Ideas on local AI
Product updates, engineering deep dives, community stories, and analysis from the Gotchi team.
Why locally hosted AI is the future of artificial intelligence
Compute is moving to the edge, open models are closing the quality gap, and privacy law is tightening. The next decade of AI runs on hardware you own.
Local AI for personal use: a private assistant that's actually yours
Journaling, email, health questions, family planning - the things you'd never paste into a cloud chatbot are exactly what a local AI is for.
Local AI for the enterprise: security, compliance, and predictable cost
Cloud AI pilots stall in legal review. On-premise AI ships. Why regulated organizations are standardizing on locally hosted inference.
The future of AI agents runs on your own hardware
Agents that read your inbox, touch your files, and act on your behalf need deep access. That access should never live in someone else's cloud.
Small models, big shift: why efficient local LLMs are winning
The frontier gets the headlines, but 7B–70B open models get the work done. Distillation and quantization changed what 'good enough' means.
How to run an LLM on your laptop in 2026: the complete guide
Hardware requirements, model selection, quantization explained in plain language - everything you need to go from zero to local inference.
Your code never leaves: local LLMs as private coding assistants
Proprietary algorithms, unreleased features, client codebases - the strongest argument for a local coding assistant is what it can't leak.
AI privacy in 2026: what really happens to your cloud prompts
Retention windows, training opt-outs, subpoenas, and breaches - a clear-eyed look at cloud AI data practices, and the architecture that sidesteps all of it.
Local AI for small businesses and startups: enterprise capability, zero cloud bill
Client confidentiality, thin margins, and no time for vendor reviews - why small teams may benefit from local AI more than anyone.
The next decade of AI: why inference is moving to the edge
Datacenter economics, NPU-equipped consumer chips, and open-weight releases point the same direction: the center of gravity in AI is shifting to your device.
The economics of local vs cloud AI in 2026
We ran the numbers on 12,000 users. The average knowledge worker saves $480/year by running AI locally. Here's the breakdown.
Introducing agent templates
Start from pre-built workflows for email triage, code review, and daily standups. Customize to fit your needs.
Why we built on Ollama
Ollama handles model management beautifully. We decided to build on it rather than reinvent the wheel. Here's why.
DeepSeek V3: local benchmark results
We tested DeepSeek V3 against Llama 3.1 70B on Apple Silicon. Results were surprising - especially on reasoning tasks.
How a design studio automated client briefs
Meridian Design cut project kickoff time from 3 days to 4 hours using Gotchi agents. Here's their setup.
MCP explained: the protocol connecting AI to everything
Model Context Protocol is the USB-C of AI tools. Here's what it is, why it matters, and how to use it in Gotchi.
State of Local AI 2026: key findings
73% of users now run AI exclusively locally. Agent adoption grew 340%. Here are the highlights from our annual report.
Building a personal AI that respects your privacy
Personal AI doesn't have to mean giving up your data. How Gotchi's memory system keeps everything on your machine.
Ready to run AI locally?
Download Gotchi. Open core, local-first, and ready for every model you want to run.