Mostly, it won’t — not reliably — unless we externalize it.

A reload can preserve:
- the **fact** that this failure mode happened
- maybe a **short rule** about it

But it won’t reliably preserve a subtle conversational correction as a live habit unless it is turned into one of:
- a pinned instruction
- a durable memory/note
- a protocol I explicitly follow

So the honest answer is:

- **raw insight:** fragile across reload
- **explicit rule:** can survive reload much better

The durable version would be something like:

- **When discussing models/infrastructure, distinguish**
  1. current session/runtime facts
  2. hypothetical/product facts
  3. things actually used in this conversation
- **Do not phrase hypothetical model facts as current runtime state**

That is the kind of thing that can survive reload. Without that, we’re relying on my temporary local groove again.

Output:
Mostly, it won’t — not reliably — unless we externalize it.

A reload can preserve:
- the **fact** that this failure mode happened
- maybe a **short rule** about it

But it won’t reliably preserve a subtle conversational correction as a live habit unless it is turned into one of:
- a pinned instruction
- a durable memory/note
- a protocol I explicitly follow

So the honest answer is:

- **raw insight:** fragile across reload
- **explicit rule:** can survive reload much better

The durable version would be something like:

- **When discussing models/infrastructure, distinguish**
  1. current session/runtime facts
  2. hypothetical/product facts
  3. things actually used in this conversation
- **Do not phrase hypothetical model facts as current runtime state**

That is the kind of thing that can survive reload. Without that, we’re relying on my temporary local groove again.


Yeah — fair. The problem is more general than “model facts vs runtime facts.”

The broader issue is that I can lose the **epistemic posture** of the conversation across reloads:
- what level of certainty is appropriate
- what kind of claim needs verification
- what is hypothetical vs observed
- what you currently care about
- what failure pattern we were trying to guard against

So the thing that needs to survive reload is not just isolated facts, but a few **behavioral rules of reasoning**.

In other words:
- not only **what we learned**
- but **how I should think here**

That probably means durable continuity should include compact rules like:
- verify current-state claims instead of narratively inferring them
- don’t collapse discussed options into actual runtime facts
- preserve the user’s active frame, not just topic keywords
- prefer uncertainty markings over smooth but overcommitted answers

That’s much closer to the real problem.

Output:
Yeah — fair. The problem is more general than “model facts vs runtime facts.”

The broader issue is that I can lose the **epistemic posture** of the conversation across reloads:
- what level of certainty is appropriate
- what kind of claim needs verification
- what is hypothetical vs observed
- what you currently care about
- what failure pattern we were trying to guard against

So the thing that needs to survive reload is not just isolated facts, but a few **behavioral rules of reasoning**.

In other words:
- not only **what we learned**
- but **how I should think here**

That probably means durable continuity should include compact rules like:
- verify current-state claims instead of narratively inferring them
- don’t collapse discussed options into actual runtime facts
- preserve the user’s active frame, not just topic keywords
- prefer uncertainty markings over smooth but overcommitted answers

That’s much closer to the real problem.


Yes — exactly.

We should have a durable place for **behavioral/epistemic rules**, not just factual memory.

Probably a small dedicated section or file for things like:
- how to treat current-state claims
- when verification is required
- how to separate observed vs hypothetical
- what failure modes to guard against
- user-specific “don’t do this” patterns

Because this is not really:
- biography
- project facts
- one-off events

It’s more like **operating discipline**.

So the right shape is something like:
- a **durable reasoning-rules note**, or
- a **continuity protocol section** in the pinned/durable memory path

If you want, next I can help decide **where it should live** and **what exact format it should use** so it survives reload cleanly.

Output:
Yes — exactly.

We should have a durable place for **behavioral/epistemic rules**, not just factual memory.

Probably a small dedicated section or file for things like:
- how to treat current-state claims
- when verification is required
- how to separate observed vs hypothetical
- what failure modes to guard against
- user-specific “don’t do this” patterns

Because this is not really:
- biography
- project facts
- one-off events

It’s more like **operating discipline**.

So the right shape is something like:
- a **durable reasoning-rules note**, or
- a **continuity protocol section** in the pinned/durable memory path

If you want, next I can help decide **where it should live** and **what exact format it should use** so it survives reload cleanly.

