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.
Guarded Memory and Session Isolation: Protecting User State
How to design memory layers that isolate user state, prevent leakage, and enforce policy boundaries.
Prompt Injection Defense Architecture: Practical Security Layers
A security-first blueprint for protecting LLM systems from prompt injection and data exfiltration.
Secure Prompt Routing: Keeping Sensitive Inputs Isolated
How to route prompts securely across models and tools without leaking sensitive data.
Neural-Symbolic Systems: Combining LLMs With Formal Reasoning
How neural-symbolic architectures merge LLM flexibility with rule-based precision for high-stakes domains.
Model Cards and Transparency: Communicating Capabilities and Limits
A practical guide to writing model cards that communicate capabilities, limitations, and safe usage.
State Space Models and Mamba: A New Path Beyond Transformers
An engineering-focused look at state space models, Mamba, and where they outperform attention-based architectures.
RAG End-to-End Latency Budgeting: Where the Milliseconds Go
A technical guide to budgeting latency across retrieval, reranking, prompting, and generation stages.
Model Compression and Distillation: Smaller Models, Real Gains
A practical guide to compressing LLMs with quantization, pruning, and distillation while preserving quality.
Prompt Structure and Context Control: Engineering Predictable Behavior
Designing prompts with strict structure and context controls to reduce variance and improve reliability.
Retrieval Evaluation and Grounding: Measuring What Actually Matters
How to evaluate retrieval systems and grounding quality in RAG pipelines with practical metrics and workflows.
LLM Regression Testing: Preventing Silent Quality Drops
How to build regression suites that catch quality drops across prompts, models, and retrieval systems.
Sequence Parallelism: Scaling Context Without Breaking Training
A technical guide to sequence parallelism and how it improves training efficiency for long-context models.
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