LLM engineering
30 posts
A smarter model would have leaked it too.
GitHub's AI agent leaked private repos not from a bug but a design failure. How two-plane architecture, scoped tokens, and deterministic validation stop it.
A meditation app shipped a switch statement as AI
Whether a product 'really uses AI' is unanswerable and beside the point. What predicts reliability is system design: validated inputs, constrained outputs, fallbacks.
Keep the hard part
AI doesn't erode your problem-solving skills-offloading the reasoning does. Any intelligence atrophies without use; the fix is design, not avoidance.
2026's AI failures aren't model failures
AI deployments fail at orchestration, not capability. Building validated pipelines around the model - not completing the task - is the real job.
The demo passed. Two weeks later, the queue filled.
Prompt engineering treats AI as magic. Reliable LLM systems come from validation, retries, fallbacks, and monitoring - not better wording.
Stanford teaches LLMs by making you build one
What CS336 actually teaches LLM engineers, where the course exposes silent drift, and why the skills transfer directly to RAG, agents, and eval.
Hy3 is quietly winning production
Hy3 is topping OpenRouter rankings with no public lineage. NovaMind breaks down what its dominance means for pipelines, automation, and team design.
Liquid AI's 8B-A1B drop rewrites inference math
Liquid AI's 8B-A1B MoE trained on 38T tokens shifts LLM inference economics. What it means for engineering pipelines and workforce planning.
One billion fire, eight billion sit in memory
Liquid AI's 8B-A1B MoE frees compute and latency, not memory. How to match sparse-model architecture to the real constraint in your deployment.
The bottleneck moved past the model
Notes from the Mistral AI Now summit on what the new enterprise stack means for automation pipelines and workforce transformation.
![The refund letter addressed to Dear [Name]](/images/posts/the-refund-letter-addressed-to-dear-name.png)
The refund letter addressed to Dear [Name]
Why ChatGPT's first output is a draft, not a deliverable, and what production AI systems actually require beyond the prompt.
The smooth line hiding a noisy benchmark
The METR AI time horizons graph contains structural errors that mislead teams building agents, automation, and AI workflows. Here is what it actually shows.