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Hatch documentation v0.1.0 · updated 09 Aug 2026

Know what Hatch can build.
Then get the best out of it.

Hatch turns a plain-language description into a focused desktop app. This guide covers the useful part people usually discover too late: where the boundary is, what to put in the prompt, which model should do each job, what leaves your computer and what to check before relying on the result.

Best starting preset
Balanced + Auto
Best starting scope
Complete
Finished app data
Local
Outside connections
AI only

01 Start here

What Hatch is—and what it is not

Hatch is a desktop app builder for small, self-contained tools tailored to one person or one team's real workflow. It plans the app with you, generates it, runs automated checks, installs it locally and keeps a version history for later changes.

It is

  • A no-code way to turn a defined workflow into a desktop app
  • A builder for local forms, records, calculations and exports
  • A guided process with questions and a blueprint before code
  • A sandboxed host for the generated HTML, CSS and JavaScript
  • Free, AGPL-licensed software that uses your chosen AI provider

It is not

  • A general website, mobile app or server builder
  • A cloud database, sync service or multi-user platform
  • A way to automate other programs or browse the web
  • A guarantee that generated software is error-free
  • A substitute for expert review in high-risk work

02 Choose the right job

Where Hatch works best

Strong fits

  • Quote or estimate calculators using your own pricing rules
  • Job, lead, stock, inspection, shift or maintenance trackers
  • Registers, logs, checklists, timers and lightweight CRMs
  • CSV organizers, searchable local records and PDF reports
  • Tools where one person owns the data and the workflow

Poor fits

  • Live multi-user systems with accounts and permissions
  • Websites, phone apps, APIs, servers or payment systems
  • Apps that scrape sites or continuously sync online data
  • Scheduled or background automation while the app is closed
  • Medical, legal, financial or safety decisions without review

A good Hatch app has a clear user, a repeatable job, a bounded set of records and an observable result. If the value depends on a live online service, multiple simultaneous users or unattended automation, Hatch is the wrong foundation in its current form.

03 Build flow

What happens after you describe an app

  1. 01

    Describe the job

    Say who uses it, what they do, what the app remembers and what it should produce.

  2. 02

    Answer focused questions

    Hatch resolves missing fields, rules, terminology and decisions that would materially change the result.

  3. 03

    Review the blueprint

    You see the workflow, data and a sketch before generation. Treat this as the contract; mistakes here flow into the app.

  4. 04

    Generate and check

    Hatch writes the app, validates it and smoke-launches it with both empty and seeded data. Failed checks can trigger repairs.

  5. 05

    Visual review

    A strong vision-capable model judges the running app against the blueprint and can request corrective passes.

  6. 06

    Install, test and refine

    Open the shortcut, use realistic records and request precise changes. Versions are append-only, so reverting creates a safe new version.

04 Scope before speed

Pick the smallest build depth that fits

DepthUse it forQuestionsReview correctionsExpectation
FocusedOne core job, few screens and rulesUp to 2Up to 1Fastest and cheapest
AmbitiousMore screens, features and edge casesUp to 8Up to 8Can take several hours

The one-minute, roughly twenty-cent example on the home page is a measured small app—not a promise for every scope. Complete and Ambitious builds can run for hours and use substantially more model tokens. A narrow version 1 is usually easier to verify and cheaper to refine than an oversized first attempt.

05 Prompt like the operator

How to get the strongest result

Do not describe a software category. Describe the work as the person doing it: the inputs, rules, sequence, exceptions and final output.

A

Name the user and job

“One estimator creates residential cleaning quotes” is better than “make a CRM.”

B

Give the real rules

Include calculations, required fields, statuses, defaults and a representative edge case.

C

Define the output

Say what success looks like: a PDF quote, filtered list, copied summary or saved record.

D

Set the scale

Ten records and fifty thousand records imply different screens and controls.

E

Say what is out

“No payments, customer login or cloud sync” prevents scope drift and false assumptions.

F

Correct the plan early

Blueprint edits are cheaper and more reliable than repairing the wrong app later.

Example of a strong first description
I run a residential cleaning business and need a quote builder for one person.

A quote records the client's name, property size, visit frequency, base service and optional extras. Price equals the base rate by bedroom count plus selected extras, with 10% off weekly visits.

I need a clear quote summary I can copy and export as a PDF, plus a searchable history of past quotes. I usually have fewer than 500 quotes.

No online payments, customer login or cloud sync.

During questions and refinements

  • Answer decisions that change the workflow; use You decide for low-stakes details.
  • Read every blueprint section, especially stored data, calculations and “not included.”
  • After the build, test with a normal case, an empty case and a difficult edge case.
  • Request one precise behavioral change at a time: what you did, what happened and what should happen instead.
  • Use real terms from the business. The right labels make the app feel tailor-made even when the workflow is simple.

