--- title: Best Daily Dose of Data Science | Avi Chawla | Substack... description: The most upvoted Daily Dose of Data Science | Avi Chawla | Substack posts from January 2026, curated by the daily.dev community. canonical: https://daily.dev/sources/dailydoseofds/best-of/2026/01 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/sources/dailydoseofds/best-of/2026/01 og:type: website og:site_name: daily.dev og:title: Best Daily Dose of Data Science | Avi Chawla | Substack posts — January 2026 | daily.dev og:description: The most upvoted Daily Dose of Data Science | Avi Chawla | Substack posts from January 2026, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of Daily Dose of Data Science | Avi Chawla | Substack — January 2026 1. 1 [](https://daily.dev/posts/new-generative-ui-for-agents-7mg572qny "[New] Generative UI for Agents") Article Daily Dose of Data Science | Avi Chawla | Substack · 32w \[New\] Generative UI for Agents Generative UI is an emerging pattern where AI agents render actual UI components instead of just returning text responses. Unlike traditional chat interfaces, agents can now display weather cards, confirmation dialogs, data tables, and other interactive elements by selecting pre-built components and filling them with data at runtime. Three approaches exist: static (predefined components), declarative (component registry), and open-ended (raw HTML/iframes). Protocols like A2UI, AG-UI, and MCP Apps enable real-time bidirectional communication between agents and frontends. CopilotKit has open-sourced a complete implementation for React with integrations for LangGraph, CrewAI, and other agent frameworks. MiniMax also launched Agent Desktop, a desktop environment where AI agents can browse the web, manage files, and automate developer tasks. 110 4 2. 2 [](https://daily.dev/posts/phases-of-ml-modeling-3hzj0bmsd "Phases of ML Modeling") Article Daily Dose of Data Science | Avi Chawla | Substack · 32w Phases of ML Modeling ML systems should evolve through four distinct phases rather than jumping straight to complex models. Start with simple heuristics and rules (Phase 1), then move to basic ML models like logistic regression (Phase 2), optimize through feature engineering and hyperparameter tuning (Phase 3), and only adopt complex models like deep neural networks when simpler approaches are exhausted (Phase 4). This staged approach reduces risk, improves debuggability, and ensures each phase's best model becomes the baseline for the next, encouraging incremental progress and evidence-driven decision-making. 91 1 3. 3 [](https://daily.dev/posts/6-components-of-context-engineering-0oewqo6qr "6 Components of Context Engineering") Article Daily Dose of Data Science | Avi Chawla | Substack · 34w 6 Components of Context Engineering Context engineering is the practice of optimizing how information flows to AI models, comprising six core components: prompting techniques (few-shot, chain-of-thought), query augmentation (rewriting, expansion, decomposition), long-term memory (vector/graph databases for episodic, semantic, and procedural memory), short-term memory (conversation history management), knowledge base retrieval (RAG pipelines with pre-retrieval, retrieval, and augmentation layers), and tools/agents (single and multi-agent architectures, MCPs). While model selection and prompts contribute only 25% to output quality, the remaining 75% comes from properly engineering these context components to deliver the right information at the right time in the right format. 87 3 4. 4 [](https://daily.dev/posts/your-rag-system-has-a-hidden-ux-problem-oytm7rbu2 "Your RAG System Has a Hidden UX Problem") Article Daily Dose of Data Science | Avi Chawla | Substack · 33w Your RAG System Has a Hidden UX Problem RAG systems often use semantic retrieval but fall back to keyword-based highlighting, creating a UX disconnect where users can't see why documents are relevant. Zilliz released an open-source semantic highlighting model that identifies semantically relevant text spans instead of just keyword matches. The bilingual model (English/Chinese) handles 8K context windows, runs fast enough for production use, and outperforms existing solutions on both in-domain and out-of-domain benchmarks. It's being integrated into Milvus as a native API and is available on Hugging Face under MIT license. 26 5. 5 [](https://daily.dev/posts/foundations-of-ai-engineering-and-llmops-puboq65dr "Foundations of AI Engineering and LLMOps") Article Daily Dose of Data Science | Avi Chawla | Substack · 34w Foundations of AI Engineering and LLMOps Part 3 of an LLMOps course is now available, covering attention mechanisms, transformer architectures, mixture-of-experts, and the fundamentals of pretraining and fine-tuning with hands-on code demos. LLMOps extends traditional MLOps principles to address the unique engineering challenges of managing large language models like Llama, GPT, and Claude in production, focusing on reliability, accuracy, security, and cost-effectiveness. The course aims to provide systems-level thinking for building production-ready LLM applications with clear explanations, examples, diagrams, and implementations. 25 6. 6 [](https://daily.dev/posts/build-agents-that-can-learn-like-humans-hew5n9evu "Build Agents That Can Learn Like Humans") Article Daily Dose of Data Science | Avi Chawla | Substack · 32w Build Agents That Can Learn Like Humans ART (Agent Reinforcement Trainer) is an open-source framework that simplifies reinforcement learning for LLMs by eliminating manual reward function engineering. It uses GRPO (Group Relative Policy Optimization) where agents attempt tasks multiple times, an LLM judge compares attempts, and the model learns from relative performance. Unlike traditional RL frameworks limited to simple chatbot interactions, ART supports multi-turn conversations, tool calls, and integrates with LangGraph, CrewAI, and ADK. It combines vLLM for model serving and Unsloth for GRPO training, enabling developers to fine-tune small open-source models to outperform larger closed-source alternatives on specific tasks. 14 [See all Daily Dose of Data Science | Avi Chawla | Substack archives](/sources/dailydoseofds/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/sources/dailydoseofds/best-of/2026/01#page","url":"https://daily.dev/sources/dailydoseofds/best-of/2026/01","name":"Best Daily Dose of Data Science | Avi Chawla | Substack Posts — January 2026","description":"The most upvoted Daily Dose of Data Science | Avi Chawla | Substack posts from January 2026, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/sources/dailydoseofds/best-of/2026/01#items","numberOfItems":6,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/new-generative-ui-for-agents-7mg572qny","name":"[New] Generative UI for Agents"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/phases-of-ml-modeling-3hzj0bmsd","name":"Phases of ML Modeling"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/6-components-of-context-engineering-0oewqo6qr","name":"6 Components of Context Engineering"},{"@type":"ListItem","position":4,"url":"https://daily.dev/posts/your-rag-system-has-a-hidden-ux-problem-oytm7rbu2","name":"Your RAG System Has a Hidden UX Problem"},{"@type":"ListItem","position":5,"url":"https://daily.dev/posts/foundations-of-ai-engineering-and-llmops-puboq65dr","name":"Foundations of AI Engineering and LLMOps"},{"@type":"ListItem","position":6,"url":"https://daily.dev/posts/build-agents-that-can-learn-like-humans-hew5n9evu","name":"Build Agents That Can Learn Like Humans"}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Sources","item":"https://daily.dev/sources"},{"@type":"ListItem","position":3,"name":"Daily Dose of Data Science | Avi Chawla | Substack","item":"https://daily.dev/sources/dailydoseofds"},{"@type":"ListItem","position":4,"name":"Best of","item":"https://daily.dev/sources/dailydoseofds/best-of"},{"@type":"ListItem","position":5,"name":"January 2026"}]}]} ```