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  <url><loc>https://huiahub.com/videos/but-what-is-cross-entropy-compression-is-intelligence-part-2-glyg</loc><lastmod>2026-10-04T05:46:57.698Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/GlYgs6v2YfU/hqdefault.jpg</video:thumbnail_loc><video:title>But what is cross-entropy? | Compression is Intelligence Part 2</video:title><video:description>3Blue1Brown explains where the loss function for training LLMs comes from, building cross-entropy up from optimal codes. Good for anyone who trains or studies language models.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/GlYgs6v2YfU</video:player_loc><video:duration>2031</video:duration><video:publication_date>2026-07-16T11:56:04.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/prompt-caching-explained-stop-overpaying-for-ai-agents-skm4</loc><lastmod>2026-10-04T05:46:57.461Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/SkM4k4SKvCM/hqdefault.jpg</video:thumbnail_loc><video:title>Prompt Caching Explained: Stop Overpaying for AI Agents</video:title><video:description>A 17-minute explainer on what prompt caching stores and the agent-code mistakes that break it, for developers paying for long AI agent sessions.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/SkM4k4SKvCM</video:player_loc><video:duration>1036</video:duration><video:publication_date>2026-08-10T03:15:10.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/agent-memory-explained-complete-architecture-ayfz</loc><lastmod>2026-10-04T05:46:57.247Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/aYfZN8t6AQs/hqdefault.jpg</video:thumbnail_loc><video:title>Agent Memory EXPLAINED - Complete Architecture</video:title><video:description>A 28-minute walk through how Mem0 stores, ingests and retrieves agent memories, for developers who want long-term memory that goes past a single vector search.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/aYfZN8t6AQs</video:player_loc><video:duration>1714</video:duration><video:publication_date>2026-08-17T03:15:35.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/run-local-models-in-pi-llama-cpp-gguf-and-the-llama-command-5dsf</loc><lastmod>2026-10-04T05:46:57.009Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/5DsFr19wJFg/hqdefault.jpg</video:thumbnail_loc><video:title>Run Local Models in Pi: llama.cpp, GGUF, and the /llama Command</video:title><video:description>A six-minute Hugging Face quickstart for Pi users who want a private, offline coding agent running on a local GGUF model through llama.cpp.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/5DsFr19wJFg</video:player_loc><video:duration>371</video:duration><video:publication_date>2026-09-08T15:00:00.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/flow-matching-for-beginners-arig23498-hxqz</loc><lastmod>2026-10-04T05:46:56.774Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/hXQZ6lyRCsI/hqdefault.jpg</video:thumbnail_loc><video:title>Flow matching for Beginners | @ariG23498</video:title><video:description>A 28-minute beginner's explanation of flow matching on the Hugging Face channel that leans on visuals over equations, for people who want the intuition behind modern image generators.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/hXQZ6lyRCsI</video:player_loc><video:duration>1702</video:duration><video:publication_date>2026-09-15T20:37:58.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/transformers-js-v4-3-structured-output-in-the-browser-innv</loc><lastmod>2026-10-04T05:46:56.564Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/InnVG6pOPpM/hqdefault.jpg</video:thumbnail_loc><video:title>Transformers.js v4.3: Structured Output in the browser</video:title><video:description>An 11-minute Hugging Face walkthrough of Transformers.js 4.3 for JavaScript developers who need models in the browser to return valid JSON every time.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/InnVG6pOPpM</video:player_loc><video:duration>668</video:duration><video:publication_date>2026-09-16T07:49:24.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/nemotron-3-diarization-who-said-that-pzuu</loc><lastmod>2026-10-04T05:46:54.516Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/PZuuOXNB3Vw/hqdefault.jpg</video:thumbnail_loc><video:title>Nemotron 3 Diarization - Who Said That?</video:title><video:description>Sam Witteveen explains speaker diarization and runs NVIDIA's Nemotron 3 Diarization on a full podcast, a 13-minute look for anyone building multi-speaker transcription.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/PZuuOXNB3Vw</video:player_loc><video:duration>790</video:duration><video:publication_date>2026-09-23T15:36:07.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/gemini-3-8-flash-tts-with-voice-cloning-gdub</loc><lastmod>2026-10-04T05:46:53.706Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/GDUBXR-ql78/hqdefault.jpg</video:thumbnail_loc><video:title>Gemini 3.8 Flash TTS  with Voice Cloning</video:title><video:description>Sam Witteveen walks through Gemini 3.8 Flash TTS, from voice design and cloning to pricing, with Colab code, for developers choosing a text-to-speech model.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/GDUBXR-ql78</video:player_loc><video:duration>1031</video:duration><video:publication_date>2026-09-24T17:15:13.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/in-context-retrieval-weaviate-podcast</loc><lastmod>2026-10-24T13:05:23.605Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/gCGEg7jJXh0/hqdefault.jpg</video:thumbnail_loc><video:title>In-Context Retrieval with Siddharth Gollapudi - Weaviate Podcast #146!