06 Give useful references

Attachments Hatch can use

You can attach up to five images or text references while describing an app. High-value attachments include a screenshot of an existing tool, a photo of a paper form, a sketch of the desired screen, a sample CSV and a short rules document.

Accepted
Images, CSV, TXT, Markdown and JSON
Limit
5 files
Text read
First 20,000 characters per file
Not accepted
PDF and Excel .xlsx references

07 Model guide

Use the strongest model where judgment matters

Hatch divides the work into roles. This is why a mixed setup often beats paying for the most expensive model on every token.

RoleWhat it doesWhat matters most
BlueprintDefines the plan and helps judge the finished appReasoning, product judgment and vision
ImplementorWrites the actual app from the approved planCoding reliability, output capacity and speed
FixerApplies repairs and later refinementsInstruction following and cost efficiency
Final reviewerLooks at the running app against the blueprintThe strongest available vision model
Recommended for most people: use Balanced with models on Auto. For the highest quality, use Best with Auto. Pin models manually when Auto does not map a provider's newest family cleanly to strong, middle and small roles.

Current provider guidance

ProviderStrongest setupBalanced setupHatch-specific note
Anthropic Pin Claude Fable 5 for Blueprint/review if your account exposes it; otherwise Opus 5 Opus 5 Blueprint, Sonnet 5 Implementor, Sonnet 5 Fixer Current Hatch Auto defaults to Opus 5 / Sonnet 5 / Sonnet 5
OpenAI gpt-5.6-sol for Blueprint, Implementor and review gpt-5.6-sol Blueprint, gpt-5.6-terra Implementor, gpt-5.6-terra Fixer Pin these explicitly; Hatch may classify the whole 5.6 family as strong
OpenRouter Use Best + Auto; Hatch assigns strong models throughout Kimi K3 Blueprint/review, GPT-5.6 Luna Pro Implementor and DeepSeek V4 Flash Fixer These are Hatch's current stage-specific OpenRouter Auto targets when present
Local A strong vision-language coding model that your hardware can run reliably Use a cloud Blueprint model and local Implementor/Fixer when possible If the server exposes one model, Hatch uses it for every stage

Preset behavior

  • Cheapest: smaller clarification and repair models, mid-tier planning and building, with a strong final review
  • Balanced: mid-tier clarification and repair, strong planning, building and final review
  • Best: strongest available model at every stage

Local model reality

  • Hatch warns about models that appear smaller than 20B parameters
  • 32B+ is a practical starting point, not a quality guarantee
  • The Blueprint role must accept images for visual review
  • Local speed and context limits can dominate a large build

Model availability changes. Refresh the provider list before pinning. Anthropic describes Fable 5 as its highest-capability model, Opus 5 for complex coding and Sonnet 5 as the quality/speed balance. OpenAI's current guide positions GPT-5.6 Sol for complex reasoning and coding, Terra for intelligence/cost balance and Luna for cost-sensitive volume. Kimi K3 is a multimodal coding model with long-context and visual iteration strengths. Hatch's other OpenRouter stage choices are implementation defaults from the current app source. See the first-party model sources.

08 Hard capability boundary

What a generated app can and cannot do

It canIt cannotPractical alternative
Use forms, lists, tables, charts and modalsBuild a website or phone appKeep it as a desktop workflow
Store records locallySync a shared cloud databaseExport to a file in a synced folder
Open files you explicitly chooseScan arbitrary folders or disksSelect one or multiple files yourself
Save CSV, JSON, text, images and real PDFs through a dialogWrite files silently anywhereChoose the destination each time
Use the clipboard and open links in your browserLog in or act on a website for youPrepare content, then open the target site
Show notifications while openRun scheduled or in the background while closedShow due items when the app opens
Use AI when the app has the AI permissionCall other APIs, fetch URLs, scrape or send webhooksPaste or import data; use AI only where appropriate
Prepare email text and a mailto: linkSend email, SMS or messages itselfOpen a pre-filled draft for you to send
Perform calculations, search, filter and canvas image workRun programs, shell commands or scriptsKeep computation inside the generated app

09 Know where data goes

Data and privacy

Stays on your computer

  • Records saved inside finished apps
  • Generated app packages and version history
  • Provider keys, held by the operating system credential store
  • Local diagnostic logs and build transcripts

Sent to your chosen model

  • Your app description and answers
  • Attached images and text references
  • Blueprint and generated code during review or repair
  • Runtime AI prompts from apps that have AI permission

A finished app without AI permission works offline. An app with AI permission needs the connected cloud provider or local model server; its runtime prompt is sent there. Using local models keeps generation local, subject to the behavior and configuration of that server.

