AI and Machine Learning
Applied AI guidance for computer vision, voice, agents, RAG, workflow automation, TinyML, and AI-enabled connected products.
AI and Machine Learning articles
How to Benchmark an Edge LLM: TTFT, TPOT, Memory, Thermals, and Power
An edge LLM benchmark needs separate TTFT, TPOT, memory, thermal, and whole-system power results under a fixed model and workload.
Read article
How to Select Edge Hardware for LLM Inference: Memory, Bandwidth, Accelerators, and Power
A supplier's “8 GB RAM, 6 TOPS NPU” line does not establish that a field device can answer questions about a long service manual offline.
Read article
Choosing an Edge LLM Runtime: Start with Devices and Model Artifacts
Compare llama.cpp, RKLLM, ExecuTorch, and MLC LLM by target device, model artifact, acceleration backend, and release responsibility—not an unverified speed ranking.
Read article
Offline RAG for Equipment Manuals: Versioned Evidence Before Answers
A technician asks how to reset an E42 alarm. A local model produces a convincing procedure, but cannot identify the machine variant, manual edition, or page it used.
Read article
When an RK3588 Edge AI Box Fits Industrial Vision—and When It Does Not
A 6-TOPS RK3588 does not automatically deliver a production vision system. Learn how to qualify camera, RKNN, latency, thermal, recovery, and rollback boundaries.
Read article
How to Choose a Local AI Model for Enterprise Deployment
The most expensive mistake in local AI selection is not choosing the second-best model. It is downloading a popular model before defining where data may go.
Read article
FunASR for AI Voice Projects: Offline, Edge, or Cloud ASR?
FunASR belongs on the shortlist when audio must remain in a factory, clinic, meeting room, or private network; transcription must continue during an Internet outage.
Read article
n8n AI Workflow Automation for Business Systems
n8n is most useful when it turns hand-offs scattered across webhooks, CRM, email, forms, tickets, and internal APIs into a visible process that can be replayed and.
Read article
YOLO for Vision Inspection: From Acceptance Criteria to Edge Deployment
YOLO is a strong fit for a specific class of vision problem: determine whether a visible object is present, identify its class.
Read article
When to Use Dify Instead of Custom AI App Development
If the main job is to connect user input, knowledge retrieval, model calls, branches, tools, and output into an observable AI process.
Read article
When to Use LlamaIndex for RAG Knowledge Bases
LlamaIndex is not the right answer for every “upload documents and ask questions” project. It becomes valuable when the hard part is turning PDFs, websites.
Read article
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.
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.