Guides, research notes and what we learn building models.
We compared OpenClaw running on fine-tuned local models against GPT-4o across five common agent tasks. Here's where fine-tuned models win, where they don't, and what the numbers say.
Read the article
Cloud APIs send PHI, personal data, and privileged material to third-party servers. Map the OpenClaw data flow and deploy compliantly with local models.
Read the article
OpenClaw defaults to cloud APIs that charge per token. Point it at an OpenAI-compatible Ollama endpoint and run fine-tuned local models with no API keys.
Read the article
Which open-source model should run your OpenClaw agents? Llama 3.1, Qwen 2.5, Mistral and Phi-3 compared on tool use, speed, RAM and fine-tuning.
Read the article
Patching CVEs and vetting ClawHub skills misses the real OpenClaw exposure: every file and email routed to a cloud API. Local models remove it.
Read the article
Hourly cloud GPU rental rates, on-premise purchase costs and API pricing for 2026, plus the break-even math for agencies running self-hosted AI.
Read the articleHow healthcare teams compare on-premise AI with cloud APIs under HIPAA: what a BAA really covers, PHI in fine-tuning data, and why on-premise is the default.
Read the articleAttorney-client privilege makes cloud AI a non-starter for most law firms. Here's why on-premise, fine-tuned AI models are the only path forward — and the opportunity for agencies that can deliver them.
Read the articleAI agencies burning through API credits can slash costs by 90% or more by switching to fine-tuned local models. Here's the math, the method, and the migration path.
Read the articleHow Unsloth, Axolotl and Ertas Studio compare on setup time, VRAM efficiency, multi-GPU support, experiment tracking and GGUF export, plus which to pick.
Read the articleHow to architect HIPAA-compliant automation workflows using self-hosted n8n and local LLM inference — with practical examples for clinical note summarisation and appointment triage.
Read the articleVibe-coded apps with AI features face a brutal cost cliff at scale. Here's how indie developers are cutting AI costs by 95% with fine-tuned local models — without rewriting their apps.
Read the articleLegal services represent one of the largest untapped markets for AI agencies. Here's the market landscape, demand signals, and a go-to-market strategy for agencies targeting law firms.
Read the articleWhen does prompt engineering hit its ceiling for legal AI tasks? A practical comparison of prompt engineering and fine-tuning for contract review, with a decision framework for agencies.
Read the articleA step-by-step guide to fine-tuning an LLM: prepare JSONL data, pick a base model, set LoRA hyperparameters, evaluate, and export GGUF to run locally.
Read the articleGenerate training data from GPT-4o and Claude, curate it aggressively, and fine-tune a smaller model with LoRA that runs on your own infrastructure.
Read the articleA practical guide for indie developers who want to replace expensive cloud AI APIs with a self-hosted fine-tuned model — without becoming an ML engineer.
Read the articleHow AI agencies can use fine-tuned LoRA adapters to deliver white-label AI solutions — one base model, dozens of client-specific adapters, premium pricing.
Read the articleMost AI agencies don't have ML engineers on staff. Here's how to fine-tune production-quality legal AI models using Ertas Studio — no Python, no GPU rental, no ML expertise required.
Read the articleA step-by-step guide for non-ML developers who want to fine-tune an AI model on their app's actual usage data — from collecting training examples to deploying the model.
Read the articleAn end-to-end guide to fine-tuning AI models for healthcare — covering data de-identification, clinical NLP training, on-premise deployment, and compliance validation.
Read the articleHow AI agencies can serve dozens of clients from a single base model using LoRA adapter hot-swapping — the architecture behind scalable, cost-effective multi-tenant AI.
Read the articlePractical techniques for generating high-quality synthetic training data using frontier models — covering prompt engineering, data augmentation, and quality filtering for fine-tuning datasets.
Read the articleHow to price AI agency work when your costs run per-token. Flat subscription, per-token pass-through and hybrid compared, with the margin math for each.
Read the article