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A complete guide to Dify, the open-source AI application platform: its core positioning, key differences from Coze, workflow-building capabilities, and enterprise private deployment advantages.
sqlite-utils 4.1 Released: The --code …
sqlite-utils 4.1 brings practical new features: the --code option for generating rows via Python, type overrides to solve ZIP-code leading-zero loss, STRICT table mode switching, drop-index, and more. A deep dive into the design and AI-assisted dev workflow.

A step-by-step guide to locally deploying the Dify open-source AI platform using BT Panel on a VMware virtual machine, covering Ubuntu setup, Docker config, and image pull troubleshooting—beginner-friendly.

A step-by-step guide to locally deploying the open-source Dify AI platform using the BT Panel on a VMware virtual machine—covering Ubuntu setup, Docker config, and image pull troubleshooting.

Dify is a low-code AI app platform supporting chatbots, Agents, and workflows. Compatible with DeepSeek, ChatGPT, and more. Learn cloud and local deployment options.

A beginner's guide to Dify covering Docker deployment, MySQL setup, model integration, five app types (Chatbot/Agent/Workflow), and publishing — build LLM apps fast.

A complete guide to Dify — covering deployment, five core app types (chatbot, Agent, workflow, and more), LLM integration, and publishing for zero-experience developers.

A complete guide to deploying Dify 1.8.0: Docker setup, environment config, five app types explained, and workflow-building tips for beginners.
Microsoft Open-Sources Ontology Playgr…
Microsoft's open-source Ontology Playground is a zero-backend static web app for visually designing ontologies, with RDF/XML export and Microsoft Fabric IQ integration.

Deutsche Telekom partners with OpenAI to embed generative AI across the full call lifecycle — live translation, in-call assistance, and post-call summaries. Containment rate hits 50%, costs drop. A deep dive into telecom AI transformation.

Cursor designer Rio: AI compresses build loops dramatically, but risks flooding the world with mediocrity. From Glass UI principles to the migration of craft — why human agency, taste, and responsibility remain software's true core.

A college student's MLOps 100-day challenge documents the full journey from Python engineering and Git to Docker, model deployment, and monitoring. A practical roadmap for data scientists transitioning to ML engineering.

A wind farm digital twin built on Microsoft Fabric and Azure AI Foundry, fusing Finnish LIDAR terrain, real-time telemetry, and conversational AI for smart industrial operations.

Should you implement ML algorithms from scratch or just use sklearn? This guide breaks down the optimal learning path for ML engineers by career stage and company type.

Task routing is hailed as a silver bullet for LLM cost reduction, but routing strategy design, model training, and self-hosting each carry hidden engineering costs. This deep dive helps smaller teams evaluate ROI and offers a phased implementation path.
SendLang: Rethinking Email Automation …
SendLang is a DSL for email automation that uses declarative syntax to separate trigger conditions, content templates, and send timing — tackling logic coupling in traditional email systems.
Building RL-Powered Autonomous Researc…
How NVIDIA NeMo combines reinforcement learning to train agent skills and build an Autoresearch workflow capable of autonomously running ML experiments end-to-end.
Agentic Loop Explained: The Three-Loop…
A deep dive into the Agentic Loop — breaking down the three-layer architecture of reasoning, tool use, and orchestration to help developers build and debug reliable AI agent systems.

Model training failure is the norm in research, not the end. Using a real DiT fine-tuning failure on weather radar as a case study, this guide offers a systematic three-layer debugging methodology — data, training convergence, and evaluation — to help deep learning practitioners diagnose issues and iterate efficiently.

A complete guide to Dify, the low-code AI app platform: five app types, multi-model setup, Docker deployment, and enterprise data security. Build LLM-powered workflows and Agents at minimal cost.