Use case
Developers using Codex, Claude Code, or Claude Desktop, when screenshots or design images accumulate across a multi-turn coding conversation, must decide before the next message which historical images keep being sent to the model.
The old approach is manually starting a new session, deleting history, or relying on model-side automatic context compression, with no way to pick image by image what to keep.
Historical images keep occupying context in long sessions, inflating request size and cost and potentially crowding out attention on the current task; this pain is inferred from the product capability and multi-turn conversation structure, as the candidate provides no user complaint text.
xOcto's call
Demand is evidenced
Trend: as coding agents move into long sessions, context size itself becomes something users must manage manually rather than relying only on model-side compression. Entry point: start from long-session cost and context governance, offering auditable context trimming and spend visibility to teams that heavily use coding agents; no pricing is disclosed in the candidate, so no revenue model should be assumed.
Reason to use it
Why users would choose it
Inference: compared with starting a new session or deleting history, it lets users check which images to keep before the next message, turning unchecked ones into placeholders, reducing the burden of losing context by restarting and controlling request size; without retention or repeat-use evidence, it cannot be claimed to be embedded in long-term workflows.
Where the easy answer breaks down
The tension worth following
An English validation note will follow from the public evidence.
If this is your job
Worth trying. Inference: compared with starting a new session or deleting history, it lets users check which images to keep before the next message, turning unchecked ones into placeholders, reducing the burden of losing context by restarting and controlling request size; without retention or repeat-use evidence, it cannot be claimed to be embedded in long-term workflows.
Entry and what to borrow
Trend: as coding agents move into long sessions, context size itself becomes something users must manage manually rather than relying only on model-side compression. Entry point: start from long-session cost and context governance, offering auditable context trimming and spend visibility to teams that heavily use coding agents; no pricing is disclosed in the candidate, so no revenue model should be assumed.