Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for Chinese-language developers and power users who want to test whether an LLM-backed whole-sentence input method is faster or less distracting than traditional candidate-picking IMEs.
Who should use it
Who should skip it
Consider madeye/ds-input lower priority if you already have a working solution in this category.
About this signal
madeye/ds-input is tracked by RepoRadar as a input method in the Local AI section. It was first seen on 2026-06-28 and last updated on 2026-06-28. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. madeye/ds-input leads on workflow potential (8.9) and open-source/build quality (8.4); its lowest signal is momentum (5.0), so factor that in before investing setup time. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned madeye/ds-input a composite score of 7.8 out of 10, placing it in the Silver tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
It sends what you type to the configured model endpoint, so anyone handling private text should point it at a trusted local server or a provider covered by their own data policy; Whole-sentence conversion removes manual candidate picking, so first use should stay on low-stakes text until you trust the model and timeout settings.