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Explains how AI agents exchange information and coordinate actions through structured messages, communication patterns like pub-sub and request-response.
Covers OpenAI's InstructGPT research from 2022, including the three-stage RLHF training process, supervised fine-tuning, reward modeling.
Explains how multiple AI agents collaborate through specialization, parallel processing, and coordination.
Covers EleutherAI's The Pile, the early 825GB open-source dataset that broadened access to high-quality training data for large language models.
See how AI agents use planning to handle complex, multi-step tasks. Topics include task decomposition, sequential execution.
Covers choosing the right approach for your LLM project: using pre-trained models as-is, enhancing them with context injection and RAG.
Covers the core concepts of LLM workflows: connecting language models to tools, handling responses, and building intelligent systems.
Explains how AI agents execute multi-step plans sequentially, handle failures gracefully, and adapt when things go wrong.
Use Monte Carlo simulation to model and analyze stock market returns, estimate future performance.
Explains how AI agents break down complex goals into manageable subtasks. Topics include task decomposition strategies, sequential vs parallel tasks.
Covers XGBoost (eXtreme Gradient Boosting), including second-order Taylor expansion, regularization techniques, split gain optimization, ranking loss functions.
Covers the 2020 scaling laws discovered by Kaplan et al. Explains how power-law relationships predict model performance from scale.
Define what your AI agent can and cannot do through access controls, action policies, rate limits, and scope boundaries.
Covers SHAP values covering mathematical foundations, feature attribution, and practical implementations for explaining any machine learning model.
Explains how AI agents perceive their environment through inputs, tool outputs, and memory.
Explains what an environment means for AI agents, from digital assistants to physical robots. Explains how environment shapes perception, actions.
Covers LightGBM gradient boosting framework, including leaf-wise tree growth, histogram-based binning, GOSS sampling, exclusive feature bundling.
Covers FlashAttention introduced in 2022. Explains how IO-aware attention computation enabled 2-4x speedup and 5-10x memory reduction.
Explains how AI agents maintain continuity across sessions with ephemeral, session, and persistent state management.
Covers CatBoost (Categorical Boosting), including categorical feature handling, target statistics, symmetric trees, ordered boosting, regularization techniques.
Covers OpenAI's CLIP, the early vision-language model that enables zero-shot image classification through contrastive learning.
Structure AI agents with clear architecture patterns. Build organized agent loops, decision logic.
Covers instruction tuning introduced in 2021. Explains how fine-tuning on diverse instruction-response pairs changed language models.
Covers Isolation Forest covering unsupervised anomaly detection, path length calculations, harmonic numbers, anomaly scoring.
Build a complete AI agent memory system combining conversation history and persistent knowledge storage.
Explains how Position Interpolation extends transformer context windows by scaling position indices to stay within training distributions.
Covers boosted trees and gradient boosting, covering ensemble learning, loss functions, sequential error correction, and scikit-learn implementation.
Covers OpenAI's DALL·E 2, the major text-to-image generation model that combined CLIP-guided diffusion with high-quality image synthesis.
Explains how AI agents store and retrieve information across sessions using vector databases, embeddings, and semantic search.
Examines the fundamental barriers limiting transformer context windows, including quadratic memory scaling, position encoding extrapolation failures.
Codex adapted GPT-3 for code completion and generation. Covers its training data, natural-language prompts, benchmark results, and role in developer tools.
Covers Random Forest covering ensemble learning, bootstrap sampling, random feature selection, bias-variance tradeoff, and implementation in scikit-learn.
Covers OpenAI's DALL·E, the early text-to-image generation model that extended transformer architectures to multimodal tasks.
Build a working calculator tool for your AI agent from scratch. Topics include the complete workflow from Python function to tool integration.
Covers CART (Classification and Regression Trees), including mathematical foundations, Gini impurity, variance reduction.
GPT-3 showed that scaling autoregressive transformers could enable in-context and few-shot learning. Covers its architecture, data, prompting, and limitations.
Call language models from Python code, including GPT-5, Claude Sonnet 4.5, and Gemini 2.5. Covers API integration, error handling.
Covers Google's T5 (Text-to-Text Transfer Transformer) introduced in 2019. Explains how the text-to-text framework unified diverse NLP tasks.
Design effective tool interfaces for AI agents, from basic function definitions to multi-tool orchestration.
Covers logistic regression covering mathematical foundations, the logistic function, optimization algorithms, and practical implementation.
Explains how Longformer combines sliding window and global attention to process documents of 4,096+ tokens with O(n) complexity instead of O(n²).
Implement sparse attention patterns including local windows, strided attention, and block-sparse methods.
Covers Poisson regression for count data analysis. Topics include mathematical foundations, maximum likelihood estimation, rate ratio interpretation.
Transformer-XL extends context with segment recurrence and relative positions. Covers its architecture, efficiency, training method, and influence.
Spline regression models nonlinear relationships with piecewise polynomials. Covers B-splines, knot selection, natural cubic splines, and Python.
Covers multinomial logistic regression covering mathematical foundations, softmax function, coefficient estimation.
Covers OpenAI's GPT-1 and GPT-2 models. Explains how autoregressive pretraining with transformers enabled transfer learning across NLP tasks.
Covers BERT (Bidirectional Encoder Representations from Transformers), including masked language modeling, bidirectional context understanding.
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Why
I created this space so readers can learn together, ask questions, and make sense of difficult ideas.