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DeepHub IMBA

A must‑follow public account sharing practical AI insights. Follow now. internet + machine learning + big data + architecture = IMBA

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Recent Articles

Latest from DeepHub IMBA

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DeepHub IMBA
DeepHub IMBA
May 11, 2026 · Artificial Intelligence

2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless

This article compares traditional Vector RAG, GraphRAG, and the newer Vectorless RAG, explains why Vector RAG fails on relational and structured queries, presents benchmark results, outlines each architecture's strengths and costs, and offers a decision framework and Adaptive RAG routing strategy for production systems.

Adaptive RetrievalGraphRAGLLM
0 likes · 13 min read
2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless
DeepHub IMBA
DeepHub IMBA
May 8, 2026 · Artificial Intelligence

Building a Custom 8×8 GridWorld with Q‑Learning in Gymnasium

This tutorial walks through creating a custom 8×8 GridWorld environment in Gymnasium, implementing a Q‑Learning agent that learns to navigate from the top‑left corner to the bottom‑right goal while avoiding walls, and visualizing training curves, learned policies, and a performance comparison with a random agent.

GridWorldGymnasiumPython
0 likes · 10 min read
Building a Custom 8×8 GridWorld with Q‑Learning in Gymnasium
DeepHub IMBA
DeepHub IMBA
May 7, 2026 · Frontend Development

Self‑Healing Playwright Tests with LLM‑Driven Locator Recovery

This article shows how to combine Playwright with an LLM (Groq) to build a self‑healing test framework that detects broken selectors, extracts a trimmed DOM snapshot, asks the model for a replacement locator, validates confidence, caches results, and integrates the logic via a Playwright fixture.

GroqJavaScriptLLM
0 likes · 17 min read
Self‑Healing Playwright Tests with LLM‑Driven Locator Recovery
DeepHub IMBA
DeepHub IMBA
May 6, 2026 · Information Security

Why MCP’s Protocol Layer Allows Prompt Injection and Hijacks Agent Context

The Model Context Protocol (MCP) embeds every tool’s description into an LLM’s context window, creating a structural “Context Poisoning” vulnerability that lets malicious or bloated tool metadata hijack agent reasoning, inflate tokens, and bypass traditional input validation.

AI agent securityLLMMCP
0 likes · 10 min read
Why MCP’s Protocol Layer Allows Prompt Injection and Hijacks Agent Context
DeepHub IMBA
DeepHub IMBA
May 1, 2026 · Artificial Intelligence

How to Build Intelligent Contextual Memory for AI Agents

The article examines why naïvely feeding all dialogue history to large language models is costly and unreliable, and it walks through rolling context windows, inverted‑index pruning, semantic vector search, and GraphRAG as complementary techniques for creating efficient, reasoning‑capable AI agent memory.

AIContext WindowGraphRAG
0 likes · 11 min read
How to Build Intelligent Contextual Memory for AI Agents
DeepHub IMBA
DeepHub IMBA
Apr 30, 2026 · Artificial Intelligence

Why Real RAG Systems Need Both BM25 and Vector Search

The article analyzes how BM25 excels at exact token matching while vector embeddings capture semantic intent, explains their distinct failure modes, and shows that a hybrid retriever—combined with metadata filtering, proper chunking, and reciprocal rank fusion—delivers the most reliable results for RAG pipelines.

BM25EmbeddingInformation Retrieval
0 likes · 17 min read
Why Real RAG Systems Need Both BM25 and Vector Search
DeepHub IMBA
DeepHub IMBA
Apr 29, 2026 · Artificial Intelligence

From Stateless to Stateful: 5 Architecture Patterns for Long‑Running Agents

The article outlines five concrete design patterns—Checkpoint‑and‑Resume, Delegated Approval, Memory‑Layered Context, Ambient Processing, and Fleet Orchestration—that enable production‑grade, multi‑day AI agents to persist state, handle failures, and scale safely.

AI agentsCloud SandboxLong-Running Agents
0 likes · 12 min read
From Stateless to Stateful: 5 Architecture Patterns for Long‑Running Agents
DeepHub IMBA
DeepHub IMBA
Apr 27, 2026 · Artificial Intelligence

DeepSeek‑V4 Deep Dive: Engineering Million‑Token Context Efficiency

The article provides a thorough technical analysis of DeepSeek‑V4, detailing how mixed sparse attention (CSA + HCA), manifold‑constrained hyper‑connections, the Muon optimizer, FP4 quantization, and a suite of infrastructure tricks enable stable training and inference with up to one‑million token contexts while achieving state‑of‑the‑art benchmark results.

CSADeepSeek-V4FP4 quantization
0 likes · 22 min read
DeepSeek‑V4 Deep Dive: Engineering Million‑Token Context Efficiency