10 Keep cost in the formula

How model costs work

Hatch is free and adds no markup. Cloud API usage is billed directly by the provider behind your key. A ChatGPT or Claude subscription does not automatically include API usage.

  • The home-page example used $0.16 in model calls on Balanced. It is a measured small build, not a universal average.
  • Scope, model prices, retries, attachment size and later refinements all affect total cost.
  • Balanced is the default value choice. Cheapest trades planning/build quality for cost; Best applies the strongest model throughout.
  • Local models have no per-token provider bill, but use your hardware and can take much longer.
  • Hatch tracks reported monthly spend and shows a guardrail, set to $20 by default. Treat it as tracking—not a provider-side hard stop.
  • Set a real spending limit with the provider. Runtime AI inside each generated app has its own Hatch-enforced budget, $2 by default.

11 Two export formats

Share with or without Hatch

FormatRecipient needsPortabilityImportant limits
.hatchappHatch installedSmall and cross-platformImport does not recreate the full editable builder project or conversation
Standalone ZIPNo Hatch; extract the whole folderSame OS and CPU architecture as the exporterLarge; runtime AI features do not work because credentials are never bundled

A .hatchapp import shows the author, description and requested permissions before installation and runs static validation. Current exports do not include the app's saved records. Standalone apps may also trigger unsigned-app warnings from Windows or macOS.

12 Sandboxed, then reviewed

Safety model

Generated apps run with Node.js integration off, context isolation on and a restrictive content policy. They receive only declared capabilities such as local storage, file pickers, notifications or AI. Hatch scans generated files, launches the app in hidden empty and seeded test states, and uses a visual reviewer before presenting it as ready.

What this reduces

  • Silent access to arbitrary files
  • Shell and process execution
  • Unapproved outside network calls
  • Obvious startup and rendering failures

What it cannot guarantee

  • Every business rule is correctly understood
  • Every calculation or edge case is correct
  • The interface is accessible to every user
  • The app is suitable for regulated or high-risk decisions

13 Current release

Known limitations worth knowing up front

  • No direct Google provider. Current connections are Anthropic, OpenAI, OpenRouter and OpenAI-compatible endpoints. A compatible gateway may expose other models.
  • No internet except AI. Generated apps cannot fetch APIs, scrape pages, use webhooks or silently connect elsewhere.
  • No background or scheduled work. Notifications work only while the app is open.
  • No cloud sync or live collaboration. Saved app data is local to one computer.
  • Limited build references. Up to five images/CSV/text/Markdown/JSON files; no PDF or Excel reference import.
  • Imported apps are not full projects. A shared package does not restore the original build conversation for editing.
  • No hosted gallery. Sharing is file-based.
  • Unsigned installers and exports. Windows SmartScreen and macOS may display warnings.
  • Model access varies. Your provider account, credits, regional availability and rate limits still apply.
  • Quality varies. Long or underspecified apps can exceed a model's practical reasoning or output reliability even when token limits are sufficient.

14 When a build struggles

Common fixes

SymptomLikely causeTry this
No model is availableNo valid key or local endpointConnect a provider in Settings, refresh models and verify API billing separately from any chat subscription
Provider or rate-limit errorCredits, rate limit or temporary provider failureCheck the provider dashboard, wait, then resume or retry; the Hatch project remains local
Screenshot is ignoredBlueprint model lacks visionChoose a vision-capable model for Blueprint and reviewer roles
Local build is poor or stallsModel too small, slow or short-contextUse a larger local model, narrow the scope, or use a strong cloud model for Blueprint
The wrong app was builtAmbiguous or incorrect blueprintReturn to the plan when possible; otherwise state one exact mismatch and expected behavior
A refinement made things worseChange affected another pathRevert to the known-good version, then request a smaller, testable change
Standalone AI does nothingCredentials are intentionally excludedShare the .hatchapp version for use inside a configured Hatch installation

15 Before you rely on it

A short final checklist

  1. 01

    The app has one clear user and a bounded primary job.

  2. 02

    The blueprint names the right fields, terminology, calculations and exclusions.

  3. 03

    The selected Blueprint model supports images and is strong enough for the scope.

  4. 04

    You tested normal, empty and edge-case records with sample data.

  5. 05

    You verified calculations, exports, deletion and recovery yourself.

  6. 06

    You know whether build material or runtime AI prompts go to a cloud provider.

  7. 07

    The provider—not only Hatch—has an appropriate spending limit.

  8. 08

    The app is not being treated as independently verified high-risk software.

Sources Model guidance

First-party model references

Recommendations above were checked on 09 August 2026. Model names, access and pricing can change; consult your provider before a large build.

The shortest useful version

Start narrow. Review the plan. Test the result.

Balanced + Auto and Complete depth are the sensible defaults. Move to Best when the workflow or stakes justify the extra model cost.

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