From notebook to production: patterns for reliable inference services
A practical look at deployment, observability, and failure modes when shipping models.
Placeholder entry. In production, this will link to a structured Markdown or MDX article.
Articles
A technical, engineering-first set of notes on building AI systems in the real world. These are placeholders for now, but the structure is ready for Markdown or MDX content.
#01
Reasonable baselines: building surprisingly strong systems with simple models
Why strong baselines still matter, and how to construct them when data and time are limited.
Placeholder entry. In production, this will link to a structured Markdown or MDX article.
Prompting as programming: thinking clearly about LLM system behavior
Treating prompts, tools, and evaluation as part of one coherent system design process.
Placeholder entry. In production, this will link to a structured Markdown or MDX article.
Data first: designing labeling and feedback loops that actually converge
Data quality, sampling, and feedback pipelines from an engineering perspective.
Placeholder entry. In production, this will link to a structured Markdown or MDX article.
Latency, throughput, and cost: trade-offs in real-time AI products
Concrete ways to think about constraints before you commit to an architecture.
Placeholder entry. In production, this will link to a structured Markdown or MDX article.
Measuring what matters: simple evaluation setups for messy systems
How to design evaluation loops when your system is part heuristic, part model, part UX.
Placeholder entry. In production, this will link to a structured Markdown or MDX article.
Reading papers like an engineer, not a reviewer
A workflow for extracting implementation details and constraints from research quickly.
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LLM tooling stacks: queues, workers, and failure handling
A ground-level view of how production LLM systems are wired behind the UI.
Placeholder entry. In production, this will link to a structured Markdown or MDX article.
Working with non-ideal data: logs, traces, and improvisation
Strategies for learning from imperfect logs and partial ground truth in real systems.
Placeholder entry. In production, this will link to a structured Markdown or MDX article.
Career paths in AI engineering that don’t start with “researcher”
Concrete, engineering-focused paths into AI work for students and early-career developers.
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Content pipeline
The articles page is wired to accept static content now, but the layout is intentionally simple so it can be swapped to Markdown or MDX sources later with minimal refactoring.