
Mining insights in data science, machine learning, analytics, and AI • Edited by @mattmayo13 • KD stands for "knowledge discovery"
San Juan, PR
Joined February 2009
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Build standardized custom NER pipelines in a few simple steps, powered by the reasoning capabilities of LLMs. f.mtr.cool/rcueu57tq6
Explore five popular AI agent skill collections that help coding agents plan, test, debug, review, and build better software. f.mtr.cool/k6yb851wz5
Don't miss Snowflake Expedition 2026 – a global virtual experience designed to turn data engineering, analytics, and AI into high-impact, business-ready workflows. Whether you’re a builder or a leader, Expedition offers tailored learning paths so you can go heavy on execution and focus on real business outcomes!
What to expect:
⚡ AI Luminary fireside chat with Satya Nadella, Chairman and CEO, Microsoft and Sridhar Ramaswamy, CEO, Snowflake
⚡ Technical hands-on labs, live demos, and minimal slides.
⚡ A first look at Snowflake CoCo, AI agent-building tools, and enterprise AI workflows.
⚡ Real-world strategies from industry-leading organizations using the Snowflake AI Data Cloud.
📅 Dates: November 3–6, 2026
💻 Location: Free Virtual Event
🔗 Register now for free: snowflake.com/en/expedition/…
🚀 Ready to turn agentic AI into real business impact?
Join Google Cloud for Trusted Agents, Real Outcomes, an online event exploring how Agentic Data Cloud can help organizations build trusted AI workflows and scale AI initiatives.
Discover what it takes to move from AI experimentation to practical business outcomes.
📅 November 5 | 10 AM CET (EMEA)
🔗 Register now:
cloudonair.withgoogle.com/ev…
#GoogleCloud #AgenticAI #DataEngineering
A structured four-phase roadmap for building the technical fluency, evaluation skills, and governance literacy needed to succeed as an AI product manager in 2026. f.mtr.cool/ynp3nv0udk
Five self-hosted Docker containers replace the public utility sites where developer data quietly leaks. Learn which ones handle JWT decoding, data transformation, file conversion, API testing, and diagram rendering locally. f.mtr.cool/ucs0ycewv5
Claude Haiku 5.5 cuts list prices by 90% and adds adaptive thinking and a 1M token context window. Learn what broke from Haiku 4.5, and build classification, extraction, and sub-agent workflows in Python using the new API. f.mtr.cool/rx0dwyavza
Choosing the right framework for your large language model application comes down to one question: what are you actually optimizing for? f.mtr.cool/2diftvo27j
AI makes writing code cheap. Verifying it is still your job. f.mtr.cool/oam879b6s8
From static retrieval pipelines to autonomous reasoning engines: a complete breakdown of how these two architectures work, where each falls short, and how to choose the right one for your use case. f.mtr.cool/xfktrryzi2
World models could be the next big shift in AI. Learn more right now. f.mtr.cool/afnwyhm5xr
Abacus AI Honest Review: Key Criticisms, Facts & What Users Should Know
kdnuggets.com/2026/10/abacus…
This review highlights the main critiques of Abacus AI, its price, user reviews, reliability of workflows, and real-life use cases to assist you in evaluating its advantages and disadvantages.
f.mtr.cool/40j5jmedr5
OGX is an open-source agentic API server for building OpenAI-compatible AI applications, using any model and any infrastructure. f.mtr.cool/ko8rls0if7
Find open-source alternatives to OpenAI API, Midjourney, Claude Code, Notion, Wispr Flow, Grammarly, and NotebookLM that you can run locally or self-host for more privacy, control, and lower cost. f.mtr.cool/mco8wthv7j
Leveraging Scikit-LLM for one of the simplest yet most practical text classification tasks utilized in the modern AI landscape: few-shot classification. f.mtr.cool/nfaz5ygxoq
Want to understand what makes LLMs fast? Build the systems yourself. f.mtr.cool/n70zh7baub
A four-level technical roadmap for building agentic AI systems, from single cognitive loops and stateful memory through multi-agent swarms and production observability. f.mtr.cool/zj1fimcmyq
Five AutoHotkey v2 scripts that handle repetitive Windows desktop work around data analysis: launching a project workspace, converting clipboard IDs to Python or SQL lists, fixing file paths, and snapping windows into place. f.mtr.cool/wfvmni3g5l

