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DeepHub IMBA

Jun 2, 2026 · Artificial Intelligence

Multi-Agent Systems: Coordinators, Specialized Agents, and Communication Mechanisms

The article explains why single-agent AI architectures struggle with complex tasks and argues that future AI will rely on multi‑agent systems featuring a coordinator, specialized research, planning, critic, and execution agents, shared memory or message‑passing communication, and hierarchical or decentralized coordination for scalability and robustness.

AI architectureCoordinatorcommunication protocols

0 likes · 8 min read

Multi-Agent Systems: Coordinators, Specialized Agents, and Communication Mechanisms

DeepHub IMBA

Jun 1, 2026 · Artificial Intelligence

The Essence of Prompt Engineering: Roles, Tasks, Context, Format, and Constraints

Prompt engineering designs inputs for large language models by combining clear intent, relevant context, explicit format, and constraints, turning ambiguous queries into reliable, high‑quality outputs through a structured, iterative process illustrated with concrete examples and advanced techniques.

AI communicationLLM reliabilityLarge Language Models

0 likes · 23 min read

The Essence of Prompt Engineering: Roles, Tasks, Context, Format, and Constraints

DeepHub IMBA

May 31, 2026 · Artificial Intelligence

Chunking Strategies for Video RAG: Pause‑Based, Sliding‑Window, and LLM‑Driven Methods

The article examines how to chunk transcribed video text for Retrieval‑Augmented Generation, comparing pause‑based, overlapping‑window, length‑based fallback, and LLM‑driven topic chunking methods, and shows how combining fine‑grained and thematic chunks yields a multi‑layered pipeline that improves context coverage for both precise and broad queries.

LLMRAGVideo Transcription

0 likes · 8 min read

Chunking Strategies for Video RAG: Pause‑Based, Sliding‑Window, and LLM‑Driven Methods

DeepHub IMBA

May 29, 2026 · Fundamentals

lat.md: Transform Any Project Code into a Queryable Knowledge Graph

lat.md builds a persistent, verified knowledge graph from code, documentation, and media by splitting documents into linked fragments, automatically scanning and validating them, and enforcing a "summary first" rule to keep AI‑driven project maps accurate and up‑to‑date.

AI integrationautomated verificationcode documentation

0 likes · 7 min read

lat.md: Transform Any Project Code into a Queryable Knowledge Graph

DeepHub IMBA

May 28, 2026 · Artificial Intelligence

AutoGen Multi‑Agent Demo: Coder, Reviewer, and Executor Automatically Complete a Code Review

The article explains how Microsoft’s AutoGen framework enables a Planner‑Executor‑Critic loop and a three‑agent GroupChat workflow, providing step‑by‑step Python code that configures AssistantAgent, UserProxyAgent, and ReviewerAgent to generate, review, and execute code automatically, and discusses the system’s advantages, scalability, and real‑world deployments.

AutoGenGroupChatLLM

0 likes · 13 min read

AutoGen Multi‑Agent Demo: Coder, Reviewer, and Executor Automatically Complete a Code Review

DeepHub IMBA

May 27, 2026 · Artificial Intelligence

Testing Four Non‑Vector RAG Approaches: BM25, GraphRAG, Tree Search, and Agentic Search

The article evaluates four non‑vector Retrieval‑Augmented Generation methods—BM25 lexical search, GraphRAG graph traversal, Tree‑Search document navigation, and an Agentic search loop—using a small JSON‑based corpus, showing each method’s strengths, weaknesses, and when to combine them for production‑grade retrieval.

Agentic SearchBM25GraphRAG

0 likes · 12 min read

Testing Four Non‑Vector RAG Approaches: BM25, GraphRAG, Tree Search, and Agentic Search

DeepHub IMBA

May 26, 2026 · Artificial Intelligence

Agentic AI Design Patterns: Pros, Cons, and Use Cases of Six Architectures

The article breaks down six common agentic AI design patterns—Single Agent, Sequential Agents, Parallel Agents, Loop & Critic, Coordinator & Sub‑agents, and Sub‑Agents as Tools—detailing their implementation structures, strengths, weaknesses, and ideal application scenarios, helping practitioners choose the right architecture for scalable LLM workflows.

AI architectureAgentic AIDesign Patterns

0 likes · 9 min read

Agentic AI Design Patterns: Pros, Cons, and Use Cases of Six Architectures

DeepHub IMBA

May 23, 2026 · Artificial Intelligence

Reason → Act → Observe: Building an Agentic Loop with LangChain and Python

This article explains what an agentic loop is, contrasts it with single‑pass chatbots, outlines its five stages, shows a visual architecture, walks through a concrete multi‑step example, provides Python pseudocode and a LangChain implementation, and discusses when to use or avoid such loops.

AI AgentsAgentic LoopLLM

0 likes · 8 min read

Reason → Act → Observe: Building an Agentic Loop with LangChain and Python

DeepHub IMBA

May 22, 2026 · Fundamentals

Inside Python’s Automatic Memory Management: Core Mechanisms and Optimization Guide

The article breaks down Python’s memory system layer by layer, explaining stack vs. heap, reference counting, generational garbage collection, the true effect of the del statement, built‑in optimizations like integer caching, string interning and __slots__, and shows how to process a 20 GB CSV efficiently with generators.

PythonReference Counting__slots__

0 likes · 12 min read

Inside Python’s Automatic Memory Management: Core Mechanisms and Optimization Guide

DeepHub IMBA

May 21, 2026 · Artificial Intelligence

Add Step‑Level Diagnostics to PyTorch Training in Three Lines with TraceML

TraceML provides a lightweight, step‑level profiler for PyTorch training that requires only a few code changes—initializing the library and wrapping each training step—to generate real‑time diagnostics and a compact JSON summary, helping engineers quickly identify whether data loading, forward, backward, or optimizer phases dominate execution time.

ML infrastructurePyTorchTraceML

0 likes · 6 min read

Add Step‑Level Diagnostics to PyTorch Training in Three Lines with TraceML