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

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TL;DR

Threlmark’s architecture makes the disk the primary data contract, eliminating databases in favor of file-based storage. This approach improves offline capability, data portability, and system transparency, with specific safety measures in place.

Threlmark’s new architecture designates the local disk as the definitive source of truth, removing reliance on traditional databases or servers. This approach is explained in the original analysis. This approach simplifies data synchronization, enhances offline usability, and makes data portable across tools, offering a resilient and transparent system for project management and collaboration.

Threlmark’s system treats each data item as a separate file stored directly on the disk, avoiding centralized databases. It employs atomic file operations—writing to temporary files before renaming—to prevent corruption and ensure data integrity during updates. The directory structure functions as a formal contract, enabling external tools to read and modify data without proprietary interfaces.

This design reduces complexity associated with concurrency and merge conflicts, as each item’s file is isolated. The system can reconstruct overall project views by reading individual files, supporting self-healing and consistency even if some files are missing or corrupted. Developers must carefully manage directory structures and update logic to balance performance and simplicity.

Disk is the contract: inside Threlmark’s architecture — ThorstenMeyerAI.com
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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
SANDISK 2TB Extreme Portable SSD (Old Model) - Up to 1050MB/s, USB-C, USB 3.2 Gen 2, IP65 Water and Dust Resistance, Updated Firmware - External Solid State Drive - SDSSDE61-2T00-G25

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Get NVMe solid state performance with up to 1050MB/s read and 1000MB/s write speeds in a portable, high-capacity…

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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
AMBER X Smart Personal Cloud Storage Device Data and Media Files, Built-in 512GB High-Speed SSD with USB Storage, Plex and Home Assistant/iOS/Android/Windows/Mac Compatible

AMBER X Smart Personal Cloud Storage Device Data and Media Files, Built-in 512GB High-Speed SSD with USB Storage, Plex and Home Assistant/iOS/Android/Windows/Mac Compatible

Easy to Set Up and Use Home-based Personal Cloud Data Backup for All Your Smart Devices

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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
WAVLINK USB 3.0 and USB C to SATA Dual-Bay External Hard Drive Docking Station for 2.5/3.5 Inch HDD/SSD with UASP (6Gbps), Support Offline Clone/Duplicator Function [16TB X2 ]-Black

Support USB 3.0 external, SATA-I/II/III internal; Works great with 2.5 inch and 3.5 inch SATA HDD/SSD.

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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
SamData USB Flash Drive 8GB 1 Pack USB 2.0 Thumb Drive Swivel Memory Stick Data Storage Jump Drive Zip Drive Drive with Led Indicator (Black, 8GB-1Pack)

SamData USB Flash Drive 8GB 1 Pack USB 2.0 Thumb Drive Swivel Memory Stick Data Storage Jump Drive Zip Drive Drive with Led Indicator (Black, 8GB-1Pack)

[Package Offer]: 1 Pack USB Flash Drive 8GB Available in black.

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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.
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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 Single Source of Truth Changes Data Management

By making the disk the primary data contract, Threlmark eliminates vendor lock-in and increases data portability, allowing users to edit files with simple tools like text editors. Offline access becomes seamless, and the system gains resilience against crashes or network failures. This approach shifts complexity from centralized database management to ensuring file integrity and conflict resolution, which can lead to more transparent and flexible workflows. For a detailed overview, see the original analysis.

However, this method requires careful handling of concurrent edits and conflict resolution, as well as managing many small files, which introduces new challenges. Overall, this architecture promotes system transparency, ease of inspection, and extensibility, potentially influencing future design choices in local-first applications.

Background and Rationale for a File-Based Data Approach

Traditional project management tools rely heavily on centralized databases or cloud servers, which can introduce issues such as vendor lock-in, limited offline capabilities, and complex synchronization protocols. Threlmark’s approach reimagines this model by treating the local disk as the ultimate source of truth, inspired by the principles of local-first software design. This shift aligns with recent trends emphasizing data ownership, portability, and offline resilience.

The idea builds on prior developments in file-based storage and atomic operations, but Threlmark integrates these into a formal architecture with explicit directory structures serving as data contracts. This allows external tools and manual edits to interact directly with data files, simplifying interoperability and reducing dependency on proprietary systems.

“Treating the disk as the contract for data fundamentally changes how we think about system resilience and interoperability. It’s about transparency and control, as detailed in the original analysis.”

— Thorsten Meyer, Threlmark developer

Unresolved Challenges and Areas for Further Development

While Threlmark’s approach offers many advantages, it is still early to assess how well it scales in complex, multi-user environments with high concurrency. Handling merge conflicts and ensuring consistency across many small files pose ongoing technical challenges. Additionally, the impact on performance with large datasets or numerous files remains to be fully evaluated.

It is also unclear how the system manages manual edits that deviate from expected formats or how external tools will adopt and adhere to the directory structure contract at scale.

Next Steps for Threlmark’s Local-First System Development

Threlmark plans to refine its conflict resolution strategies, improve performance in large projects, and develop tooling to better support manual and external modifications. Future updates may include enhanced self-healing mechanisms and broader integration options, aiming to demonstrate the system’s robustness in real-world, multi-user scenarios.

Community feedback and case studies will likely influence further enhancements, with the goal of establishing a widely adopted pattern for local-first architecture based on disk as the contract.

Key Questions

How does Threlmark prevent data corruption when multiple tools edit files simultaneously?

Threlmark employs atomic write operations, where updates are first written to a temporary file and then renamed over the original, preventing corruption during crashes or interruptions.

Can users manually edit data files without breaking the system?

Yes, the directory structure is designed to be transparent, allowing manual edits. However, users should follow the established data formats to avoid inconsistencies.

What are the main tradeoffs of using a file-based system instead of a database?

While it increases transparency and portability, managing many small files can introduce filesystem overhead and complexity in maintaining relationships between items, especially under high concurrency.

Will this architecture work for large, collaborative projects?

It is still under development; scalability in multi-user environments with high concurrency remains a challenge to be addressed in future iterations.

How does Threlmark handle conflicts when multiple edits happen simultaneously?

Current strategies include atomic writes and tolerant merging, but conflict resolution in complex scenarios is an area for ongoing improvement.

Source: ThorstenMeyerAI.com