GitHub

Documentation

# WELDR Next Directions Deep Dive

This document unpacks the five follow-up directions that emerged from the weldr-vision-growing-beyond-nextjs-sql.md conversation. For each initiative we clarify what it actually entails today, why it matters, tradeoffs to expect, and a rough effort/priority signal so we can stack-rank intelligently.

Latest prioritization signal (external synthesis)

  • • Absolute bedrock: turn DSL → IR → Canon into durable artifacts before touching anything else or the system stays brittle.
  • • Next wedge: implement the Ownership Manifest on top of the pipeline; start small (file-level, Managed vs Custom) to earn trust quickly.
  • • Strategic leverage: modularize renderers/data adapters once the pipeline is stable so we can ship schema-flex preview storage and alternative stacks without refactors.
  • • Messaging: publish the “stack-aware, not stack-locked” story early to keep scope aligned, but realize it does not block engineering.
  • • SVDOM editor: acknowledged as an enticing experience layer but explicitly out of the current plan—treat as future exploration only.

    • → Practical sequencing: (1) Pipeline, (2) Manifest, (3) Adapter modularization (plus schema-flex preview store), (4) Messaging refresh, (5) Optional future SVDOM work.

    1. DSL → IR → Canon pipeline as first-class artifacts

    What “first-class” really implies

  • • The DSL, IR, and Canonical graph exist as durable, queryable objects (tables, APIs, and file artifacts), not just transient data that flows through a codegen script.
  • • Every mutation (chat edit, UI edit, repo diff) produces an event that replays through “parse → normalize → canonicalize”, and we capture snapshots/diffs for each stage.
  • • Tooling (CLI, dashboards, tests) talks to the pipeline directly: e.g. weldr model diff uses stored Canon snapshots, not ad-hoc regeneration.

    • Today we do materialize pieces of the IR, but they mostly live as blobs tied to specific commands. Making the pipeline “first-class” means standardizing schemas, identifiers, storage, and APIs so other systems — schedulers, guardrails, renderers — can subscribe/inspect without bespoke glue.

    Why it matters (pros)

  • • Deterministic regeneration: canonical snapshots + deterministic canonicalization give us stable codegen inputs, reducing “AI drift”.
  • • Multi-surface editing: chat, schema form, and SVDOM editor can all emit events against the same pipeline, avoiding bespoke merge logic per editor.
  • • Observability & auditing: stored snapshots/time-travel enable debugging (“why did auth break yesterday?”) and compliance stories.
  • • Unlocks automation: workers can diff Canon versions to trigger selective codegen, tests, or notifications without re-running the entire orchestrator.

    • Costs / risks (cons)

  • • Schema ossification: once we expose pipeline artifacts publicly we must version/migrate them carefully.
  • • Storage and perf overhead: Canon snapshots + event logs grow quickly; need pruning/compaction strategies.
  • • Failure choreography: partial writes or crashing midway through canonicalization can leave inconsistent states; requires transactional orchestration.
  • • Tooling surface area: CLIs, APIs, and dashboards all need to understand the pipeline, which increases maintenance.

    • Effort & dependencies

    | Slice | Scope | Effort | | --- | --- | --- | | Pipeline schema + storage | Define tables/JSONB layout for DSL events, IR nodes, Canon snapshots, version IDs | Medium | | Canonicalization worker | Deterministic service/queue that consumes events, writes artifacts, emits metrics | Medium-High | | Tooling & introspection | CLI endpoints, admin UI, test harness hooking into stored artifacts | Medium |

    Dependencies: requires stable App Model schema definitions, event bus (Supabase functions or background worker), and storage budgets (likely Postgres JSONB + S3 for large artifacts).

    Open questions

  • • Do we expose Canon snapshots to customers or keep them internal? (affects backward compatibility requirements.)
  • • How often do we snapshot (every event vs checkpoint) to balance storage vs diff granularity?
  • • Should we store the generated code artifacts alongside Canon so we can reconstruct code without rerunning renderers?

