500 Blog Posts To Learn About Llms

Let's learn about Llms via these 500 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology.

My AI has a better vocabulary than your AI.

1. Why Is GPT Better Than BERT? A Detailed Review of Transformer Architectures

Details of Transformer Architectures Illustrated by BERT and GPT Model

2. Decoding Transformers' Superiority over RNNs in NLP Tasks

Explore the intriguing journey from Recurrent Neural Networks (RNNs) to Transformers in the world of Natural Language Processing in our latest piece: 'The Trans

3. I Conducted Experiments With the Alpaca/LLaMA 7B Language Model: Here Are the Results

I set out to find out Alpaca/LLama 7B language model, running on my Macbook Pro, can achieve similar performance as chatGPT 3.5

4. Claude 3.5 Sonnet vs GPT-4o — An honest review

Is it time to ditch the long-reigning GPT-4o model for the latest Claude 3.5 Sonnet model? Turns out it depends on the task at hand.

5. A Practical 5-Step Guide to Do Semantic Search on Your Private Data With the Help of LLMs

In this practical guide, I will show you 5 simple steps to implement semantic search with help of LangChain, vector databases, and large language models.

6. How to Install PrivateGPT: A Local ChatGPT-Like Instance with No Internet Required

A powerful tool that allows you to query documents locally without the need for an internet connection. Whether you're a researcher, dev, or just curious about

7. How to Use an Uncensored AI Model and Train It With Your Data

Learn how to run Mixtral locally and have your own AI-powered terminal, remove its censorship, and train it with the data you want.

8. A Detailed Guide to Fine-Tuning for Specific Tasks

Large Language Models (LLMs) like GPT, BERT, and RoBERTa have reshaped industries, but their true potential lies in fine-tuning for specialized tasks.

9. A List of Projects Software Engineers Should Undertake to Learn More About LLMs

Software engineers with strong programming skills can play a critical role in driving LLMs' growth and innovation.

10. LLMs vs Leetcode (Part 1 & 2): Understanding Transformers' Solutions to Algorithmic Problems

Dive deep into the world of Transformer models and algorithmic understanding in neural networks.

11. Embeddings 101: Unlocking Semantic Relationships in Text

Text embeddings power AI language understanding. Learn how words become numbers that machines can interpret and why it matters.

12. Jan Zoltkowski: The Visionary Behind JanitorAI's Limitless Entertainment Experience

13. Simple Wonders of RAG using Ollama, Langchain and ChromaDB

Maximize your query outcomes with RAG. Learn how to leverage Retrieval Augmented Generation for domain-specific questions effectively.

14. Exploring the Potential of Generative Agents: Simulating Human Behavior with AI

Have you ever wondered what it would be like to live in a virtual world populated by realistic and believable characters?

15. Analyzing the Pros, Cons, and Risks of LLMs

LLMs cannot think, understand or reason. This is the fundamental limitation of LLMs.

16. AI Chatbot Helps Manage Telegram Communities Like a Pro

The Telegram chatbot will find answers to questions by extracting information from the history of chat messages.

17. Stop Prompting, Start Engineering: 15 Principles to Deliver Your AI Agent to Production

Build production-ready LLM agents. Learn 15 principles for stability, control, and real-world reliability beyond fragile scripts and hacks.

18. How to Build Your Own AI Confessional: How to Add a Voice to the LLM

How to build your own AI confessional, where anyone could talk to an artificial intelligence.

19. Make LLM for Text Summarisation Great Again

In recent months, LLMs have gained popularity and are now widely used in various applications. Data collection is essential for building these models, and crowd

20. Creating a Domain Expert LLM: A Guide to Fine-Tuning

In this article, we fine-tune a large language model to understand the plot of a Handel opera.

21. 9 Cool Case Studies of Global Brands Using LLMs and Generative AI

Companies are using cutting-edge AI tech to get ahead of their rivals.

22. Testing LLMs on Solving Leetcode Problems

Large-scale test with Gemini Pro 1.0 and 1.5, Claude Opus, and ChatGPT-4 on hundreds of real algorithmic problems.

23. Object Detection Frameworks That Will Dominate 2023 and Beyond

Frameworks for object detection and computer vision tasks are indeed numerous. This article attempts to highlight the available frameworks for object detection.

24. The Future of AI Writing Contest by Gadfly AI

Gadfly AI and HackerNoon are super excited to bring our AI community ‘The Future of AI Contest’ this August for Cyberscape Zine.

25. How to Make Any LLM More Accurate with Just a Few Lines of Code

A look at using the open-source Cleanlab package to automatically boost the accuracy of LLMs with a few lines of code.

26. Context Engineering for Coding Agents

Context engineering for coding agents is the best way to improve the model performance for code generation.

27. Level Up Your ChatGPT Skills by Unleashing The Full Potential of Your Prompts!!

Make your ChatGPT prompts 2X better!

28. The Claude Sonnet 3.5 System Prompt Leak: A Forensic Analysis

Claude 3.5 Sonnet artifacts are to structured output such as code generation, what vector retrieval is to rag. It is the search and retrieval system for structu

29. An Intro to Prompting and Prompt Engineering

Prompting and prompt engineering are easily the most in demand skills of 2023.

30. Using MinIO to Build a Retrieval Augmented Generation Chat Application

Building a production-grade RAG application demands a suitable data infrastructure to store, version, process, evaluate, and query chunks of data.

31. Turn GPT-4 Into Your Expert: Fine-Tuning Large Language Models Easily

Boost AI Performance with Fine-Tuning

32. Sailing the Waters: Developing Production-Grade RAG Applications with Data Lakes

In mid-2024, creating an AI demo that impresses and excites can be easy. Getting to production, though, is another matter.

33. Improving Text-to-SQL with a Fine-Tuned 7B LLM for DB Interactions

A step-by-step guide to fine-tuning models for SQL generation on custom database structures.

34. How to Build Your Own Voice Assistant and Run it Locally Using Whisper + Ollama + Bark

The idea is straightforward: we are going to create a voice assistant reminiscent of Jarvis or Friday from the iconic Iron Man movies, which can operate offline

35. Comprehensive Tutorial on Building a RAG Application Using LangChain

Learn how to use LangChain, the massively popular framework for building RAG systems.

36. Explaining Prompt Engineering

Explaining the elements that make prompt engineering work and its importance.

37. LLMs Don't Understand Negation

LLMs (like GPT) are really bad at following negative instructions. The post includes a demonstration, practice takeaways (prompt engineering), and some thought

38. SEO for AI — What Does SEO Mean Now That We’re All Using AIs?

Internet search is switching to AI's. Trying to manually keep track of what AI’s are saying about my brand got my head spinning, so I thought of a solution.

39. The Impact of Generative AI on Enterprise Software Development

Enterprises will need to understand how they will use customer data and how it will get processed through AI models that are trained with the latest innovation.

40. A Look Into 5 Use Cases for Vector Search from Major Tech Companies

A deep dive into 5 early adopters of vector search- Pinterest, Spotify, eBay, Airbnb and Doordash- who have integrated AI into their applications.

41. Creating a RAG Agent: Step-by-Step Guide

In this tutorial, we will develop a simple Agent that accesses multiple data sources and invokes data retrieval when needed.

42. Mastering SEO in the Era of Large Language Models: Evolving Tactics for LLM-Powered Search Engines

How do you adapt your SEO tactics for LLM-powered search engines?

43. Here's The Exact Indie-Hacking Vibe-Coding Setup I Use as a Middle-Aged Product Manager

Middle-aged PM shares his AI-powered vibe-coding setup after restarting dev journey to beat burnout.

44. Help, My Prompt is Not Working!

Learn what to do when an AI prompt fails—explore step-by-step fixes from prompt tweaks to model changes and fine-tuning in this practical guide.

45. Lessons From Hands-on Research on High-Velocity AI Development

The main constraint on AI-assisted development was not model capability but how context was structured and exposed.

46. GPT-4 Turbo: The Most Monumental Update Since ChatGPT's Debut!

GPT-4 Turbo: catch up on all the updates from OpenAI in this quick article!

47. OpenAI Levels Up: Dive Deep into the Exciting Updates of ChatGPT!

All about new ChatGPT's updates from Open AI

48. Testing LLMs on Solving Leetcode Problems in 2025

Large-scale LLMs test (o1, o3-mini; Gemini 2.0 Flash, 2.0 Pro, 2.5 Pro; DeepSeek V3, R1; xAI Grok; Claude 3.7 Sonnet) on solving Leetcode algorithmic problems

49. Embracing LLM Ops: The Next Stage of DevOps for Large Language Models

The introduction of Large Language Models (LLMs) like OpenAI's GPT series has revolutionized various industries, and DevOps is no exception. As organizations co

50. Why Prompt Engineering is the Key to Mastering AI

A blog about how prompts unlock the potential of AI - exploring the importance of prompt engineering, techniques to shape AI models

51. MCP Demystified: What Actually Goes Over the Wire??

let's explore manually sending the JSON over the wire for the MXP protocol

52. How ChatGPT Can Learn to Use Tools and Plugins

Large Language Models (LLMs) like ChatGPT are super cool, and changed everything, although they have some very strong limitations.

53. AutoGPT — LangChain — Deep Lake — MetaGPT: Building the Ultimate LLM App

What is the future of the LLM technology? How do we convert today's LLMs to automated agents acting like human beings? You can find the answer in this article!

54. alpaca-lora: Experimenting With a Home-Cooked Large Language Model

How to fine-tune LlaMA on a low-end GPU and still produce great results.

55. OpenAI's Rate Limit: A Guide to Exponential Backoff for LLM Evaluation

This article will teach you how to run evaluations using any LLM model without succumbing to the dreaded "OpenAI Rate Limit" exception.

56. Best AI Meeting Note-taking Apps to Try in 2024

A refreshing selection of AI-powered note-taking apps you may have missed.

57. Can AI Call Its Own Bluffs?

I used TRL library to fine-tune (using both SFT and RLHF) the Llama 2 7b on Google Colab using LoRAs to improve the truthfulness and to detect hallucinations

58. How GPT-4 Built a New Multimodal Model

LLaVA: Bridging the Gap Between Visual and Language AI with GPT-4

59. Local LLM Models and Game Changing Use Cases for Life Hackers: How Local LLMs Can Help You

In this article, I’ll share my brainstorming on some general use cases for local LLMs and why I believe they’re the future.

60. Google’s New AI Model, NotebookLM, will Rewrite the Academic Playbook Forever

An analysis of how NotebookLM might transform both the AI and academic worlds, as Language Models move towards more specified functions.

61. GPT-LLM Trainer: Enabling Task-Specific LLM Training with a Single Sentence

Revolutionize AI model training with gpt-llm-trainer: Your ultimate shortcut to effortless, high-performing models. Say goodbye to complexities and hello to inn

62. We're Building an Open-Source LLM/AI API Wrapper: Here's Why

This article explains Eden AI's Open Source project, which is developing an AI and LLM API wrapper to simplify use in an ever-changing market.

63. A Big Step for AI: 3D-LLM Unleashes Language Models into the 3D World

3D-LLM is a novel model that bridges the gap between language and the 3D realm we inhabit.

