AI and Machine Learning
Articles related to the integration of machine learning and computer vision in IoT projects could go here. This would include tutorials on frameworks like OpenMV, Open...
AI and Machine Learning articles
When to Use LlamaIndex for RAG Knowledge Bases
LlamaIndex fits RAG systems that need multi-source ingestion, repeatable indexing, tunable retrieval, and evaluation. Small, stable, or fully managed knowledge bases o...
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How to Build Practical AI Agents with OpenAI APIs
Practical OpenAI agent development is not just a chatbot demo. It requires clear tool definitions, authorization, structured outputs, human approval, logging, retries,...
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AI Vision Workstation vs Handheld Scanner: How to Choose for Warehouse Recognition
An AI vision workstation fits warehouse workflows that need visual verification, operator identity, photo evidence, and WMS or ERP writeback. A handheld scanner is bet...
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AI Warehouse Recognition Workstation Use Cases
An AI warehouse recognition workstation fits workflows where item recognition, barcode scanning, image capture, operator identity, and WMS or ERP updates must happen a...
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When to Use LangGraph for AI Agent Workflows
LangGraph is a strong fit for AI agent workflows that need explicit state, loops, human review, checkpoint recovery, and observability. For one-shot answers, fixed API...
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Ollama for Local AI and Private Deployment: Enterprise Use Cases and Limits
Ollama is useful for local prototypes, private knowledge-base validation, offline assistants, and low-concurrency edge inference, but it should not be treated as a ful...
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How to Choose an AI Development Toolchain for Enterprise Projects
Enterprise AI toolchains should be chosen by task chain, not tool popularity. OpenAI, Dify, Ollama, YOLO, and FunASR belong to different layers: model capability, work...
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Home Assistant Voice: Local, Cloud, or Hybrid?
Home Assistant Voice should not be framed as simply local versus cloud. Wake word, speech-to-text, intent handling, text-to-speech, and device execution each have diff...
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Dify Workflow Template Patterns for Smart Home and IoT Automation
Dify Workflow is useful for reusable smart home and IoT automation templates, but it should not replace the device control plane. This guide covers event summaries, al...
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Why TinyML on ESP32-S3 Bottlenecks on Memory, Quantization, and Real-Time Inference
ESP32-S3 can run TinyML, but production success depends less on AI instructions alone and more on SRAM, tensor arena sizing, INT8 quantization, operator support, PSRAM...
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The Hard Part of Multimodal Edge Systems Is Latency, Sync, and Operations
In multimodal edge systems, the hardest part is rarely whether a model can run. It is whether voice, video, and event streams stay aligned, low-latency, diagnosable, a...
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ESP32-S3 Edge AI in Practice: Deep Optimization of TensorFlow Lite Micro Inference Performance
Deep dive into ESP32-S3 TinyML optimization, covering TFLM setup, INT8 quantization, memory tuning, PSRAM trade-offs, and real-world performance limits.
Read articleEvaluating a AI and Machine Learning project?
Share the device, data flow, network condition, target users, and business system boundaries. We can help you decide what to build first.