8bit.tr Journal
Ideas, frameworks, and playbooks for modern product teams.
Clear, practical articles about building digital products that people love. Short, useful, and built for teams that ship.
Distributed Inference and Load Balancing: Serving LLMs at Planet Scale
A systems-level guide to distributed inference, load balancing, and traffic shaping for large-scale LLM services.
AI Model Evaluation Playbook: Metrics, Benchmarks, and Reality Checks
How to evaluate AI models with the right metrics, human review loops, and production-grade benchmarks.
Benchmark Leakage and Contamination: Keeping Evaluation Honest
How to detect benchmark leakage, prevent contamination, and build reliable evaluation pipelines.
Retrieval-Augmented Generation (RAG): Architecture, Pitfalls, and Best Practices
A practical guide to building RAG systems that are accurate, fast, and easy to maintain in production.
Kernel Fusion and Inference Kernels: Squeezing Latency Out of GPUs
A deep dive into kernel fusion, custom kernels, and GPU-level optimizations for fast LLM inference.
LLM Architecture From Scratch: The Building Blocks That Matter
A clear, technical walk-through of modern LLM architecture, from tokenization and attention to training loops and inference trade-offs.
Differential Privacy for LLM Training: Protecting Data at Scale
A practical guide to applying differential privacy in LLM training without destroying model utility.
C and C++ in AI Systems: The Performance Layer Behind Modern ML
A professional deep dive into how C and C++ power AI systems under Python, from kernels and runtimes to deployment at scale.
Shipping Fast Without Burning Out: A Sustainable Release Rhythm
A sustainable release rhythm for small teams: weekly cadence, focus rituals, quality systems, and energy-aware planning.
Multi-Tenant Token Budgeting: Fairness, Cost, and Performance
Designing token budgets for multi-tenant LLM systems while preserving fairness and latency targets.
Model Ensemble Strategies: Aggregating Confidence for Better Answers
How to use model ensembles to improve accuracy, confidence, and robustness in LLM systems.
AI Product Design Checklist for 2026
A practical AI product design checklist covering trust boundaries, feedback loops, reliability, and launch operations.
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