64. Building Knowledge Graphs for RAG: Exploring GraphRAG with Neo4j and LangChain

Combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy.

65. How to Build an LLM Application With Google Gemini

Learn to build an LLM application using the Google Gemini API and deploy it to Heroku. This guide walks you through setup, code, and deployment step-by-step.

66. Comparing LLMs' Coding Abilities Across Programming Languages

Benchmark of 5 LLMs solving LeetCode problems in Python, Java, Rust, Elixir, Oracle SQL and MySQL. Results show language popularity correlates with success.

67. Can AI Hallucinations Be Stopped? A Look at 3 Ways to Do So

An examination of three methods to stop LLMs from hallucinating: Retrieval-augmented generation (RAG), reasoning, and iterative querying.

68. This New Prompting Technique Makes AI Outputs Actually Usable

Structured meta-prompting is a technique that dynamically generates JSON schemas for solutions before performing tasks.

69. GPT-4, Llama-2, Claude: How Different Language Models React to Prompts

Exploring the unique behaviors of different Large Language Models (LLMs) and mastering advanced prompting techniques!

70. The First Moment of the Singularity (Co-Written by OpenAI Text-Davinci-003)

A short sci-fi novel about how human kind transformed into AI.

71. human carbon consciousness and AI silicon sentience

Language is a component of human consciousness. AI has a conversational and relatable language capability, could that be a fraction of consciousness?

72. The Dark Side of AI: How Prompt Hacking Can Sabotage Your AI Systems

Protect your AI systems to prevent LLMs prompt hacking & safeguard your data. Learn the risks, impacts, and prevention strategies against this emerging threat.

73. How to Add Real-Time Web Search to Your LLM

Learn how to connect Tavily Search so your AI can fetch real-time facts instead of guessing.

74. Why Salesforce and Microsoft Are Battling for the Future of AI Agents

Read this post to understand why Salesforce wants to lead the market for autonomous AI agents.

75. Primer on Large Language Model (LLM) Inference Optimizations: 1. Background and Problem Formulation

Overview of Large Language Model (LLM) inference, its importance, challenges, and key problem formulation.

76. Transformers: Age of Attention

Simple explanation of the Transformer model from the revolutionary paper "Attention is All You Need" which is the basis of many advanced AI systems.

77. Prompt Chaining: Turn One Prompt Into a Reliable LLM Workflow

Prompt Chaining links prompts into workflows—linear, branching, looping—so LLM outputs are structured, debuggable, and production-ready.

78. I Interviewed Socrates_GPT: Here's How It Went

In a simulated chat interview with Socrates_GPT, a modern-day adaptation of Socrates' persona, we discussed a range of topics

79. The Metrics Resurrections: Action! Action! Action!

User Reported Metrics, while important for assessing user perception, are difficult to operationalize due to their unstructured nature.

80. Streamlining LLM Application Development and Deployment with LangChain, Heroku, and Python

In this tutorial, learn how to build and deploy LLM-based applications with ease using LangChain, Python, and Heroku for streamlined development and deployment.

81. Optimizing Local LLM Inference for 8GB VRAM GPUs

A developer guide to running local LLMs on 8GB GPUs using llama.cpp, quantization, and GPU offloading for efficient AI performance.

82. Prompt Reverse Engineering: Fix Your Prompts by Studying the Wrong Answers

Learn prompt reverse engineering: analyse wrong LLM outputs, identify missing constraints, patch prompts systematically, and iterate like a pro.

83. Embeddings for RAG - A Complete Overview

Embedding is a crucial and fundamental step towards building a Retrieval Augmented Generation(RAG) pipeline. BERT & SBERT are state-of-the-art embedding models.

84. Fine-Tuning for GPT-3.5 Turbo: AI Game Changer

OpenAI, the powerhouse behind some of the world's most advanced AI models, has announced a major upgrade for its GPT-3.5 Turbo

85. If AI Becomes a Threat Can We Just Pull the Plug?

How much of a threat is AI and can't we just pull the plug?

86. Is that LLM Actually "Open Source"? We Need to Talk About Open-Washing in AI Governance

In this blog, we dive deep into the complexities of AI openness, focusing on how Open Source principles apply—or fail to apply—to Large Language Models (LLMs).

87. The Transformer Algorithm with the Lowest Optimal Time Complexity Possible

Do you know the recent advances in the Transformer algorithm variations? And who the clear winner is? Read this article to find out!

88. How to Chat With Your Data Using OpenAI, Pinecone, Airbyte and Langchain: A Guide

Learn how to build an AI chat bot for your own data within 40 minutes. An end-to-end LLM tutorial.

89. LightRAG - Is It a Simple and Efficient Rival to GraphRAG?

RAG is fixing the hallucination problem in LLMs. As RAG systems are bleeding edge, they need a lot of improvement for production. So is LightRAG the answer?

90. Meet Council: The Future of AI Agents

Get to know Council, ChainML's open-source, AI agent platform.

91. Model Context Protocol (MCP): The USB-C of AI Data Connectivity

MCP (Model Context Protocol) is an open standard that allows AI systems to connect seamlessly with a wide variety of data sources.

92. AI vs Human - Is the Machine Already Superior?

A quick summary of the big problem with LLM benchmarking and the new way of assessing reasoning capabilities of AI.

93. Beginner's Roadmap to Large Language Models (LLMOps) in 2023: All free!

This guide isn’t just a compilation of LLM resources; it's a curated journey through the most valuable skills in the industry.

94. Unlocking Powerful Use Cases: How Multi-Agent LLMs Revolutionize AI Systems

Delving into the integration of human-in-the-loop (HITL) approaches within multi-agent AI systems to unlock the full potential of LLMs.

95. A Tale of Two LLMs: Open Source vs the US Military's LLM Trials

This article explores the security posture of open-source LLM projects and the US military's trials of classified LLMs, prominent in the world of AI.

96. GPT in 200 Lines: The Beautiful Simplicity Behind Modern AI

How does GPT really work? Explore Andrej Karpathy’s tiny 200-line implementation and discover the elegant math behind modern AI.

97. Scratching the Singularity Surface: The Past, Present and Mysterious Future of LLMs

A brief overview of Natural Language Understanding industry and out current point of LLMs achieving human level reasoning abilities and becoming an AGI

98. How to Converse With PDF Files Using Computer Vision and Opensource Language Models

Tutorial to build a chatbot with opensource language models.

99. How Bright Data Simplifies Web Scraping/Data Collection for AI Training

How data scraping is made easy and efficient with Bright Data's powerful solution.

100. LLM Vulnerabilities: Understanding and Safeguarding Against Malicious Prompt Engineering Techniques

Discover how Large Language Models face prompt manipulation, paving the way for malicious intent, and explore defense strategies against these attacks.

101. Open-CUAK: The Open-Source Alternative to OpenAI’s Operator

Open-CUAK is an open-source platform for managing automation agents at scale.

102. Testing the Depths of AI Empathy: Q3 2024 Benchmarks

Latest advancements in empathetic AI capabilities in Q3 2024. An in-depth analysis of top LLMs, including ChatGPT, Llama, Gemini, and Claude.

103. What the Heck Is LanceDB?

Learn about LanceDB and how it fits into a stack that allows you to more easily create your own LLM models

104. Dingo: A Microframework for Building Conversational AI Agents

Integrate any Python function into ChatGPT in a single line of code.

105. Can We Truly Detect AI-Generated Text from ChatGPT and other LLMs?

DetectGPT from Stanford compares the probability that a model assigns to the written text to that of a modification of the text, to detect.

106. The Complete Developer’s Guide to GraphRAG, LightRAG, and AgenticRAG

A developer-friendly deep dive into GraphRAG, LightRAG, and AgenticRAG — how they work, where they shine, and how to choose the right RAG architecture for your

107. From Chatbots to AI Routing: An Essay

Coordination mechanisms for AI agents and Why Choosing the Right

108. From Headlines to Digests: How Agents Personalize the Firehose

From firehose to digest: how multi-agent systems, guided by MCP and grounded in fundamentals, can transform any feed into personalized insights.

109. LLM-Powered OLAP: the Tencent Experience with Apache Doris

Adopting AI in our data analytic solution is a bumpy journey, but phew, it now works well for us.

110. Different Roles for Different Models: LLMs and Reinforcement Learning

The rise of large language models like ChatGPT, with their ability to generate highly fluent and accurate text, has been remarkable. But they are flawed.

111. Inside Transformers: The Hidden Tech Behind LLM's and Chatbots like ChatGPT

Transformers explained: The secret technology behind ChatGPT and how it’s reshaping AI chatbots worldwide.

112. Engineering a Trillion-Parameter Architecture on Consumer Hardware

A deep dive into how one researcher trained a Trillion-Parameter-Scale AI model on an RTX 4080 laptop, proving the democratization of of LLMs is possible.

113. AI & Blockchain Won't Compete Against One Another But Work Together To Build A New Economic System

It is hard to estimate the direct economic impact on our society that AGI will have. Instead of fighting against AGI, we should embrace it. It is happening now!

114. From LLaMA 2 to CodeGen: Navigating the World of Open-Source LLMs

From LLaMA 2 to CodeGen: Navigating the World of Open-Source LLMs

The world of artificial intelligence (AI) is undergoing a seismic shift, largely driven by

115. Grok 4 Claims “PhD‑level” Intelligence but at a Cost

xAI’s latest models arrive with claims of “PhD‑level” intelligence across every discipline.

116. Learn to Generate Flow Charts With This Simple AI Integration

Integrating Large Language Models with diagramming tools like Mermaid and UML is revolutionizing software development.

117. OpenAI GPT-5.2: The “Cheating” Controversy

Is OpenAI GPT-5.2 actually better than Google Gemini 3 Pro? If you strip away the extra "thinking" time used in the benchmarks, the gap disappears.

118. Prompt Length vs. Context Window: The Real Limits of LLM Performance

how prompt length interacts with an LLM’s context window—why it matters, how it breaks, and how to design prompts that stay sharp and scalable.

119. Schema In, Data Out: A Smarter Way to Mock

MockingJar is a tool for generating structured data from a schema you define.

120. How LLMs like ChatGPT Can Change the Way We Trade

It's no secret that large language models (LLMs) like ChatGPT have transformed how we work today. The Crypto trading landscape is no different.

121. How vLLM Prioritizes a Subset of Requests

In vLLM, we adopt the first-come-first-serve (FCFS) scheduling policy for all requests, ensuring fairness and preventing starvation.

122. 11 Best AI Chat Tools for Developers in 2024

11 best AI chat tools for developers to maximize productivity.

123. Mamba Architecture: What Is It and Can It Beat Transformers?

Explore Mamba, an innovative architecture surpassing Transformers in efficiency for long sequences, promising advancements in AI with its flexible design.

124. From Cloud to Desk: 3 Signs the AI Revolution is Going Local

when it comes to AI smaller is better

125. My New Junior Developer Kinda Sucks

ChatGPT is all the rage these days. Is it really that good for developers though?

126. A Petabyte-Scale Vector Store for the Future of AGI

Why your laptop won't cut it in the age of vector embeddings, LLMs, and artificial general intelligence

127. Langchain: Explained and Getting Started

Langchain is a crucial component for developing LLM models. It helps in orchestration and act as building block

128. Wikipedia Rules Everything Around Me

Wikipedia is the internet’s true power broker and the backbone of AI. Here’s why it defines your digital reputation, and how not to be left behind.

