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Insights on AI, Machine Learning, Web Development, and emerging technologies from industry experts.
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301–312 of 523
Most ML projects fail not because ML is hard but because ML was the wrong tool. A decision tree, a regex, or a database query solves most 'AI' problems faster.
Mahmudul Haque Qudrati
CEO & ML Engineer
A vector database stores embeddings and finds the most similar ones to a query. SQL cannot do this. ChromaDB, Pinecone, Weaviate, pgvector, and Qdrant compared.
Mahmudul Haque Qudrati
CEO & ML Engineer
An AI agent is an LLM that can take actions and loop until a goal is achieved. The four components, the ReAct loop, what production agents actually do, and honest limits.
Mahmudul Haque Qudrati
CEO & ML Engineer
Building an AI agent requires an LLM with tool calling and a loop that runs until completion. Five steps with working code, common failure patterns, and when to use a framework vs. build from scratch.
Mahmudul Haque Qudrati
CEO & ML Engineer
Assistants respond to requests. Agents pursue goals autonomously. The technical differences, when you actually need an agent vs. an assistant, and an honest 2026 state-of-the-art.
Mahmudul Haque Qudrati
CEO & ML Engineer
Multi-agent systems coordinate specialized agents to handle tasks too complex for one agent. Four coordination patterns, real use cases, frameworks, and the hard problems that come with distribution.
Mahmudul Haque Qudrati
CEO & ML Engineer
An AI gateway sits between your application and LLM providers to handle routing, fallback, caching, rate limiting, and cost tracking. This guide compares Cloudflare AI Gateway, LiteLLM, Portkey, and building your own.
Mahmudul Haque Qudrati
CEO & ML Engineer
Microsoft's LayoutLMv3 pretrains on text, bounding boxes, and image patches together, enabling form understanding, receipt parsing, and document VQA without separate OCR fine-tuning.
Mahmudul Haque Qudrati
CEO & ML Engineer
Neptune.ai tracks ML experiments, stores artifacts and metrics, and enables team collaboration on model comparisons - bridging the gap between prototype notebooks and production model management.
Mahmudul Haque Qudrati
CEO & ML Engineer
Step-by-step framework for calculating monthly active user workloads, tokens-per-session averages, and cloud margins before launching an AI feature.
Mahmudul Haque Qudrati
CEO & ML Engineer
How to systematically measure retrieval context relevance, generation faithfulness, and answer correctness in production search systems.
Mahmudul Haque Qudrati
CEO & ML Engineer
A plain-English guide to how LLMs actually work: tokens, attention, training vs inference, why they hallucinate, and what context windows mean for your workflow.
Mahmudul Haque Qudrati
CEO & ML Engineer