Verifisert mot live Microsoft Learn (foundry/openai/how-to/fine-tuning-cost-management,
prompt-caching, use-your-data):
- RFT (Reinforcement Fine-Tuning) lagt til (filen dekket kun SFT/DPO): tidsbasert «pris = treningstid
× timepris + grader-tokens», ~$100/time core training for o4-mini-2025-04-16, per-job-tak $5000
(pauses + deploybart checkpoint ved tak, kan resume uten videre pristak). Cost-control-strategier med.
- Cache-TTL-tabell hadde FEIL akse («Foundry Classic vs AI Services») → korrigert til in-memory
(5-10 min/maks 1t, alle GPT-4o+) vs extended (24h, GPT-5-serien+gpt-4.1, default for nyere).
- prompt_cache_key ~15 req/min overflow-caveat lagt til.
- On Your Data deprecation 2026-10-14 flagget (3 steder: token-est-seksjon, Foundry-seksjon, kilde #6).
- tiktoken model-set manglet o1-mini/o3-mini → lagt til.
- Header 2026-02→2026-06, verification-date 2026-02-04→2026-06. Priser urørt (kanonisk peker). validate 239/0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add /ultraresearch-local for structured research combining local codebase
analysis with external knowledge via parallel agent swarms. Produces research
briefs with triangulation, confidence ratings, and source quality assessment.
New command: /ultraresearch-local with modes --quick, --local, --external, --fg.
New agents: research-orchestrator (opus), docs-researcher, community-researcher,
security-researcher, contrarian-researcher, gemini-bridge (all sonnet).
New template: research-brief-template.md.
Integration: --research flag in /ultraplan-local accepts pre-built research
briefs (up to 3), enriches the interview and exploration phases. Planning
orchestrator cross-references brief findings during synthesis.
Design principle: Context Engineering — right information to right agent at
right time. Research briefs are structured artifacts in the pipeline:
ultraresearch → brief → ultraplan --research → plan → ultraexecute.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>