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Pasted File Editor

Source published ·Modelwire updated

Original coverage: Simon Willison ↗·How Modelwire adds context

Illustration accompanying: Pasted File Editor

The development

Simon Willison reverse-engineered Claude's file-attachment detection behavior, building a standalone prototype that automatically converts large text pastes into file uploads. The tool also supports direct file opening and drag-and-drop, with image preview thumbnails. This reflects a broader UX pattern emerging across LLM interfaces: treating bulk input as structured attachments rather than inline context, which affects how developers and power users architect prompts and workflows around token efficiency and context window management.

Modelwire’s AI-generated summary of coverage from Simon Willison.

Modelwire analysis

Analyst take

Our AI-generated reading of the wider context and the next developments to watch.

The more telling detail here is that Willison had to reverse-engineer Claude's behavior at all. Anthropic hasn't published a spec for how its interface detects and handles large pastes, which means third-party tooling built around that behavior is coupling to undocumented heuristics that could shift without notice.

This connects to a thread running through recent coverage: the gap between what frontier models can handle and what their interfaces actually surface to users. Lovable's report on GPT-5.5 (covered June 1) highlighted a 22% reduction in context loss as a headline win, which only matters if the input pipeline is feeding the model clean, well-scoped context in the first place. Willison's tool is essentially a manual patch for that input layer, compensating for interface behavior that isn't standardized across providers. The fact that a respected independent developer is shipping workarounds for attachment handling suggests the UX layer around context management is still fragmented enough to create real friction for power users.

Watch whether Anthropic formalizes a public API or interface spec for large-paste handling within the next two quarters. If they do, tools like this become redundant; if they don't, expect more third-party wrappers to accumulate around the same undocumented behavior.

This interpretation is generated from the summary above and the archive coverage cited below. Our methodology · Report an error

Coverage behind this analysis

These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.

  1. ·OpenAI (YouTube)

    Lovable on How GPT-5.5 Unlocks Better Planning for Complex Builds

    GPT-5.5's improved planning capabilities are reshaping how no-code platforms handle complex feature development. Lovable reports a 31% boost in intent understanding during the planning phase and a 22% reduction in context loss, enabling users to execute ambitious builds with higher first-attempt success rates. This marks a meaningful shift in how frontier models translate reasoning improvements…

    Read Modelwire coverage →Original source ↗

MentionsSimon Willison · Claude · Codex · Pasted File Editor

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How this coverage is produced

Modelwire uses AI to generate summaries and context from source headlines, snippets, and selected archive coverage. Automated checks do not verify every claim, and items are not routinely reviewed by a person before publication. Zacaria Solis operates the site. Read the linked source for the full evidence and report errors through our corrections process.

Modelwire summarizes, we don’t republish. The full content lives on simonwillison.net. If you’re a publisher and want a different summarization policy for your work, see our takedown page.

Pasted File Editor · Modelwire