    • Storage implementation snapshot (current)

  • • app_model_event stores the replayable log with both global position and per-chat indices; it’s populated directly from DSLManager.recordChange.
  • • app_model_canon_version, app_model_ir_node, and app_model_ir_edge keep the canonical graph plus normalized IR nodes/edges with deterministic hashes and metadata.
  • • scripts/pipeline/run-canonicalizer.ts is the worker/CLI entry point that replays DSL → Canon and persists the Canon + IR artifacts.

    • 2. Ownership manifest & guardrails expansion

    Current state vs goal

  • • Today: we track high-level “managed vs custom” heuristics and manually preserve @custom zones, but the manifest isn’t a formal artifact with schema, diffing, or enforcement logic.
  • • Goal: a structured manifest (likely JSON/DSL + DB representation) that enumerates surfaces (files, components, workflows) with states Managed/Hybrid/Owned and rules for how renderers/agents interact with each state.

    • Pros

  • • Predictable edits: developers know exactly which files the AI may touch, eliminating “mystery overwrites”.
  • • Progressive adoption story: we can market/measure migrations from Managed → Owned surfaces, aligning with the wedge described in the vision doc.
  • • Safer regeneration: manifest-aware codegen lets us regenerate only managed zones, speeding up cycles and reducing merge conflicts.
  • • Enables policy features: e.g. teams can require approvals before flipping a surface to Managed, or block regen on Owned code unless tests pass.

    • Cons / risks

  • • Requires accurate detection: we need robust diffing to understand when a user manually edits a Managed surface so we can alert rather than clobber.
  • • Additional UX complexity: users must understand/maintain manifest entries; poor tooling could feel bureaucratic.
  • • Renderer coupling: each renderer must honor manifest states, which introduces coordination overhead as we add adapters.
  • • Potential deadlocks: if a surface is marked Owned but contains generated dependencies, regen may fail without clear remediation flows.

    • Effort & components

    | Component | Notes | Effort | | --- | --- | --- | | Manifest schema & storage | File format + DB table; API for CRUD, versioning, linking to repo paths | Medium | | Enforcement hooks | Codegen + CLI that read manifest and gate mutations | Medium-High | | Developer tooling | UI in dashboard / CLI commands to inspect/edit/approve manifest changes | Medium | | Detection/alerts | Watcher detecting manual edits in managed surfaces → notifications / suggested manifest updates | High |

    Dependencies: needs stable pipeline IDs (to tie manifest entries to App Model nodes) and repo instrumentation (git diff watchers or FS hooks).

    Open questions

  • • Do we store the manifest in-repo (for Git transparency) or centrally (for SaaS control)? Hybrid model?
  • • How do we represent nested ownership (e.g. component-level vs file-level) without confusion?
  • • Should manifest changes themselves go through approvals/PRs?

    • 3. SVDOM pipeline + interactive editor (deprioritized for now)

    Definition

  • • SVDOM = structured representation of UI trees (component type, props, layout, data bindings) that lives alongside the App Model.
  • • Pipeline: (a) App Model authoring can output SVDOM (either from chat or forms), (b) NextReactRenderer translates SVDOM → React/Next files, (c) visual editor manipulates the SVDOM directly and replays through pipeline.
  • • “Interactive editor” = DevTools/Figma-style tree + inspector linked to SVDOM nodes with live preview, diff history, and integration with ownership manifest (e.g. toggling nodes to custom code segments).
  • • Reality check: this initiative wasn’t part of the original north-star plan and only surfaced in a later conversation. Given current capacity it is explicitly deferred until the core pipeline + manifest efforts land.

    • Pros

  • • Shared UI language: AI, structured editors, and renderers manipulate the same model; easier diffing + collaborative editing.
  • • Deterministic layout: SVDOM enforces design system primitives so generated UIs stay on-brand and composable.
  • • Cross-renderer flexibility: once SVDOM is renderer-agnostic we can emit Next, Remix, or design tokens without rewriting the authoring experience.
  • • UI-focused tooling: we can build features like “highlight Managed vs Custom nodes,” inline data binding inspectors, etc.

