Opus 4.7 vs GLM 5.1: is mixing models worth it?
A couple of months ago, I compared Opus vs GLM by having both of them do a task for me. It’s not that surprising that Opus was best. But what if we ge…
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Upgrading Kiwi-chan’s Brain: Pushing a 30GB "Frankenstein" GPU Rig to the Limit with Qwen 3.6-35B-A3B
Upgrading Kiwi-chan’s Brain: Pushing a 30GB "Frankenstein" GPU Rig to the Limit with Qwen 3.6-35B-A3B If you’ve been following my journey of building …
Why Strict JSON Mode Doesn't Stop Hallucinated Tool Calls
Book: AI Agents Pocket Guide Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go My project: …
Why I Built an AI That Tries to Destroy Your Legal Argument
The Kill Switch Protocol: Mandatory adversarial search in production LLM systems Most AI systems suffer from the same fatal flaw: they're desperate to…
7 AI Gateways That Actually Work in Production (2026 Guide)
Let me start with an admission. I resisted using an AI gateway for longer than I should have. My reasoning was the kind engineers convince themselves …
Strategic LLM Adoption: A Director's Guide to Fine-Tuning Models for Domain-Specific Applications
Strategic LLM Adoption: A Director's Guide to Fine-Tuning Models for Domain-Specific Applications As AI continues to reshape enterprise technology sta…
Жизнь сквозь призму LLM. Часть 1 — почему мы будем платить не за сервис, а за доступ к мышлению
Разговор про большие языковые модели до сих пор слишком часто ведётся по одной из двух схем. Либо восторг: «смотрите, нейросеть уже пишет код и тексты…
Claude Code with Local LLMs and ANTHROPIC_BASE_URL: Ollama, LM Studio, llama.cpp, vLLM
Native Anthropic endpoints, tool-call compatibility, and context-window sizing for local Claude Code. Last tested: April 2026. See Changelog at the bo…
Stop Getting Rate-Limited: Building Bulletproof LLM API Consumption Patterns
You know that feeling when your chatbot suddenly stops responding at 2 AM because you hit the rate limit on your LLM provider? Yeah, we've all been th…
Multi-Model LLM Orchestration with OpenRouter
Multi-model LLM orchestration is the practice of routing AI requests to different models based on what each task needs — speed, cost, reasoning depth,…
🦀 PicoClaw Deep Dive 🤖 — A Field Guide to Building an Ultra-Light AI Agent in Go 🐹
A comprehensive, actionable guide to the principles, techniques, and architecture behind sipeed/picoclaw — written so you can build a similar system f…
DeepSeek-V4 Changes the Context Game for Agents — And Your Memory Architecture Should Adapt
A million-token context window built specifically for agentic workloads. That's the feature in DeepSeek-V4 that stopped me mid-scroll this week — not …
LLM-as-Judge: Automated Quality Gate for LLM Outputs in Production
LLM-as-Judge is a pattern where one language model evaluates another model's outputs against defined criteria. An automatic quality gate: every respon…
A Unified View of AI Evolution: From Machine Learning to LLMs, RAG, and Fine-Tuning
Artificial Intelligence has progressed far beyond its early rule-based origins. What once depended on predefined logic has evolved into systems that c…
I built an open-source tool to distill books into knowledge graphs
I have a bad habit: I buy books faster than I read them. Not because I'm lazy — I start most of them. But somewhere around chapter 3, I lose the threa…
Tesla, Meta, and Google: Nearly $350B in 2026 AI Capex
Book: LLM Observability Pocket Guide Also by me: AI Agents Pocket Guide My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude C…
Reading list (21st to 27th April)
What I am currently reading These are the current online posts that I enjoyed reading and made me think. AI If you are not the model, you are the harn…
GEO / AI Search Thread
REPLIES R1 Citation patterns in ChatGPT are not backlink signals in new packaging. Pages that get cited consistently have dense entity co-occurrence —…
Deep Dive into Open Agent SDK (Part 6): Multi-LLM Providers and Runtime Controls
An Agent shouldn't be locked to a single LLM provider. Different tasks suit different models — simple questions use cheap models, complex reasoning us…
(The Voice) Multilingual Layer
The Catalyst: One Language, Many Attack Surfaces The comfortable fiction is: “We wrote English rules, so the model is safe.” The truth: LLMs are multi…
GPT-5.5 Just Dropped. Here's What the Benchmarks Are Hiding.
GPT-5.5 landed April 23, 2026. I've been in the benchmark data since the moment it dropped — and I need to tell you the number OpenAI didn't put in an…
Harness Engineering with Nothing but Markdown
If coding agents aren't your primary battlefield, "harness engineering" probably feels like a distant concept. Scrolling through a timeline full of ar…
Why Your Shipping Speed Hasn't Changed Since You Started Using AI
I'm going to be honest with you. Most engineers using AI assistants today are shipping at the same speed as before. They have Cursor. They have Claude…
My Harness Is Not a Cage. It's an Org Chart.
Your AI agent did not fail because the model was weak. It failed because it made a decision no one had authorized it to make. Maybe it skipped an esca…
Supervise a multi-agent setup with Local LLMs
There’s a popular misconception that local LLMs are not useful for anything beyond passing “trust me, bro” benchmarks. In reality, they can be surpris…
How to Benchmark LLM Inference Performance: TTFT, ITL, and Throughput Metrics
When deploying large language models to production, measuring performance accurately is critical. Whether you're using vLLM, SGLang, TensorRT-LLM, or …
One Open Source Project a Day (No. 48): karpathy/autoresearch —— Launching the Era of Self-Evolving AI Laboratories
Introduction "This may be the story of how it all began." —— Andrej Karpathy This is the No.48 article in the "One Open Source Project a Day" series. …
DeepSeek V4: Million-Token Context That Actually Works
DeepSeek V4: Million-Token Context That Actually Works Most long-context models are benchmarks in search of a use case. DeepSeek V4 flips the script—i…
Why LLM Agents Fail: Four Mechanisms of Cognitive Decay and the Reasoning Harness Layer
LLM agents fail in four predictable, mechanism-level ways. Attention decay, reasoning decay, sycophantic collapse, hallucination drift. The current st…
Stop Building One Giant Prompt: A Better Way to Design LLM Systems
## Most early LLM apps start the same way: “Let’s just put everything into one prompt and let the model handle it.” So we write a prompt that tries to…