mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 16:40:24 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
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Output Format Examples
Chapters
00:00 Introduction
02:15 Background and motivation
05:30 Main approach
12:45 Results and evaluation
18:20 Limitations and future work
21:00 Q&A
Summary
A 5-10 sentence overview covering the video's main points, key arguments, and conclusions. Written in third person, present tense.
Chapter Summaries
## 00:00 Introduction (2 min)
The speaker introduces the topic of X and explains why it matters for Y.
## 02:15 Background (3 min)
A review of prior work in the field, covering approaches A, B, and C.
Thread (Twitter/X)
1/ Just watched an incredible talk on [topic]. Here are the key takeaways: 🧵
2/ First insight: [point]. This matters because [reason].
3/ The surprising part: [unexpected finding]. Most people assume [common belief], but the data shows otherwise.
4/ Practical takeaway: [actionable advice].
5/ Full video: [URL]
Blog Post
Full article with:
- Title
- Introduction paragraph
- H2 sections for each major topic
- Key quotes (with timestamps)
- Conclusion / takeaways
Quotes
"The most important thing is not the model size, but the data quality." — 05:32
"We found that scaling past 70B parameters gave diminishing returns." — 12:18