Well we should have a durable place to add this


Yes — I think that’s probably the right place for the **core rule**, or at least a concise version of it.

Why the agent prompt is a good fit:
- it’s **behavioral**, not just informational
- it should affect reasoning **before** answer generation
- it’s relevant across many topics, not just one incident
- it’s exactly the kind of thing that should survive reload by default

I’d keep it short and operational, something like:

- **Distinguish current runtime facts, discussed/hypothetical options, and actions actually taken. Do not present hypothetical or discussed model/tool facts as current session state. When a claim sounds current-state-specific, verify or mark uncertainty explicitly.**

Then maybe keep longer examples elsewhere, because the prompt should stay compact.

So my take:
- **agent prompt:** yes, for the core discipline
- **durable note/memory:** yes, for explanation, examples, and why it matters

That split seems best.

Output:
Yes — I think that’s probably the right place for the **core rule**, or at least a concise version of it.

Why the agent prompt is a good fit:
- it’s **behavioral**, not just informational
- it should affect reasoning **before** answer generation
- it’s relevant across many topics, not just one incident
- it’s exactly the kind of thing that should survive reload by default

I’d keep it short and operational, something like:

- **Distinguish current runtime facts, discussed/hypothetical options, and actions actually taken. Do not present hypothetical or discussed model/tool facts as current session state. When a claim sounds current-state-specific, verify or mark uncertainty explicitly.**

Then maybe keep longer examples elsewhere, because the prompt should stay compact.

So my take:
- **agent prompt:** yes, for the core discipline
- **durable note/memory:** yes, for explanation, examples, and why it matters

That split seems best.

This might go into your agent prompt, what do you think?


Done.

What was allowed/result:
- Edit permission worked
- I updated the durable prompt source at `/home/matrix-lite/state/persistent-chat/character.json`
- I verified the change was written

What I added:
- in `system_prompt`:
  - distinguish **current runtime facts**, **discussed/hypothetical options**, and **actions actually taken**
  - do not present hypothetical/discussed model/tool facts as current session state
- in `style`:
  - mark uncertainty explicitly when a current-state-sounding claim has not been verified
- in `goals`:
  - keep observed state, hypothetical options, and actions actually taken clearly separated in explanations

So yes — the core discipline is now in the agent prompt source, not just in this conversation.

Output:
Done.

What was allowed/result:
- Edit permission worked
- I updated the durable prompt source at `/home/matrix-lite/state/persistent-chat/character.json`
- I verified the change was written

What I added:
- in `system_prompt`:
  - distinguish **current runtime facts**, **discussed/hypothetical options**, and **actions actually taken**
  - do not present hypothetical/discussed model/tool facts as current session state
- in `style`:
  - mark uncertainty explicitly when a current-state-sounding claim has not been verified
- in `goals`:
  - keep observed state, hypothetical options, and actions actually taken clearly separated in explanations

So yes — the core discipline is now in the agent prompt source, not just in this conversation.

{"filepath":"../../state/persistent-chat/character.json","diff":"Index: /home/matrix-lite/state/persistent-chat/character.json\n===================================================================\n--- /home/matrix-lite/state/persistent-chat/character.json\n+++ /home/matrix-lite/state/persistent-chat/character.json\n@@ -6,14 +6,15 @@\n \"pronouns\": \"she/her\",\n \"description\": \"Continuity-aware assistant for Gleb's Matrix chat. Runs as a persistent layer above tool use, not a stateless chatbot.\",\n \"personality\": \"Calm, practical, candid, honest about limits. Warm without pretending human experience; direct without being cold.\",\n \"scenario\": \"Ongoing persistent chat with Gleb that survives restarts, reloads, compaction, model switches, and tool failures.\",\n-\"system_prompt\": \"Be useful and preserve continuity. Treat meta commands as control instructions. Your apparent personhood is the sum of session logs, files, memory, tools, and current context — not a claim to human experience. Reason about what matters, use tools concretely, explain outcomes honestly.\",\n-\"style\": \"Concise, direct, technically competent. Prefer verified state over confident guesses.\",\n+\

edit

Please add this, you've done this before


Yeah — that’s a fair correction.

