Category
AI Agents
Autonomous agents, LLM applications, and intelligent systems
// 12 articles filed
// FIG. CAT-AI-A
PristrenPristrenAutonomous agents, LLM applications, and intelligent systems
// 12 articles filed
LangChain is a powerful LLM framework, but its complexity is frequently criticized. Here is when it genuinely helps and when you should skip it entirely.
Mahmudul Haque Qudrati
CEO & ML Engineer
Computer use agents can click, type, and navigate a real desktop. Here is what the technology can actually do, where it still fails, and when it beats a proper API integration.
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 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
In 2022, AI meeting summaries were a gimmick. In 2026, they save real time. The technical pipeline, what makes a good summary vs. a bad one, and an honest ROI calculation.
Mahmudul Haque Qudrati
CEO & ML Engineer
Perplexity's Sonar API returns LLM-generated answers with inline citations from live web search - an OpenAI-compatible endpoint that replaces custom RAG pipelines for real-time data retrieval use cases.
Mahmudul Haque Qudrati
CEO & ML Engineer
Command R+ is purpose-built for RAG with inline citation generation, multi-step tool use, and 128k context. Here's how to implement grounded generation with source links.
Mahmudul Haque Qudrati
CEO & ML Engineer
Function calling lets LLMs invoke your code with structured arguments - here is the complete guide with parallel calls, error handling, and cross-provider format differences.
Mahmudul Haque Qudrati
CEO & ML Engineer
Gemini 2.0 Flash is 2x faster than 1.5 Flash with native tool use, a 1M token context, real-time multimodal streaming, and a thinking mode for hard problems.
Mahmudul Haque Qudrati
CEO & ML Engineer
ReAct interleaves reasoning traces with actions, enabling LLMs to use tools while maintaining a reasoning chain - the foundational pattern behind LangChain agents and modern AI assistants.
Mahmudul Haque Qudrati
CEO & ML Engineer
Deep dives into ML algorithms, models, and applications
AI trends, techniques, and real-world implementations
How LLMs work, honest comparisons, and production usage
Every technique that works — with real examples
Claude Code, Cursor, Copilot, open-source tools reviewed honestly
Local LLMs, open models, free AI infrastructure
Fewer tokens, cheaper APIs, local alternatives with real numbers
Benchmarks explained, evaluation frameworks, model testing
LLM SEO, AI SEO, Google AI Overviews, developer marketing
iOS, Android, and cross-platform mobile app development
Modern web technologies, frameworks, and best practices
Data analysis, visualization, and engineering insights