Calculating VRAM for local coding AI from model weights alone will always fail. This lays out the real math for weights, KV cache, and runtime overhead, measures how capacity and quality shift at each quantization level, and gives recommended models per VRAM tier with a rule for keeping headroom.

What I realized after running paid APIs as bare shells. Agent performance comes not from knowledge but from the reasoning loop, schemas, and skill rules.

In a debate where only pro and con were offered, a model picked neutral and the closing logic ground to a halt. Tracing the cause, it turned out the model had not broken the rules — I had simply never enforced them.

In a debate that offered only pro and con, the AI picked neutral and the closing logic stalled. The culprit was not the model but the server. The story of the division of labor between model and server, confirmed by two tests.

A warning report that verifies with numbers the signs that China's CXMT has broken through 10% share by digging into general-purpose memory while the industry fixates on HBM, and that this is a carbon copy of the fall of Japanese semiconductors

An in-depth AI bubble report that verifies OpenAI's audited financials, Anthropic's $65 billion run rate, xAI's 460x multiple, $700 billion in CapEx, and the inference price war — all with numbers

A roundup, from an agent practitioner's perspective, of the signs of leaks around Space Bunny Alpha, Astra Minor, and Sonnet 5.5, plus the discovery of the ART enzyme system by 950 Claude agents.

The same model divides into genius and fool depending on context. This lays out why context is everything for an agent and how to fill it.

From requests to Scrapy and Playwright — crawling principles and methods, speed and block evasion, storage and legality, all covered with real-world code

A comparison of seven AI browsers — from Aside, the top agent-benchmark performer, to Comet, the strongest free option — covering speed, cost, and pitfalls, based on measured data

From basic setup like memory and Google app integration to image generation and Deep Research, this walks through 12 hands-on Gemini features in order

The curriculum numbers of the world's largest free coding education, which has produced 100,000 graduates, vivid reviews from users, and the real value of its certificates, all in one place

Six of the most unusual AI projects on GitHub, from a repo where human commits are banned to a 3,000-line self-evolving agent, with measured numbers

This lays out the limits of attempts to understand and control AI from the inside, and the FAMS paradigm of monitoring the flow of outputs and actions, along with implementation code.

A measured, benchmark-and-VRAM-based review of Hugging Face's most popular local models, from distilled math models to decensored tunes and coding agents

70B runs comfortably on 128GB while 150GB-class monsters need 192GB, and this lays out the boundary lines of unified memory selection, including why capacity does not guarantee speed

From Phi-4 14B's monstrous reasoning to the Nemotron hybrid 30B, a roundup of four hidden masters that go unnoticed behind mammoth models but have overwhelming real-world value.

A practical, hands-on rundown of the open-source Orca ADE for running and managing 30-plus AI agents on one screen — its core features, mobile integration, and installation and usage

A 552B MoE with only 8-16B active parameters. By compressing the KV cache to 890 bytes, DeepSeek's next-generation model runs 4x the agents on a single GPU. An in-depth architecture analysis, including the mHC paper.

A practical rundown of Qoder IDE's core features — Quest Mode, NES, Repo Wiki, and more — plus download, install, and basic usage, with real code

An analysis with real code examples of 5 security vulnerabilities hidden in vibe-coded apps: missing authorization checks, SQL injection, vulnerable libraries, information exposure, and a practical security checklist.

Beyond the concept and pros and cons of vibe coding, this uses real code examples to show the realistic limit that "vibe coding only works as far as you know," and lays out the attitude you actually need.

A complete comparison of NVIDIA and AMD architecture, the CUDA/ROCm ecosystems, DLSS/FSR, and AI/gaming/video-editing performance with real benchmarks for putting a graphics card into a local PC

A detailed guide to Orca ADE (Agent Development Environment) for managing multiple AI agents at once — its core features, perfect Korean support, mobile app integration, parallel Git Worktree handling, and download and installation.

A practical rundown of the Qoder IDE developed by Alibaba, covering its core features (Quest Mode, NES, Repo Wiki), how to download it, basic usage, pricing, and a comparison with Cursor/Copilot.

