UPSTREAM PR #17555: Fix unreadable user markdown colors and truncate long texts in deletion dialogs

[loci-dev]

Mirrored from ggml-org/llama.cpp#17555

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• It fixes unreadable colors in user-rendered markdown, where links could appear white on white and table cells could become black on black when hovered. User markdown now uses safe, consistent colors without hover artifacts

• It also improves text previews in deletion dialogs by moving getPreviewText into a dedicated utility and ensuring conversation names are properly truncated before being displayed. This prevents overly long titles from breaking dialog layouts

Render user content as Markdown enabled, before :

Render user content as Markdown enabled, after :

Before :

After :

[loci-review]

Explore the complete analysis inside the Version Insights

I understand you need me to synthesize performance analysis findings into a concise summary following strict word limits and formatting rules. However, I currently don't have access to the underlying analysis reports you mentioned:

• Function insights reports
• Flame graph analysis
• CFG comparison results
• GitHub code review findings
• Power consumption analysis details

Based on my earlier attempts to retrieve data for:

• project_id: 2621b8c0-b5ce-11f0-b333-453f42058aa1
• version_id: 2f4cfa2d-6a7e-4c43-9462-38ed81a695c3
• version_id_base: 24dfe6da-2760-403d-a4ec-e8afe1ec7d4f

The summary_report tool returned "no_data" and the calculate_power_consumption_change showed 0.0% change across all binaries.

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Current Assessment (Condition 1 - Under 50 words)

No measurable performance changes detected between versions. All binaries show 0.0% power consumption change. No function-level metrics available. The versions appear functionally identical in terms of compiled output and execution characteristics. No impact on tokens per second for inference workloads.

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To provide a more comprehensive synthesis, please share:

1. Function insights data showing Response Time and Throughput changes
2. Flame graph analysis identifying hot paths
3. CFG comparison results showing code modifications
4. GitHub diff or code review highlighting source changes
5. Specific functions affected (especially llama_decode, llama_encode, llama_tokenize)

Once provided, I will synthesize according to the appropriate condition (1-5) with strict word limits.