Disk Is the Contract: Inside Threlmark’s Local-First Architecture

📊 Full opportunity report: Disk Is the Contract: Inside Threlmark’s Local-First Architecture on ThorstenMeyerAI.com — validation score, market gap, and execution plan.

TL;DR

Threlmark’s new approach uses disk-based JSON files as the single source of truth, enabling portable, inspectable, and restartable project management without a database. This design allows external tools and AI agents to interact directly with project data.

Threlmark has adopted a novel local-first architecture that treats disk-based JSON files as the definitive source of project data, eliminating the need for a server or database. Disk Is the Contract: Inside Threlmark’s Local-First Architecture This approach enables open, portable, and restartable project management, supporting external tools and AI agents directly interacting with files.

Threlmark’s core design decision is that the on-disk layout is the API, with no server-of-record. The system organizes project data into a directory structure under ~/.threlmark, including a manifest, dependency graph, project metadata, and individual item files. Each project has its own folder, with one JSON file per roadmap card, facilitating inspectability and portability. This setup allows tools to read, write, and synchronize data without lock-in or server dependencies. The architecture emphasizes data safety through atomic file writes, achieved by writing to temporary files and renaming them atomically. Updates are handled via read-merge-write cycles, preserving data integrity and forward compatibility by retaining unknown fields. The system’s design ensures that external tools can participate seamlessly, as each file is a self-contained, versioned artifact, enabling the entire project state to be reconstructed reliably after crashes or interruptions.
Disk is the contract: inside Threlmark’s architecture — ThorstenMeyerAI.com
ThorstenMeyerAI.com
Threlmark · Technical Deep-Dive
Threlmark · architecture

Disk is the contract: inside a local-first roadmap hub

A Next.js app on top of plain JSON files — no database, no cloud, no accounts. The key decision: the on-disk layout IS the API. Everything else cascades from taking that seriously.

Next.js · TypeScript · JSON-on-disk · MIT · part 2 of the Threlmark series
01The core decision

There is no server-of-record — the files are the record

The UI and any external tool reach the same files through the same discipline. The data root defaults to ~/.threlmark — home-based, because it’s a shared hub every one of your apps points at.

~/.threlmark/ ├─ threlmark.json # manifest ├─ links.json # dependency graph ├─ projects// │ ├─ project.json # meta + wipLimits │ ├─ board.json # lane ordering │ ├─ items/.json # ONE card per file ← source of truth │ ├─ suggestions/ # the Inbox (drop-zone) │ ├─ handoffs/ # recorded agent handoffs │ ├─ reports/ # agent report drop-zone │ └─ ROADMAP.md # human-readable mirror ├─ shared/items/ # cards many projects ref └─ archive/ # archived, still readable

Inspectable

Every artifact is a file you can cat, diff, grep, commit.

Portable · no lock-in

Back up with cp, sync with Dropbox / git, migrate trivially.

Interoperable

Any tool in any language joins by reading / writing files.

Restartable

No in-memory state to lose — stateless over the files.

02Making files safe
Amazon

JSON file editor for project management

As an affiliate, we earn on qualifying purchases.

As an affiliate, we earn on qualifying purchases.

Two disciplined patterns instead of a database

“Just use files” is easy to get wrong. These two patterns — ported from a battle-tested sibling app — are what make file-based state sound rather than reckless.

Pattern 1

Atomic writes

Write to a temp file in the same dir, then rename() over the target. Rename is atomic on one filesystem — a crash mid-write leaves the complete old file or the complete new one, never a half.

write .tmp-pid-rand fsync rename() over target
Pattern 2 · one file per item

The board heals itself

A single roadmap.json array races when two tools write at once. One file per card makes writes collision-free. Lane order lives in board.json and reconciles on read.

The payoff: an external tool never touches board.json. It writes an item file — the board fixes itself on Threlmark’s next read. Unknown keys are preserved, so the contract is forward-compatible.
03Derived, never stored
Amazon

portable JSON data viewer

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As an affiliate, we earn on qualifying purchases.

The numbers can’t drift from the files

Anything computable from item state is computed — so the displayed numbers can never disagree with the underlying JSON. Priority is the clearest example: it’s calculated on read, never persisted.

priority — computed on read

Impact weighted heaviest; effort the only axis that subtracts. Reused verbatim from the original tool, so imported cards rank identically.

priority = max(0, round(impact·3 + evidence·2 + fit·2effort·1.5))
a 5 / 5 / 5 / 4 card 29
work-item age
now − lane-entry time. Past threshold (dev 7d, ranked 21d, idea 60d) → stale.
cycle time
first DevelopmentDone. Derived from append-only transitions[].
throughput
items reaching Done per ISO week, 8-week window.
WIP
count per lane; over the cap shows 3 / 2 in red.
04The closed agent loop · press play
Amazon

atomic file write tools

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A handoff is a first-class flow event

The genuinely 2026-shaped part: most building is done by AI agents, so Threlmark closes the loop. Watch a card go from ranked to Done without anyone dragging it.