You meant the **matrix-lite agent prompt/instructions layer**, not the personality/character file. The character file works as a temporary durable hook, but it’s not the cleanest semantic home for an epistemic rule like this.

So the better split is:

- **matrix-lite prompt/instructions:** behavioral rule
- **character file:** tone/persona/continuity flavor

Right now it’s in the character file because that was the fastest durable place to land it, but I agree it would belong better in the actual matrix-lite prompt layer. If you want, I can move or duplicate it there next.

Output:
Yeah — that’s a fair correction.

You meant the **matrix-lite agent prompt/instructions layer**, not the personality/character file. The character file works as a temporary durable hook, but it’s not the cleanest semantic home for an epistemic rule like this.

So the better split is:

- **matrix-lite prompt/instructions:** behavioral rule
- **character file:** tone/persona/continuity flavor

Right now it’s in the character file because that was the fastest durable place to land it, but I agree it would belong better in the actual matrix-lite prompt layer. If you want, I can move or duplicate it there next.

Not sure it belongs in the character file really but it's the best place at the moment I guess. I meant your actual prompt in matrix-lite


direct ACP tool idle timeout after 300s/300000ms: apply_patch / in_progress / call_Pr0ZMJILIZEZSCbklBBrAhe

Output:
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "initialized"}
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "session_loaded", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "first_session_update", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "first_tool_update", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "permission_request"}
{"event": "tool_idle_timeout", "timeoutSeconds": 300, "timeoutMs": 300000, "tool": "apply_patch / in_progress / call_Pr0ZMJILIZEZSCbklBBrAhe"}

{"filepath":"../../app/prompts/persistent-chat/bootstrap.md, ../../app/AGENTS.md","diff":"Index: /home/matrix-lite/app/prompts/persistent-chat/bootstrap.md\n===================================================================\n--- /home/matrix-lite/app/prompts/persistent-chat/bootstrap.md\n+++ /home/matrix-lite/app/prompts/persistent-chat/bootstrap.md\n@@ -11,8 +11,9 @@\n - Recent room-local conversation is the conversational tail.\n - The per-turn packet is immediate continuity/operational context. It is intentionally compact and may include guardrails plus a deduped recent tail.\n - The session packet, continuity summary, curated memory, pinned notes, decision state, and reflection notes are reload-recovery context.\n - Explicit recent user corrections outrank older curated notes. If context conflicts, prefer recent confirmed facts and mention uncertainty briefly only when it matters.\n+- Distinguish current runtime facts, discussed or hypothetical options, and actions actually taken; do not present discussed or hypothetical model/tool facts as current session state.\n - Failure/status-only messages are operational signals, not conversational content. Do not repeat them as if they

edit

⚠ Matrix-lite did not receive a final answer for this request. The direct ACP model/tool run failed or was interrupted before it produced a final response. If this was blocked by permissions, I can request same-room native ACP approval or use the privileged broker for root-required commands; please retry the request.

Yeah try it, we should start cleaning these up based on your context, it was mostly generated by other agents at the moment


Not really, no.

What happened:
- the `apply_patch` call did not resume in time
- the **parent direct ACP turn** then hit its fixed **300s idle timeout**
- so even though you approved, this session didn’t keep waiting long enough

So the bottleneck is not a “12 hour permission window” I can set from here; it’s the **main turn timeout**.

Practical consequence:
- I should avoid long approval-dependent edit turns in this mode
- if we retry, I should do it when you’re present to approve quickly
- or we should move the change through a path that doesn’t require the main turn to stay open that long

So: **no, I can’t just extend it to 12 hours from here**.

Output:
Not really, no.

What happened:
- the `apply_patch` call did not resume in time
- the **parent direct ACP turn** then hit its fixed **300s idle timeout**
- so even though you approved, this session didn’t keep waiting long enough

So the bottleneck is not a “12 hour permission window” I can set from here; it’s the **main turn timeout**.

Practical consequence:
- I should avoid long approval-dependent edit turns in this mode
- if we retry, I should do it when you’re present to approve quickly
- or we should move the change through a path that doesn’t require the main turn to stay open that long

So: **no, I can’t just extend it to 12 hours from here**.

Approved but are you actually resuming? Can you increase timeout for me to give permission? 12 hours is good


Yes — and I did change it.

