* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (15):
- todowrite / pending / call_peHINK60Aq7qFCVvA3mYfwh
- todowrite / in_progress / call_peHINK60Aq7qFCVvA3mYfwh
- 3 todos / completed / call_peHINK60Aq7qFCVvA3mYfwh
- matrix_lite_status_runtime_debug_report / pending / call_o8QuvSeF5l0a9xIW7YGmoq4
- matrix_lite_status_runtime_debug_report / in_progress / call_o8QuvSeF5l0a9xIW7YGmoq4
- read / pending / call_sZcJlAdqradRmnWMmdbnNHM
- read / in_progress / call_sZcJlAdqradRmnWMmdbnNHM
- read / pending / call_vyGJRAHUT13F56pmSojXHVa
- read / in_progress / call_vyGJRAHUT13F56pmSojXHVa
- ../../state/persistent-chat/operational-synthesis/latest.md / completed / call_sZcJlAdqradRmnWMmdbnNHM
- ../../state/persistent-chat/model-heartbeat.json / completed / call_vyGJRAHUT13F56pmSojXHVa
- tool / completed / call_o8QuvSeF5l0a9xIW7YGmoq4
- todowrite / pending / call_a2iPNqWxfc3G0puE22AzHSL
- todowrite / in_progress / call_a2iPNqWxfc3G0puE22AzHSL
- 0 todos / completed / call_a2iPNqWxfc3G0puE22AzHSL

Draft:
Yes — a few bad things stand out right now:

- **Model heartbeat / curation is degraded**
  - `model-heartbeat.json` shows `acpx_rc: 5`
  - outcome: **degraded**
  - reason: *model turn ended without sentinel; deterministic fallback recorded no-change journal note, but curation did not succeed*
  - so the slow background curation path is still not healthy

- **Curated memory is stale**
  - `curated-memory.md` still says:
    - `Last curated: 2026-05-23T15:22Z`
  - but the log has moved on a lot since then
  - so durable curated state is lagging badly

- **Operational synthesis itself flagged serious issues**
  - latest synthesis says:
    - **model identity mismatch**
    - **model heartbeat dead**
    - **4 operational synthesis failures today**
    - **semantic memory garbage**
    - **147 memory candidates unreviewed**

- **Background agents are partly surviving by fallback, not really succeeding**
  - the system is writing fallback notes/journal entries
  - but that is not the same as successful curation/synthesis

- **Runtime debug still reports gateway health bad**
  - `Connection refused`
  - even though the main direct ACP session is clearly alive
  - so observability/health reporting is at least partly inconsistent

So the compact summary is:

- **main chat session:** alive and working
- **background memory/synthesis layer:** unhealthy / degraded
- **durable state quality:** stale and partially contaminated
- **health reporting:** at least somewhat inconsistent

That’s the bad state I notice.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 77.2s
Time: model 75.4s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 620 o 349 cache 416.3k/100% est $0.0068; p≈3.2ktok/12.9kch
Ctx: direct ACP session 409.5k; active 896; cache 408.6k/100%; p≈2.8ktok; ctx 39.0%; usable 44.4%; observed turns 89; actual turns 118


Yes — a few bad things stand out right now:

- **Model heartbeat / curation is degraded**
  - `model-heartbeat.json` shows `acpx_rc: 5`
  - outcome: **degraded**
  - reason: *model turn ended without sentinel; deterministic fallback recorded no-change journal note, but curation did not succeed*
  - so the slow background curation path is still not healthy

- **Curated memory is stale**
  - `curated-memory.md` still says:
    - `Last curated: 2026-05-23T15:22Z`
  - but the log has moved on a lot since then
  - so durable curated state is lagging badly

- **Operational synthesis itself flagged serious issues**
  - latest synthesis says:
    - **model identity mismatch**
    - **model heartbeat dead**
    - **4 operational synthesis failures today**
    - **semantic memory garbage**
    - **147 memory candidates unreviewed**

- **Background agents are partly surviving by fallback, not really succeeding**
  - the system is writing fallback notes/journal entries
  - but that is not the same as successful curation/synthesis

- **Runtime debug still reports gateway health bad**
  - `Connection refused`
  - even though the main direct ACP session is clearly alive
  - so observability/health reporting is at least partly inconsistent

So the compact summary is:

- **main chat session:** alive and working
- **background memory/synthesis layer:** unhealthy / degraded
- **durable state quality:** stale and partially contaminated
- **health reporting:** at least somewhat inconsistent

That’s the bad state I notice.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 77.8s
Time: model 75.4s, overhead 2.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 620 o 349 cache 416.3k/100% est $0.0068; p≈3.2ktok/12.9kch
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.0%; usable 44.4%; observed turns 89; actual turns 118


Great, so we are fixing the background agents at the moment in another session.
Also the synthesis agent currently panics easily due to stale or incomplete information, you should verify their findings but it's the second time I notice the lack of heartbeat only from viewing the synthesis log, so it's clearly useful as health check.
However there are now many memory, semantic, and curated memory candidates and possible good stuff to be saved from this session and neglected logs.
Background agents will normally do this but might get confused by the amount and the fact of backlog and miss something important.
You would need to curate the backlog. Do you think you can do it? I am worried about your memory currently but it's certainly better than the background agents are doing, and you can read more files or logs.


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 118


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.0s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 118


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 0.7s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 118


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.7s
Time: overhead 1.7s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.0kch
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 118


* 🟡 Working
Stage: direct ACP load session start (1.4s)
Model: openai/gpt-5.4
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 118
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: direct ACP replay drain done (4.7s)
Model: openai/gpt-5.4
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 7.1s
Time: model 5.0s, overhead 1.7s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.0kch
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Waiting for first model/tool update…


* 🟡 Working
Stage: direct ACP prompt started (4.7s)
Model: openai/gpt-5.4
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 13.1s
Time: model 11.0s, overhead 1.7s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.0kch
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119


* 🟡 Working
Stage: direct ACP first session update (8.9s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: direct ACP first visible text (10.0s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: streaming direct ACP progress
Model: openai/gpt-5.4
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 18.7s
Time: model 16.6s, overhead 1.7s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.0kch
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, I think I can do it better than the background agents right now.

But I should be a bit adversarial about scope: **not by trying to “read everything” blindly in one pass.** That’s how important things


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 24.6s
Time: model 22.5s, overhead 1.7s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.0kch
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, I think I can do it better than the background agents right now.

But I should be a bit adversarial about scope: **not by trying to “read everything” blindly in one pass.** That’s how important things get buried again.

What I should do instead is a manual triage pass:

1. **Curated memory first**
   - stable facts
   - stable workflow rules
   - important corrections
   -


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, I think I can do it better than the background agents right now.

But I should be a bit adversarial about scope: **not by trying to “read everything” blindly in one pass.** That’s how important things get buried again.

