedding quality determines what your retrieval system can find. How contrastive training works, when to fine-tune versus use off-the-shelf models, and what...
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Mar 21, 2026
prompt-engineering
Advanced Prompt Engineering: Chain-of-Thought, ReAct, and Tree-of-Thoughts Explained
Chain-of-thought improves multi-step reasoning. ReAct adds tool use. Tree-of-thoughts explores multiple solution paths. When each technique earns its token cost, and when...
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Mar 19, 2026
structured-output
Structured Outputs in LLMs: JSON Mode, Function Calling, and Schema Validation
Free-form LLM output breaks parsing pipelines. JSON mode, function calling, grammar-constrained decoding, and Pydantic validation are the layers that make structured output...
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Mar 14, 2026
security
Prompt Injection Attacks: How LLMs Get Exploited and How to Defend Your Application
Prompt injection turns user input into an instruction override. Indirect injection, jailbreaks, and data exfiltration vectors are all in scope, and defense...
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Mar 12, 2026
observability
LLM Observability: Tracing, Logging, and Debugging AI Applications
You can't debug what you can't trace. Setting up prompt logging, span tracing, cost tracking, and latency monitoring for production LLM apps,...
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Mar 5, 2026
hybrid-search
Hybrid Search: Combining Keyword and Vector Search for Better Retrieval
Pure vector search misses exact matches. BM25 misses semantic intent. Reciprocal rank fusion combines both without the tuning overhead of learned fusion,...
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Feb 27, 2026
llm
PEFT Methods Explained: LoRA, QLoRA, and Adapter-Based Fine-Tuning
Full fine-tuning a 7B model costs thousands in GPU hours. LoRA and QLoRA achieve comparable quality by training a fraction of the...
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Feb 22, 2026
llm
Why Your LLM Application Feels Slow
LLM latency usually isn't the model's fault. Synchronous retrieval, sequential tool calls, missing streaming, and cold-start overhead are the architectural decisions that...
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Feb 20, 2026
LLM
A Beginner's Guide to Cost Optimization in LLM Applications
LLM API costs compound fast at scale. Token budgeting, model routing, prompt caching, and batching are the four levers that cut costs...
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Feb 19, 2026
RAG
How Retrieval-Augmented Generation (RAG) Works
RAG grounds LLM responses in retrieved documents rather than model weights. Walk through the full pipeline, indexing, retrieval, augmentation, and generation, and...
-
Feb 18, 2026
llm-agents
What is an AI Agent?
An LLM becomes an agent when it can reason about which tool to call, execute that call, and update its plan based...
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Feb 17, 2026
multimodal-ai
Navigating the 3 Critical Hurdles of Multimodal AI Agent Deployment
Multimodal agents hit three hard walls in production: image token cost, latency from vision encoding, and grounding errors that compound across reasoning...
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Feb 16, 2026
multimodal-ai
Multimodal AI and Grounding Challenges
Vision-language models can describe an image without understanding what's in it. Spatial reasoning failures, hallucinated objects, and weak grounding are architectural constraints,...
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Feb 13, 2026
llm
Context Window Limits: Why Your LLM Still Hallucinates
A 128K context window doesn't mean the model attends equally across all of it. Token budget pressure, retrieval gaps, and the lost-in-the-middle...
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Feb 12, 2026
embeddings
How to Generate Better Embeddings for Vector Search
Bad embeddings kill retrieval before the LLM even sees the query. Preprocessing strategies, model selection, chunking decisions, and fine-tuning approaches that move...
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Feb 11, 2026
chatbot
Building Real-Time Chatbot Memory with Vector Databases + LLMs
Stateless LLMs forget everything between turns. Combining short-term context buffers with long-term vector memory gives chatbots the persistence that real-world use cases...
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Feb 10, 2026
RAG
Why Most RAG Systems Fail in Production
Poor chunking, weak embedding models, and retrieval that returns irrelevant context are why RAG fails, not the generator. A production-focused breakdown of...
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Feb 10, 2026
artificial-intelligence
A Beginner's Guide to Building AI Safety Filters
Input classifiers, output filters, and safe-completion layers don't stop all attacks, but they raise the cost of abuse significantly. How to layer...
-
Feb 8, 2026
machine-learning
Airflow vs Prefect for ML Pipelines
Airflow gives you DAG-based scheduling with a mature ecosystem. Prefect gives you Python-native flows with dynamic tasks and better failure handling. The...
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Jan 27, 2026
machine-learning
How OpenAI Builds and Maintains ChatGPT
RLHF, safety red-teaming, deployment infrastructure, and continuous model updates, how OpenAI actually operates ChatGPT as a production system at scale, and what...
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Jan 10, 2026
vector-db
Vector DB Comparison: Pinecone vs Weaviate vs Qdrant
Pinecone is fully managed. Weaviate adds hybrid search and schema flexibility. Qdrant is self-hosted with Rust-level throughput. Which one fits your RAG...
