Posts

Most LLM applications start simple: one prompt, one response, ship it. Then requirements grow. The task needs to search the web, then read the results, then decide whether to search again, then synthesize everything. You add more logic. Then you need one agent to write a plan and another to execute it. Suddenly you are managing state, routing decisions, and failure modes across multiple LLM calls, and a simple chain is not the right abstraction anymore. ...

Most LLM applications are deployed without a meaningful evaluation system. The developer prompts the model a few times, the outputs look reasonable, and it ships. Then users start complaining about specific failure cases, the developer adjusts the prompt, checks a few examples again, and ships again. This cycle is not engineering. It is guessing. Evals are what turns LLM development from guessing into engineering. They let you measure whether a change actually improved things, catch regressions when you update your prompt or switch models, and understand the failure modes of your application before users do. This guide covers how to build an eval system that is actually useful, not just theoretically correct. ...

Everyone knows to tell the model to “think step by step.” That was 2022. In 2026, the basics are table stakes, and the developers extracting the most value from LLMs are using techniques that go well beyond the starter guides. This article covers what actually works: the patterns that experienced LLM engineers use in production, the failure modes they have learned to avoid, and the reasoning behind why these techniques work rather than just a list of tips to copy. ...

When you are building an LLM-powered application, your development environment is not just where you write code. It is where you run the model, iterate on prompts, manage API keys, handle dependencies, and eventually deploy. The choice between Replit and GitHub Codespaces is not just about which cloud IDE is nicer to use. It determines your entire development loop for building AI applications. Both platforms have invested heavily in AI features over the past two years, but they have taken very different approaches. Replit is betting on AI as the primary way you interact with your environment. Codespaces is betting on familiar tooling with AI as a powerful layer on top. Here is what each approach looks like in practice. ...

Qwen3.5-4B GGUF Quants Compared: KLD Quality Loss vs. Inference Speed on Intel Lunar Lake If you’re running local LLMs on a Lunar Lake laptop, every quantization decision is a tradeoff. Pick too aggressive a quant and your Qwen3.5-4B outputs turn to mush. Pick too conservative a quant and you’re watching tokens trickle in at a speed that kills any productivity gain. This guide maps every major Qwen3.5-4B GGUF quant against its Kullback-Leibler Divergence (KLD) quality score and real-world tokens-per-second on Intel’s Core Ultra 200V (Lunar Lake) silicon, so you can make the call yourself. ...

Disclosure: I earn a commission from Cursor when you sign up via my link. I also have an affiliate relationship with Replit. All opinions are based on hands-on use. The AI coding assistant space looked very different eighteen months ago. GitHub Copilot was the obvious default. Today there are four or five serious tools competing for the same keyboard real estate, and the performance gap between them is measurable in hours of developer time per week. Choosing the wrong one costs you more than the subscription fee. ...

Apfel: The Free AI Already Living on Your Mac There’s a capable AI assistant sitting idle inside your Mac right now, and most people have no idea it exists. Apfel, a free and open-source project that surfaced on Hacker News earlier this year, wraps Apple’s own on-device AI infrastructure into a clean, privacy-first assistant that runs entirely on your machine. No API key. No $20/month subscription. No data streaming to a server farm in Oregon. ...

The Claude Code Source Leak: Fake Tools, Frustration Regexes, and Undercover Mode When developers reverse-engineered Claude Code’s compiled JavaScript bundle, they didn’t just find a system prompt. They found a window into how Anthropic thinks about AI behavior at the most granular level: placeholder tools that exist to shape cognition rather than perform actions, regex patterns monitoring your emotional state, and an identity-concealment layer built for whitelabel deployments. This is what Anthropic didn’t put in the docs. ...

How CERN Runs Ultra-Compact AI on FPGAs to Filter 40 Million Collisions Per Second Every second, the Large Hadron Collider smashes protons together 40 million times. Each collision produces a blizzard of subatomic debris — and buried somewhere in that noise might be a Higgs boson decay, a hint of dark matter, or a particle that rewrites physics entirely. The catch? There is no storage system on Earth that could record all of it. CERN’s answer is one of the most impressive deployments of AI in any scientific field: ultra-compact neural networks running on FPGAs, making life-or-death filtering decisions in under one microsecond, in real time, on custom silicon. ...

The AI landscape is shifting fast, and Anthropic just sent a clear signal that the next wave of models will be dramatically more capable than what we’ve seen so far. The phrase being used internally — “step change” — is not marketing language. In the AI industry, it has a specific meaning, and it matters. This piece breaks down what Anthropic’s announcement actually means, why this particular moment is significant in the context of AI’s development history, and what concrete steps developers and businesses should take right now to be positioned when the new model ships. ...