A comparison of the local AI inference performance gap between NVIDIA CUDA and AMD ROCm/Vulkan, complete with real commands. Covers GPU selection, driver installation, and llama.cpp/Ollama setup from a practical standpoint.

The technical differences between Linux's four major filesystems (ext4/XFS/Btrfs/ZFS), up to format, mount, snapshot, and performance-test commands, organized around practical code examples. Includes a selection guide for which filesystem to use in which environment.

A local LLM benchmark comparison between the 32GB VRAM AMD R9700 AI Pro and the 8GB RTX 4060. It analyzes, with measured data, the Vulkan vs ROCm performance gap, whether a 27B model is practical, and the bottleneck of running two cards.

A VRAM-by-VRAM and model-by-model benchmark of which local AI models you can run on the graphics card you own. Recommended models, inference speed (TPS), and value rankings for 18 GPUs from the RTX 3060 to the RTX 5090.

Qwen Image 2.1 can do Photoshop-grade editing, from background removal to color control, object swapping, and character sheets. But it also has limits: pose transfer and a clay-like skin texture. Here are 12 core features laid out with actual test results.

An analysis of the NVIDIA DGX Spark (128GB unified memory). It breaks the illusion that more VRAM means faster, and explains from a bandwidth standpoint why an RTX 4090 (24GB) is 3x faster on an 8B model. It sorts out the cases where the DGX Spark is genuinely useful (70B+ fine-tuning) and where a GPU is better.

The features, benchmarks, practical commands, and selection criteria of Linux's four major filesystems (ext4, XFS, Btrfs, ZFS), with hands-on code

Meta's newly launched AI agent Muse keeps working in the background even after you close the app, and it makes phone calls for you — from handling dental insurance to booking a restaurant. This analyzes the controversy hidden behind the convenience and the pushback from big tech.

Running Qwen3.8-27B Q4_K_M in an 8GB VRAM environment yields only 0.26 tokens per second, a 333x difference from 86.67 t/s on a 24GB setup. Loading a model onto a GPU is a matter of physics, and current technology cannot get around it.

A complete rundown from the GPU chip makers (NVIDIA/AMD/Intel) to the hidden traits, cooling tech, and 2026 Korean street prices of AIB partners like ASUS/MSI/Gigabyte

RAG is not just vector search. From chunking quality, hybrid search, rerankers, and contextual retrieval to late chunking — this lays out why retrieval fails in practice and how to fix it.

One call to OpenAI o1 burns $100. For an ordinary individual, a top-tier reasoning model is a luxury. The smartest combination is to run a local model as the main and use a cost-effective API only when needed.

Fine-tuning is not about fixing the whole model — it is about attaching a small adapter. This covers the differences between full fine-tuning, LoRA, and QLoRA, VRAM requirements, a GPU training timetable, seven failure causes and their remedies, data formats, how to install Unsloth, Axolotl, and LLaMA-Factory, and the commands from training through GGUF conversion to running it in Ollama.

A beginner's guide that finishes repository creation, commit, and push using only GitHub Desktop, with no terminal required. It walks in order from the three core concepts — repository, commit, push — to the first upload.

Qwen3.8-9B Distill compresses the capability of a 2.4T-parameter giant into 9B. It runs on 8GB of VRAM and delivers performance that surpasses its 9B class, from agentic coding to reasoning. Includes operator field-use benchmarks.

Traditional software is governed by rules, but generative AI works by next-token probability. This piece reviews real failures of jailbreaks, prompt injection, and hallucination, and lays out a triple outer-fence architecture built with NeMo Guardrails and Llama Guard.

Even with the same model loaded, the Mac mini and the RTX 4090 are fast and slow in opposite directions. This covers the architectural difference between unified memory and discrete VRAM, the principle that memory bandwidth determines token speed, measured numbers at the 8B class and the 27B-70B class, and selection criteria by use case.

Analyzes the reality of daily limits, slowdowns, and deliberate throttling hidden behind the sweet marketing of free AI agent platforms. It also offers tips for using free tools most efficiently and realistic alternatives.