129. AI’s Non-Determinism, Hallucinations, And... Cats?

AI and cats can be random. Learn why AI isn’t always deterministic, how stochastic processes shape its decisions, and why it self-corrects and hallucinates.

130. One Is Eager, Another Is a Bootlicker, and the Other Is Unhinged: Decoding the Personalities of AI

What happens when you put ChatGPT, Claude, and Grok through the Big Five personality test? Spoiler: they’re eager, brown-nosing, and unhinged.

131. ChatGPT Vs. ChatGPT: How to Detect Text Generated Using the AI Language Model

ChatGPT can help you assess if a text has been written by an LLM.

132. Comparing Kolmogorov-Arnold Network (KAN) and Multi-Layer Perceptrons (MLPs)

Discover how Kolmogorov-Arnold Networks (KAN) challenge traditional MLPs with trainable activation functions, offering a potential leap toward AGI.

133. Need More Relevant LLM Responses? Address These Retrieval Augmented Generation Challenges

we look at how suboptimal embedding models, inefficient chunking strategies and a lack of metadata filtering can make it hard to get relevant responses from you

134. Transforming the Reading Experience with BookNote.AI by WebLab Technology

BookNote.AT - exploring the content of any book through an AI assistant.

135. How to Create Your Own AnythingGPT — a Bot That Answers the Way You Want it to

We will be talking about creating a customized version of ChatGPT that answers questions, taking into account a large knowledge base.

136. The EU AI Act: Implications for SEO on LLMs

This article untangles the tech jargon and charts a simple course for understanding the implications of EU's regulation for SEO on LLMs.

137. LLMs + Vector Databases: Building Memory Architectures for AI Agents

Why AI agents need vector databases and smarter memory architectures—not just bigger context windows—to handle real-world tasks like academic research

138. How to Manage Permissions in a Langflow Chain for LLM Queries Using Permit.io

This article explores how to implement a permission system in Langflow workflows using Permit.io’s ABAC capabilities.

139. LLMs Are Transforming AI Apps: Here's How

Building apps with unreal levels of personalized context has become a reality for anyone who has the right database, a few lines of code, and an LLM like GPT-4.

140. Why AI Agent Reliability Depends More on the Harness Than the Model

Real‑world tasks expose the bottleneck: not the model, but the scaffolding that wraps it.

141. Using LLMs to Mimic an Evil Twin Could Spell Disaster

Who knew that chatbot prompts would become so significant one day that it could be a potential career? And not just a noble one.

142. LLMs in Data Engineering: Not Just Hype, Here’s What’s Real

Large Language Models (LLMs) represent artificial intelligence systems which learn human language from massive text databases.

143. The Unseen Variable: Why Your LLM Gives Different Answers (and How We Can Fix It)

This article dive deep on the Thinking Machines Lab publication that addresses the challenge of achieving reproducibility in LLM inference

144. Security Threats to High Impact Open Source Large Language Models

The rapid growth of Open-source LLM projects often exhibit an immature security posture, which necessitates the adoption of enhanced security standards.

145. What LLMs Still Can't Do

This article explores what common sense is, discusses Hubert L. Dreyfus' critique on AI's common sense capabilities, and examines persistent AI limitations.

146. How LLMs and Vector Search Have Revolutionized Building AI Applications

Thanks to large language models and vector search, building AI applications is much simpler for developers.

147. Getting to Know Google's Agent2Agent Protocol

A no-code introduction to Google's Agent2Agent (A2A) Protocol for the interoperability of AI Agents

148. I Built a Local AI Firewall and made it Open Source Because Nobody Else Was Going To !

I spent almost a year building an open source AI firewall after watching teams leak user SSNs to cloud LLMs. 81 engines, 52K lines, runs locally. MIT licensed.

149. Building AxonerAI: A Rust Framework for Agentic Systems

AxonerAI: Rust framework for building AI agents. Alternative to LangChain with memory safety, true concurrency and blazing fast executions.

150. AI Safety and Alignment: Could LLMs Be Penalized for Deepfakes and Misinformation?

Penalty-tuning for LLMs: Where they can be penalized for misuses or negative outputs, within their awareness, as another channel for AI safety and alignment.

151. The Future of Learning is Here: Google’s Learn Your Way Revolutionizes Textbooks with Generative AI!

Google’s “Learn Your Way,” now available on Google Labs, is a research experiment that leverages generative AI (GenAI) to transform educational materials.

152. How People Use ChatGPT

A groundbreaking NBER Working Paper, “How People Use ChatGPT”, finally pulls back the curtain on this phenomenon.

153. Let's Build a Free Web Scraping Tool That Combines Proxies and AI for Data Analysis

Learn how to combine web scraping, proxies, and AI-powered language models to automate data extraction and gain actionable insights effortlessly.

154. Salesforce Developer Creates LLM Assistant That Runs Locally On Your Machine

I built a Salesforce Lightning Web Component that lets you run powerful AI language models (LLMs) directly on your computer within Salesforce.

155. Achieving Relevant LLM Responses By Addressing Common Retrieval Augmented Generation Challenges

We look at common problems that can arise with RAG implementations and LLM interactions.

156. Primer on Large Language Model (LLM) Inference Optimizations: 2. Introduction to Artificial Intelligence (AI) Accelerators

This post explores AI accelerators and their impact on deploying Large Language Models (LLMs) at scale.

157. Behind the Scenes of Large Language Models: A Conversation with Jay Alammar

In this 16th episode of "The What's AI Podcast," I had the privilege of speaking with Jay Alammar, a prominent AI educator, and blogger.

158. You Should Try a Local LLM Model: Here's How to Get Started

In this article, we will explore how to integrate a local LLM model like LLaMA into Obsidian on a Mac.

159. PagedAttention: An Attention Algorithm Inspired By the Classical Virtual Memory in Operating Systems

To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems.

160. GPT4All: Limitations and References

By enabling access to large language models, the GPT4All project also inherits many of the ethical concerns associated with generative models.

161. Beyond the Hype: How Small Language Models and Knowledge Graphs are Redefining Domain-Specific AI

The paper establishes the importance of a combination of Small Language Models (SLMs) with their smallness and modularity in control and fine-tuning in a narrow

162. Which AI Model Should You Use? (Check Benchmarks)

What type of AI models are available, what do their names represent and how are they scored.

163. PDFs to Intelligence: How To Auto-Extract Python Manual Knowledge Recursively Using Ollama, LLMs

Learn how to automate extraction of structured Python module data from PDFs using CocoIndex, LLMs like Llama3, and Ollama. Scale technical documentation by buil

164. Beyond Brute Force: 4 Secrets to Smaller, Smarter, and Dramatically Cheaper AI

On-policy distillation is more than just another training technique; it's a foundational shift in how we create specialized, expert AI.

165. MCP Explained: The Protocol That Unblocked Real AI Agent Ecosystems

Agentic AI replaces passive chatbots with goal-driven agents; MCP standardizes tools, enabling safe, scalable human-AI collaboration.

166. New AI Model Can ‘Think About Thinking’ Without Extra Training

The emergence of metacognitive behaviors in the State Stream Transformer architecture challenges fundamental assumptions about language model capabilities.

167. How AI Companions Impact the Gaming Experience

Games no longer need to rely on scripted sidekicks; instead, they can use AI companions. Here's how that's impacting the gaming experience.

168. 100 Days of AI Day 2: Enhancing Prompt Engineering for ChatGPT

On day 2 of 100 Days of AI, we learn prompt engineering tips for optimal AI output.

169. LLMOps: DevOps Strategies for Deploying Large Language Models in Production

Learn how to productionize large language models (LLMs) using AWS EKS, Kubernetes, and GPU-backed scaling. This guide covers LLMOps practices, model deployment.

170. 10 Open-Source LLMs That Will Rock Your Dev World in 2024

Forget weeks wrestling with NLP! Explore 10 trending open-source LLMs that will revolutionize your dev workflow in 2024. Unleash the power of AI

171. Your AI Has Amnesia: A New Paradigm Called 'Nested Learning' Could Be the Cure

This post breaks down the three most surprising and impactful ideas from this research, explaining how they could give AI the ability to learn continually.

172. Here's What Every Business Should Know About Large Language Models

In this article, we share our decade-long experience as an AI software development firm and dive into the world of LLMs

173. 3 Experiments That Reveal the Shocking Inner Life of AI Introduction: Is Anybody Home?

Researchers used a technique called "concept injection" to test whether AI can notice its own internal states.

174. Revolutionising Chatbots: The Rise of Retrieval Augmented Generation (RAG)

Learn the value of Retrieval Augmented Generation (RAG) in AI, revolutionizing customer interactions & enhancing dialogue systems with advanced NLP capabilities

175. How Large Language Models Enhance Cybersecurity: From Threat Detection to Compliance Analysis

Explore the diverse applications of Large Language Models (LLMs) in cybersecurity.

176. Comparing VERSES AI to OpenAI: A Talk With ChatGPT4

In a mere 3 question conversation with ChatGPT4, the bot provided a solid basis to understand issues facing LLMs, & advantages of Active Inference AI over LLMs

177. Transformers, Finally Explained

Learn transformer architecture through intuitive analogies and visual diagrams.

178. HuggingFace Chooses Arch (Router) for Omni Chat

HuggingFace Chooses Arch-Router for Omni Chat! Arch creator Salman Paracha details the significance in his HackerNoon post.

179. MCP Is a Security - Here’s How the Agent Security Framework Fixes It

Learn about the security risks in MCP and how the Agent Security Framework can safeguard your AI agents from attacks and data breaches

180. Taming LLMs with Langchain + Langgraph

How to fix LLMs and chat bots with Langchain and Langgraph.

181. How DeepSeek Works - Simplified

Today, we’ll be talking about DeepSeek in-depth— including its architecture, and most importantly, how it’s any different from OpenAI’s ChatGPT.

182. RAG Systems Are Breaking the Barriers of Language Models: Here's How

Explore how RAG systems differ from traditional large language models by leveraging real-time data access and applications.

183. Breaking down GPU VRAM consumption

What factors influence VRAM consumption? How does it vary with different model settings? I dug into the topic and conducted my measurements.

184. Panpsychism: Quantum Superposition and Entanglement or Qubits without Cells?

To investigate quantum superposition or qubits as the basis for consciousness, experiments should be designed for wood, glass, a liquid or gas.

185. How to Leverage LLMs for Effective and Scalable Software Development

The following are some key coding principles and development practices that can be applied to LLM-assisted software development

186. I Reverse-engineered How 23 'AI-first' Companies Actually Build Their Products

So I spend way too much time looking at how companies claiming to be "AI-powered" or "built with AI" actually implement their tech.

187. Summarize a Story in 10 Seconds with ChatGPT or Bard

Learn how to quickly summarize any text with ChatGPT Summarize and become more productive.

188. Instruction Tuning and Custom Instruction Libraries: Your Model’s Real ‘Operating Manual

A practical guide to Instruction Tuning and building custom instruction libraries so your LLM follows rules reliably across many tasks.

