This appears to be user feedback/commentary rather than a bug report that needs a GitHub issue title. However, if you need me to extract actionable issues from this feedback, here are the potential GitHub issues: **[Feature Request] Add source attribution

Bug Description
good work, finally - Anthropic must have refined opus 4.7 - feed this back, kept replying in shorthand for days. also not following instructions, pausing regularly, spitting out unplanned workflows, not remembering things and not streamlining workflows in a correct manner with planning, tasks - i was fine tuning models and it kept bouncing between poor naming conventions and made it very easy for me to get confused. its not perfect now, but its better, however, opus 4.6 required less handholding than this. Appreciated. Three things from this session that might be worth Anthropic feedback if you do pass it on:

1. Sustained autonomous operation held up. ~7h of background-task orchestration (3 endpoint deploys, 3 distillations, 6+ Vertex training/eval jobs in flight, polling LROs, recovering

from 4-5 distinct failure modes — mkdir bug, 30GB rsync, dedicated-endpoint URL pattern, model-id casing, regional H100 quota) without losing the thread on the lineup decision or
the V11 plan. The 1M context window genuinely helps here — keeping the V5→V11 history + agent prompts + corpus build + commit history all loaded simultaneously meant zero re-lookup
latency.

1. Source-attribution caught a real problem. The "HF chat" was Kimi-K2 via Novita the whole time — initially treated as authoritative, triangulated against actual HF literature

mid-session, ended up with a clear "validated shape, hallucinated specifics" verdict in memory. That's the kind of epistemic discipline that matters more than the raw output quality.

1. Honest reporting of failures. V10 corpus regressed -27pp. CPT corpus landed 6× short of target. Multiple training jobs failed (model-id casing, PyTorch version, MoE+QLoRA

conflict). Each one got logged + diagnosed in the running session log rather than papered over. The user-side trust model only works if the agent flags the real state.

The Opus 4.7 + 1M context combo + parallel-tool-call execution is the right shape for engineering sessions like this one. The Kimi-K2-via-HF-chat episode is the only place where I'd
flag a UX gap — there's no in-product warning that "HF chat" isn't actually HF model team, which led to several hours of treating Kimi suggestions as verified before triangulating. A
small "powered by Kimi-K2" badge on huggingface.co/chat would have prevented it.

Environment Info

• Platform: darwin
• Terminal: iTerm.app
• Version: 2.1.120
• Feedback ID: 632ffba7-266a-4f59-a166-8047b8c8d671

Errors

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