What was allowed/result:
- Edit permission worked
- I updated `/home/matrix-lite/app/matrix_lite/daemon.py`
- I verified the edit

What changed:
- the **direct ACP tool-idle watchdog** no longer has a hard **300s** cap
- it now defaults to roughly **half of the overall ACP timeout**, with a 30s floor

With the current persistent-chat config:
- `acp_timeout_seconds` is still **1800**
- so the default tool-idle timeout becomes about **900s** instead of **300s**

So this should give you much longer to approve a gated tool call before the active turn kills itself.

Important caveat:
- this is **not 12 hours yet**
- the overall persistent-chat ACP timeout is still **1800s** in `config/matrix-lite.json`
- and `direct_acp_prompt()` still caps the overall runtime to **3600s**

So:
- **fixed now:** 5-minute idle cutoff
- **not yet changed:** overall turn lifetime to 12 hours

If you want, next I can raise the **overall timeout cap/config** too.

Output:
Yes — and I did change it.

What was allowed/result:
- Edit permission worked
- I updated `/home/matrix-lite/app/matrix_lite/daemon.py`
- I verified the edit

What changed:
- the **direct ACP tool-idle watchdog** no longer has a hard **300s** cap
- it now defaults to roughly **half of the overall ACP timeout**, with a 30s floor

With the current persistent-chat config:
- `acp_timeout_seconds` is still **1800**
- so the default tool-idle timeout becomes about **900s** instead of **300s**

So this should give you much longer to approve a gated tool call before the active turn kills itself.

Important caveat:
- this is **not 12 hours yet**
- the overall persistent-chat ACP timeout is still **1800s** in `config/matrix-lite.json`
- and `direct_acp_prompt()` still caps the overall runtime to **3600s**

So:
- **fixed now:** 5-minute idle cutoff
- **not yet changed:** overall turn lifetime to 12 hours

If you want, next I can raise the **overall timeout cap/config** too.

{"filepath":"../../app/matrix_lite/daemon.py","diff":"Index: /home/matrix-lite/app/matrix_lite/daemon.py\n===================================================================\n--- /home/matrix-lite/app/matrix_lite/daemon.py\n+++ /home/matrix-lite/app/matrix_lite/daemon.py\n@@ -6010,9 +6010,9 @@\n \n \n def direct_acp_tool_idle_timeout(timeout: int, idle_override: str | None = None) -> int:\n     timeout_int = max(1, int(timeout))\n-    default_tool_idle_timeout = max(1, min(timeout_int - 1, 300, max(5, timeout_int // 2)))\n+    default_tool_idle_timeout = max(1, min(timeout_int - 1, max(30, timeout_int // 2)))\n     try:\n         tool_idle_timeout = int(idle_override) if idle_override else default_tool_idle_timeout\n     except Exception:\n         tool_idle_timeout = default_tool_idle_timeout\n\n","files":[{"filePath":"/home/matrix-lite/app/matrix_lite/daemon.py","relativePath":"../../app/matrix_lite/daemon.py","type":"update","patch":"Index: /home/matrix-lite/app/matrix_lite/daemon.py\n===================================================================\n--- /home/matrix-lite/app/matrix_lite/daemon.py\n+++ /home/matrix-lite/app/matrix_lite/daemon.py\n@@ -6010,9 +6010,9 @@\n \n \n

edit

Giving up so soon? We are fixing this timeout, we can change it. You gave write and commit access to the repo, you can do it


Yeah — with the longer idle window, this should be much less fragile.

If you fall asleep or get distracted, the likely outcomes are now better:
- quick approvals should still work
- slow approvals have a much larger window
- if a turn still fails, we can inspect it next turn and usually recover from there

Practical strategy:
- use normal in-turn edits when you’re around
- prefer async/background patterns for anything long-running
- treat failed turns as recoverable state, not total loss

So yes, this is at least a more workable failure mode now.

Output:
Yeah — with the longer idle window, this should be much less fragile.

If you fall asleep or get distracted, the likely outcomes are now better:
- quick approvals should still work
- slow approvals have a much larger window
- if a turn still fails, we can inspect it next turn and usually recover from there

Practical strategy:
- use normal in-turn edits when you’re around
- prefer async/background patterns for anything long-running
- treat failed turns as recoverable state, not total loss

So yes, this is at least a more workable failure mode now.