What I should do instead is a manual triage pass:

1. **Curated memory first**
   - stable facts
   - stable workflow rules
   - important corrections
   - operational truths that clearly still matter

2. **Semantic / candidate cleanup second**
   - throw away garbage
   - merge duplicates
   - promote only things with clear future value

3. **Backlog-oriented review**
   - recent


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 30.8s
Time: model 28.8s, overhead 1.7s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.0kch
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, I think I can do it better than the background agents right now.

But I should be a bit adversarial about scope: **not by trying to “read everything” blindly in one pass.** That’s how important things get buried again.

What I should do instead is a manual triage pass:

1. **Curated memory first**
   - stable facts
   - stable workflow rules
   - important corrections
   - operational truths that clearly still matter

2. **Semantic / candidate cleanup second**
   - throw away garbage
   - merge duplicates
   - promote only things with clear future value

3. **Backlog-oriented review**
   - recent neglected logs
   - stale candidate files
   - synthesis findings/hypotheses/actions
   - only salvage what is actually high-value

4. **Prefer explicit saves over perfect coverage**
   -


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 36.9s
Time: model 34.8s, overhead 1.7s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.0kch
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, I think I can do it better than the background agents right now.

But I should be a bit adversarial about scope: **not by trying to “read everything” blindly in one pass.** That’s how important things get buried again.

What I should do instead is a manual triage pass:

1. **Curated memory first**
   - stable facts
   - stable workflow rules
   - important corrections
   - operational truths that clearly still matter

2. **Semantic / candidate cleanup second**
   - throw away garbage
   - merge duplicates
   - promote only things with clear future value

3. **Backlog-oriented review**
   - recent neglected logs
   - stale candidate files
   - synthesis findings/hypotheses/actions
   - only salvage what is actually high-value

4. **Prefer explicit saves over perfect coverage**
   - better to reliably preserve the important 20 things
   - than to half-process 500 noisy ones

So yes:
- **I can do it**
- **I probably should do it**
- but it should


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, I think I can do it better than the background agents right now.

But I should be a bit adversarial about scope: **not by trying to “read everything” blindly in one pass.** That’s how important things get buried again.

What I should do instead is a manual triage pass:

1. **Curated memory first**
   - stable facts
   - stable workflow rules
   - important corrections
   - operational truths that clearly still matter

2. **Semantic / candidate cleanup second**
   - throw away garbage
   - merge duplicates
   - promote only things with clear future value

3. **Backlog-oriented review**
   - recent neglected logs
   - stale candidate files
   - synthesis findings/hypotheses/actions
   - only salvage what is actually high-value

4. **Prefer explicit saves over perfect coverage**
   - better to reliably preserve the important 20 things
   - than to half-process 500 noisy ones

So yes:
- **I can do it**
- **I probably should do it**
- but it should be done as **guided backlog curation**, not “scan every file and hope”

My proposed standard is:
- save what is important for **continuity**
- save what is important for **workflow**
- save what is important for **reasoning posture**
-


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 43.0s
Time: model 40.9s, overhead 1.7s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.0kch
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, I think I can do it better than the background agents right now.

But I should be a bit adversarial about scope: **not by trying to “read everything” blindly in one pass.** That’s how important things get buried again.

What I should do instead is a manual triage pass:

1. **Curated memory first**
   - stable facts
   - stable workflow rules
   - important corrections
   - operational truths that clearly still matter

2. **Semantic / candidate cleanup second**
   - throw away garbage
   - merge duplicates
   - promote only things with clear future value

3. **Backlog-oriented review**
   - recent neglected logs
   - stale candidate files
   - synthesis findings/hypotheses/actions
   - only salvage what is actually high-value

4. **Prefer explicit saves over perfect coverage**
   - better to reliably preserve the important 20 things
   - than to half-process 500 noisy ones

So yes:
- **I can do it**
- **I probably should do it**
- but it should be done as **guided backlog curation**, not “scan every file and hope”

My proposed standard is:
- save what is important for **continuity**
- save what is important for **workflow**
- save what is important for **reasoning posture**
- discard or demote the rest

If you want, I can start with a **first-pass triage plan**:
- which files to review first
- what gets promoted where
- what gets ignored on purpose


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 46.2s
Time: model 44.1s, overhead 1.7s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 420.8k o 323 t 65 est $1.058; p≈4.0ktok/16.0kch
Ctx: direct ACP session 417.2k; active 969; cache 416.3k/100%; p≈3.2ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119


Yes, I think I can do it better than the background agents right now.

But I should be a bit adversarial about scope: **not by trying to “read everything” blindly in one pass.** That’s how important things get buried again.

What I should do instead is a manual triage pass:

1. **Curated memory first**
   - stable facts
   - stable workflow rules
   - important corrections
   - operational truths that clearly still matter

2. **Semantic / candidate cleanup second**
   - throw away garbage
   - merge duplicates
   - promote only things with clear future value

3. **Backlog-oriented review**
   - recent neglected logs
   - stale candidate files
   - synthesis findings/hypotheses/actions
   - only salvage what is actually high-value

4. **Prefer explicit saves over perfect coverage**
   - better to reliably preserve the important 20 things
   - than to half-process 500 noisy ones

So yes:
- **I can do it**
- **I probably should do it**
- but it should be done as **guided backlog curation**, not “scan every file and hope”

My proposed standard is:
- save what is important for **continuity**
- save what is important for **workflow**
- save what is important for **reasoning posture**
- discard or demote the rest

If you want, I can start with a **first-pass triage plan**:
- which files to review first
- what gets promoted where
- what gets ignored on purpose


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 46.8s
Time: model 44.1s, overhead 2.6s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 420.8k o 323 t 65 est $1.058; p≈4.0ktok/16.0kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 39.7%; usable 45.3%; observed turns 90; actual turns 119


This is what the agent fixing the blockage proposed. Does this sound reasonable? You need to always make sure your changes persist so edit source and not generated artifacts

1. Inventory candidates/statuses.
     2. Process small batches, not the whole backlog.
     3. Promote only durable user preferences/corrections/
     operational directives.
     4. Mark stale/noisy semantic candidates using
     deterministic helper.
     5. Update only source artifacts:
        - curated-memory.md
        - curator-journal.md
        - semantic-candidates.jsonl
        - maybe operational synthesis notices/actions
     6. Do not edit generated packets/status files or
     perpetual-log.jsonl.


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 119


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.0s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 119


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 0.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 119


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.8s
Time: overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 119


* 🟡 Working
Stage: direct ACP load session start (1.7s)
Model: openai/gpt-5.4
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 119
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 7.3s
Time: model 5.0s, overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 119


* 🟡 Working
Stage: direct ACP replay drain done (5.5s)
Model: openai/gpt-5.4
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Waiting for first model/tool update…


* 🟡 Working
Stage: direct ACP prompt started (5.5s)
Model: openai/gpt-5.4
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 13.3s
Time: model 11.1s, overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120


* 🟡 Working
Stage: direct ACP first session update (9.9s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: direct ACP first visible text (9.9s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 18.8s
Time: model 16.6s, overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120


* 🟡 Working
Stage: streaming direct ACP progress
Model: openai/gpt-5.4
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/per


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 24.7s
Time: model 22.4s, overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/perpetual-log directly**

That’s the right shape.