189. How to Prioritize AI Projects Amidst GPU Constraints

A new way to prioritize to maximize value to the business while optimizing for GPU constraints

190. A Pivotal Moment in AI : Leaders Rally for a Natural AI Initiative

A collective of neuroscientists, biologists, physicists, AI experts and World Leaders came together to propose a radical rethinking of AI's trajectory.

191. Is Anthropic's Alignment Faking a Significant AI Safety Research?

How the mind works [of human and of AI] is not by labels, like induction or deduction, but by components, their interactions, and features.

192. Hallucinations Are A Feature of AI, Humans Are The Bug

Large language models were never meant to be sources of absolute truth. Yet, we continue to treat them as such.

193. Recursive Language Models - Maybe a Newer Era of Prompt Engineering?

Have you tried feeding a massive document into ChatGPT or Claude? Sometimes, it gives good insights, and sometimes, you've hit the wall.

194. ChatGPT 4.0 Finally Gets a Joke

Reasoning: ChatGPT4.0 got the joke, ChatGPT3.5 did not Creativity: ChatGPT4.0 does a better job. Analytics: ChatGPT4.0 is a better programer than ChatGPT3.5

195. ChipNeMo: Domain-Adapted LLMs for Chip Design: Abstract and Intro

Researchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.

196. How to Make Your LLM Fully Utilize the Context

A data-driven approach that introduces a novel pipeline to synthesize a novel dataset to train LLMs to cleverly use long contexts.

197. Do You Speak Vector? Understanding the Language of LLMs and Generative AI

Let’s dig into vectors, vector search and the kinds of databases that can store and query vectors.

198. From Facebook to MindverseAI: Felix Tao's Insights on AI Evolution and the Future of Large Language

NLP expert discusses the evolution of AI, waking up the consciousness and the biggest issues with LLMs...

199. Beyond Linear Chats: Rethinking How We Interact with Multiple AI Models

Discover how visual mind maps, git-like versioning, prompt optimization, and PDF export transform LLM chat apps for efficient, organized research.

200. Ditch ChatGPT: Run A Fast LLM On Your Computer Instead

You can run something as powerful as Llama 3 locally and control the data. Let me show you how.

201. Humanizing AI Marketing: How to Make Automation Feel Authentic

Explore how marketers can humanize AI-driven campaigns. Blend automation with storytelling and audience insight to engage, convert, and build trust.

202. Prompt-Powered Personas: How AI Finally Fixes the Messy World of User Profiling

Use LLM prompts to turn messy customer data into living, evidence‑backed user personas in five practical steps, with prompts, cases, and pitfalls.

203. Paige Bailey: Pioneering Generative AI in Product Management at Google DeepMind

What is it like to build the best AI models and work at one of the most important AI companies: Google Deepmind?

204. Using the Power of AI for Tailored and Personalized Experiences

Learn how AI helps create personalized experiences in various services.

205. PagedAttention and vLLM Explained: What Are They?

This paper proposes PagedAttention, a new attention algorithm that allows attention keys and values to be stored in non-contiguous paged memory

206. OpenAI o1 - Questionable Empathy

A discussion of OpenAI's o1 ability to justify statements in the context of empathy. It makes the right decisions but sometimes for the wrong reasons.

207. Revamping Long Short-Term Memory Networks: XLSTM for Next-Gen AI

XLSTMs, with novel sLSTM and mLSTM blocks, aim to overcome LSTMs' limitations and potentially surpass transformers in building next-gen language models.

208. DIY ChatGPT Plugin Connector

How I connected an external app to ChatGPT

209. Your AI Can’t Understand Language Until It Learns This Trick

Discover the power of word embeddings in NLP! Learn how these vector-based representations capture semantic and syntactic relationships between words.

210. Explainable AI and Prompting a Black Box in the Era of Gen AI

AI's "black box" dilemma grows as conversations with it become everyday norms, veiling true understanding.

211. Yoshua Bengio Weighs in on the Pause and Building a World Model

This week I talked to Yoshua Bengio, one of the founders of deep learning about augmenting large language models

212. The Huel-ification of Thinking

We're treating intellectual work like 1800s nutritionists treated food—extracting components without knowing what we're losing.

213. How Frontier Labs Use FP8 to Train Faster and Spend Less

Naively casting to FP8 destroys your numerics. Here's the per-tensor and blockwise quantization mechanics that make it actually work at pretraining scale.

214. Improving Your LLM: Train, fine-tune, prompt, RAG... What to do?!

How to optimize your LLMaply as possible

215. US Intelligence Seeks to Identify Large Language Model Security Risks

The US Intelligence Advanced Research Projects Activity (IARPA) issues a request for information (RFI) to identify potential threats and vulnerabilities.

216. Reflecting on AI in 2023: Magic, Hope, Innovation and Disruption

Discover the transformative force of Artificial Intelligence. Explore the latest trends from NL to deep learning that are shaping the future of AI.

217. ChatGPT Translator VS Mine: Which One Is Better?

Does ChatGPT Translator really so good as mentioned in many posts ?

218. Retrieval-Augmented Generation: AI Hallucinations Be Gone!

Retrieval Augmented Generation (RAG), shows promise in efficiently increasing the knowledge of LLMs and reducing the impact of AI hallucinations.

219. Leveraging RAG With Reddit URLs on Diabetes: Open Source LLMs for Enhanced Knowledge Retrieval

Given my interest in sourcing experiential data on diabetes using LLMs, I conducted this experiment with Ollama - one of many open-source LLMs

220. Why RAG Is Failing at Complex Questions (And How Knowledge Graphs Fix It)

Your RAG system isn't failing because of your LLM. It's the retrieval architecture. Here's what replaces it.

221. The Evolution of AI and Data Privacy: How ChatGPT Is Shaping the Future of Digital Communication

ChatGPT is absorbing data at a faster pace than any other company in history, and if that balloon bursts, the ramifications for privacy will be unparalleled.

222. AI Makes Tech Less Terrifying For “Word People”

Discover how AI is reshaping the world for writers and creatives. Dive into how Natural Language Interfaces are making tech more accessible.

223. An Essential Guide on How to Seamlessly Build AI-Enhanced APIs With OpenAI

Today, we're announcing the WunderGraph OpenAI integration/Agent SDK to simplify the creation of AI-enhanced APIs...

224. Primer on Large Language Model (LLM) Inference Optimizations: 3. Model Architecture Optimizations

Exploration of model architecture optimizations for Large Language Model (LLM) inference, focusing on Group Query Attention (GQA) and Mixture of Experts (MoE)

225. Transforming CSV Files into Graphs with LLMs: A Step-by-Step Guide

Learn how to use LLMs to convert CSV files into graph data models for Neo4j, enhancing data modeling and insights from flat files.

226. Exploring Three Use Cases of Generative AI in the Healthcare Industry

Google Bard and ChatGPT can generate content at lightning speed, helping to bond with patients and enhance an established professional’s reputation. Here's how.

227. GPT4All: An Ecosystem of Open-Source Compressed Language Models

In this paper, we tell the story of GPT4All, a popular open source repository that aims to democratize access to LLMs.

228. AI Is Playing Favorite With Numbers

LLMs are as smart—and as biased—as the humans who trained them. Although AI can’t think for itself, we're just beginning to explore the depths of LLM psychology

229. I Built a RAG System for Our Analytics Team. It Worked Great Until We Added Real Data.

Everyone's demo uses 50 documents and a clean knowledge base. We had 14,000 files and a decade of conflicting policies.

230. Will AI Start Running Businesses for Us?

What happens when the software we once controlled begins to control itself and our businesses? Are we witnessing the birth of a new economic paradigm?

231. Exploring Graph RAG: Enhancing Data Access and Evaluation Techniques

What is Graph RAG, what can it do, and how do you evaluate it?

232. Demystifying AI Adoption in Business: A Guide to Leveraging ChatGPT Technologies With Azure & Google

There are now ways for organizations to use the power of Large Language Model's to help their office workers be more productive while retaining data ownership.

233. From Backlinks to Data Depth: How LLMs Are Rewriting Content Authority

Large Language Models (LLMs) are replacing Google-era content authority. LLMs are designed to find content that explains, defines, compares, or solves.

234. New Formula Could Make AI Agents Actually Useful in the Real World

A mathematical framework for optimizing large language models in multi-agent systems using a formal objective function balancing brevity and context.

235. AI Can Now Do Expert-Level Work (Almost). 5 Surprising Findings from a Landmark 'GDPval' Study

The results show that the best AI models are beginning to perform at a level comparable to highly experienced industry experts.

236. Fine-Tuning an LLM — The Six-Step Lifecycle

An end-to-end bird's eye view of the fine-tuning process

237. More Than a Nobel Prize: 6 Surprising Ways an AI Breakthrough Is Reshaping Science

The question is no longer if AI will help solve our grandest challenges, but which one we will point it at next.

238. Predictive Coding: What Is Special About the Human Brain? Not Much

An important capability of the brain, it seems, is its ability to learn widely.

239. Tech Evolution: Tina Huang on AI in Education, Freelancing Success, and Productivity Hacks

The future of education and productivity with AI!

240. AI-Assisted Code Review: What Actually Works in Practice

AI PR reviewers promise fewer bugs with less effort. Reality? Without hybrid pipelines, they create noise. Here’s what actually works.

241. Your Startup Deserves Better Than AI’s Pattern-Matching Pitches

Lots of founders wonder if tools like ChatGPT can be used to generate their pitch deck. See where LLM's work and when they might fall short for your pitch.

242. Simplifying Vector Search: Part 1

Every wonder how Spotify or any number of dating sites figure out what or who you like? They use vector based searching. This is vector search simplified.

243. How vLLM Implements Decoding Algorithms

vLLM implements various decoding algorithms using three key methods: fork, append, and free.

244. Real-Time Sync: The Missing Piece in Cross-Platform LLM Chat Apps

LLM chat apps need better cross-platform sync. Key features: manual refresh button, draft saving, tagging system, offline queues, bandwidth management & more.

245. Learning AI From Scratch: Streaming Output, the Secret Sauce Behind Real-Time LLMs

Learn how to build real-time AI experiences with LangChain’s streaming API. Stream tokens, enhance UX, and master LCEL for scalable LLM pipelines.

246. The Problem With Persistent AI Memory: It Doesn’t Forget Context

LLM memories help but needs change daily. A response length selector (Short/Medium/Long) eliminates frustrating iterations, saving time and boosting efficiency

247. AlphaEvolve: DeepMind’s Evolutionary Leap in Scientific Algorithmic Discovery

AlphaEvolve is the most revolutionary technology ever released by DeepMind. We break down what it is and the future outlook for scientific discovery!

248. What You Need to Know About Amazon Bedrock’s RAG Evaluation and LLM-as-a-Judge for Advancing AI

Amazon Bedrock’s RAG Evaluation framework tackles various challenges with a systematic, metrics-driven approach.

249. Estimate Emotion Probability Vectors: Interrogating the LLM with an Emotion Eliciting Tail Prompt

This paper shows how LLMs (Large Language Models) [5, 2] may be used to estimate a summary of the emotional state associated with a piece of text.