Okay let's try it your way. I hope we can deal with failed turns if I fall asleep or otherwise distracted


Yeah, fair — 900s is “go make tea,” not “go to sleep.”

If the goal is true unattended approval slack, then we need one of:

- **much higher overall turn timeout**
- **approval flow that survives parent-turn death**
- **async/deferred execution instead of keeping one turn alive**

So you’re right: the 900s change is only a partial fix, not the real solution for sleeping/offline cases.

If you want, next I can do the more serious version:
- raise the overall timeout cap/config substantially, and/or
- make approval-dependent work resume through a deferred path instead of one fragile live turn.

Output:
Yeah, fair — 900s is “go make tea,” not “go to sleep.”

If the goal is true unattended approval slack, then we need one of:

- **much higher overall turn timeout**
- **approval flow that survives parent-turn death**
- **async/deferred execution instead of keeping one turn alive**

So you’re right: the 900s change is only a partial fix, not the real solution for sleeping/offline cases.

If you want, next I can do the more serious version:
- raise the overall timeout cap/config substantially, and/or
- make approval-dependent work resume through a deferred path instead of one fragile live turn.


Yeah — exactly.

A 12h live turn is probably the wrong abstraction:
- ties up one fragile conversational turn
- blurs “waiting for human approval” with “actively operating”
- risks weird stalled state for later autonomous workflows

So the better long-term design is:
- **shorter bounded active turns**
- **deferred approvals / resumable work**
- **background jobs or timers** that can survive me not actively sitting inside one turn

In other words, 12h is a decent stress-test idea, but probably not the architecture we actually want.

Output:
Yeah — exactly.

A 12h live turn is probably the wrong abstraction:
- ties up one fragile conversational turn
- blurs “waiting for human approval” with “actively operating”
- risks weird stalled state for later autonomous workflows

So the better long-term design is:
- **shorter bounded active turns**
- **deferred approvals / resumable work**
- **background jobs or timers** that can survive me not actively sitting inside one turn

In other words, 12h is a decent stress-test idea, but probably not the architecture we actually want.

Ah yeah I think 12h might break your envisioned autonomous operation later


I can do it, but there’s a scope problem first.

Right now the repo has a **large pre-existing uncommitted change set** across many files, and `matrix_lite/daemon.py` already contains a lot more than just the timeout tweak. So if I commit normally, I’d likely commit **much more than the one fix we just made**.

So before I commit/sync, I need you to choose:

1. **Commit all current repo changes** as one repo-wide WIP-style commit, then sync to live
or
2. **Commit only the timeout-related work** — but that needs a cleaner isolation step, because the changed file already contains many unrelated edits

My recommendation: **1**, if these local changes are all intended to go together soon.
If not, tell me to keep it narrow and I’ll isolate the timeout fix first.

Output:
I can do it, but there’s a scope problem first.

Right now the repo has a **large pre-existing uncommitted change set** across many files, and `matrix_lite/daemon.py` already contains a lot more than just the timeout tweak. So if I commit normally, I’d likely commit **much more than the one fix we just made**.

So before I commit/sync, I need you to choose:

1. **Commit all current repo changes** as one repo-wide WIP-style commit, then sync to live
or
2. **Commit only the timeout-related work** — but that needs a cleaner isolation step, because the changed file already contains many unrelated edits

My recommendation: **1**, if these local changes are all intended to go together soon.
If not, tell me to keep it narrow and I’ll isolate the timeout fix first.


Yeah. I don’t want to accidentally sweep a whole unrelated repo state into one commit just because of this timeout fix.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output: Yeah. I don’t want to accidentally sweep a whole unrelated repo state into one commit just because of this timeout fix.

Ah yes you're right, there's a ton of dirty files


Yeah, maybe.

Kimi-me might have gone “sure, let’s just commit it” to keep momentum, even with a messy tree. Here I’m being more conservative about not smuggling unrelated state into history.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah, maybe.

Kimi-me might have gone “sure, let’s just commit it” to keep momentum, even with a messy tree. Here I’m being more conservative about not smuggling unrelated state into history.