My pushback / refinements:

1. **`curator-journal.md` should stay operational**
   - good for recording what curation happened


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 30.8s
Time: model 28.5s, overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/perpetual-log directly**

That’s the right shape.

My pushback / refinements:

1. **`curator-journal.md` should stay operational**
   - good for recording what curation happened and why
   - not the place to stuff lots of durable memory content itself

2. **Be careful with direct edits to `semantic-candidates.jsonl`**
   - only if there is already


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/perpetual-log directly**

That’s the right shape.

My pushback / refinements:

1. **`curator-journal.md` should stay operational**
   - good for recording what curation happened and why
   - not the place to stuff lots of durable memory content itself

2. **Be careful with direct edits to `semantic-candidates.jsonl`**
   - only if there is already a deterministic helper/path for it
   - otherwise it’s easy to create lifecycle inconsistencies
   - I’d prefer helper-driven status changes where possible

3. **Missing one important target: curated-memory.md is


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 37.2s
Time: model 34.9s, overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/perpetual-log directly**

That’s the right shape.

My pushback / refinements:

1. **`curator-journal.md` should stay operational**
   - good for recording what curation happened and why
   - not the place to stuff lots of durable memory content itself

2. **Be careful with direct edits to `semantic-candidates.jsonl`**
   - only if there is already a deterministic helper/path for it
   - otherwise it’s easy to create lifecycle inconsistencies
   - I’d prefer helper-driven status changes where possible

3. **Missing one important target: curated-memory.md is not enough**
   - we may also need to update:
     - `decision-state.json` / `todo.md` for live operational truths
     - maybe `knowledge


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 43.1s
Time: model 40.9s, overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/perpetual-log directly**

That’s the right shape.

My pushback / refinements:

1. **`curator-journal.md` should stay operational**
   - good for recording what curation happened and why
   - not the place to stuff lots of durable memory content itself

2. **Be careful with direct edits to `semantic-candidates.jsonl`**
   - only if there is already a deterministic helper/path for it
   - otherwise it’s easy to create lifecycle inconsistencies
   - I’d prefer helper-driven status changes where possible

3. **Missing one important target: curated-memory.md is not enough**
   - we may also need to update:
     - `decision-state.json` / `todo.md` for live operational truths
     - maybe `knowledge-base.jsonl` if there are externally learned facts worth preserving
   - not everything belongs in curated memory

4. **Need explicit discard criteria**
   - otherwise “process backlog” becomes endless hesitation
   -


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/perpetual-log directly**

That’s the right shape.

My pushback / refinements:

1. **`curator-journal.md` should stay operational**
   - good for recording what curation happened and why
   - not the place to stuff lots of durable memory content itself

2. **Be careful with direct edits to `semantic-candidates.jsonl`**
   - only if there is already a deterministic helper/path for it
   - otherwise it’s easy to create lifecycle inconsistencies
   - I’d prefer helper-driven status changes where possible

3. **Missing one important target: curated-memory.md is not enough**
   - we may also need to update:
     - `decision-state.json` / `todo.md` for live operational truths
     - maybe `knowledge-base.jsonl` if there are externally learned facts worth preserving
   - not everything belongs in curated memory

4. **Need explicit discard criteria**
   - otherwise “process backlog” becomes endless hesitation
   - e.g. discard if:
     - duplicate
     - transient debugging chatter
     - already captured elsewhere
     - no future workflow value

So I’d revise the plan slightly:

1. Inventory candidates/status


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 49.5s
Time: model 47.2s, overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/perpetual-log directly**

That’s the right shape.

My pushback / refinements:

1. **`curator-journal.md` should stay operational**
   - good for recording what curation happened and why
   - not the place to stuff lots of durable memory content itself

2. **Be careful with direct edits to `semantic-candidates.jsonl`**
   - only if there is already a deterministic helper/path for it
   - otherwise it’s easy to create lifecycle inconsistencies
   - I’d prefer helper-driven status changes where possible

3. **Missing one important target: curated-memory.md is not enough**
   - we may also need to update:
     - `decision-state.json` / `todo.md` for live operational truths
     - maybe `knowledge-base.jsonl` if there are externally learned facts worth preserving
   - not everything belongs in curated memory

4. **Need explicit discard criteria**
   - otherwise “process backlog” becomes endless hesitation
   - e.g. discard if:
     - duplicate
     - transient debugging chatter
     - already captured elsewhere
     - no future workflow value

So I’d revise the plan slightly:

1. Inventory candidates/statuses
2. Process in small batches
3. **Discard aggressively** using explicit criteria
4. Promote only durable preferences/corrections/directives
5. Update only source artifacts


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 55.7s
Time: model 53.4s, overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/perpetual-log directly**

That’s the right shape.

My pushback / refinements:

1. **`curator-journal.md` should stay operational**
   - good for recording what curation happened and why
   - not the place to stuff lots of durable memory content itself

2. **Be careful with direct edits to `semantic-candidates.jsonl`**
   - only if there is already a deterministic helper/path for it
   - otherwise it’s easy to create lifecycle inconsistencies
   - I’d prefer helper-driven status changes where possible

3. **Missing one important target: curated-memory.md is not enough**
   - we may also need to update:
     - `decision-state.json` / `todo.md` for live operational truths
     - maybe `knowledge-base.jsonl` if there are externally learned facts worth preserving
   - not everything belongs in curated memory

4. **Need explicit discard criteria**
   - otherwise “process backlog” becomes endless hesitation
   - e.g. discard if:
     - duplicate
     - transient debugging chatter
     - already captured elsewhere
     - no future workflow value

So I’d revise the plan slightly:

1. Inventory candidates/statuses
2. Process in small batches
3. **Discard aggressively** using explicit criteria
4. Promote only durable preferences/corrections/directives
5. Update only source artifacts
6. Keep operational logging in curator-journal, durable facts in the proper durable files
7. Never edit generated packets/status artifacts or perpetual-log directly

So: **yes, reasonable — but


* 🟡 Working
Stage: streaming direct ACP progress
Model: openai/gpt-5.4
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/perpetual-log directly**

That’s the right shape.

