Qwen3.5 users share OpenCode JSON fix

// 83d agoTUTORIAL

Qwen3.5 users share OpenCode JSON fix

A LocalLLaMA user reports strong coding performance from Qwen3.5 122B/397B but recurring JSON write errors in OpenCode when tool output is emitted as an object instead of a string. Their practical fix is to enforce a system-prompt rule requiring JSON files to be written via a bash heredoc.

// ANALYSIS

This is a useful field fix for local-agent workflows, but it highlights brittle tool-call formatting boundaries rather than a true model capability gap.

–The workaround is prompt-level and easy to apply across test runs, so it is immediately useful for practitioners.
–The failure mode appears in structured file-writing paths, where minor output-shape drift can break automation pipelines.
–Using heredoc sidesteps strict JSON serialization expectations in tool interfaces and reduces format mismatch risk.
–Because this is community guidance from Reddit, teams should still validate against their own OpenCode/tooling stack and model quantization setup.

// TAGS

qwen3-5opencodellmai-codingcliprompt-engineering

DISCOVERED

83d ago

2026-03-05

PUBLISHED

83d ago

2026-03-05

RELEVANCE

7/ 10

AUTHOR

BitXorBit

// KEEP READING

More AI developer news from the feed

EXPLORE FULL FEED

UPDATE4h ago

Cursor adds dedicated subagents for skills

Cursor now allows developers to execute tool-heavy or research-intensive agent skills within dedicated subagents. This architectural shift isolates noisy background tasks, keeping the main chat context clean and focused.

UPDATE5h ago

YouTube moves AI labels to video player

YouTube is moving its AI content disclosures from video descriptions to more prominent placements beneath the player and on Shorts overlays. Starting in May, the platform will use internal signals to automatically label photorealistic AI content that creators fail to disclose.

OPEN SOURCE8h ago

Taste Skill kills AI "frontend slop"

Taste-Skill is an open-source framework that provides portable "agent skills" to enforce high-end design principles in AI-generated code. By injecting specific design directives and "anti-slop" rules, it enables LLMs to produce editorial-grade UIs that bypass generic, boilerplate-heavy AI templates.