    • Cons / risks

  • • High UX lift: building a polished tree/inspector/editor is a product on its own (selection, undo/redo, keyboard nav, preview sync).
  • • Expressiveness tradeoffs: SVDOM must balance constraints vs flexibility; too constrained and power users bail, too flexible and diffing becomes messy.
  • • Custom code integration: bridging from SVDOM nodes to Owned React components requires clear boundaries and compiler directives.
  • • Performance: syncing large trees, running live preview, and handling concurrent edits can stress the client/backend.

    • Effort & dependencies

    | Workstream | Notes | Effort | | --- | --- | --- | | SVDOM schema revamp | Ensure React-flavored but renderer-agnostic tree, component registry, data binding spec | Medium | | Renderer integration | NextReactRenderer consumes SVDOM; handles slots, server/client components, manifest boundaries | Medium-High | | Editor UX | Tree view, inspector, preview, event logging, custom block toggles | High | | Collaboration & diffing | Live updates, history, multi-user editing, hooking into pipeline events | High |

    Dependencies: requires first-class pipeline so editor can read/write SVDOM snapshots, plus ownership manifest to annotate nodes with states.

    Open questions

  • • How deep do we go before shipping v1? (e.g., static read-only tree vs full drag/drop?)
  • • Do we allow arbitrary React props/functions inside SVDOM or restrict to declarative bindings with escape hatches?
  • • Is the editor a standalone desktop/web tool, or integrated into the WELDR cloud dashboard exclusively?

    • 4. Renderer / Data adapter modularization

    Clarification

  • • Today much of the Next.js + Drizzle logic is interwoven with core orchestration.
  • • Goal: carve renderer/adapters into explicit packages implementing interfaces (e.g., packages/renderer-next-react, packages/data-postgres-drizzle) so the App Model interacts only through contracts.

    • Pros

  • • Future stacks: ability to add SvelteKit, Remix, Django, or alternative data adapters without uprooting the App Model.
  • • Internal velocity: renderer teams can ship independently; bugs stay scoped to packages.
  • • Community contributions: open-core story becomes credible when adapters are swappable modules.
  • • Testing: we can snapshot App Model fixtures and run them across multiple renderers in CI.

    • Cons / risks

  • • Interface design upfront: we must freeze stable contracts early, which is hard while the App Model is still evolving.
  • • Shared utilities: duplication or complex shared libs might appear if adapters need common helpers (e.g., component definitions, auth scaffolding).
  • • Version skew: keeping adapters compat with core releases requires semver discipline and integration tests.
  • • Build complexity: bundling multiple renderers might increase package graph complexity and tooling overhead (pnpm workspace management, tree-shaking, etc.).

    • Effort & dependencies

    | Task | Notes | Effort | | --- | --- | --- | | Define renderer/data interfaces | Contracts for inputs/outputs, file artifact structures, capabilities metadata | Medium | | Refactor existing code | Move Next.js + Drizzle implementations into packages with shared utils | Medium-High | | Adapter selection logic | Config in App Model/CLI to choose renderer combos, fallback handling | Medium | | Testing harness | Golden App Model fixtures run through adapters, diff outputs | Medium |

    Dependencies: requires canonical pipeline artifacts as adapter inputs, plus manifest metadata so adapters know which files are managed.

    Open questions

  • • Do we support multiple adapters per project (e.g., Next frontend + Django backend) or one “stack template” at a time?
  • • How do we represent adapter-specific capabilities (e.g., Next server actions) without polluting the core model?
  • • Do we allow third-party adapters to plug in at runtime, or require build-time registration?

    • 5. Public positioning & feature grid

    Definition

  • • Produce the “tight 2–3 paragraph About WELDR” plus feature bullet grid requested in the original note; align with “stack-aware, not stack-locked” messaging and highlight progressive ownership.

    • Draft narrative

    WELDR is the stack-aware application model that lets teams hand more of their product surface to AI without giving up ownership. The platform turns every edit—chat, form, or visual tooling—into DSL → IR → Canon artifacts so you can regenerate code deterministically, diff past versions, and plug the same timeline into tests, dashboards, or compliance workflows.