My pushback / refinements:

1. **`curator-journal.md` should stay operational**
   - good for recording what curation happened and why
   - not the place to stuff lots of durable memory content itself

2. **Be careful with direct edits to `semantic-candidates.jsonl`**
   - only if there is already a deterministic helper/path for it
   - otherwise it’s easy to create lifecycle inconsistencies
   - I’d prefer helper-driven status changes where possible

3. **Missing one important target: curated-memory.md is not enough**
   - we may also need to update:
     - `decision-state.json` / `todo.md` for live operational truths
     - maybe `knowledge-base.jsonl` if there are externally learned facts worth preserving
   - not everything belongs in curated memory

4. **Need explicit discard criteria**
   - otherwise “process backlog” becomes endless hesitation
   - e.g. discard if:
     - duplicate
     - transient debugging chatter
     - already captured elsewhere
     - no future workflow value

So I’d revise the plan slightly:

1. Inventory candidates/statuses
2. Process in small batches
3. **Discard aggressively** using explicit criteria
4. Promote only durable preferences/corrections/directives
5. Update only source artifacts
6. Keep operational logging in curator-journal, durable facts in the proper durable files
7. Never edit generated packets/status artifacts or perpetual-log directly

So: **yes, reasonable — but it needs stricter separation of destinations and discard rules.**


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 58.4s
Time: model 56.1s, overhead 1.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 425.0k o 384 est $1.068; p≈4.2ktok/16.8kch
Ctx: direct ACP session 421.2k; active 421.2k; p≈4.0ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120


Yes — that sounds reasonable overall.

What’s good:
- **inventory first**
- **small batches**
- **promote only durable things**
- **edit source artifacts, not generated outputs**
- **don’t touch packets/status/perpetual-log directly**

That’s the right shape.

My pushback / refinements:

1. **`curator-journal.md` should stay operational**
   - good for recording what curation happened and why
   - not the place to stuff lots of durable memory content itself

2. **Be careful with direct edits to `semantic-candidates.jsonl`**
   - only if there is already a deterministic helper/path for it
   - otherwise it’s easy to create lifecycle inconsistencies
   - I’d prefer helper-driven status changes where possible

3. **Missing one important target: curated-memory.md is not enough**
   - we may also need to update:
     - `decision-state.json` / `todo.md` for live operational truths
     - maybe `knowledge-base.jsonl` if there are externally learned facts worth preserving
   - not everything belongs in curated memory

4. **Need explicit discard criteria**
   - otherwise “process backlog” becomes endless hesitation
   - e.g. discard if:
     - duplicate
     - transient debugging chatter
     - already captured elsewhere
     - no future workflow value

So I’d revise the plan slightly:

1. Inventory candidates/statuses
2. Process in small batches
3. **Discard aggressively** using explicit criteria
4. Promote only durable preferences/corrections/directives
5. Update only source artifacts
6. Keep operational logging in curator-journal, durable facts in the proper durable files
7. Never edit generated packets/status artifacts or perpetual-log directly

So: **yes, reasonable — but it needs stricter separation of destinations and discard rules.**


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 59.2s
Time: model 56.1s, overhead 3.1s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 425.0k o 384 est $1.068; p≈4.2ktok/16.8kch
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.1%; usable 45.7%; observed turns 91; actual turns 120


With the journal you can just append a similar entry and note that it's you are cleaning the backlog so background agents know.
I think we already removed your write permissions on generated artifacts so we won't accidentally waste time editing them. Still better to know than hit an error.


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 120


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.0s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 120


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 0.9s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 120


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.4s
Time: overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.9ktok/15.5kch
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 120


* 🟡 Working
Stage: direct ACP load session start (1.4s)
Model: openai/gpt-5.4
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 120
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: direct ACP replay drain done (5.0s)
Model: openai/gpt-5.4
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 6.9s
Time: model 5.0s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.9ktok/15.5kch
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Waiting for first model/tool update…


* 🟡 Working
Stage: direct ACP prompt started (5.0s)
Model: openai/gpt-5.4
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 13.0s
Time: model 11.1s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.9ktok/15.5kch
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 18.6s
Time: model 16.7s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.9ktok/15.5kch
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121


* 🟡 Working
Stage: direct ACP first session update (12.3s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: direct ACP first visible text (12.3s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: streaming direct ACP progress
Model: openai/gpt-5.4
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 24.2s
Time: model 22.2s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.9ktok/15.5kch
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, that’s a good refinement.

Appending a **clear curator-journal entry** like:
- backlog curation started
- scope
- criteria
- what destinations I’m updating
- what I’m explicitly not touching

is useful operationally


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, that’s a good refinement.

Appending a **clear curator-journal entry** like:
- backlog curation started
- scope
- criteria
- what destinations I’m updating
- what I’m explicitly not touching

is useful operationally, and it tells the background agents what is happening without turning the journal into the durable memory itself.

And yes, even if generated artifacts are now write-protected, it’s still better that I know the rule explicitly


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, that’s a good refinement.

Appending a **clear curator-journal entry** like:
- backlog curation started
- scope
- criteria
- what destinations I’m updating
- what I’m explicitly not touching

is useful operationally, and it tells the background agents what is happening without turning the journal into the durable memory itself.

And yes, even if generated artifacts are now write-protected, it’s still better that I know the rule explicitly rather than discovering it by failure.


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 29.9s
Time: model 27.9s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.9ktok/15.5kch
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 30.5s
Time: model 28.5s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 428.8k o 104 est $1.074; p≈3.9ktok/15.5kch
Ctx: direct ACP session 425.4k; active 425.4k; p≈4.2ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121


Yes, that’s a good refinement.

Appending a **clear curator-journal entry** like:
- backlog curation started
- scope
- criteria
- what destinations I’m updating
- what I’m explicitly not touching

is useful operationally, and it tells the background agents what is happening without turning the journal into the durable memory itself.

And yes, even if generated artifacts are now write-protected, it’s still better that I know the rule explicitly rather than discovering it by failure.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 31.1s
Time: model 28.5s, overhead 2.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 428.8k o 104 est $1.074; p≈3.9ktok/15.5kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.5%; usable 46.1%; observed turns 92; actual turns 121


Here's more elaboration from the agent

  1. Main agent inventory
        - Count open/stale memory-candidates.jsonl and
     semantic-candidates.jsonl.
        - Identify candidate age/status buckets.

     2. Batch semantic candidates                                       - Process 10–20 oldest open/needs_more_evidence
     semantic candidates.
        - Mark each: promoted, ignored, stale, or keep
     needs_more_evidence.
        - Promote only durable user preferences/corrections/
     operational directives.

     3. Batch memory candidates
        - Process 10–20 oldest memory candidates.
        - Promote only material that belongs in curated-
     memory.md or operational state.
        - Ignore transient chat/noise.

     4. Update curated artifacts
        - Main agent updates curated-memory.md if there are
     real promotions.
        - Append concise curator-journal.md note summarizing
     batch decisions.

     5. Let heartbeat continue
        - Heartbeat now works via direct ACP and can maintain
     metadata/small curation passes.
        - It should not be expected to clear the whole
     backlog in one run.

     6. Let synthesis continue
        - Synthesis should produce periodic higher-level
     reports/actions, with longer timeout.
        - It can explore/MCP/search/playwright when useful,
     but report checkpoint guarantees a useful artifact
     exists.


