idapt Blog
Guides
How to get more out of idapt's models, agents, and files.
Fix a Failing Test on Your Own Machine From a Chat
A worked idapt Code run: two permissions, one prompt, and an agent that reproduces a failing test on your laptop, fixes it, and hands back the diff.
Cancel ChatGPT Plus Without Losing Your History
The switcher's checklist: export your ChatGPT archive, import every conversation into idapt in minutes, and know exactly what maps where before you cancel.
Capability-Matched Cost Cutting: Pay for the Ceiling You Use
Most AI spend goes to flagship models doing non-flagship work. How to match benchmark tier to task tier, verify the swap, and keep quality where it matters.
Conversation Branching: Three Patterns That Beat Starting Over
Edit a message to fork reality, regenerate with a different model, and keep every path: three branching patterns for getting more out of one conversation.
Bring Your Own Keys: BYOK in Practice
How BYOK works in idapt: connect provider API keys, route matching models on your accounts, and mix BYOK with platform billing and local inference.
One Prompt, Five Models: Comparing Side by Side
How to run the same prompt across several models in idapt with parallel tabs, when triangulation beats trusting one answer, and how to pick a keeper.
Connect Your Tools to idapt Over MCP
idapt is an MCP server: connect Claude Code, editors, or any MCP client and reach your Drive, agents, chats, and computers through the open protocol.
Cost Controls That Professionals Expect
How idapt makes AI spend legible: pre-send estimates, per-request pricing in the app, run budgets, plan allowances, and a usage log you can audit.
Fan Out Big Jobs With Subagents
How subagent delegation works in idapt: an orchestrator splits work across parallel agents and merges results, with patterns for research, review, and extraction.
Generate Images Side by Side
How to run one image brief across two models in idapt, which model fits which job, and how iteration works when generation lives inside your chats.
Give Your Agent a Memory
How agent memory works in idapt: what agents remember across chats, how to read and correct it, and the difference between memory, context, and files.
How the Auto-Router Picks Your Model
What idapt's Auto mode actually does: how it matches a prompt to a model tier, when it escalates, what it costs, and when to pin a model instead.
From Prompt to Deployed Site on a Cloud Computer
A worked end-to-end run: an idapt agent scaffolds a site, runs it on a cloud computer, fixes what breaks, and exposes it on a public URL.
A Research Workflow That Cites Itself
The four-step idapt research loop: sources into Drive, cited search, subagent reading in parallel, and multi-model verification before anything ships.
Run Local Models With Ollama in idapt
The full local-inference guide: pair a machine, pull the right models for your hardware, set prefer-local routing, and verify where each reply ran.
Script Your Workspace: The idapt CLI
The idapt CLI drives your whole workspace from the terminal: chats, files, agents, computers, and generation, with five copy-paste recipes to start.
Build on idapt: SDK Quickstart
The @idapt/sdk in ten minutes: authenticate, chat with any model, read and write Drive files, and run agents from TypeScript.
Team Workspaces: Roles, Sharing, and Shared Context
How teams run idapt: workspaces as the collaboration boundary, member roles, shared agents and files, and the governance controls that keep it sane.
The Autonomy Dial: From Read-Only to Full Control
How idapt bounds what an AI agent may do: four autonomy levels per chat, confirmations on consequential actions, budgets, and complete run traces.
The Model Treadmill
The best AI model changes every few months. Chasing it by switching apps is the treadmill; the exit is infrastructure that outlives any model.
Voice Mode, End to End
How voice works in idapt: real-time conversation with any model, barge-in, transcription, spoken replies, and the workflows where voice beats typing.
Why Real Work Needs an AI Workspace
Chat answers questions; work produces artifacts. Why files, agents, computers, and 200+ models in one workspace beat a standalone chatbot for real output.
Write Your First Skill in Ten Minutes
A worked example of authoring an idapt skill: turn your most-pasted prompt into a single file every agent in the workspace applies automatically.
Your First Automation: An Agent That Runs Monday Mornings
Build a scheduled agent in idapt: a Monday digest that searches, compares against last week, and files a cited report before you sit down.
How to Import Your ChatGPT History Into idapt
A five-minute guide: export your ChatGPT data, upload the ZIP, and get your whole conversation history (branches, tool calls, and images) in idapt.