Author: JAMIUL ISLAM
Emergent Planning in LLMs: How AI Predicts the Future Before Speaking
Discover how advanced AI models predict entire responses before speaking. Explore emergent planning in LLMs, the science behind internal blueprints, and why this matters for future AI agents.
Model Cards for Generative AI: A Compliance Guide to What You Must Publish
Learn how to create compliant model cards for generative AI. This guide covers essential elements, governance vs. compliance, regulatory drivers like the EU AI Act, and tools for automation.
Tensor Parallelism 101: How to Run Large Language Models on Multiple GPUs
Learn how tensor parallelism enables running large language models on multiple GPUs by splitting weight matrices. Understand hardware requirements, implementation steps, and comparisons with other parallelism strategies.
Adversarial Examples for Large Language Models: Jailbreaks and Overrides
Explore how adversarial examples and jailbreaks bypass safety filters in LLMs. Learn about text suffix attacks, visual perturbations, and why current alignment methods fail against these exploits.
In-Context Learning in LLMs: How Models Learn from Prompts Without Training
Discover how in-context learning allows LLMs to master new tasks from prompts alone. We explore the mechanics, benefits over fine-tuning, and expert tips for optimizing your prompts.
Refactoring AI-Generated Codebases: A Step-By-Step Architecture Rescue Plan
A practical guide to rescuing architectures built by LLMs. Learn how to use static analysis, characterization tests, and theme-based refactoring to eliminate technical debt and restore reliability to AI-generated codebases.
How to Use Cursor for Multi-File Changes in Large Codebases (2026 Guide)
Learn how to use Cursor's multi-agent AI to safely refactor large codebases. We cover Composer mode, best practices, pitfalls, and comparisons with Aider and Copilot.
Maximize LLM Scaling Utilization: Scheduling Strategies for 2026
Learn how to maximize GPU utilization during LLM scaling using advanced scheduling strategies like continuous batching and PagedAttention. Compare vLLM, Sarathi-Serve, and ExeGPT for cost-effective inference.
Shadow Prompting and Data Exfiltration Risks in LLM Workflows: A Security Guide
Explore the hidden dangers of shadow prompting and data exfiltration in LLM workflows. Learn how attackers bypass security, the financial costs of breaches, and practical steps to protect your organization.
Multi-Agent LLM Systems: How Role Specialization Drives Better Results
Explore how multi-agent LLM systems use role specialization to solve complex tasks better than single models. Compare frameworks like Chain-of-Agents, MacNet, and LatentMAS.
Prompt Hygiene for Factual Tasks: How to Eliminate Ambiguity in LLM Instructions
Learn how prompt hygiene eliminates ambiguity in LLM instructions, reducing hallucinations by up to 63% and securing AI systems against injection attacks.
How to Train Non-Developers to Ship Secure Vibe-Coded Apps in 2026
Learn how to train non-developers to build secure vibe-coded apps. Cover key vulnerabilities, platform comparisons, and practical steps to mitigate risks in AI-assisted development.