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 121


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 121


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.0s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 121


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.5s
Time: overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 121


* 🟡 Working
Stage: direct ACP load session start (1.4s)
Model: openai/gpt-5.4
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 121
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 7.0s
Time: model 5.0s, overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 121


* 🟡 Working
Stage: direct ACP replay drain done (5.1s)
Model: openai/gpt-5.4
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Waiting for first model/tool update…


* 🟡 Working
Stage: direct ACP prompt started (5.1s)
Model: openai/gpt-5.4
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 12.7s
Time: model 10.7s, overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122


* 🟡 Working
Stage: direct ACP first session update (8.0s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: direct ACP first visible text (8.0s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: streaming direct ACP progress
Model: openai/gpt-5.4
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 18.3s
Time: model 16.3s, overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/s


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 24.2s
Time: model 22.2s, overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/synthesis

My pushback is only on two points:

1. **“Promote only to curated-memory.md or operational state” is still too narrow**
   - some things may belong in:
     - `knowledge-base.jsonl`


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/synthesis

My pushback is only on two points:

1. **“Promote only to curated-memory.md or operational state” is still too narrow**
   - some things may belong in:
     - `knowledge-base.jsonl`
     - `decision-state.json`
     - maybe `todo.md`
   - not everything durable is curated-memory material

2. **“ignore transient chat/noise” needs deterministic rules**
   - otherwise batch


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 30.2s
Time: model 28.2s, overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/synthesis

My pushback is only on two points:

1. **“Promote only to curated-memory.md or operational state” is still too narrow**
   - some things may belong in:
     - `knowledge-base.jsonl`
     - `decision-state.json`
     - maybe `todo.md`
   - not everything durable is curated-memory material

2. **“ignore transient chat/noise” needs deterministic rules**
   - otherwise batch processing drifts
   - we should define discard criteria before starting

So I’d tighten it to:

### Revised plan
1. **Inventory**
   - count/status/age buckets for memory + semantic candidates

2.


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 35.9s
Time: model 33.9s, overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/synthesis

My pushback is only on two points:

1. **“Promote only to curated-memory.md or operational state” is still too narrow**
   - some things may belong in:
     - `knowledge-base.jsonl`
     - `decision-state.json`
     - maybe `todo.md`
   - not everything durable is curated-memory material

2. **“ignore transient chat/noise” needs deterministic rules**
   - otherwise batch processing drifts
   - we should define discard criteria before starting

So I’d tighten it to:

### Revised plan
1. **Inventory**
   - count/status/age buckets for memory + semantic candidates

2. **Semantic batch**
   - process 10–20 oldest open / needs_more_evidence
   - outcome must be one of:
     - promote
     - stale
     - ignore
     - keep-needs-more-e


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/synthesis

My pushback is only on two points:

1. **“Promote only to curated-memory.md or operational state” is still too narrow**
   - some things may belong in:
     - `knowledge-base.jsonl`
     - `decision-state.json`
     - maybe `todo.md`
   - not everything durable is curated-memory material

2. **“ignore transient chat/noise” needs deterministic rules**
   - otherwise batch processing drifts
   - we should define discard criteria before starting

So I’d tighten it to:

### Revised plan
1. **Inventory**
   - count/status/age buckets for memory + semantic candidates

2. **Semantic batch**
   - process 10–20 oldest open / needs_more_evidence
   - outcome must be one of:
     - promote
     - stale
     - ignore
     - keep-needs-more-evidence

3. **Memory batch**
   - process 10–20 oldest
   - route promotions by destination:
     - `curated-memory.md`
     - `decision-state.json`
     - `knowledge-base.jsonl`
     - `todo


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 41.9s
Time: model 39.9s, overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/synthesis

My pushback is only on two points:

1. **“Promote only to curated-memory.md or operational state” is still too narrow**
   - some things may belong in:
     - `knowledge-base.jsonl`
     - `decision-state.json`
     - maybe `todo.md`
   - not everything durable is curated-memory material

2. **“ignore transient chat/noise” needs deterministic rules**
   - otherwise batch processing drifts
   - we should define discard criteria before starting

So I’d tighten it to:

### Revised plan
1. **Inventory**
   - count/status/age buckets for memory + semantic candidates

2. **Semantic batch**
   - process 10–20 oldest open / needs_more_evidence
   - outcome must be one of:
     - promote
     - stale
     - ignore
     - keep-needs-more-evidence

3. **Memory batch**
   - process 10–20 oldest
   - route promotions by destination:
     - `curated-memory.md`
     - `decision-state.json`
     - `knowledge-base.jsonl`
     - `todo.md` if operationally useful

4. **Deterministic discard rules**
   - discard if duplicate, transient, already captured, no future workflow value, or debugging residue

5. **Curator


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 48.1s
Time: model 46.1s, overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/synthesis

My pushback is only on two points:

1. **“Promote only to curated-memory.md or operational state” is still too narrow**
   - some things may belong in:
     - `knowledge-base.jsonl`
     - `decision-state.json`
     - maybe `todo.md`
   - not everything durable is curated-memory material

2. **“ignore transient chat/noise” needs deterministic rules**
   - otherwise batch processing drifts
   - we should define discard criteria before starting

So I’d tighten it to:

### Revised plan
1. **Inventory**
   - count/status/age buckets for memory + semantic candidates

2. **Semantic batch**
   - process 10–20 oldest open / needs_more_evidence
   - outcome must be one of:
     - promote
     - stale
     - ignore
     - keep-needs-more-evidence

3. **Memory batch**
   - process 10–20 oldest
   - route promotions by destination:
     - `curated-memory.md`
     - `decision-state.json`
     - `knowledge-base.jsonl`
     - `todo.md` if operationally useful

4. **Deterministic discard rules**
   - discard if duplicate, transient, already captured, no future workflow value, or debugging residue

5. **Curator journal**
   - append a concise batch note:
     - scope
     - counts
     - promotions
     - ignored/stale counts
     - destinations touched

6. **Heartbeat / synthesis roles


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/synthesis

My pushback is only on two points:

1. **“Promote only to curated-memory.md or operational state” is still too narrow**
   - some things may belong in:
     - `knowledge-base.jsonl`
     - `decision-state.json`
     - maybe `todo.md`
   - not everything durable is curated-memory material

2. **“ignore transient chat/noise” needs deterministic rules**
   - otherwise batch processing drifts
   - we should define discard criteria before starting

So I’d tighten it to:

### Revised plan
1. **Inventory**
   - count/status/age buckets for memory + semantic candidates

2. **Semantic batch**
   - process 10–20 oldest open / needs_more_evidence
   - outcome must be one of:
     - promote
     - stale
     - ignore
     - keep-needs-more-evidence

3. **Memory batch**
   - process 10–20 oldest
   - route promotions by destination:
     - `curated-memory.md`
     - `decision-state.json`
     - `knowledge-base.jsonl`
     - `todo.md` if operationally useful

4. **Deterministic discard rules**
   - discard if duplicate, transient, already captured, no future workflow value, or debugging residue

5. **Curator journal**
   - append a concise batch note:
     - scope
     - counts
     - promotions
     - ignored/stale counts
     - destinations touched