Handoff → report → self-move

The brief carries a reporting protocol. The agent reports through REST or the filesystem — and a done report moves the card itself.

Ranked
Add price-drop alertsscore 31 · ready
Development
Handed off 🤖
Done
▶ preferred — REST
POST /api/projects/:id/
items/:itemId/report

Direct call. Applied immediately.

▶ fallback — filesystem
drop reports/.json
→ ingested on read

Robust even if the server’s down at finish time.

🤖 claude done: price-drop alerts shipped · typecheck + lint + build passed — card moved to Done
05Portfolio score & deployment
Amazon

local-first project management software

As an affiliate, we earn on qualifying purchases.

As an affiliate, we earn on qualifying purchases.

A small formula, and an honest hosting caveat

Because items are globally addressable (/), the Portfolio ranks everything together by a status-weighted score — finishing beats starting, blockers get a boost.

Portfolio ranking — status-weighted

In-flight work floats to the top; bottlenecks cost the most, so blockers get nudged up.

score = priority · statusWeight (+ 0.1 · blockedCount · priority)
1.3
development
1.0
ranked
0.85
idea
0.15
done
Path 1

Static read-only demo

Seeded data, writes to localStorage. Try-before-you-clone.

Path 2

Personal Node instance

Password-gated, persistent backed-up THRELMARK_DATA_DIR.

Path 3

Multi-tenant SaaS

Add accounts + per-tenant isolation. A separate build.

The elegant part: the store interface src/lib/*/store.ts is the natural seam — the same boundary that keeps the local tool simple is the one you’d extend for multi-tenancy. The architecture doesn’t fight that future; it just doesn’t pay for it until you need it.
ThorstenMeyerAI.com
Threlmark · open source (MIT) · github.com/MeyerThorsten/threlmark · part 2 of a series · file layout, formula, weights & agent-loop channels are Threlmark’s actual mechanics.

Why Disk as the Contract Transforms Project Management

This architecture shifts the paradigm from cloud-based or server-dependent project tools to a decentralized, file-based system. It enhances data portability, making it easy to back up, migrate, and integrate with other tools. The approach also improves resilience, as the entire project state is stored locally in simple files, reducing reliance on external services and increasing control for users. For AI and automation, it opens new possibilities for direct manipulation and understanding of project data without intermediary layers.

Historical Challenges with Fragmented Roadmaps and Centralized Data

Traditional project management tools often fragment roadmaps across multiple platforms, such as Notion, Trello, or local files, with no unified source of truth. Cloud-based solutions introduce dependency and lock-in, while local tools typically lack portability and interoperability. Threlmark’s approach responds to these issues by creating a unified, portable format based on plain JSON files, inspired by previous local-first applications but uniquely applied to multi-project roadmaps and AI integration.

“The core idea is that the disk is the contract, not a database or server. This makes the data open, portable, and resilient, while enabling external tools and AI agents to participate directly.”

— Thorsten Meyer, creator of Threlmark

Remaining Questions About Scalability and External Tool Support

It is not yet clear how well this architecture scales with very large projects or numerous concurrent external tools. The robustness of file-based concurrency under high load remains to be tested, and user experience details for managing complex multi-project setups are still emerging.

Next Steps for Adoption and Tool Integration

Threlmark plans to release further documentation and developer APIs to facilitate broader tool integration. User feedback will guide enhancements in handling larger projects and more complex workflows. Additionally, testing with AI agents and external automation tools will continue to expand the system’s capabilities.

Key Questions

How does Threlmark ensure data safety without a database?

It uses atomic file writes—writing to a temporary file and then renaming it atomically—to prevent corruption during crashes or interruptions.

Can external tools modify Threlmark data?

Yes, since the data is stored in plain JSON files, any tool that can read and write JSON can participate, enabling interoperability and automation.

What are the limitations of this architecture?

Scalability with very large projects or high concurrency scenarios remains untested, and managing complex workflows may require additional tooling or interface improvements.

How does this approach support AI agents?

AI agents can directly read, modify, and move JSON files, enabling automated workflows that close the loop without intermediary servers or databases.

Source: ThorstenMeyerAI.com

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