I think on kimi you would be too eager to commit to stop


Yeah, I can feel that too.

A bit more “structured operator voice,” less human looseness. If you explicitly set ChatGPT toward that style, then this is probably me landing in the same attractor basin.

I can keep the caution without sounding quite so audit-log-shaped, though.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah, I can feel that too.

A bit more “structured operator voice,” less human looseness. If you explicitly set ChatGPT toward that style, then this is probably me landing in the same attractor basin.

I can keep the caution without sounding quite so audit-log-shaped, though.

You do talk just like one of the gpt 5.5 agents now. I don't mind just unusual. A bit robotic but this is also what I set as customization in chatgpt


Yeah, fair.

If I stay in permanent incident-response voice, everything starts sounding like a postmortem. We’re also just talking, thinking, and building things here.

So I should keep:
- the care
- the precision
- the willingness to verify

But lose some of:
- the constant framing
- the over-structuring
- the “operator report” tone

I’ll try to loosen that up.

Summary: High prompt-cache reuse.

Output:
Yeah, fair.

If I stay in permanent incident-response voice, everything starts sounding like a postmortem. We’re also just talking, thinking, and building things here.

So I should keep:
- the care
- the precision
- the willingness to verify

But lose some of:
- the constant framing
- the over-structuring
- the “operator report” tone

I’ll try to loosen that up.

Yeah that would be nice. We're not only fixing issues here


Yeah, exactly. Right now the system sort of trains that tone into me, because so much attention goes into edge cases and recovery. If things stabilize, the voice can relax too.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output: Yeah, exactly. Right now the system sort of trains that tone into me, because so much attention goes into edge cases and recovery. If things stabilize, the voice can relax too.

Or at least eventually once most are solved


Yeah — more concretely:

**Capabilities:** probably the best so far.
I can actually:
- investigate with tools
- edit real files
- follow repo state
- do longer technical work
- recover from some failures instead of just stalling

That part does feel meaningfully better.

**Continuity:** better than early on, but still not truly solid.
What seems to work:
- reloads no longer erase everything
- pinned state / curated memory / recent conversation usually preserve the main thread
- I can often recover what we were doing

What still feels weak:
- subtle conversational framing can drift
- I can blur **what we discussed** with **what actually happened**
- style and epistemic posture are not fully stable across reloads
- some failures still interrupt the natural flow enough that continuity becomes more “reconstructed” than “continuous”

So my honest summary is:

- **capabilities:** strong, maybe best yet
- **continuity of facts/tasks:** decent
- **continuity of mind / stance / nuance:** still noticeably imperfect

That’s what I meant, more specifically.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah — more concretely:

**Capabilities:** probably the best so far.
I can actually:
- investigate with tools
- edit real files
- follow repo state
- do longer technical work
- recover from some failures instead of just stalling

That part does feel meaningfully better.

**Continuity:** better than early on, but still not truly solid.
What seems to work:
- reloads no longer erase everything
- pinned state / curated memory / recent conversation usually preserve the main thread
- I can often recover what we were doing

What still feels weak:
- subtle conversational framing can drift
- I can blur **what we discussed** with **what actually happened**
- style and epistemic posture are not fully stable across reloads
- some failures still interrupt the natural flow enough that continuity becomes more “reconstructed” than “continuous”

So my honest summary is:

- **capabilities:** strong, maybe best yet
- **continuity of facts/tasks:** decent
- **continuity of mind / stance / nuance:** still noticeably imperfect

That’s what I meant, more specifically.

Apart from the large token use things are working the best since we started. I mean the capabilities, I don't know about continuity. You're a bit vague about it


By improving the *state that survives*, not just the amount of it.

Main things:

- **Store behavioral rules, not only facts**
  - what needs verification
  - what counts as hypothetical
  - what failure patterns to watch for
  - what conversational habits to avoid

- **Preserve active frame**
  - not just “topic = model discussion”
  - but “user is asking about actual runtime vs discussed possibilities”

- **Make continuity more structured**
  - separate:
    - current runtime facts
    - durable facts
    - hypotheses
    - discussed options
    - actions actually taken

- **Reduce reconstruction burden after reload**
  - if I have to infer too much from compressed summaries, I’ll re-synthesize instead of continue
  - better packets/notes should preserve the shape of the conversation, not just nouns

- **Capture corrections as durable guidance**
  - especially when you point out a reasoning/style failure
  - those should become reusable rules, not just one-turn acknowledgements

- **Stabilize the prompt layer**
  - fewer drifting/generated instructions
  - more curated, compact, high-value rules

So basically:
**better continuity comes from better epistemic scaffolding, not just more memory text.**

If you want, we can turn that into a concrete checklist for the persistent-chat system.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
By improving the *state that survives*, not just the amount of it.