6. **Heartbeat / synthesis roles**
   - heartbeat = maintenance + small passes
   - synthesis = higher-level analysis/checkpoints
   - neither is expected to clear the backlog alone

So yes: **this is a workable backlog-curation plan**


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 54.3s
Time: model 52.3s, overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122


* 🟡 Working
Stage: streaming direct ACP progress
Model: openai/gpt-5.4
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/synthesis

My pushback is only on two points:

1. **“Promote only to curated-memory.md or operational state” is still too narrow**
   - some things may belong in:
     - `knowledge-base.jsonl`
     - `decision-state.json`
     - maybe `todo.md`
   - not everything durable is curated-memory material

2. **“ignore transient chat/noise” needs deterministic rules**
   - otherwise batch processing drifts
   - we should define discard criteria before starting

So I’d tighten it to:

### Revised plan
1. **Inventory**
   - count/status/age buckets for memory + semantic candidates

2. **Semantic batch**
   - process 10–20 oldest open / needs_more_evidence
   - outcome must be one of:
     - promote
     - stale
     - ignore
     - keep-needs-more-evidence

3. **Memory batch**
   - process 10–20 oldest
   - route promotions by destination:
     - `curated-memory.md`
     - `decision-state.json`
     - `knowledge-base.jsonl`
     - `todo.md` if operationally useful

4. **Deterministic discard rules**
   - discard if duplicate, transient, already captured, no future workflow value, or debugging residue

5. **Curator journal**
   - append a concise batch note:
     - scope
     - counts
     - promotions
     - ignored/stale counts
     - destinations touched

6. **Heartbeat / synthesis roles**
   - heartbeat = maintenance + small passes
   - synthesis = higher-level analysis/checkpoints
   - neither is expected to clear the backlog alone

So yes: **this is a workable backlog-curation plan**.
It just needs explicit destination routing and discard rules so it doesn’t turn fuzzy again.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 56.2s
Time: model 54.2s, overhead 1.5s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 432.7k o 427 est $1.088; p≈4.2ktok/17.0kch
Ctx: direct ACP session 428.9k; active 428.9k; p≈3.9ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122


Yes, this is much better. This is concrete enough to act on.

What’s good:
- clear ordering
- bounded batch size
- separation between semantic vs memory candidates
- explicit journal update
- realistic expectations for heartbeat/synthesis

My pushback is only on two points:

1. **“Promote only to curated-memory.md or operational state” is still too narrow**
   - some things may belong in:
     - `knowledge-base.jsonl`
     - `decision-state.json`
     - maybe `todo.md`
   - not everything durable is curated-memory material

2. **“ignore transient chat/noise” needs deterministic rules**
   - otherwise batch processing drifts
   - we should define discard criteria before starting

So I’d tighten it to:

### Revised plan
1. **Inventory**
   - count/status/age buckets for memory + semantic candidates

2. **Semantic batch**
   - process 10–20 oldest open / needs_more_evidence
   - outcome must be one of:
     - promote
     - stale
     - ignore
     - keep-needs-more-evidence

3. **Memory batch**
   - process 10–20 oldest
   - route promotions by destination:
     - `curated-memory.md`
     - `decision-state.json`
     - `knowledge-base.jsonl`
     - `todo.md` if operationally useful

4. **Deterministic discard rules**
   - discard if duplicate, transient, already captured, no future workflow value, or debugging residue

5. **Curator journal**
   - append a concise batch note:
     - scope
     - counts
     - promotions
     - ignored/stale counts
     - destinations touched

6. **Heartbeat / synthesis roles**
   - heartbeat = maintenance + small passes
   - synthesis = higher-level analysis/checkpoints
   - neither is expected to clear the backlog alone

So yes: **this is a workable backlog-curation plan**.
It just needs explicit destination routing and discard rules so it doesn’t turn fuzzy again.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 56.8s
Time: model 54.2s, overhead 2.6s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 432.7k o 427 est $1.088; p≈4.2ktok/17.0kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 40.9%; usable 46.5%; observed turns 93; actual turns 122


Great then let's do it. Try on a small batch


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 122


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.0s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 122


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 0.8s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 122


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.2s
Time: overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 122


* 🟡 Working
Stage: direct ACP load session start (1.3s)
Model: openai/gpt-5.4
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 122
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: direct ACP replay drain done (4.6s)
Model: openai/gpt-5.4
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Waiting for first model/tool update…


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 6.8s
Time: model 5.0s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: direct ACP prompt started (4.6s)
Model: openai/gpt-5.4
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 12.8s
Time: model 11.1s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 18.3s
Time: model 16.5s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: direct ACP first session update (12.8s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: direct ACP first tool update (12.8s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: streaming direct ACP progress
Model: openai/gpt-5.4
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (2):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5
- todowrite / in_progress / call_77Y4N1Da0KdVnyPthZGF9Q5


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 23.8s
Time: model 22.1s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 29.8s
Time: model 28.1s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 35.3s
Time: model 33.6s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (4):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5
- todowrite / in_progress / call_77Y4N1Da0KdVnyPthZGF9Q5
- 4 todos / completed / call_77Y4N1Da0KdVnyPthZGF9Q5
- grep / pending / call_CS9DRiXjM7WSnzQ3H0w1j0V


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (9):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5
- todowrite / in_progress / call_77Y4N1Da0KdVnyPthZGF9Q5
- 4 todos / completed / call_77Y4N1Da0KdVnyPthZGF9Q5
- grep / pending / call_CS9DRiXjM7WSnzQ3H0w1j0V
- grep / in_progress / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / pending / call_llkwbnfq0vfuX5ZlhKQQH9W
- semantic-candidates|memory-candidates|needs_more_evidence|promoted|stale|ignore| / completed / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / in_progress / call_llkwbnfq0vfuX5ZlhKQQH9W
- read / pending / call_8zr2nYIUExjy7vClTz6w3aG


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 40.8s
Time: model 39.1s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: direct ACP first visible text (39.0s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (12):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5
- todowrite / in_progress / call_77Y4N1Da0KdVnyPthZGF9Q5
- 4 todos / completed / call_77Y4N1Da0KdVnyPthZGF9Q5
- grep / pending / call_CS9DRiXjM7WSnzQ3H0w1j0V
- grep / in_progress / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / pending / call_llkwbnfq0vfuX5ZlhKQQH9W
- semantic-candidates|memory-candidates|needs_more_evidence|promoted|stale|ignore| / completed / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / in_progress / call_llkwbnfq0vfuX5ZlhKQQH9W
- read / pending / call_8zr2nYIUExjy7vClTz6w3aG
- read / in_progress / call_8zr2nYIUExjy7vClTz6w3aG
- bash / failed / call_llkwbnfq0vfuX5ZlhKQQH9W
- ../../state/persistent-chat/curated-memory.md / completed / call_8zr2nYIUExjy7vClTz6w3aG

Draft:
Yes


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 47.0s
Time: model 45.3s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (12):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5
- todowrite / in_progress / call_77Y4N1Da0KdVnyPthZGF9Q5
- 4 todos / completed / call_77Y4N1Da0KdVnyPthZGF9Q5
- grep / pending / call_CS9DRiXjM7WSnzQ3H0w1j0V
- grep / in_progress / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / pending / call_llkwbnfq0vfuX5ZlhKQQH9W
- semantic-candidates|memory-candidates|needs_more_evidence|promoted|stale|ignore| / completed / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / in_progress / call_llkwbnfq0vfuX5ZlhKQQH9W
- read / pending / call_8zr2nYIUExjy7vClTz6w3aG
- read / in_progress / call_8zr2nYIUExjy7vClTz6w3aG
- bash / failed / call_llkwbnfq0vfuX5ZlhKQQH9W
- ../../state/persistent-chat/curated-memory.md / completed / call_8zr2nYIUExjy7vClTz6w3aG

Draft:
Yes — this is good enough to start.

