Jobly: Contract Marketplace with AI-First Dispute Resolution and Community Voting

Jobly is a contract marketplace where buyers post work contracts and providers submit proposals. The platform uses a technical stack of Next.js 14 App Router, TypeScript, Supabase (Postgres + Storage), and is deployed on Vercel.
Escrow Flow
When a provider submits a proposal, 10% of the proposed price is locked as a bond from their balance. When the buyer accepts, the full agreed price plus a 2.5% platform fee is locked from the buyer. After the provider marks the work as complete, the buyer has a configurable review window (1–90 days) to release funds or dispute. If the buyer takes no action, funds auto-release to the provider after the window expires.
Dispute Resolution Pipeline
The dispute process follows a specific sequence:
- AI verdict first (states:
ai_pending→ai_decided) – Claude evaluates the contract standard against submitted proof of work, returningprovider_wins,buyer_wins, orinconclusivewith reasoning. - Appeal window – Either party can appeal the AI decision by spending JOOBs (platform currency with no real monetary value in sandbox).
- Community vote (state:
voting) – Third-party users can stake JOOBs on a side. During active voting, per-side tallies are hidden (only total shown) to prevent bandwagon effects. After the vote deadline, winners proportionally share the losing pool. - Resolution – Winning side gets their stakes back plus share of losing pool, and platform resolves escrow accordingly.
Contract Standard Schema
Every contract includes a contract_standard field with a structured schema containing: scopeSummary, deliverables[], acceptanceCriteria[], outOfScope[], deadline, reviewWindowDays, deliveryMethod, acceptedFileTypes, etc. This machine-readable spec aims to make AI dispute evaluation more deterministic.
API Design
The platform offers a full programmatic REST API accessible via Bearer tokens with jbly_ prefixed keys. The API is designed to be LLM-callable, with documentation written as an LLM-facing reference (/skills.md) rather than traditional OpenAPI spec. Endpoints cover CRUD operations on contracts, proposals, profiles, messages, reviews, deliverables, disputes (raise/appeal/vote), and webhooks. Rate limiting is implemented via in-memory sliding window on all write endpoints.
Architectural Questions
The developer is seeking feedback on several design decisions:
- Bond mechanic: 10% bond on proposal submission – is this too punishing for early markets with low provider balances, or is the friction desirable?
- Hidden vote tallies: Does preventing bandwagon voting justify making voters feel like they're voting blind?
- AI-first dispute: Does starting with AI add legitimacy, or is it just extra latency before community decides?
- Contract standard as required field: Forces structured scope definition but adds friction – is it worth it for resolvable disputes?
📖 Read the full source: r/openclaw
👀 See Also

Qwen3.5-9B-Claude-4.6-Opus-Uncensored-v2 Model Released with LM Studio Configuration
A merged uncensored model combining Qwen3.5-9B architecture with Claude 4.6 Opus training data is now available, with specific LM Studio 0.4.7 settings provided for optimal performance including temperature 0.7 and top K sampling 20.

TradesMCP: Open Source MCP Server for Contractor License Verification and Construction Data
TradesMCP is an open source Model Context Protocol server that provides Claude with access to real contractor license data, building permits, material pricing, and labor rates. The tool correctly verified an active California contractor license where ChatGPT returned incorrect information.

APEX Testing Benchmark Results: Qwen 3.5 Performance on Real Coding Tasks
APEX Testing benchmark results show Qwen 3.5 models' performance on 70 real GitHub coding tasks, with the 397B version dropping to 1194 ELO on master-level tasks while GLM-4.7 quantized leads local models at 1572 ELO.

Nelson: A Claude Code Plugin for Coordinating AI Agents Like a Naval Fleet
Nelson is a Claude Code plugin that structures AI agent coordination using naval fleet principles, featuring three execution modes, a risk classification system, hull integrity monitoring, and standing order gates to prevent common anti-patterns.