#large-language-models

Sentetik Veri üretimi

Öncelikle uzun süredir blog yazamadım çünkü .Hem iş hayatı,hem akademik çalışmalar,hem yüksek lisansla uğraşırken çok vakit…

When Does an Enterprise Actually Need MoE?

The first article in this series was about system reality: latency, memory, concurrency, and deployment constraints. This second article…

Industry Foresight: Wan 3.0 is on the Horizon

In the current AI video generation community, a “holding our breath” atmosphere is spreading. Recently, with SeaArt officially releasing…

Precisão e Recall: o trade-off por trás da caixa de busca

Uma das interfaces humano-máquina mais eficientes já criadas foi a de conduzir um veículo. Apesar da enorme evolução da mecânica, da…

LLMOps 101: How to Actually Monitor a Model in Production

Traditional monitoring answers “is it up.” LLM monitoring has to answer “is it still good” — and that’s a fundamentally harder question…

Aether: Decoding, CPU-Resident Compute & GPU Scheduling, Running a 120B MoE on a Single Consumer…

In Part 1, we introduced Aether and the core ideas behind running a 120B MoE model on a single consumer GPU. If you missed it, you can read…

Rethinking MoE: How DeepSeek Redefined Mixture of Experts Architecture

One of the most prominent trends in the development of Large Language Models (LLMs) in recent years has been the increasing size of the…

How I Structured an ICM Workspace for Drafting Mini-Courses

What’s actually inside CLAUDE.md and CONTEXT.md, and how the layout maps onto Interpretable Context Methodology.

Colibri Runs a 744B Model on 25 GB of RAM. The Real Breakthrough Is Weight Streaming

Colibri treats VRAM, RAM and NVMe as one inference hierarchy. That makes frontier-scale Mixture-of-Experts models accessible on smaller…

LLM Evaluation & Alignment: Zero to Hero

Everything you need to understand how we measure and shape language models — explained from scratch, with diagrams, worked examples, and…

Not Every Telecom AI Needs a 600B Parameter Model

What if the future of telecom AI isn't about building bigger models—but building smarter systems?

GenRec: Towards LLM-Native Recommendation at Netflix

Authors: Ying Li, Arjun Rao, Shradha Sehgal

You Don’t Always Need a Frontier Model. You Need One That Fits.

Building real applications with right-sized, open-weight models using Gemma 4

Unbelievable! Run Kimi K3–2.8 Trillion Parameters — on a Single 4GB GPU

The new king of open source is three points behind Claude. Here is how to run it on the single 4GB GPU card in your desktop.

MCP Server Explained: Tools vs Resources vs Prompts

AI Engineer Interview Preparation

Can You Fine-Tune a Model on Just a Few Hundred Examples and Get a Personalized Assistant?

An experiment in small-data fine-tuning, and the wall I ran into with a 0.6B model and 300 examples

Diffusion Language Models Explained: From Theory to Training Pipeline

Think of solving a jigsaw puzzle. Traditional language models place one puzzle piece after another until the picture is complete. Diffusion…

Model Abliteration 101

How do people remove safeguards from AI models permanently? And what’s’ the price to humanity?

Iterative Workflows in LangGraph | Agentic AI using LangGraph | Class 8 |

Overview: Iterative Workflows

TOP AI Network Biweekly Report: July 8, 2026 -July 21, 2026

Over the past two weeks, TOP AI Network has continued to focus on a practical question: how can AI better support the way teams actually…

I’m completely done with LLMs in the enterprise

Step 39 of a 40 step workflow

A 2.8-Trillion-Parameter Open Model Just Shipped With Full Weights Coming in 10 Days.

Some numbers are worth sitting with before moving on to the next headline. This is one of them.

Why Every Enterprise AI Agent Needs a Rollback Strategy (Before It Becomes Your Most Expensive…

Your AI agent probably doesn’t need coffee breaks. Unfortunately, it also doesn’t know when it’s confidently making terrible decisions.

🚀 Inkling — A 975 Billion Parameter OS Model

Open-source LLM just got a new member, Inkling from Thinking Machines!