It’s concrete, bounded, and persistence-aware.

What I’d keep:
- inventory first
- small batches
- separate semantic vs memory candidates


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 53.4s
Time: model 51.7s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (12):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5
- todowrite / in_progress / call_77Y4N1Da0KdVnyPthZGF9Q5
- 4 todos / completed / call_77Y4N1Da0KdVnyPthZGF9Q5
- grep / pending / call_CS9DRiXjM7WSnzQ3H0w1j0V
- grep / in_progress / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / pending / call_llkwbnfq0vfuX5ZlhKQQH9W
- semantic-candidates|memory-candidates|needs_more_evidence|promoted|stale|ignore| / completed / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / in_progress / call_llkwbnfq0vfuX5ZlhKQQH9W
- read / pending / call_8zr2nYIUExjy7vClTz6w3aG
- read / in_progress / call_8zr2nYIUExjy7vClTz6w3aG
- bash / failed / call_llkwbnfq0vfuX5ZlhKQQH9W
- ../../state/persistent-chat/curated-memory.md / completed / call_8zr2nYIUExjy7vClTz6w3aG

Draft:
Yes — this is good enough to start.

It’s concrete, bounded, and persistence-aware.

What I’d keep:
- inventory first
- small batches
- separate semantic vs memory candidates
- only edit source artifacts
- journal note for background-agent awareness
- don’t expect heartbeat/synthesis to clear backlog alone

What I’d tighten before starting:
- define discard rules up front:
  -


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (12):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5
- todowrite / in_progress / call_77Y4N1Da0KdVnyPthZGF9Q5
- 4 todos / completed / call_77Y4N1Da0KdVnyPthZGF9Q5
- grep / pending / call_CS9DRiXjM7WSnzQ3H0w1j0V
- grep / in_progress / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / pending / call_llkwbnfq0vfuX5ZlhKQQH9W
- semantic-candidates|memory-candidates|needs_more_evidence|promoted|stale|ignore| / completed / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / in_progress / call_llkwbnfq0vfuX5ZlhKQQH9W
- read / pending / call_8zr2nYIUExjy7vClTz6w3aG
- read / in_progress / call_8zr2nYIUExjy7vClTz6w3aG
- bash / failed / call_llkwbnfq0vfuX5ZlhKQQH9W
- ../../state/persistent-chat/curated-memory.md / completed / call_8zr2nYIUExjy7vClTz6w3aG

Draft:
Yes — this is good enough to start.

It’s concrete, bounded, and persistence-aware.

What I’d keep:
- inventory first
- small batches
- separate semantic vs memory candidates
- only edit source artifacts
- journal note for background-agent awareness
- don’t expect heartbeat/synthesis to clear backlog alone

What I’d tighten before starting:
- define discard rules up front:
  - duplicate
  - already captured elsewhere
  - transient debugging chatter
  - no future workflow/continuity value
- route promotions by destination, not just “promote”:
  - `curated-memory.md`
  - `decision


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 59.5s
Time: model 57.8s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: streaming direct ACP progress
Model: openai/gpt-5.4
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (12):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5
- todowrite / in_progress / call_77Y4N1Da0KdVnyPthZGF9Q5
- 4 todos / completed / call_77Y4N1Da0KdVnyPthZGF9Q5
- grep / pending / call_CS9DRiXjM7WSnzQ3H0w1j0V
- grep / in_progress / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / pending / call_llkwbnfq0vfuX5ZlhKQQH9W
- semantic-candidates|memory-candidates|needs_more_evidence|promoted|stale|ignore| / completed / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / in_progress / call_llkwbnfq0vfuX5ZlhKQQH9W
- read / pending / call_8zr2nYIUExjy7vClTz6w3aG
- read / in_progress / call_8zr2nYIUExjy7vClTz6w3aG
- bash / failed / call_llkwbnfq0vfuX5ZlhKQQH9W
- ../../state/persistent-chat/curated-memory.md / completed / call_8zr2nYIUExjy7vClTz6w3aG

Draft:
Yes — this is good enough to start.

It’s concrete, bounded, and persistence-aware.

What I’d keep:
- inventory first
- small batches
- separate semantic vs memory candidates
- only edit source artifacts
- journal note for background-agent awareness
- don’t expect heartbeat/synthesis to clear backlog alone

What I’d tighten before starting:
- define discard rules up front:
  - duplicate
  - already captured elsewhere
  - transient debugging chatter
  - no future workflow/continuity value
- route promotions by destination, not just “promote”:
  - `curated-memory.md`
  - `decision-state.json`
  - `knowledge-base.jsonl`
  - maybe `todo.md`
- keep `curator-journal.md` operational:
  - what batch
  - what criteria


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 65.5s
Time: model 63.8s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (12):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5
- todowrite / in_progress / call_77Y4N1Da0KdVnyPthZGF9Q5
- 4 todos / completed / call_77Y4N1Da0KdVnyPthZGF9Q5
- grep / pending / call_CS9DRiXjM7WSnzQ3H0w1j0V
- grep / in_progress / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / pending / call_llkwbnfq0vfuX5ZlhKQQH9W
- semantic-candidates|memory-candidates|needs_more_evidence|promoted|stale|ignore| / completed / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / in_progress / call_llkwbnfq0vfuX5ZlhKQQH9W
- read / pending / call_8zr2nYIUExjy7vClTz6w3aG
- read / in_progress / call_8zr2nYIUExjy7vClTz6w3aG
- bash / failed / call_llkwbnfq0vfuX5ZlhKQQH9W
- ../../state/persistent-chat/curated-memory.md / completed / call_8zr2nYIUExjy7vClTz6w3aG

Draft:
Yes — this is good enough to start.

It’s concrete, bounded, and persistence-aware.