</video:title><video:description>A 56-minute Weaviate Podcast episode for search and RAG engineers on using an LLM's attention over a million-token context as the retriever, and where that breaks.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/gCGEg7jJXh0</video:player_loc><video:duration>3374</video:duration><video:publication_date>2026-10-01T14:04:14.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/image-decision-models-for-rpa-forms-scans-and-screenshots-l8yx</loc><lastmod>2026-10-04T05:46:49.886Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/L8YxigQoLaM/hqdefault.jpg</video:thumbnail_loc><video:title>Image Decision Models for RPA: Forms, Scans and Screenshots</video:title><video:description>Sam Witteveen tries two open decision models, ImaJev 4B and Jev Omni, on forms and screenshots for RPA, useful for automation builders who need a model to make yes/no or multiple-choice calls.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/L8YxigQoLaM</video:player_loc><video:duration>983</video:duration><video:publication_date>2026-10-02T14:30:27.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/your-coding-agent-is-6-months-out-of-date-jakub-hojsan-exa-ckhp</loc><lastmod>2026-10-04T05:46:49.673Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/cKhpeEBnT1o/hqdefault.jpg</video:thumbnail_loc><video:title>Your Coding Agent Is 6 Months Out of Date — Jakub Hojsan, Exa</video:title><video:description>For developers whose coding or review agents misjudge new code: why a search tool alone falls short, and how Exa trims pages down to what the model needs.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/cKhpeEBnT1o</video:player_loc><video:duration>743</video:duration><video:publication_date>2026-10-02T15:30:31.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/why-99-accurate-browser-agents-still-fail-derek-meegan-browserbase-5xi</loc><lastmod>2026-10-04T05:46:48.791Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/5xi_S1f9sDU/hqdefault.jpg</video:thumbnail_loc><video:title>Why 99% Accurate Browser Agents Still Fail — Derek Meegan, Browserbase</video:title><video:description>A 17-minute talk for anyone moving a browser agent from demo to production: the math of compounding step errors and an architecture that works around it.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/5xi_S1f9sDU</video:player_loc><video:duration>1009</video:duration><video:publication_date>2026-10-02T21:30:15.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/yolo-mode-safely-microvm-sandboxes-for-any-agent-rowan-christmas-docker-oe-l</loc><lastmod>2026-10-04T05:46:48.576Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/OE_lLNCNfQo/hqdefault.jpg</video:thumbnail_loc><video:title>YOLO Mode, Safely: MicroVM Sandboxes for Any Agent — Rowan Christmas, Docker</video:title><video:description>A 12-minute Docker talk for developers who let coding agents run unattended: why an agent's built-in guardrails fall short and how microVM sandboxes contain an agent.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/OE_lLNCNfQo</video:player_loc><video:duration>695</video:duration><video:publication_date>2026-10-03T00:30:06.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/your-llm-app-returned-200-ok-it-was-still-wrong-marina-petzel-datadog-rtoj</loc><lastmod>2026-10-04T05:46:48.360Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/rTojoVotlD8/hqdefault.jpg</video:thumbnail_loc><video:title>Your LLM App Returned 200 OK. It Was Still Wrong. — Marina Petzel, Datadog</video:title><video:description>An 18-minute talk for engineers who run LLM apps in production: which cost, safety and quality metrics to track beyond latency and error rates.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/rTojoVotlD8</video:player_loc><video:duration>1081</video:duration><video:publication_date>2026-10-03T01:30:33.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/i-built-a-personal-ai-agent-on-a-raspberry-pi-jeremy-adams-neo4j-ouze</loc><lastmod>2026-10-04T05:46:48.144Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/oUZEt4EiPbk/hqdefault.jpg</video:thumbnail_loc><video:title>I Built a Personal AI Agent on a Raspberry Pi — Jeremy Adams, Neo4j</video:title><video:description>Jeremy Adams of Neo4j builds a wearable agent on a Raspberry Pi with NanoClaw, WhatsApp and graph memory, a 20-minute talk for builders of small personal agents.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/oUZEt4EiPbk</video:player_loc><video:duration>1210</video:duration><video:publication_date>2026-10-03T16:30:34.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/an-interaction-is-all-you-need-ivan-leo-google-deepmind-8avb</loc><lastmod>2026-10-04T05:46:47.451Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/8aVbXXvJUY4/hqdefault.jpg</video:thumbnail_loc><video:title>An Interaction Is All You Need — Ivan Leo, Google DeepMind</video:title><video:description>Ivan Leo of Google DeepMind explains the Gemini Interactions API and Managed Agents in 17 minutes, useful for developers building agents on Gemini.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/8aVbXXvJUY4</video:player_loc><video:duration>1024</video:duration><video:publication_date>2026-10-03T19:30:16.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/what-makes-open-models-fast-in-production-sujee-maniyam-nebius-tre1</loc><lastmod>2026-10-04T05:46:47.224Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/TRe1u7dHYiA/hqdefault.jpg</video:thumbnail_loc><video:title>What Makes Open Models Fast in Production — Sujee Maniyam, Nebius</video:title><video:description>A 20-minute talk for engineers serving open LLMs: a layer-by-layer list of inference optimizations, from NVFP4 to speculative decoding, after a Nebius product intro.