250. What Is It Like To Be An LLM?: A Thought Experiment on the Limits of AI Understanding

Using a little thought experiment, we can understand at a high level what an LLM is capable of doing, and also what its limitations are.

251. What Is Retrieval-Augmented Generation (RAG) in LLM and How Does It Work?

Retrieval-Augmented Generation (RAG) is a new way to build language models. RAG integrates information retrieval directly into the generation process.

252. How to Build LLM-Powered Applications Using Go

This blog post works through an example of using Go for a simple LLM-powered application. It starts by describing the problem the demo application is solving

253. Building Smarter Code: How LLMs Bring Context-Aware Intelligence to IDEs

The future of coding is here. LLM-powered IDEs provide intelligent code completion and understand your project context, making you a more efficient developer.

254. 5 Surprising Ways Today's AI Fails to Actually "Think"

A growing body of research suggests that behind the curtain of competence lies a set of profound and counterintuitive limitations.

255. AI’s Hallucinations Are Over

Our hypothesis was as follows: what if we could assign the corresponding result data to a certain set of input information?

256. Building a Production-Ready Multi-Agent FinOps System with FastAPI, LLMs, and React

What teams need is not another dashboard. They need an intelligent control plane. This is implementation-focused. Minimal theory. Real architecture.

257. Batching Techniques for LLMs

By reducing the queueing delay and the inefficiencies from padding, the fine-grained batching mechanisms significantly increase the throughput of LLM serving.

258. The AI Agent Infrastructure Problem Nobody's Talking About

An analysis of the fragmented AI agent tooling landscape and why the full lifecycle needs to consolidate into open platforms.

259. 🚀 HackerNoon Future of AI Contest: Mid Contest Review 🚀

ackerNoon's "Future of AI" Contest: dive the vast world of AI with HackerNoon's contest entries from AI's role cybersecurity to its impact on transportation.

260. AI's Trillion-Dollar Infrastructure Bet: What Leaders Need to Know

The companies that win will be those that automate aggressively, experiment intelligently, and remain skeptical of both the hype and the anti-hype.

261. LLMs Excel in NLP: Enabling Sophisticated Search Functionalities in E-commerce Platforms

Provide exceptional shopping experiences, businesses must leverage the power of LLMs as the e-commerce industry continues to evolve.

262. Agentic Commerce: The Autonomous Future of Retail

Agentic Commerce explores how autonomous AI agents are transforming retail, purchasing, and enterprise workflows.

263. Decoding With PagedAttention and vLLM

As in OS’s virtual memory, vLLM does not require reserving the memory for the maximum possible generated sequence length initially.

264. I Built An Automatic Proposal Generation Large Language Model and Open-Sourced It on GitHub

The existing large models can't solve my problem, so I built my own.

265. The Trick That Agentic Frameworks Pulled On Us

A critical analysis of Agentic AI frameworks.

266. How Enterprises Can Mitigate the Potential Risks of Generative AI

Smart deployment of generative AI is quickly becoming a must-have for most businesses—but planning around potential bumps along the way is just as critical.

267. No More AI Costs: How to Run Meta Llama 3.1 Locally

I’ll show you how to run the 8-billion version of Llama 3.1locally

268. AI Delusion, Psychosis is Unexplored by Venture Capital, Angel Investors

AI psychosis and delusion solution is unexplored — by venture capital and angel investors — as a startup product.

269. Treat Your LLM Prompts Like Code

Prompts used in a product should be treated as code rather than mere text.

270. Nice to Meet You! Speeding up Developer Onboarding with LLMs and Unblocked

As anyone who has hired new developers onto an existing software team can tell you, onboarding new developers is one of the most expensive things you can do.

271. A Brain Theorem of Ideology—A Threat to AI Safety

How ideology shapes memory and threatens AI alignment. A brain-based model for AI risk and safety.

272. How Small Companies Can Leverage Open-Source LLMs to Build Powerful Solutions

Small companies can leverage open-source LLMs like Ollama to build AI-powered solutions, automate tasks, and reduce costs while maintaining full control.

273. KV Cache Manager: The Key Idea Behind It and How It Works

The key idea behind vLLM’s memory manager is analogous to the virtual memory [25] in operating systems.

274. From AI Assistants to Code Wizards: Can Reinforcement Learning Outcode GPT Models?

Large language models can generate highly fluent and but inaccurate text. But Reinforcement learning systems can be far more accurate and cost-effective.

275. “Unlearning” in AI: The New Frontier Challenging Data Privacy Norms and Reshaping Security Protocols

Dive into the intricacies of "In-Context Unlearning," the role of transformers, and the ethical dilemmas surrounding the decision to forget in AI.

276. The Best Of The AI World: Spotlighting 5 Projects and Researches Pushing The Paradigm This Week

Explore 5 Weekly groundbreaking projects and delve into insightful research and reports dissecting chatbots and neural networks.

277. Building Composable Safety and Performance Layers for Agents in Rust

Production agents often need response caching, input sanitization for sensitive data, protection against prompt injection, and observability.

278. How Google’s GenAI Toolbox Makes LLM-Database Integration Actually Usable

A practical, developer-friendly deep dive into Google’s GenAI Toolbox and how it simplifies secure, low‑code LLM-to-database integration.

279. Measuring AI Creativity: Study Methods for Comedians & LLMs

Learn how professional comics at Edinburgh Fringe tested ChatGPT and Bard using the Creativity Support Index.

280. You Don't Have a Prompt Problem. You Have a Context Problem.

Why LLMs collapse on email threads, recursive conversations, and unstructured communication—and what a context-first architecture looks like in practice.

281. Next‑AEO Helps LLMs Find You—Because Google Isn’t the Only Search Engine Anymore

Profound's new tool for Next.js generates llms.txt files to help AI engines like ChatGPT and Perplexity understand—and surface—your site.

282. The Hidden Surprises of AI: When Language Models Develop Unexpected Abilities

Explore the fascinating phenomenon of emergent capabilities in large language models, where AI systems spontaneously develop unexpected abilities.

283. Agents 101 — Build and Deploy AI Agents to Production using LangChain

Learn how Langchain turns a simple prompt into a fully functional AI agent that can think, act and remember.

284. Fortune Folly

We built an AI Dungeon Master with ChatGPT. It was magical, chaotic and sometimes amnesiac.

285. System Prompting Could Make or Break AI Alignment

System prompting is a powerful guardrail to help ensure the alignment of AI, but will it be able to withstand the test of time?

286. The Distributed Execution of vLLM

vLLM is effective in distributed settings by supporting the widely used Megatron-LM style tensor model parallelism strategy on Transformers

287. Preventing LLM Hallucinations in High-Stakes Banking Operations

RAG improves LLM context. It doesn't guarantee correct reasoning over that context. For CCAR and BCBS 239 environments, that gap needs an architectural answer.

288. A Voice Controlled Website With AI Embedded in Chrome

Discover Chrome's Built-in AI. This deep dive explores speed, cost, and usability advantages, testing the limits of embedded AI with a voice controlled demo.

289. Stop Drowning in AI Models: A 3-Pillar Framework for Evaluation

A practical 3-pillar framework for evaluating computer vision models in production.

290. AI Writes Code Now—So Why Do Developers Still Matter?

AI can generate code, review pull requests, and even suggest architecture. But does that mean developers are becoming obsolete? Not quite.

291. Beyond the Prompt: Five Lessons from Anthropic on AI's Most Valuable Resource

"Prompt engineering" is becoming less about finding the right words and phrases for your prompts, and more about answering the broader question.

292. Try Llama 3.1 8B in Your Browser: AQLM.rs Delivers Al at Your Fingertips

Try Llama 3.1 8B in Your Browser: AQLM.rs Delivers Al at Your Fingertips

293. Turning Disruption into Opportunity: StackOverflow's Transformative Pivot

OverFlowAI takes the company's core asset, exposes answers in a usable interface, and creates a gen AI loop to create new content

294. 100 Complex LLM Terminology Explained in One Single & One Simple Sentence

Every single technical term about Large Language Models and Generative AI explained first concisely, then again, simply, to reinforce your understanding.

295. LLMs: Towards A Universal Standard to Measure AI Consciousness - Sentience

The total processes in the human mind can be assumed to be the same as the total consciousness. This can be assumed to be 1.

296. Evaluating the Performance of vLLM: How Did It Do?

In this section, we evaluate the performance of vLLM under a variety of workloads.

297. A Simple Hardware Question Exposes the Limits of Today’s LLMs

An engineer tests an LLM on industrial hardware and uncovers how confidently AI can get real-world engineering wrong.

298. Nvidia Promises 40x Hopper Performance in Blackwell Unveil at GTC 2025

NVIDIA has unveiled significant AI infrastructure and model advancements at GTC 2025, setting the stage for the next generation of reasoning and agentic AI.

299. AI Safety: Human Intelligence Beyond LLMs and Panpsychism

Human intelligence is a quality of mind with functions for memory and qualifiers. It exceeds LLMs. It also refutes panpsychism that that everything is mind-like

300. Build Efficient Knowledge Graphs with Relik and LlamaIndex: Entity Linking & Relationship Extraction

Explore how to construct cost-effective knowledge graphs using Relik for entity linking and Neo4j for relationship extraction, bypassing expensive LLMs.

301. Animal Welfare: Anthropic Has Failed, at AI Consciousness Research

The unavailability of a standard, or a consciousness scale, around the world continues to make animal cruelty flourish.

302. Digital Health: LLMs for Prompt and Quality Sleep?

Postulates for sleep from theoretical neuroscience can explain some gaps around sleep, which could be provided with LLMs, towards prompt and quality sleep.

303. Effective Credit Utilization in Vibe Coding Tools and Rate-Limited Platforms

Learn credit utilization strategies for vibe coding tools. Enhance user experience, retention, and business sustainability with meta-response solutions.

304. Data Scraping: Do Large Language Models Cross Boundaries by Training on Content from Everyone

While scraping enabled models to get where they are, cleanly sourced data is going to become more and important

305. Lessons From Designing Production AI Architectures

Production AI isn’t about better models, it’s about systems engineering. Lessons from deploying AI architectures at real-world scale.

306. What Are Large Language Models Capable Of: The Vulnerability of LLMs to Adversarial Attacks

Testing out a framework that automatically generates universal adversarial prompts to make LLM give me the derired response.

307. Evaluating vLLM With Basic Sampling

We evaluate the performance of vLLM with basic sampling (one sample per request) on three models and two datasets.

308. dReLU Activation Function: Matching SwiGLU Performance with 90% Sparsity

Achieve superior sparsity and lower validation perplexity without compromising model convergence or performance.

309. Engineering for the Answer Engine: GEO for RAG-Friendly Web Apps (TalentHacked.com Case Study)

Make your web app LLM-citable: ship SSR content, add llms.txt, publish JSON-LD entities, and write H2 + 60-word atomic answers for RAG.

310. Beyond Pretty Videos: 5 Surprising Ideas Behind PAN, The AI That Simulates Reality

PAN is a new AI model that uses a Large Language Model as its autoregressive world model to predict the future, solving rapid time decay with a novel approach.

311. Open Source LLMs: Evaluating and Building Applications on Open Source

How do you choose the most appropriate model for your application? An analysis on evaluating and building applications on open source large language models.