    Progressive ownership guardrails make regeneration trustworthy. You decide which files stay Managed, Hybrid, or Custom, and WELDR enforces that manifest every time a template, agent, or teammate touches the repo. Developers can invite AI back into high-churn surfaces while insulating hand-written code and pairing automated PRs with human approvals.

    Because renderers and data adapters are modular, WELDR ships with opinionated Next.js + Supabase defaults but is “stack-aware, not stack-locked.” Preview runtimes run in the browser via iframe/WebContainer, templates can be applied from the gallery or CLI, and adapters for additional frameworks slot into the same canonical pipeline without rewrites.

    Pros

  • • Sales/marketing clarity: consistent copy across website, decks, docs.
  • • Internal alignment: canonized messaging anchors product decisions (e.g., we can reject features that don’t support the wedge).
  • • Onboarding asset: easy handout for investors, partners, or new hires.

    • Cons

  • • Needs upkeep: as roadmap evolves copy can become stale; requires owner.
  • • Alignment overhead: getting buy-in from product/marketing takes time.
  • • Limited direct product impact: purely comms, so opportunity cost vs engineering work.

    • Effort & dependencies

    | Deliverable | Notes | Effort | | --- | --- | --- | | Messaging draft | 2–3 paragraph blurb + feature grid (use vision doc + this deep dive) | Low | | Review loop | Feedback from product/marketing/founder, iterate copy | Low-Medium | | Publication | Update docs/website, share with GTM team | Low |

    Dependencies: needs crisp articulation of other initiatives (pipeline, manifest, SVDOM) so messaging references real capabilities.

    Feature grid draft

    | Pillar | What it unlocks | Proof / Next Step | | --- | --- | --- | | Progressive Ownership Manifest | Declare which surfaces are Managed, Hybrid, or Custom so AI edits never clobber hand-written code. | Schema + enforcement hooks spec’d in §2; next step is to formalize the manifest artifact and wire it into renderers. | | Canonical Pipeline Artifacts | Replayable DSL → IR → Canon timeline with deterministic regeneration, diffing, and observability. | Tables + worker entry point exist (app_model_*, scripts/pipeline/run-canonicalizer.ts); initiative #1 tracks the durability hardening. | | Stack-Aware Renderer & Data Adapters | Swap or mix adapters (Next.js, future SvelteKit/Django) without changing the App Model or CLI workflow. | Interfaces scoped in §4; roadmap includes extracting current renderers into packages and documenting adapter contracts. | | Runtime-accurate Preview & Sandbox | Iframe/WebContainer runtime matches shipped experience and keeps test harnesses gated. | Runtime alignment initiative (#2) covers locking down app/test-* and refreshing docs so GTM copy links to the real flow. | | Template-to-Production Loop | Templates ship with CLI + gallery flows, seeded chats, and bundle metadata for reproducible launches. | Recent blog/TaskOps template exports demonstrate the loop; doc updates will link to catalog entries and CLI commands. |

    Open questions

  • • Who owns updates when roadmap shifts?
  • • Do we create variants per audience (developers vs execs) or keep one canonical blurb?

    • Cross-initiative considerations & prioritization thoughts

  • • Sequencing: Pipeline-as-first-class unlocks nearly every other initiative (manifest references model IDs, adapters consume canonical artifacts). SVDOM is deliberately paused until those land.
  • • Resourcing: Ownership manifest is a backend/platform-heavy effort; renderer modularization can run partially in parallel once Canon artifacts stabilize; messaging just needs product/marketing time.
  • • Experimentation path: prototype small slices (e.g., manifest enforcement on one feature area, adapter refactor around a single renderer) before rolling out platform-wide.
  • • Measurement: define success metrics per initiative now (e.g., % of surfaces with declared ownership, # of pipeline events stored, successful adapter fixture runs) so we can judge impact later.

    • This document should serve as the reference when we stack-rank or spin up dedicated epics around these themes.