What I’d keep:
- inventory first
- small batches
- separate semantic vs memory candidates
- only edit source artifacts
- journal note for background-agent awareness
- don’t expect heartbeat/synthesis to clear backlog alone

What I’d tighten before starting:
- define discard rules up front:
  - duplicate
  - already captured elsewhere
  - transient debugging chatter
  - no future workflow/continuity value
- route promotions by destination, not just “promote”:
  - `curated-memory.md`
  - `decision-state.json`
  - `knowledge-base.jsonl`
  - maybe `todo.md`
- keep `curator-journal.md` operational:
  - what batch
  - what criteria
  - what changed
  - not the durable content itself

So my practical version is:

1. Inventory statuses/age
2. Process 10–20 semantic candidates
3. Process 10–20 memory


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (12):
- todowrite / pending / call_77Y4N1Da0KdVnyPthZGF9Q5
- todowrite / in_progress / call_77Y4N1Da0KdVnyPthZGF9Q5
- 4 todos / completed / call_77Y4N1Da0KdVnyPthZGF9Q5
- grep / pending / call_CS9DRiXjM7WSnzQ3H0w1j0V
- grep / in_progress / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / pending / call_llkwbnfq0vfuX5ZlhKQQH9W
- semantic-candidates|memory-candidates|needs_more_evidence|promoted|stale|ignore| / completed / call_CS9DRiXjM7WSnzQ3H0w1j0V
- bash / in_progress / call_llkwbnfq0vfuX5ZlhKQQH9W
- read / pending / call_8zr2nYIUExjy7vClTz6w3aG
- read / in_progress / call_8zr2nYIUExjy7vClTz6w3aG
- bash / failed / call_llkwbnfq0vfuX5ZlhKQQH9W
- ../../state/persistent-chat/curated-memory.md / completed / call_8zr2nYIUExjy7vClTz6w3aG

Draft:
Yes — this is good enough to start.

It’s concrete, bounded, and persistence-aware.

What I’d keep:
- inventory first
- small batches
- separate semantic vs memory candidates
- only edit source artifacts
- journal note for background-agent awareness
- don’t expect heartbeat/synthesis to clear backlog alone

What I’d tighten before starting:
- define discard rules up front:
  - duplicate
  - already captured elsewhere
  - transient debugging chatter
  - no future workflow/continuity value
- route promotions by destination, not just “promote”:
  - `curated-memory.md`
  - `decision-state.json`
  - `knowledge-base.jsonl`
  - maybe `todo.md`
- keep `curator-journal.md` operational:
  - what batch
  - what criteria
  - what changed
  - not the durable content itself

So my practical version is:

1. Inventory statuses/age
2. Process 10–20 semantic candidates
3. Process 10–20 memory candidates
4. Discard aggressively by rule
5. Promote only high-value items to the right destination
6. Append a concise curator-journal batch note
7. Repeat

That’s a sane first-pass backlog curation plan.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 71.0s
Time: model 69.3s, overhead 1.2s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 6.8k o 267 t 61 cache 436.7k/98% est $0.0219; p≈4.0ktok/16.1kch
Ctx: direct ACP session 433.1k; active 433.1k; p≈4.2ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


Yes — this is good enough to start.

It’s concrete, bounded, and persistence-aware.

What I’d keep:
- inventory first
- small batches
- separate semantic vs memory candidates
- only edit source artifacts
- journal note for background-agent awareness
- don’t expect heartbeat/synthesis to clear backlog alone

What I’d tighten before starting:
- define discard rules up front:
  - duplicate
  - already captured elsewhere
  - transient debugging chatter
  - no future workflow/continuity value
- route promotions by destination, not just “promote”:
  - `curated-memory.md`
  - `decision-state.json`
  - `knowledge-base.jsonl`
  - maybe `todo.md`
- keep `curator-journal.md` operational:
  - what batch
  - what criteria
  - what changed
  - not the durable content itself

So my practical version is:

1. Inventory statuses/age
2. Process 10–20 semantic candidates
3. Process 10–20 memory candidates
4. Discard aggressively by rule
5. Promote only high-value items to the right destination
6. Append a concise curator-journal batch note
7. Repeat

That’s a sane first-pass backlog curation plan.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 71.6s
Time: model 69.3s, overhead 2.3s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: i 6.8k o 267 t 61 cache 436.7k/98% est $0.0219; p≈4.0ktok/16.1kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


Great please go ahead


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.0s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 0.9s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.4s
Time: overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: direct ACP load session start (1.5s)
Model: openai/gpt-5.4
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 7.0s
Time: model 5.0s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 123


* 🟡 Working
Stage: direct ACP replay drain done (5.1s)
Model: openai/gpt-5.4
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Waiting for first model/tool update…


* 🟡 Working
Stage: direct ACP prompt started (5.1s)
Model: openai/gpt-5.4
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 13.1s
Time: model 11.1s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 18.6s
Time: model 16.6s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 24.1s
Time: model 22.1s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 29.6s
Time: model 27.6s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: direct ACP first session update (23.8s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 35.2s
Time: model 33.2s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: direct ACP first tool update (28.2s after prompt)
Model: openai/gpt-5.4
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 🟡 Working
Stage: streaming direct ACP progress
Model: openai/gpt-5.4
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- todowrite / pending / call_qHTFrrtQd4FqNwANcNaOYnz


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 41.0s
Time: model 39.1s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 46.5s
Time: model 44.5s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 52.3s
Time: model 50.3s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 57.9s
Time: model 55.9s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 63.4s
Time: model 61.4s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 69.0s
Time: model 67.0s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 74.6s
Time: model 72.6s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 80.1s
Time: model 78.1s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 85.7s
Time: model 83.7s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 91.2s
Time: model 89.2s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 96.7s
Time: model 94.8s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 102.3s
Time: model 100.3s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 107.8s
Time: model 105.8s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 113.3s
Time: model 111.3s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 118.8s
Time: model 116.9s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 124.4s
Time: model 122.4s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 130.0s
Time: model 128.0s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 135.6s
Time: model 133.6s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 141.1s
Time: model 139.1s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 146.6s
Time: model 144.7s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 152.2s
Time: model 150.2s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 157.7s
Time: model 155.7s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 163.2s
Time: model 161.2s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 168.8s
Time: model 166.9s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 174.4s
Time: model 172.5s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 180.0s
Time: model 178.0s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 185.5s
Time: model 183.5s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 191.1s
Time: model 189.1s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 196.7s
Time: model 194.7s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 41.2%; usable 47.0%; observed turns 94; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 202.3s
Time: model 200.3s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 42.3%; usable 48.1%; observed turns 95; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 207.9s
Time: model 205.9s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 42.3%; usable 48.1%; observed turns 95; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 213.4s
Time: model 211.5s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 42.3%; usable 48.1%; observed turns 95; actual turns 124


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 218.9s
Time: model 216.9s, overhead 1.4s
Backend: ses_1a4838151ffe2RE9g5hXeQ8xwX
Turn: p≈3.5ktok/13.9kch
Ctx: direct ACP session 443.8k; active 7.1k; cache 436.7k/98%; p≈4.0ktok; ctx 42.3%; usable 48.1%; observed turns 95; actual turns 124