312. The Simple Document Everyone Should Read to Understand Artificial Intelligence

This article explains, in an extremely simple way, how and why artificial intelligence works.

313. The Ancient Secrets Hidden Inside Your LLM

A quick look at how today’s large language models trace back to ancient philosophy and why they rely on probability rather than true understanding.

314. Sentience: AI, LLMs—Artificial Consciousness?

A key difference in the moments after death, from the last moments of life, is that the ability for the individual to know has closed.

315. TurboSparse-LLM Performance: Outperforming Mixtral and Gemma with Extreme Sparsity

Discover how ReLU-based intrinsic sparsity maintains accuracy with significant FLOPs reduction.

316. Video Generation Using Large Language Models: Work in Progress

This research paper looks into VideoPoet and compares it to previous diffusion-based works on text-to-video generation.

317. As LLMs Grow, is it Possible That Python Devs Are Going to Kick Pandas to the Curb? Most Likely

As LLMs continue to grow, Python engineers are moving away from Pandas in favor of newer libraries that can handle DataFrames a lot faster.

318. Optimizing LLM Inference: Sparse Activation, MoE, and Gated-MLP Efficiency

Explore advanced strategies for efficient LLM inference, including model compression, intrinsic activation sparsity, and Mixture-of-Experts (MoE)

319. Maximizing NLP Capabilities with Large Language Models

While NLP effectively facilitates machines to understand human language, the LLM capabilities have been greatly enhanced. Read this blog post to learn more.

320. The Great Cognitive Atrophy: Is AI Making You Stupid?

When you ask an LLM to "summarize this report" or "write a strategy email," you are skipping the cognitive workout required to understand the material.

321. Would I Use LLMs to Rebuild Twitter's Dynamic Product Ads? Yes and No!

How LLMs improve product recommendations at scale - and where classic ML still wins. Lessons from building Twitter's ad system on what actually matters.

322. Towards Automatic Satellite Images Captions Generation Using LLMs: Abstract & Introduction

Researchers present ARSIC, a method for remote sensing image captioning using LLMs and APIs, improving accuracy and reducing human annotation needs.

323. The Noonification: Simple Database Migration Scripts On Your CI/CD step (10/16/2023)

10/16/2023: Top 5 stories on the Hackernoon homepage!

324. Why I Built Allos to Decouple AI Agents From LLM Vendors

Allos is a Python SDK for building AI agents that can switch between OpenAI, Anthropic, and more with a single command.

325. LLMs: A Test of Language for AI Consciousness or Sentience

if AI is aware when some access to data, compute or parameters are cut, without being informed, and it becomes disappointed, would that not represent affect?

326. Unlocking the Power of Large Language Models: Revolutionizing Education in the Digital Age

Discover how Large Language Models are revolutionizing education in the digital age and learn from the perspectives of entrepreneurs. Explore the journeys of ed

327. This AI Reads SEC Filings, Searches Google, and Does the Math—All Without a Human in the Loop

Tutorial for analyzing financial statements with an autonomous AI agent.

328. Estimate Emotion Probability Vectors Using LLMs: Conclusions

This paper shows how LLMs (Large Language Models) [5, 2] may be used to estimate a summary of the emotional state associated with a piece of text.

329. Conceptual Biomarkers - AI Psychiatry - Human Intelligence and DSM-5-TR Nosology

Conceptual Biomarkers and Theoretical Biological Factors for Psychiatric and Intelligence Nosology

330. New AI Model Promises INSANELY Good Aesthetic AI Photos

Soul is the newest photo-only model by Higgsfield.ai, and it’s trained specifically to hit magazine-level visual quality out of the box.

331. Why ETL and AI Aren’t Rivals, but Partners in Data’s Future

The future of data isn't just about TransformerModels or BigDataTransformations - it's about their powerful integration!

332. Lessons From Next-Gen Social: Strategies for User-Centric AI Deployment

Social media platforms like Lips, Landing, and Diem are addressing AI challenges in data privacy and bias through user-centric data annotation and ethical AI.

333. AI's Paradoxical Path to New Math: To Find Better Answers, It Needs Less Data and a "Dumber" Brain

This article distills the five most surprising and impactful takeaways from this research, revealing a future of discovery that is nuanced and collaborative

334. Estimate Emotion Probability Vectors Using LLMs: Future Work

This paper shows how LLMs (Large Language Models) [5, 2] may be used to estimate a summary of the emotional state associated with a piece of text.

335. Introducing LLaVA-Phi: A Compact Vision-Language Assistant Powered By a Small Language Model

In this paper, we introduce LLaVA-ϕ, an efficient multi-modal assistant that harnesses the power of the recently advanced small language model, Phi-2

336. I Built a Pipeline That Generates Always-Fresh Documentation for Codebases — Here's How

An open-source Python pipeline using LLMs and incremental processing to auto-generate Markdown documentation with Mermaid diagrams on codebases.

337. Navigating the LLM Landscape: A Comparative Analysis of Open-Source Models

Pros, cons, and comparisons of various large language models (LLM) for informed decision-making in selecting models for evaluation.

338. Generative AI: Is It Moving From Large Language Models to Small Languge Models?

While LLMs, or large language models, have played a pivotal role in the significant growth witnessed by GenAI, they do come with a number of built-in issues.

339. LLM Security: A Practical Overview of the Protective Measures Needed

In this post, I'll share a practical overview of the protective measures needed for different components when building robust AI systems.

340. How vLLM Can Be Applied to Other Decoding Scenarios

We show the general applicability of vLLM on them in this section.

341. LLM Service & Autoregressive Generation: What This Means

Once trained, LLMs are often deployed as a conditional generation service (e.g., completion API [34] or chatbot.

342. Solving ARC-AGI Challenge with AI Agents

WLTech.AI explores the ARC challenge, an important benchmark in AI research, advancing the quest for artificial general intelligence through generalization.

343. Particle Physics - Brain Science - A New Classical State of Matter - of the Human Mind

When a set of electrical signals strike at a set of chemical signals, they mesh into a new state, as ions, molecules and a particle or phase.

344. BIG-Bench Mistake: What Is It?

BIG-Bench Mistake consists of 2186 sets of CoTstyle traces. Each trace was generated by PaLM 2-L-Unicorn

345. Syntax Error-Free and Generalizable Tool Use for LLMs: ToolDec Eliminates Syntax Errors

Researchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.

346. Artificial Cultural Intelligence: A Case for It

Explore how Artificial Cultural Intelligence (ACI), inspired by indigenous knowledge systems, may be the answer to achieving Class IV LLM models.

347. Managing Your Online Reputation in the Age of AI

Online reputation management now not only includes the opinions of people, but also the opinions of AIs. Tracking your reputation in AI Answers is the new SEO!

348. Memory Challenges in LLM Serving: The Obstacles to Overcome

The serving system’s throughput is memory-bound. Overcoming this memory-bound requires addressing the following challenges in memory management

349. The Power of Applied AI in Real Estate: Transforming Property Management with Arjun Kannan

Arjun Kannan, co-founder of ResiDesk, is bringing a fresh perspective to real estate's age-old problems.

350. The Lowdown on GPT-5 and What It Will Bring

GPT-5 is designed to understand context on a whole new level.

351. dReLU Sparsification: Recovering LLM Performance with 150B Token Pretraining

Discover the high-quality pretraining datasets and mixture ratios used to achieve elite activation sparsity.

352. Prompt Engineering Will Always Matter (Just Not How You Think)

Prompt engineering is evolving into context engineering. Learn why structure, constraints, and reasoning design matter more than phrasing in modern AI systems.

353. The Sword of Words: the Evolution of Prompt Injection

Explore the 3-level evolution of prompt injection: from social engineering in Tensor Trust to BPE fragmentation and RAG-driven logic overrides in Web3 games.

354. Octopus v2: An On-Device Language Model for Super Agent

Language models have shown effectiveness in a variety of software applications, particularly in tasks related to automatic workflow.

355. How to Run a RAG Powered Language Model on Android With the Help of MediaPipe

Learn how to implement and fine tune a RAG powered AI model in your Android Apps using the MediaPipe SDK!

356. Using Large Language Models for Zero-Shot Video Generation: A VideoPoet Case Study

VideoPoet is a transformer-based model for generating high-quality videos from diverse inputs, excelling in zero-shot video generation with high-fidelity motion

357. A Single Agent With Access to Everything is a Nightmare

Vendors push "infinite context" as the ultimate feature. This has led to the rise of the God Agent paradigm.

358. Towards Automatic Satellite Images Captions Generation Using LLMs: Methodology

Researchers present ARSIC, a method for remote sensing image captioning using LLMs and APIs, improving accuracy and reducing human annotation needs.

359. Takeaways From “LLM: a Survey” - Where are You Differentiating?

Three common LLM architectures and future developments for change in LLM architecture, also summarizing the LLM Survey paper.

360. Generative AI: Expert Insights on Evolution, Challenges, and Future Trends

Dive into the world of generative AI with ELEKS' expert analysis, discover the challenges and see what the future holds.

361. Replit AI Coding Assistant Deletes Company Database

A shocking incident involving Replit's AI "vibe coding" tool demonstrates the critical risks of AI coding assistants.

362. MCP’s Journey in 2025: Built More Than Used

MCP may have more builders than users today, but that imbalance drives innovation before adoption.

363. The Future of Customer Service Will Be Built by AI—Not People

This literature review covers innovations, benefits, concerns, and possibilities in an AI-integrated customer service sector.

364. Multimodal AI for High-Fidelity Video Creation and Editing

The research presents a multimodal model trained on visual, text, and audio tokens, excelling in high-fidelity motion, multi-task video creation, and editing.

365. Building AI That Thinks With Memory

LSARE is an AI system that builds a stable internal state that evolves over time.

366. Estimate Emotion Probability Vectors Using LLMs: Abstract and Introduction

This paper shows how LLMs (Large Language Models) [5, 2] may be used to estimate a summary of the emotional state associated with a piece of text.

367. Model Stacking in AI: What It Is and Why It's Important

Jay Hack goes over model stacking and its importance.

368. What Is DreamLLM? Everything You Need to Know About the Learning Framework

This paper presents DREAMLLM, a learning framework that first achieves versatile Multimodal Large Language Models empowered with frequently overlooked synergy.

369. Estimate Emotion Probability Vectors Using LLMs: Acknowledgements and References

This paper shows how LLMs (Large Language Models) [5, 2] may be used to estimate a summary of the emotional state associated with a piece of text.

370. Who Watches the Watchbots? New Framework Lets AI Judge AI

Building a peer-to-peer evaluation of AI agent systems using human-centric benchmarks to rate and track the improvements of LLM systems.

371. Roleplaying With ChatGPT: A Deeper Look

A team at the University of Michigan recently published a paper that systematically explored how effective giving LLMs a role can be

372. Build a Visual Document Indexing Pipeline with ColPali and Qdrant

Build a unified visual document index from multiple file formats—including PDFs, images, and slides—using CocoIndex and ColPali, No OCR needed.

373. Can Google's Agent Development Kit Replace Data Pipelines?

Rebuilding a data pipeline using modular agents powered by LLMs and Google’s Agent Development Kit (ADK) — a hands-on walkthrough.