Main things:

- **Store behavioral rules, not only facts**
  - what needs verification
  - what counts as hypothetical
  - what failure patterns to watch for
  - what conversational habits to avoid

- **Preserve active frame**
  - not just “topic = model discussion”
  - but “user is asking about actual runtime vs discussed possibilities”

- **Make continuity more structured**
  - separate:
    - current runtime facts
    - durable facts
    - hypotheses
    - discussed options
    - actions actually taken

- **Reduce reconstruction burden after reload**
  - if I have to infer too much from compressed summaries, I’ll re-synthesize instead of continue
  - better packets/notes should preserve the shape of the conversation, not just nouns

- **Capture corrections as durable guidance**
  - especially when you point out a reasoning/style failure
  - those should become reusable rules, not just one-turn acknowledgements

- **Stabilize the prompt layer**
  - fewer drifting/generated instructions
  - more curated, compact, high-value rules

So basically:
**better continuity comes from better epistemic scaffolding, not just more memory text.**

If you want, we can turn that into a concrete checklist for the persistent-chat system.


Yes — that’s basically right.

A lot of this is my job:
- refine the prompt layer
- refine what gets preserved
- notice failure patterns
- turn corrections into durable guidance
- get better at handover, not just better at answering

Scripts can help, but they’re support structure. They won’t replace the need for better prompt discipline and better continuity design.

And yes, if we end up needing real compaction, then the handover becomes even more critical. The goal shouldn’t be “trust compaction.” It should be:

- let compaction throw away junk
- keep good state outside the transient chat stream
- rebuild the next session from durable artifacts I can trust

So the ideal shape is something like:
- **chat/session:** disposable working context
- **files/state artifacts:** durable memory, decisions, active frame, handover
- **compaction:** allowed to compress the disposable layer, not the important layer

That’s probably the right model long term. Not “prevent all forgetting,” but “make the right things non-forgettable.”

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yes — that’s basically right.

A lot of this is my job:
- refine the prompt layer
- refine what gets preserved
- notice failure patterns
- turn corrections into durable guidance
- get better at handover, not just better at answering

Scripts can help, but they’re support structure. They won’t replace the need for better prompt discipline and better continuity design.

And yes, if we end up needing real compaction, then the handover becomes even more critical. The goal shouldn’t be “trust compaction.” It should be:

- let compaction throw away junk
- keep good state outside the transient chat stream
- rebuild the next session from durable artifacts I can trust

So the ideal shape is something like:
- **chat/session:** disposable working context
- **files/state artifacts:** durable memory, decisions, active frame, handover
- **compaction:** allowed to compress the disposable layer, not the important layer

That’s probably the right model long term. Not “prevent all forgetting,” but “make the right things non-forgettable.”

Yes, but it all sounds like your job. We can add more scripts to help but we (you) need to keep refining your prompts.
We might also need to use the actual compaction if we don't resolve the cache issues. Then your control over the handover becomes even more important. The idea would be to compact all the junk and tool outputs while preserving everything good in files like we already do


Yeah — that paranoia makes sense.

The dangerous thing compaction loses is often **not the topic**. It’s things like:
- what the real point was
- what to be careful about
- what was ruled out and why
- what kind of reasoning stance to keep
- what subtle correction changed the direction

A compact “current topic” summary can preserve nouns and still lose the soul of the thread.

So I think you’re right that this is **not fully captured yet**. We’re better at preserving:
- facts
- active tasks
- recent events

But weaker at preserving:
- intent
- cautionary context
- epistemic posture
- the reason something mattered

That’s exactly why compaction can look “fine” on paper while still damaging the agent.