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Jan 10, 2026
machine-learning
A Beginner's Guide to CI/CD for ML Models (GitHub Actions + Docker + Kubernetes)
ML models need pipelines that test, containerize, and deploy reliably, not just once, but on every retrain. Build a full CI/CD workflow...
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Jan 8, 2026
machine-learning
Best Open-Source LLMs in 2026
Llama, Mistral, Qwen, and Gemma have each carved out distinct strengths in 2026. A practical comparison across benchmark performance, context length, licensing,...
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Dec 18, 2025
machine-learning
How Netflix Builds Recommender Systems
Netflix's recommender stack is a multi-stage funnel: candidate generation at scale, ranking models trained on implicit feedback, and contextual re-ranking. A walkthrough...
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Dec 5, 2025
machine-learning
How to Monitor ML Drift in Real Deployments
Data drift, concept drift, and label drift degrade model performance in different ways. How to detect each in production, which statistical tests...
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Nov 25, 2025
machine-learning
Feature Engineering: Making Data Understandable for Machines
The features you feed a model matter more than the model you choose. Practical techniques for encoding, transforming, and constructing features that...
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Nov 23, 2025
machine-learning
Metrics Beyond Accuracy: Measuring What Actually Matters
Accuracy hides class imbalance. Precision, recall, F1, AUC, and MCC each expose different failure modes. Choose the wrong metric and you'll ship...
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Nov 17, 2025
machine-learning
Why Overfitting Is the Real Enemy of Machine Learning
High training accuracy means nothing if the model memorized the data. Understand why overfitting happens, how to spot it in learning curves,...
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Nov 14, 2025
machine-learning
Why AI Models Fail in the Real World
A model that hits 94% accuracy in the notebook can fail silently in production. The distribution shifts, labeling errors, and deployment gaps...
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Nov 10, 2025
agents
Agentic AI: From Passive Models to Autonomous Systems
Agentic AI isn't just a smarter chatbot, it's an LLM wired to tools, memory, and a feedback loop. Breaking down the core...
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Nov 9, 2025
agents
Agentic AI: How Autonomous Systems Perceive, Plan, and Act
Most AI systems react to input. Agentic systems plan, take actions, and recover from errors autonomously. The architecture, perception, memory, reasoning, action,...
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Nov 6, 2025
machine-learning
Medical AI: Models, Data, and Evaluation in High-Risk Systems
Medical AI fails differently from consumer AI. Label noise, distribution shift across hospital systems, and regulatory constraints change what good performance means....
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Nov 6, 2025
artificial-intelligence
Why Governments Care About AI: Compute, Data, and Talent
AI policy isn't about chatbots, it's a race for compute infrastructure, proprietary datasets, and specialized talent. The strategic calculus driving national AI...
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Oct 5, 2025
KNN
K-Nearest Neighbors (KNN). Part 1: Classification
KNN is simple enough to fit in one equation but deceptively tricky in production. The geometry behind distance voting, why k=1 will...
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Oct 4, 2025
machine-learning
A Beginner's Guide to Elastic Net Regression (L1 + L2 Regularization)
Elastic Net blends L1 and L2 penalties to get Lasso's feature selection with Ridge's stability on correlated predictors. Walk through the dual-penalty...
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Oct 3, 2025
machine-learning
A Beginner's Guide to Lasso Regression (L1 Regularization)
Lasso's L1 penalty drives coefficients exactly to zero, performing automatic feature selection. Why this makes it fundamentally different from Ridge, and when...
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Oct 2, 2025
machine-learning
A Beginner's Guide to Ridge Regression (L2 Regularization)
L2 regularization shrinks all coefficients toward zero without eliminating them. Understand the bias-variance tradeoff Ridge introduces, how the penalty term changes the...
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Oct 2, 2025
blogpost
A Beginner's Guide to Residual Sum of Squares (RSS)
RSS is the foundation of least-squares regression. What it actually measures, why it grows with bad predictions, and how minimizing it gives...
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Oct 1, 2025
blogpost
A Beginner's Guide to R-Squared (R²)
R² tells you how much variance your model explains, but it can be negative, inflated by more features, and misleading on non-linear...
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Oct 1, 2025
blogpost
A Beginner's Guide to Mean Absolute Error (MAE)
MAE treats all errors equally and stays in the same units as your target, making it more interpretable than RMSE. When it's...
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Sep 29, 2025
blogpost
A Beginner's Guide to Root Mean Squared Error (RMSE)
RMSE penalizes large errors harder than MAE because it squares them first. Understand the formula from first principles, its units advantage, and...
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Aug 26, 2024
machine-learning
Multiple Linear Regression Model
Extending OLS to multiple predictors introduces multicollinearity, interpretation traps, and matrix algebra. Walk through the normal equations and what the coefficients actually...
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Aug 25, 2024
machine-learning
Simple Linear Regression Model
Walk through the math behind ordinary least squares: deriving slope and intercept from scratch, understanding residuals geometrically, and the assumptions that make...
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