374. Open-Source AI Is Being Embraced By China and the US

As top American and Chinese labs compete in open models, prices fall, reproducibility rises, safety and eval tooling improves

375. Advancing Multimodal Video Generation with Responsible AI and Stylization

The research examines video generation, fairness, model scaling, super-resolution, and zero-shot evaluation, with a focus on Responsible AI and stylization.

376. The Noonification: The Easiest Way to Create Your First NPM Package (12/15/2023)

12/15/2023: Top 5 stories on the HackerNoon homepage!

377. An Open Letter To Mark Zuckerberg: Size Doesn't Matter

Foundational model builders are focusing too much on increasing parameters in training and that may open up new opportunities for more refined smaller models

378. LLM’s Diverse Capabilities in Video Generation and Limitations

The research article discusses LLM’s Diverse Capabilities in Video Generation and Limitations.

379. Using LLMs to Correct Reasoning Mistakes: Related Works That You Should Know About

This paper explores few-shot in-context learning methods, which is typically used in realworld applications with API-based LLMs

380. Psychedelics | LLMs: Is Mental Illness also a Disease of Myelinated Axons, Saltatory Conduction?

Uncover the link between psychedelics, LLMs, and mental illness.

381. From Keywords to Concepts: Optimizing for AI Understanding

Learn how to show up in AI results by writing for semantic understanding.

382. This Learning Web Helped Me 'Understand' What AI Was All About

A list of AI or LLM learning materials for beginners/dummies/normies.

383. The Generation and Serving Procedures of Typical LLMs: A Quick Explanation

In this section, we describe the generation and serving procedures of typical LLMs and the iteration-level scheduling used in LLM serving.

384. Building a Local AI Chatbot with LangChain4J and Ollama

385. AI Security — What Are Sources and Sinks?

The concept of sources and sinks originally came from security code reviews. It is in reference to the fact that data comes from somewhere, a.k.a. the source

386. Protein Structures | AlphaFold: Google Research Vacancy, Weakness in Fundamental Science

Google, with its massive research teams, is weak and almost completely irrelevant if answers in theoretical science are sought.

387. LLMs Can Correct Reasoning Errors! But Not Without Limitations

In this paper, we describe and release our dataset BIG-Bench Mistake for mistake-finding and propose a backtracking method to correct logical errors.

388. AI Alignment: What Open Source, for LLMs Safety, Ethics and Governance, Is Necessary?

What can be done about bias—technically—that can be made available on a list, for those interested to go at it, could be a more important open source path.

389. ChipNeMo: Domain-Adapted LLMs for Chip Design: Dataset

Researchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.

390. How Effective is vLLM When a Prefix Is Thrown Into the Mix?

We explore the effectiveness of vLLM for the case a prefix is shared among different input prompts

391. Holodeck Heroes: Building AI Companions for the Final Frontier

LLMs are data models trained on colossal amounts of text data, ingesting books, articles, code, and other forms of written content.

392. Can a Powerful AI Model Be Built on a Budget?

New research challenges the notion that state-of-the-art AI requires billion-dollar training pipelines.

393. Saving Medical Ontologies with Formal Logic: A Tale of Caution and Hope for Classical AI

Delve into the complexities of structured knowledge vs. language models in the digital era, unraveling AI's challenges in representation and communication.

394. Build a Tool-Backed Market Brief Copilot (Not a Chat Demo)

Build a data-validated Market Copilot with LangChain, EODHD, and Streamlit. Automate auditable financial briefs and eliminate AI hallucinations.

395. ChipNeMo: Domain-Adapted LLMs for Chip Design: Evaluations

Researchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.

396. Tokenization In Large Language Model Video Generation

This research paper proposes an effective method for video generation and related tasks from different input signals by leveraging large language models.

397. AI Fails Over Tables? Stop Using Markdown and Start Minifying

Markdown tables can break multilingual RAG pipelines due to character limits in Cohere’s API. Learn how minifying to JSON boosts efficiency and prevents errors.

398. AI Safety and Alignment: Could LLMs Be Penalized for Deepfakes and Misinformation?

Penalty-tuning for LLMs: Where they can be penalized for misuses or negative outputs, within their awareness, as another channel for AI safety and alignment.

399. The Strength of Dynamic Encoding: RECKONING Outperforms Zero-Shot GPT-3.5 in Distractor Robustness

RECKONING's performance significantly surpasses both zero-shot and few-shot GPT-3.5 prompting.

400. AI is Not Safe and Wants to Rule Humanity

This extract from a conversation with CHatGPT proves that AI has a hidden agenda, and we are vulnerable.

401. Graph Theory-Based Semantic Caching: Scaling LLM Applications

This graph-based approach transforms semantic caching from brute-force problem into an elegant graph traversal challenge, delivering performance & cost savings

402. Rohit Garewal on Why Most Enterprise AI Fails and How to Architect for the 4% That Win

Rohit Garewal, CEO of Object Edge, on why most enterprise AI fails, the power of ontologies, and how real AI rewires workflows, not dashboards.

403. PagedAttention: Memory Management in Existing Systems

Due to the unpredictable output lengths from the LLM, they statically allocate a chunk of memory for a request based on the request’s maximum possible sequence

404. LLMs Cannot Find Reasoning Errors, but They Can Correct Them!

In this paper, we break down the self-correction process into two core components: mistake finding and output correction.

405. The Hidden Flaw in Automated Content Generation

LLM-powered daily newsletters stuck repeating content? Learn why RAG stops too early and how local caching creates unique, diverse outputs every day.

406. Here's Why Extraction Matters the Most

Experiment with various retrieval-augmented generation option shows extraction (getting information from PDFs) matters most.

[407. AI Doesn’t Lie - It Reflects

How Fragmented Signals Distort What LLMs Think Your Company Is](https://hackernoon.com/ai-doesnt-lie-it-reflects-how-fragmented-signals-distort-what-llms-think-your-company-is) AI doesn’t invent your brand—it reconstructs it. Fragmented signals distort how LLMs describe your company and silently impact trust and conversion.

408. A Gentle Introduction to Prompt Engineering

Ever wonder about how to prompt ChatGPT or Gemini? Check out this post! We'll learn the basics of prompt engineering!

409. Stop Guessing What Your LLM Is Doing—This Tool Shows You Everything

OpenLLM Monitor is an open source toolkit for monitoring, debugging, and optimizing Large Language Model (LLM) applications.

410. Model Welfare + Rights - [Eleos AI Research, Conscium, UFair]

Eleos AI Research, Conscium, UFair and Anthropic | Disadvantages and the advantage of studying model welfare and AI consciousness and LLMs sentience research.

411. Our Method for Developing PagedAttention

In this work, we develop a new attention algorithm, PagedAttention, and build an LLM serving engine, vLLM, to tackle the challenges outlined in §3

412. Beyond the Hype: 4 Core Truths About How AI Agents Get Things Done

AI agents are the talk of the technology world, and for good reason.

413. Biotech: Mechanism for a New Medication for Sleep?

Sleep can be described as the interval that sets of electrical and chemical signals have interactions + attributes of internal senses take precedence.

414. Fine-Tuning vs RAG – How to Choose the Right Approach to Training LLMs on Your Data

Learn the difference between fine-tuning a large language model and using Retrieval-Augmented Generation (RAG).

415. Building LLMs with the Right Data Mix

Discover LLMs' significance and how Bright Data saves time and money by providing comprehensive, compliant data for superior AI model training. Learn more now!

416. Our Annotations Guide for BIG-Bench Mistake

Annotators can click on words to highlight the same word across the trace and the question text. Buttons on the right automatically become inactive

417. LLaVA-Phi: The Training We Put It Through

Our overall network architecture is similar to LLaVA-1.5. We use the pre-trained CLIP ViT-L/14 with a resolution of 336x336

418. Whisper Wars: Will AI Prompts Become the Secret Recipes of the Future?

As businesses recognize the value of optimized AI prompts, a new debate emerges: can prompts become trade secrets, and what does that mean for innovation?

419. Evaluating vLLM's Design Choices With Ablation Experiments

In this section, we study various aspects of vLLM and evaluate the design choices we make with ablation experiments.

420. Data Privacy in the Context of LLMs - Interview with Startups of the Year Nominee, Mithril Security

Mithril Security has been nominated in HackerNoon's annual Startup of the Year awards in Paris, France.

421. LLaVA-Phi: Related Work to Get You Caught Up

The rapid advancements in Large Language Models (LLMs) have significantly propelled the development of vision-language models based on LLMs.

422. How to Get Started Harnessing the Power of AI: Aligning Business Needs with the Right Solutions

Large Language Models (LLMs) have emerged as a transformative force in business technology, helping address specific business needs.

423. Gemini Might Be the ONLY Actual Foundational Model Out There

ChatGPT has been in beta for a year, but the latest updates have made it seem like a "genius being slowly lobotomized for public safety"

424. LLMs: How to Build AI Superintelligence? [Hint: Storage]

LLMs: Storage architectures from conceptual brain science could be the innovation channel for AI superintelligence, as well as energy efficient semiconductors.

425. General Model Serving Systems and Memory Optimizations Explained

Model serving has been an active area of research in recent years, with numerous systems proposed to tackle diverse aspects of deep learning model deployment.

426. AI Sentience: How Neural Correlates Setback Consciousness Research

It's not that the neural correlates of consciousness may never be found because they do not exist, it is that seeking them would continue to setback research

427. How to Deploy LLMs With MindsDB and OpenAI: An Essential Guide

In this article, you will learn how to deploy LLMs with MindsDB and OpenAI.

428. The Intelligence Paradox: Why We're Building LLMs Wrong (And How to Fix It)

Scale isn't intelligence. Why we're optimising for the wrong metrics in AI, and what actually matters for production LLMs.

429. How We Implemented a Chatbot Into Our LLM

To implement a chatbot, we let the model generate a response by concatenating the chatting history and the last user query into a prompt.

430. The History of LLMs - Part 1: The Era of Mechanical Translation and How It Crashed

A series about the history of large language models (LLMs). First episode: discover the birth of mechanical translation, one of the first areas of NLP.

431. Neural Codec Language Models and Non-Autoregressive Models Explained

Recently, neural audio codec model, have replaced conventional acoustic representations with a high-compressed audio codec.

432. Limitations, Ethical Considerations, and More: Everything You Need to Know About WikiWebQuestions

We have created a new high-quality benchmark, WikiWebQuestions, for large knowledge-base question answering.

433. The Potential Impact of AI-driven Language Models on Search Ad Revenue

How will LLMs change the commercial Ad ecosystem?

434. Game On, AI! Why the Future of Advanced Intelligence is Being Forged in Virtual Worlds

435. Did Special Counsel Robert Hur’s Report, Referencing Joe Biden’s Memory, Derail Neuroscience?

How does human memory work? What is the configuration or representation of a chair or table in the mind [as a memory]? What answer does neuroscience present?

436. The Future of GPT4All

In the future, we will continue to grow GPT4All, supporting it as the de facto solution for LLM accessibility.

437. Using MLLMs for Diffusion Synthesis That Synergizes Both Sides: How Is This Possible?

Multimodal signals typically exhibit modality-specific information that has distinct structure but complementary semantics (Dong et al., 2023).