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/TRe1u7dHYiA</video:player_loc><video:duration>1232</video:duration><video:publication_date>2026-10-03T22:30:28.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/stop-fine-tuning-to-fix-retrieval-problems-anant-srivastava-qfll</loc><lastmod>2026-10-04T05:46:47.010Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/qflLT3SoVbw/hqdefault.jpg</video:thumbnail_loc><video:title>Stop Fine-Tuning to Fix Retrieval Problems — Anant Srivastava</video:title><video:description>A 20-minute talk for engineers about to fine-tune a model to fix wrong answers: a clear rule for what belongs in the prompt, in retrieval and in weights.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/qflLT3SoVbw</video:player_loc><video:duration>1212</video:duration><video:publication_date>2026-10-04T00:00:35.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/how-ai-models-scale-beyond-a-single-gpu</loc><lastmod>2026-10-24T13:05:23.713Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/qZBibWYcKH4/hqdefault.jpg</video:thumbnail_loc><video:title>How AI Models Scale Beyond a Single GPU Across LLM Workloads</video:title><video:description>A nine-minute IBM Technology explainer for engineers who need the basic vocabulary of distributed LLM inference: the four parallelism types and prefill versus decode.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/qZBibWYcKH4</video:player_loc><video:duration>544</video:duration><video:publication_date>2026-10-06T11:00:06.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/build-and-train-glm-5-3-flash-from-scratch</loc><lastmod>2026-10-24T13:05:23.804Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/-gfgQfw2g_E/hqdefault.jpg</video:thumbnail_loc><video:title>Build &amp; Train a GLM-5.3-Flash Model From Scratch with Python</video:title><video:description>A freeCodeCamp.org walkthrough for developers who want to read, train and RL-tune a small GLM-5.3-Flash-style model in Python, with all code on GitHub.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/-gfgQfw2g_E</video:player_loc><video:duration>2666</video:duration><video:publication_date>2026-10-06T16:55:56.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/n8n-full-course-scalable-ai-automations</loc><lastmod>2026-10-24T13:05:23.936Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/YY-egxTQirA/hqdefault.jpg</video:thumbnail_loc><video:title>n8n Full Course – Architect Scalable AI Automations from Scratch</video:title><video:description>A 43-minute freeCodeCamp.org course that takes n8n from first workflow to agents, Slack approvals, error handling and cost monitoring, for builders new to the tool.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/YY-egxTQirA</video:player_loc><video:duration>2585</video:duration><video:publication_date>2026-10-09T17:00:19.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/three-questions-every-agent-engineer-should-answer</loc><lastmod>2026-10-24T13:05:24.027Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/oVKGKdIAgtg/hqdefault.jpg</video:thumbnail_loc><video:title>Three Questions Every Agent Engineer Should Be Able To Answer</video:title><video:description>A 14-minute LangChain talk on finding, reproducing and fixing agent failures using production traces, for engineers who run agents in production.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/oVKGKdIAgtg</video:player_loc><video:duration>847</video:duration><video:publication_date>2026-10-09T17:16:00.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/train-to-deploy-loop-for-specialized-ai</loc><lastmod>2026-10-23T13:05:23.154Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/13YZ1EB9hjU/hqdefault.jpg</video:thumbnail_loc><video:title>Stop Renting Intelligence: The Train-to-Deploy Loop for Specialized AI — Fireworks AI</video:title><video:description>A 19-minute Fireworks AI talk on training open models on your own data and redeploying them in a continuous loop, for teams weighing closed-model costs.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/13YZ1EB9hjU</video:player_loc><video:duration>1157</video:duration><video:publication_date>2026-10-11T22:30:33.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/sandboxes-4-ways-to-isolate-coding-agents</loc><lastmod>2026-10-23T13:05:23.251Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/iH_wV8BbS24/hqdefault.jpg</video:thumbnail_loc><video:title>Sandboxes Aren't Optional: 4 Ways to Isolate Coding Agents — Robert Brennan, OpenHands</video:title><video:description>A 14-minute talk from the OpenHands CEO comparing four ways to sandbox coding agents, for developers who run agents with broad permissions.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/iH_wV8BbS24</video:player_loc><video:duration>862</video:duration><video:publication_date>2026-10-12T05:10:18.000Z</video:publication_date></video:video></url>
  <url><loc>https://huiahub.com/videos/how-inference-keeps-up-with-the-need-for-tokens</loc><lastmod>2026-10-23T13:05:23.341Z</lastmod><video:video><video:thumbnail_loc>https://i.ytimg.com/vi/_cRgl3maiT8/hqdefault.jpg</video:thumbnail_loc><video:title>How Inference Keeps Up with Our Insatiable Need for Tokens — Cerebras &amp; Gimlet Labs</video:title><video:description>A 22-minute AI Engineer talk for inference engineers that covers runtime tricks like paged KV cache and chunked prefill, then system-level disaggregation and kernel-writing agents.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/_cRgl3maiT8</video:player_loc><video:duration>1366</video:duration><video:publication_date>2026-10-12T05:36:56.000Z</video:publication_date></video:video></url>
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