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Dynamically loading multiple LoRA adapters onto one base model saves GPU capacity but introduces artifact substitution, cache contamination, and cross-tenant mix-up risks. This article designs trusted registration, resource limits, batching isolation, and per-adapter rollback.
Git worktrees isolate indexes and working directories, but do not automatically resolve parallel ownership of files, generated targets, and shared contracts. This article uses versioned claims, monotonic generations, diff admission, and ref CAS to create a reclaimable integration protocol that late agents cannot overwrite.
One Preview environment per PR is convenient, but it can easily reuse data, credentials, and stale deployments across environments. This article defines ephemeral environment identities, seed snapshots, monotonic leases, and destruction receipts so creation, retries, closure, and rollback are all verifiable.
Successfully loading a quantized model does not make it ready for production. This article turns candidate artifacts, calibration data, task quality, tail slices, latency, throughput, and cross-hardware stability into release gates so that average scores cannot conceal localized regressions.
Changing a tokenizer affects token IDs, length budgets, special tokens, and cache semantics. This article presents a verifiable migration process based on artifact fingerprints, golden corpora, compatibility levels, dual-track routing, and versioned data.
Full-parameter fine-tuning can require dozens of GPUs, while LoRA and QLoRA bring it within reach of a single consumer card. Learn low-rank adaptation, 4-bit quantization, the full workflow from data preparation through deployment, and when fine-tuning is actually the right solution.
AI does not have to run in the cloud. Privacy, latency, offline access, and cost are making on-device inference relevant again. This article explains the state of on-device AI: models (quantized 1B–7B), frameworks (llama.cpp/MLC/ExecuTorch), environments (mobile, WebGPU browsers, and desktop), suitable use cases, and current limitations.
Every AI application must choose between GPT/Claude and Llama/Qwen. This article explains the tradeoffs in capability, convenience, data ownership, control, cost, and customization; how privacy, scale, and compliance shape the choice; and why a hybrid of closed-source primary models and open-source fallbacks often works best.
Fine-tuning follows one rule: garbage in, garbage out. This guide covers instruction-pair formats, data collection and cleaning, quality controls such as deduplication, denoising, and consistency checks, synthetic-data uses and risks, augmentation, and the right way to use LLMs to generate training data.
Cloud AI is admired for its capabilities but feared for its privacy risks. Local-first principles and on-device AI offer a new approach: data stays on-device, small local models handle routine work, and cloud enhancement is optional. It covers the principles, AI's role, architecture (local CRDT storage, on-device models, and cloud fallback), and use cases.