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1. Chat History Storage Patterns in Microsoft Agent Framework

   This article explores the critical architectural decision of where to store conversation history when building AI agents. It examines how different storage strategies impact user experience, privacy, cost, and portability. The author compares service-managed versus client-managed storage models and details how modern APIs support both linear threads and forking/branching capabilities.
   Key topics include:
   * Service-Managed vs. Client-Managed storage tradeoffs
   * Linear (single-threaded) vs. Forking-capable conversation models
   * Strategies for context window management and compaction such as truncation, summarization, and sliding windows
   * How Microsoft Agent Framework abstracts these patterns using AgentSession and ChatHistoryProvider to ensure provider-agnostic code
   * Practical implementation examples for the Responses API in different modes

   2026-04-25 Tags: llm agents, chat history, microsoft, agent framework, software, architecture, llm, context, kmeans by klotz
2. Document Clustering with LLM Embeddings in scikit-learn

   This tutorial demonstrates how to perform document clustering using LLM embeddings with scikit-learn. It covers generating embeddings with Sentence Transformers, reducing dimensionality with PCA, and applying KMeans clustering to group similar documents.

   2026-02-11 Tags: document clustering, llm embeddings, sentence transformers, scikit-learn, pca, kmeans, dimensionality reduction, natural language processing, nlp by klotz
3. Mastering Customer Segmentation with LLM

   Unlock advanced customer segmentation techniques using LLMs, and improve your clustering models with advanced techniques

   2023-09-28 Tags: llm, segmentation, kmeans, k prototype, tsne, pca, sentence, embedding, clustering, exploratory data analysis by klotz
4. python - Drawing boundary lines based on kmeans cluster centres - Stack Overflow

   from scipy.spatial import Voronoi, voronoi_plot_2d
   import pandas as pd
   import matplotlib.pyplot as plt
   from sklearn.cluster import KMeans
   
   location1="XXX"
   df = pd.read_csv(location1, encoding = "ISO-8859-1")
   
   #Run kmeans clustering
   X = df ['long','lat' » ].values #~2k locations in the UK
   y=df 'label' » .values #Label is a 0 or 1
   kmeans = KMeans(n_clusters=30, random_state=0).fit(X, y)
   centers=kmeans.cluster_centers_
   
   plt.scatter(centers :,0 » ,centers :,1 » , marker='s', s=100)
   
   
   vor = Voronoi(centers)
   fig = voronoi_plot_2d(vor,plt.gca())
   
   plt.show()

   2021-09-24 Tags: vornoi, visualization, kmeans by klotz
5. “Learn TensorFlow, the Word2Vec model, and the TSNE algorithm using rock bands”

   2018-08-19 Tags: tensorflow, tsne, kmeans, word2vec, machine learning, clustering, classification by klotz

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