Large Language Models

Building an AI Scoring Pipeline for 10,000+ Listings a Day

Phase 2: Embeddings & Semantic Search

I Compared 4,000+ AI API Prices So You Don't Have To

I Built an AI Content Team That Posts to My Blog While I Sleep

What You Can Safely Put Into ChatGPT: The Postcard Rule

Do not treat LangGraph as a longer chain: define state, interrupts, and recovery first

Reranking: Retrieve Fast, Then Reorder Precisely (Better RAG)

LLM Gateway vs MCP Gateway: Understanding the New AI Infrastructure Stack

I built a Rust entropy monitor to route LLM inference — here's what the benchmark showed

How Claude AI Actually Works: The Technical Story Behind the Scenes

Multilingual code gap exposed by Multi‑LCB

How I built pairwise AI model compare pages with Claude Haiku and a budget cap

From Code to Governance: The Complete Guide to LLM Token Optimization

The Open-Model Cost Chart Everyone's Sharing Is API Prices. Here's What Self-Hosting Actually Gets You (Measured)

Use a flat-priced, auto-routing LLM API in Aider or Cline — one npx command

We're Moving LiteLLM to Rust. Here's What the Benchmarks Look Like.

GLM-5.2 Is Cheap Because It's Subsidized, Not Efficient

The 5 Things Your LLM Benchmark Misses That Actually Decide the Winner

Turn Your Best AI Conversations Into a Blog — and Give Your Model a Memory That Lasts

How to Prove Your AI Did What It Said: A Developer's Guide to Verifiable AI Outputs

The LLM is an ALU

Why Beginners Should Ask AI More Questions Before Building An App

How Much Does It Actually Cost to Run a Local LLM? (€ per Million Tokens, Measured)

I built a local AI memory gate on a CPU, and my 7B model scored worse than my 1.5B model because it was too smart

Designing a Synthetic Data Pipeline for Persian LLM Fine Tuning: From Topic Graphs to QLoRA Evaluation

TensorSharp: .NET Native Open Source Local LLM Inference Engine

June 25 - AI, ML, and Computer Vision Meetup

We built a new AI Topology to bypass the Transformer bottleneck. Here are our first benchmark results.

Top AI Papers on Hugging Face - 2026-06-22

Dumping chat history into a vector database is the worst way to give an AI memory. Here is how I built a "Structured Memory" layer that separates conversation from durable human facts.

I wanted to build an AI companion, so my first step was limiting what the AI is actually allowed to think. Here is how I used "Semantic Intake" to force the LLM to understand context before it speaks:

RAG Systems with Claude: From Documentation to Production

'"An LLM and a harness": Nvidia''s simple thesis on what agents actually are'

How I Cut My LLM API Bill by 80% With a Simple Router

Catch LLM hallucinations with multi-model consensus

Three Rounds of Training Turn a Word-Predictor Into a Chatbot. None of Them Are Magic.

The AI Prompts I Wish I Had Right After Graduation

Sharing Claude Context Across a Team Is the New Org Chart

Literature Review Automation: Search and Analyze Hundreds of Academic Papers in Minutes

Embeddings: Turning Meaning Into Numbers

Transformers From Scratch: Assembling the Block Behind GPT

Why Multi-Head Attention Needs Position, Residuals, and Normalization

How to Stop One Customer From Blowing Up Your Entire LLM Budget

You don't need the internet to run research - document 05 ? local ai inference performance. We made sure of it.

The cloud was never necessary for Local-First AI Inference. Here's why.

Semantic caching our flaky-test summariser: 58% fewer LLM calls

I gave myself an AI advisory board — three models argue, I decide

How to Export Your Lovable Project and Accelerate Development