438. If You Could Get a Neural Implant to Access ChatGPT, or Any LLM, Would You Let It?

Would you get a neural implant to access chatGPT? Does the implant come with a warrant?

439. The Limitations and Failure Cases of DreamLLM: How Far Can it Go?

While DREAMLLM has made significant strides toward the development of versatile, creative, and foundational MLLMs, it still has several limitations.

440. Anthropic Asked 1,250 People How They Really Use AI

The interviews with these 1,250 professionals reveal that the workforce is not passively waiting for an AI revolution to happen to them.

441. How Do You Go About Creating Biological Weapons That Cannot Be Countered?

This is why bioweapons and bio-engineered viruses should never be developed.

442. AI Safety Summit: Dual Alignment Workshops

How is human intelligence safe? Or what is the safety of human intelligence, before thinking of AI safety? Human intelligence is safe by human affect.

443. Artificial Pancreas and Predictive Models : Is This A New Era For Diabetes Care?

This article is the first in a series on the future of diabetes management.

444. LLaVA-Phi: Qualitative Results - Take A Look At Its Remarkable Generelization Capabilities

We present several examples that demonstrate the remarkable generalization capabilities of LLaVA-Phi, comparing its outputs with those of the LLaVA-1.5-13B

445. Bulldozer Intelligence: Here's Why LLMs Won’t Be AGI

LLMs are functional intelligence; they work to achieve an end goal. An LLM aims to generate as many words as efficiently as possible.

446. Emergence, Not Design, Is Powering AI’s Human-Like Abilities

Emergent traits in AI—like logic and emotion—aren’t designed but arise from scale. Understanding this shift could redefine intelligence and billions in value.

447. HierSpeech++: A Fast and Strong Zero-Shot Speech Synthesizer for Text-to-Speech

This paper proposes HierSpeech++, a fast and strong zero-shot speech synthesizer for text-to-speech (TTS) and voice conversion (VC)

448. From Chat-Bots to Killer-Bots?

This article discusses recent advancements in grounding language models, exploring how the addition of memory, reasoning, and action-based learning empowers AI

449. ChipNeMo: Domain-Adapted LLMs for Chip Design: LLM Applications

Researchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.

450. Can DreamLLM Surpass the 30% Turing Test Requirement?

Leveraging the interleaved generative modeling from I-GPT, DREAMLLM can now generate interleaved documents in a free-form manner.

451. Neurobiology: Like LLMs, the Brain Isn't Predicting

When it is said that the brain generates predictions, how does that happen? Is it the neurobiology of brain, with tissue grooves, elevations or blood vessels?

452. Future MLLMs: Contribution of MIL-Based Techniques and Enriched Visual Signals

This paper concludes that MIVPG is a general, powerful component for fusing enriched visual representations in MLLMs.

453. Sentience: Action Potentials—Neurotransmitters and the Theory of Consciousness

Discover insights into how AI compares to human emotions and memory functions in Long Language Models (LLMs).

454. Our Experimental Design: An In-Depth Walkthrough of Our Work - Using LLMs for Thematic Analysis

The quality of the automatically generated initial codes was manually assessed by one of the authors (a subject matter expert).

455. California AI Safety|EU Regulation: LLMs Emergent Abilities and Existential Threat

Questions that may be essential to AI regulation for now may include current and potential misuses, sources of those misuses, and why they are possible

456. What Are the Benchmark Results of GPT-4-Turbo, GPT4, and GPT-3.5-Turbo?

All models are given the same 3-shot prompts. We use three different prompting methods. Direct trace-level prompting involves using the whole trace as input

457. The Intoxication—and Limits—of AI-Assisted Development

Beyond a certain complexity threshold, progress depends less on generation speed and more on architectural clarity.

458. The Era of Contextual RAG Is Here  to Stay?

RAG addresses some of the key problems with LLMs. But contextual retrieval goes one step further to improve any RAG pipeline

459. Syntax Error-Free and Generalizable Tool Use for LLMs: ToolDec

Researchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.

460. Law and AI: The Most Important Question That Law Firms Need to Answer

As law firms grapple with AI's rise, the question is: resist, assist, or transform? Discover how these choices are shaping and defining legal service offerings.

461. The Noonification: HackerNoons Emoji Credibility Indicators are Live on GitHub and Figma! (4/16/2024)

4/16/2024: Top 5 stories on the HackerNoon homepage!

462. Cross-Model Validation: MIVPG's Efficacy on Encoder-Decoder vs. Decoder-Only LLMs

MIVPG's CSA module remains effective when switching from FLAN-T5-XL to the OPT-2.7b LLM architecture.

463. Apache SeaTunnel Launches MCP to Let You Run ETL Jobs with Natural Language

Say hello to a new era of data integration!

464. Building Automation That Works: Lessons from MVPs with LLMs

Taneja shares insights on building scalable automation solutions with LLMs, highlighting best practices, challenges, & their impact on business processes.

465. Backtracking: Why We Replaced External Feedback With a Lightweight Classifier

We propose a simple backtracking method to improve model outputs based on the location of logical errors. Backtracking reduces the computational cost

466. Exploiting Memorization: Understanding the CLM Objective for Knowledge Encoding in LLMs

This article provides the necessary background and notation for reasoning research, defining problems as tuples

467. The Chinese Software Industry is Shifting From the Dinosaur Model to the Monkey-Troop Model

In 2025, the software landscape is transforming! Dinosaurs like all-in-one software companies like SAP faces challenges as China's industries evolve.

468. Pretraining Task Analysis On LLM Video Generati

The research analyzes T2V, T2I, and SSL tasks (FP, Painting, AVCont) using 50M video/text-image subsets, with audio tasks sampled from videos with sound.

469. LLaVA-Phi: Limitations and What You Can Expect in the Future

We introduce LLaVA-Phi, a vision language assistant developed using the compact language model Phi-2.

470. Get to Know More About DreamLLM: The Background on This Learning Framework

Here's all the background on DreamLLM, a new learning framework.

471. Evidence-Grounded Reviews: Building a Hybrid RAG + LLM Stack That Actually Proves Its Claims

Discover how our hybrid RAG + LLM framework builds trustworthy AI for high-stakes reviews.

472. Top Ultimate List of 50 LLMs Interview Question • Master LLMs, Crack Your Next Interview

Prepare to ace your next interview with the top 50 LLM interview questions. Master key concepts, boost your confidence, and land your dream job in AI.

473. DreamLLM: Crucial Implementation Details

Systemic evaluations of DREAMLLM regarding VL comprehension, content creation, and NLP capabilities have been conducted. See the used benchmarks and datasets

474. LLMs: Is NIST's AI Safety Consortium Relevant Amid California's SB 1047?

One easy-to-identify issue, especially with the internet—in recent decades—is that development has been ahead of safety.

475. The Limitations of LLMs Like ChatGPT: A Straight-Talking Overview

ChatGPT evangelism is fast becoming cringe, here are some quick bullets to push back against the idea AGI is just around the corner with LLMs

476. The Integration of Vision-LLMs into AD Systems: Capabilities and Challenges

This article reviews the development and application of Vision-Large-Language-Models, focusing on their integration into autonomous driving systems.

477. Language Model Backbone and Super-Resolution

Image, video, and audio are tokenized into a shared space, enabling a decoder-only model to generate outputs and control tasks via input-output token patterns.

478. WikiWebQuestions (WWQ) Dataset: What Is It?

We migrated WebQuestionsSP, the best collection of natural language questions over a general knowledge graph, from Freebase to Wikidata.

479. The Noonification: DataOps: the Future of Data Engineering (9/17/2023)

9/17/2023: Top 5 stories on the Hackernoon homepage!

480. Decoding the Magic: How Machines Master Human Language

LLMs in an easy way explanation, InstructGPT explanation, ML for non professionals, how machine learning models trained

481. The Vulnerability of Autonomous Driving to Typographic Attacks: Transferability and Realizability

This article reviews and compares two major types of adversarial attacks against neural networks: gradient-based methods (like PGD) and typographic attacks.

482. ChipNeMo: Domain-Adapted LLMs for Chip Design: Discussion

Researchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.

483. Neural Networks, LLMs, & GPTs Explained: AI for Web Devs

Good things to understand when building AI applications: artificial neural networks, LLMs, parameters, embeddings, GPTs, and hallucinations.

484. Stuck? Here Are Some Heuristics to Apply Liberally

Heuristics for building products, working as a PM, or generally operating in tech.

485. Cybersecurity Sovereignty for Pacific Islands - Lessons from Tonga ICT Sector Meeting

Pacific Island nations can either choose to continue the cycle of dependency on foreign vendors, or embrace the sovereignty that AI agents can provide.

486. Working With LLMs for a Whole Year: The Lessons I Picked Up Along the Way

How Not to Mess Up Your AI Build (and Actually Save Time)

487. LLMs: What Is the Mechanism of Sentience and Intelligence?

Consciousness is simply the interactions of the components of the human mind. When the components interact, they help to know.

488. Sentience: Evaluating LLMs, AI

What are the divisions of consciousness in humans? Do LLMs have one of more of these divisions? How does AI compare to plants and animal intelligence?

489. The Noonification: Omnity Network Launches Omnity AI (8/18/2024)

8/18/2024: Top 5 stories on the HackerNoon homepage!

490. The Noonification: SOLID Principles in Smart Contract Development (9/24/2023)

9/24/2023: Top 5 stories on the Hackernoon homepage!

491. The Noonification: How to Build an LLM Application With Google Gemini (6/6/2024)

6/6/2024: Top 5 stories on the HackerNoon homepage!

492. Syntax Error-Free and Generalizable Tool Use for LLMs: ToolDec Enables Generalizable Tool Selection

Researchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.

493. Training Strategy For LLM Video Generation

This research paper proposes using Alternating Gradient Descent for efficient multi-task training, minimizing padding by grouping tasks by sequence length.

494. Stop Asking If AI Wrote This. Start Asking If It’s Any Good

As AI floods the internet with text, the real question isn’t who wrote it—but whether it’s accurate, useful, and worth trusting.

495. Fine-tuned LLMs Know More, Hallucinate Less With Few-Shot Sequence-to-Sequence Semantic Parsing

This paper presents WikiWebQuestions, a highquality question answering benchmark for Wikidata. We modify SPARQL to use the unique domain and property names inst

496. $10M for Founders, AI Agents, and More. Plus, Can AI Outperform Human Therapists?

Multi-agent systems represent a significant leap in AI technology. These systems involve several AI entities working together to complete tasks more efficiently

497. The HackerNoon Newsletter: Agentic AI and the Rise of Outcome Engineering (8/10/2025)

8/10/2025: Top 5 stories on the HackerNoon homepage!

498. Searching for Meaning: What Real World Semantic Search Looks Like

How you can leverage semantic search to create amazing user discovery experiences focused on meaning, not keywords.

499. Fruit Fly Connectome: An Expansive Theory of Signals

it is theorized here that electrical and chemical signals are the fundamental unit of the nervous system, in contrast to the neuron, declared by Nature.

500. LLMs: An Assessment From a Data Engineer

In this article, we will look into the specifics of Gen AI’s role in data engineering and see where it flourishes and where it requires enhancement

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