We needed machines to teach us how to talk to each other

We built AI with limited context windows, spent three years learning to feed them carefully, and only then noticed we had been overloading…

Intelligent Enterprise Workflows with Generative AI

Abstract

Where Agentic AI Cost Actually Goes

Why cheaper model calls can still create more expensive agentic workflows

VSCode’a Ollama Modelleri Nasıl Yüklenir

Merhaba, Bugün sizlere Vscode’a ollama modellerinin nasıl yükleyebileceğinizi ve çalıştırabileceğinizi göstereceğim

GPT Nedir ve Nasıl Çalışır? Transformer Teknolojisi | DEHA

GPT, “Generative Pre-trained Transformer” ifadesinin kısaltmasıdır. Türkçede kabaca üretken, önceden eğitilmiş dönüştürücü olarak…

What Claude Code’s MCP Support Unlocks (Beyond the Marketing)

MCP isn’t Claude’s proprietary magic; it’s a Linux Foundation protocol that’s rewiring how every AI coding tool connects to external…

Running a 744 Billion Parameter AI Model on a Regular Laptop: Inside the Colibri Inference Engine

How a tiny, dependency free C program makes frontier scale Mixture of Experts models usable on hardware most developers already own

UIUC — 긴 텍스트도 찰떡같이 알아듣는 LLM의 비밀 무기, RECONTEXT 패턴

최근 대형 언어 모델(LLM)들은 책 한 권 분량을 한 번에 집어넣을 수 있을 정도로 거대한 콘텍스트 창을 제공합니다. 하지만 입력 창이 커졌다고 해서 모델이 그 안의 정보를 모두 완벽하게 이해하고 추론할 수 있는 것은 아닙니다. 방대한 입력 데이터…

🔥 DeepSeek’s DSpark — Making Speculative Decoding Faster

Photo by Piotr Łaskawski on Unsplash

Can You Spot AI Writing by Em Dash Alone? (The Math Says No)

If you’ve spent time in the online writing world, you most definitely know that usage of AI is ubiquitous. How were you able to arrive at…

Why AI Agents Forget What You Told Them: Active Memory Explained

Suppose you are learning machine learning with an AI, and you have already covered supervised learning, regression, and a few related…

Google Just Proved Reasoning Models Know Things Instant Models Can’t Reach — And That’s a Problem

A quietly published research paper explains why “thinking” AI models get facts right that faster, cheaper models simply cannot. The…

🚀Top 5 LLM Libraries Everyone Must Try (Beyond HFace and OpenAI)

AI systems are going Big. Every day, a new framework is developed. Focus on what is Important.

10 Power User Apps That Transform Your Mac & iPhone Experience

Skip system bloat — these native-like utilities are all you need.

Beyond Accuracy: How DeepTeam Brings Professional Adversarial Testing to Every LLM Application for…

Optimizing LLM Token Costs in Production: A Practical Engineering Playbook [Part 1]

Understanding Where Your Token Budget Actually Goes Before You Start Optimizing

🤯 AutoMem — Learning Memory as a Skill

How memory is learnt as a skill, just like how humans do it.

# I Tested Gemma, Phi, and Granite on South Indian Food — Here’s Why They Failed.

*How a custom 900+ prompt benchmark exposed the “Tourist Cuisine Bias” in modern lightweight LLMs.*

What Is AI Benchmarking?

What those AI scores actually mean

BenchLocal: The Local-First Desktop App That Finally Gives LLM Benchmarking a Shared Runtime and a…

Computer Use Agents: How AI Operates Through Real User Interfaces

AI Engineer Interview Preparation

The Hidden Cost of Self-Hosted LLMs

Every AI vendor pitch in Malaysia these days sounds the same. “Data sovereignty. Cost savings. Control. Just run it on-prem”

Prompt Engineering

Chapter 6 of Hands-On Large Language Models: Iterative prompt design, controlling text generation with temperature and top-p, and…

LLMs, RAG, Agents, and MCP: The AI Evolution You Need to Understand

We did not build four different kinds of AI. We kept solving the next limitation.

Building Search-Enabled Agents with DuckDuckGo: A Technical Walkthrough

A 5-minute read for developers wiring web search into agent tool-loops.

Attention Sinks: The Tokens Every LLM Keeps Looking At

A strange, counterintuitive quirk hiding inside every transformer you’ve ever used