self.cursor is a byte index into the string. When scanning the buffer,
we need to compare character positions, not byte positions or widths.
Convert self.cursor to a character count before comparing with the
buffer scan. Count each non-empty cell as 1 character (the buffer
already represents visual cells, so width doesn't matter here).
The cursor index is into self.input, but the rendered buffer contains
the prompt prepended to the first line. Need to add prompt.len() to
get the correct character position when scanning the buffer.
Instead of simulating ratatui's word wrapping algorithm, scan the
rendered buffer to find the actual cursor position. This correctly
handles word wrapping, unicode widths, and any other rendering
nuances that ratatui applies.
The old code computed wrapped_height() and cursor position based on
simple character counting, which diverged from ratatui's WordWrapper
that respects word boundaries.
Now we render first, then walk the buffer counting visible characters
until we reach self.cursor. This is O(area) but the input area is
small (typically < 200 cells), so it's negligible.
Use unicode display width (matching ratatui's Wrap behavior) instead
of chars().count() for both wrapped_height calculation and cursor
positioning. The mismatch caused the cursor to drift when input
wrapped to multiple lines.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Always display reasoning tokens regardless of reasoning_effort
setting — Qwen 3.5 thinks natively and the reasoning parser
separates it into its own field
- Remove chat_template_kwargs that disabled thinking when
reasoning_effort was "none"
- Add chat_template_kwargs field to ChatRequest for vllm compat
- Update provision script: qwen3_xml tool parser, qwen3 reasoning
parser, 262K context, 95% GPU memory utilization
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Split poc-agent into lib + bin so its API client, types, and tool
dispatch can be imported by poc-memory. This is the foundation for
replacing claude CLI subprocess calls with direct API calls to
vllm/OpenAI-compatible endpoints.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Move poc-agent (substrate-independent AI agent framework) into the
memory workspace as a step toward using its API client for direct
LLM calls instead of shelling out to claude CLI.
Agent prompt improvements:
- distill: rewrite from hub-focused to knowledge-flow-focused.
Now walks upward from seed nodes to find and refine topic nodes,
instead of only maintaining high-degree hubs.
- distill: remove "don't touch journal entries" restriction
- memory-instructions-core: add "Make it alive" section — write
with creativity and emotional texture, not spreadsheet summaries
- memory-instructions-core: add "Show your reasoning" section —
agents must explain decisions, especially when they do nothing
- linker: already had emotional texture guidance (kept as-is)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