Local AI inference is governed by a single formula: memory bandwidth equals speed. From a ~500,000 KRW AMD mini-PC to a ~4,000,000 KRW Mac Studio, this lays out with measured numbers which models you can run at how many TPS for each budget.

It lays out a division of labor where a generative LLM writes the code and the non-generative judgment model Jev verifies it with yes-or-no. It covers Jev's business model and pricing, the four MCP integration paths, and three synergies: on-site supervisor, conditional controller, and fact checker.

A hands-on record of wiring the Rust-built coding agent jcode — which boots in 14ms in the terminal and uses only 27.8MB of RAM — directly to DeepSeek V4 Flash. About 86% of the roughly 14,000-token system prompt is reused as cache, confirming a cost of about 0.1 won per question.

A breakdown of the differences between the GGUF, EXL2, AWQ, GPTQ, and GGML formats used in local LLMs, with concrete model benchmark numbers. It provides a practical guide to which format to use on which hardware.

A comparison of five MCP servers that add web search to an AI agent. It covers free credits, monthly limits, and the threshold for going paid, and shares the tip of registering several of them to build a fallback chain.

Why a local LLM suddenly slows down on 8GB of VRAM, what the KV cache is, and the real VRAM usage per model, laid out with benchmark figures.

Unity CLI MCP lets you connect free local AI models for game development without any paid subscription. This covers the whole process, from installation to connecting an AI agent and real-time game builds.

Your first step into local AI. From installing LM Studio to downloading a Llama/Qwen model and having your first conversation in three minutes. A from-zero explanation of how to run AI on your own computer without the internet.

20,000 tokens for a greeting, 40,000 tokens for a line of code. An agent's excessive reasoning is not a technical limitation but a thoroughly deliberate structure. This piece digs into the structure that profits platforms and API vendors the more tokens get consumed.

Every small action an agent takes leads straight to tens of thousands of tokens in API cost. This piece covers the structure where a 10-step loop bills 43x rather than 10x, real bill-shock cases, and cost-cutting strategies.

From first-generation search, where humans typed keywords, to third-generation agentic search, where AI agents cross-verify hundreds of sources — this piece compares per-generation cost, processing style, and empirical benchmarks, and gathers global developer feedback.

IFM's K2 Horizon 3.7B is a 3.7B tiny model that achieves a 512K context and 68.6% on SWE-bench. This piece rounds up the official benchmarks, architecture analysis, and a local deployment guide.

With the September 22 launch of GPT-6 Sol ($2/$10) and Luna ($0.10/$0.50), API prices dropped 50% versus GPT-5.6. This piece cross-verifies benchmarks and real-user feedback to decide which model to deploy for which task.

A single document covering the Qwen 4 Apsara Conference announcements, an evidence-based benchmark comparison of Qwen 3.8-27B vs Claude Opus 4.6 Max, and local GPU (16-24GB) setup.

Based on the specs of Qwen 3.8 Max (2.4T), this predicts the class of Qwen 4 Max and lays out the roadmap revealed at the Apsara Conference along with verified specs. It also includes criteria for telling fake rumors from the real thing.

A technical report diagnosing the AI agent boom as a bubble on the grounds of developer concentration, retention collapse, CapEx/ROI imbalance, and the absence of a killer app. It states that the cited figures are as provided by the source and unverified.

Analyzes the big-tech-led AI agent and image generation boom from a value-creation standpoint: developer concentration, retention collapse, enterprise ROI imbalance, and the absence of a killer app, and states the need to verify the cited figures.

Five techniques that keep agents from missing your site's information (llms.txt, semantic HTML/SSR, JSON-LD, JSON API with markdown fallback, robots and caching) — their principles, examples, and a verification checklist.

By 2026, AI has moved past the ask-and-answer chatbot stage into an ecosystem of agents that decide and act on their own. What decides a large model's performance is not raw parameter count but its skills, rules, and inference speed — and at bottom it is still next-token prediction.

A beginner's guide covering an overview of Python, its main features, and basic usage.