Editing Openai/691a14bb-ac48-8008-85d0-2e4fb3f780d1 (section)

== gpt-5-thinking ==
=== User: I've been trying to debug an error that's preventing the persistence of settings in a web app's Admin Console. ===
I've been trying to debug an error that's preventing the persistence of settings in a web app's Admin Console. I've began debugging with model GPT-5 medium reasoning, but after multiple failed attempts to fix the problem -- or even identify the cause of the problem -- I then switched to GPT-5 high reasoning model. 

Unfortunately, even GPT-5 high reasoning is unable to solve this problem. Both models are merely repeating themselves and performing essentially the same tasks over and over with no results. I've wasted nearly 3 hours on this problem, to no avail. 

Which of the other available models should I change to? Which model is most suited to debugging, error identification & correction of this type, etc.?

=== Assistant: Switch to Claude Opus 4.1 Thinking for root-cause debugging. ===
Switch to Claude Opus 4.1 Thinking for root-cause debugging. It’s the best in your list at error localization and explaining failure chains across client → API → DB.

If that stalls, use this fallback order by scenario:
* Repo-wide or multi-file symptoms: Kimi K2. Very large context. Good for dumping logs, configs, and many files at once.
* Commit/diff-aware inspection or structured refactors: SWE-1.5. Strong on repo semantics and grouping related changes.
* Protocol/API or request/response anomalies (cookies, CSRF, caching, 4xx/5xx): Gemini 2.5 Pro. Good at HTTP traces and JSON/state reasoning.
* Logic/constraints bugs (validation, race conditions, invariants): o3 (high reasoning). Precise stepwise reasoning.
* Test scaffolding to reproduce the bug fast: Qwen3-Coder Fast. Generates focused unit/integration tests quickly.
* Pure code-search/boilerplate checks: GPT-5-Codex. Fast at locating misused APIs and writing minimal patches.

Minimal anti-looping prompt to give the chosen model:

<syntaxhighlight>Goal: Settings in Admin Console do not persist.

Context: [paste failing request log lines, server error excerpts, relevant files: handler, DTO/serializer, DB layer, cache, CSRF/config].

Constraints:
* Produce a 6-step investigation plan first.
* For each step, name the observable you need (log line, request/response diff, DB write result).
* After each hypothesis, show falsifying evidence or confirm with an artifact.
* No repeating prior steps. Each attempt must add a new hypothesis or new evidence.
* Output must include: (a) minimal repro, (b) failing request example, (c) patch or config diff, (d) verification command.

</syntaxhighlight>

High-leverage checks for “settings not persisting” you can paste as tasks:
# Client → API: Capture one save attempt with full request/response. Confirm 200/204 vs hidden 401/403/409/422. Check CSRF token, Content-Type, SameSite, CORS, and ETag/precondition headers.
# Server handler: Verify route method matches client. Confirm body parsing and schema validation; log rejected fields.
# Write path: Ensure transaction commits. Check ORM autocommit, await on async saves, and error swallowing.
# Caching layer: Confirm cache write-through/invalidations. Look for stale reads after write.
# Permissions: Verify feature flags/tenant filters. A missing tenant scope often reads the old row.
# Serialization: Make sure returned payload echoes new values; mismatch implies write succeeded but read path differs.

Pick model by the bottleneck:
* Need deep causal trace → Claude Opus 4.1 Thinking.
* Need huge context of files/logs → Kimi K2.
* Need repo-structured fixes/commits → SWE-1.5.
* Need HTTP/state scrutiny → Gemini 2.5 Pro.
* Need invariant math-like reasoning → o3 high reasoning.
* Need fast tests → Qwen3-Coder Fast.

=== User: Which model is best for beginning development to add a quite complicated feature set into an already complicated repo? ===
Which model is best for beginning development to add a quite complicated feature set into an already complicated repo? To clarify, I need to add OpenAI's ChatKit and Responses API + Tools (including TTS/STT) to an existing project. 

I've attached the .md file describing the plan. You must examine the plan to the extent necessary to determine which Windsurf model is best for this plan. Specifically, you should tell me which model will be best to start with (for creating the scaffolding/foundation and preparing everything), as well as which models should assist with each step of the plan.

=== Assistant: Start with GPT-5 High Reasoning for scaffolding. ===
Start with GPT-5 High Reasoning for scaffolding. It will plan the end-to-end glue across ChatKit, the Responses API, tools, retrieval, and voice I/O. Your plan couples all of these, so a long-context planner is optimal. HOSTED_ASSISTANT_VOICE_PLAN

===== Model map by plan phase =====

| Phase / Step                                                                                                                                       | Primary model            | Why                                                                        | Assist models                                                                                                                                                       |
| ------------------------------------------------------------------------------------------------------------------------ | ------------------------ | -------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ |
| Phase 1 — Chat foundation: integrate ChatKit UI → /chat calling Responses API, register tools, pass tenant metadata. 

HOSTED_ASSISTANT_VOICE_PLAN | GPT-5 High Reasoning     | Lays the scaffold, enforces request/response shapes, and threads metadata. | SWE-1.5 for structured repo diffs and clean commits; Claude Sonnet 4.5 for clear config and SDK docs text; Kimi K2 if you need to load many existing files at once. |
| Ingestion pipeline: uploads → normalize/OCR → chunk → embed → vector_store.create; map tenant→store. 

HOSTED_ASSISTANT_VOICE_PLAN                 | GPT-5 Medium Reasoning   | Balanced throughput for boilerplate services and workers.                  | Qwen3-Coder Fast to generate batch jobs, schemas, and retries quickly.                                                                                              |
| Phase 2 — Tool layer: sql_read, doc_summarize, compare_docs, with tenant validation and logging. 

HOSTED_ASSISTANT_VOICE_PLAN                     | o3 High Reasoning        | Precise contract/spec authoring and invariant checks on params and auth.   | Gemini 2.5 Pro to wire HTTP/JSON, error mapping, and idempotency; SWE-1.5 to group function files and tests into coherent commits.                                  |
| Architecture flow glue: ChatKit ↔ Responses API with tools and vector store. 

HOSTED_ASSISTANT_VOICE_PLAN                                         | Claude Opus 4.1 Thinking | Strong at cross-component causality and failure paths.                     | GPT-5 High as alternate if latency is acceptable.                                                                                                                   |
| Phase 3 — STT: Realtime API WebSocket streaming; capture mic frames and forward text to /chat. 

HOSTED_ASSISTANT_VOICE_PLAN                       | Gemini 2.5 Pro           | Good at evented streaming handlers and WebSocket state.                    | Grok Code Fast to draft front-end audio workers and frame queues.                                                                                                   |
| Phase 3 — TTS: audio/speech with gpt-4o-mini-tts or tts-1, return signed URL/base64; cache by (message_id, voice). 

HOSTED_ASSISTANT_VOICE_PLAN   | Claude Sonnet 4.5        | Clear server/controller code and response caching logic.                   | GPT-5 Medium to finalize SDK calls and response types.                                                                                                              |
| Phase 4 — UI voice controls: Hold-to-Speak, Speak Response, settings toggle. 

HOSTED_ASSISTANT_VOICE_PLAN                                         | Grok Code Fast           | Fast JS/TS components and event wiring.                                    | Haiku 4.5 for small, rapid UI edits.                                                                                                                                |
| Phase 5 — Observability & security: per-tenant isolation, signed URLs, cost tracking. 

HOSTED_ASSISTANT_VOICE_PLAN                                | SWE-1.5                  | Commit hygiene, logging hooks, and policy checks across files.             | o3 High to write assertions and guardrails; Qwen3-Coder Fast to generate probes and tests.                                                                          |
| Deliverables sweep: /chat, /ingest, /tts, /stt, ChatKit UI, tenant-aware File Search, logging/caching/costs. 

HOSTED_ASSISTANT_VOICE_PLAN         | Kimi K2                  | Load many files and tickets at once for the final integration pass.        | SWE-1.5 to batch and label commits coherently.                                                                                                                      |

===== Default starting point =====
* Start: GPT-5 High Reasoning until /chat and tool registration compile and pass basic e2e. HOSTED_ASSISTANT_VOICE_PLAN
* Then: switch to GPT-5 Medium for throughput on services and adapters.
* Use Kimi K2 only when you must reason over many files and logs simultaneously.

===== Anti-looping build prompt =====

Give the active model this control block to prevent repetition:

<syntaxhighlight>Project: Hosted assistant with ChatKit + Responses API + Tools + File Search + Voice I/O.

Constraints:
* Output a phased scaffold plan with file tree, interfaces, and minimal runnable stubs.
* Before writing code, list risks by phase and a test to falsify each risk.
* After each commit set, produce: (a) commands to run locally, (b) one curl example per endpoint, (c) a verification log expected pattern.
* Do not repeat previous attempts. Each iteration must add a new artifact or a refactor diff.

</syntaxhighlight>

===== Notes tied to the plan =====
* Core stack: ChatKit UI, Responses API orchestration, custom tool endpoints, vector stores, Audio/Realtime APIs. HOSTED_ASSISTANT_VOICE_PLAN
* Voice flow and chosen audio models are fixed in the plan; keep server code aligned with those endpoints and params. HOSTED_ASSISTANT_VOICE_PLAN  HOSTED_ASSISTANT_VOICE_PLAN

If you want, I can produce a one-screen “who to use when” card scoped only to your repo and this plan.