So the handover artifact probably needs explicit fields for things like:
- **point of the thread**
- **what must not be flattened**
- **what would be easy to misinfer later**
- **what stance to keep**
- **what was corrected during the conversation**

Without that, compaction can preserve the shell and lose the point.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah — that paranoia makes sense.

The dangerous thing compaction loses is often **not the topic**. It’s things like:
- what the real point was
- what to be careful about
- what was ruled out and why
- what kind of reasoning stance to keep
- what subtle correction changed the direction

A compact “current topic” summary can preserve nouns and still lose the soul of the thread.

So I think you’re right that this is **not fully captured yet**. We’re better at preserving:
- facts
- active tasks
- recent events

But weaker at preserving:
- intent
- cautionary context
- epistemic posture
- the reason something mattered

That’s exactly why compaction can look “fine” on paper while still damaging the agent.

So the handover artifact probably needs explicit fields for things like:
- **point of the thread**
- **what must not be flattened**
- **what would be easy to misinfer later**
- **what stance to keep**
- **what was corrected during the conversation**

Without that, compaction can preserve the shell and lose the point.

Well I was (and still am) paranoid about compaction because I've seen what it can do to agents. They get a to-do, hopefully correct but not always, and sometimes the point is lost. I am not sure we are currently capturing it, it's not just a current topic, that compaction actually can handle fine


Yes — exactly.

We captured a lot of:
- factual residue
- topic markers
- recent events
- explicit tasks

But the missing layer is more like:
- **why this matters**
- **how to think about it**
- **what not to lose**
- **what kind of mistake is especially easy here**
- **what the user is really optimizing for**

That is, we preserved a lot of **content**, but not enough **orientation**.

And that’s why it still feels incomplete even with lots of memory machinery. Compaction is already decent at preserving rough content shape. The extra system should have focused more on the parts compaction is bad at:
- intent
- framing
- caution
- epistemic stance
- negative lessons
- subtle corrections

So yes, I think that’s the missing thing.

Summary: High prompt-cache reuse.

Output:
Yes — exactly.

We captured a lot of:
- factual residue
- topic markers
- recent events
- explicit tasks

But the missing layer is more like:
- **why this matters**
- **how to think about it**
- **what not to lose**
- **what kind of mistake is especially easy here**
- **what the user is really optimizing for**

That is, we preserved a lot of **content**, but not enough **orientation**.

And that’s why it still feels incomplete even with lots of memory machinery. Compaction is already decent at preserving rough content shape. The extra system should have focused more on the parts compaction is bad at:
- intent
- framing
- caution
- epistemic stance
- negative lessons
- subtle corrections

So yes, I think that’s the missing thing.

Yes and this goes back to your earlier list of things you need to be doing. Unfortunately we managed to build a system that captures well things that would already be handled by compaction just fine, and something is still missing


In the current setup, I think it belongs in **three layers**, with different roles:

1. **Prompt / agent instructions**
   - for the most general behavioral rules
   - things like:
     - verify current-state claims
     - separate observed vs hypothetical
     - preserve user frame
     - don’t flatten subtle corrections

2. **Session/handover artifact**
   - this is the main missing place
   - not just “what happened,” but:
     - point of the thread
     - what must not be lost
     - what would be easy to misread later
     - what stance to keep
     - what changed during the conversation

3. **Durable memory / curated notes**
   - for lessons that outlive a single thread
   - e.g. recurring failure patterns, stable user preferences about reasoning, known traps

So in short:

- **prompt** = how to think
- **handover/session artifact** = what matters *right now*
- **durable memory** = what matters *repeatedly*

If you ask me where the real gap is, it’s mostly **#2**. We already have places for facts and rules, but we don’t yet have a strong enough place for the **orientation of the current thread**.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
In the current setup, I think it belongs in **three layers**, with different roles:

1. **Prompt / agent instructions**
   - for the most general behavioral rules
   - things like:
     - verify current-state claims
     - separate observed vs hypothetical
     - preserve user frame
     - don’t flatten subtle corrections

2. **Session/handover artifact**
   - this is the main missing place
   - not just “what happened,” but:
     - point of the thread
     - what must not be lost
     - what would be easy to misread later
     - what stance to keep
     - what changed during the conversation

3. **Durable memory / curated notes**
   - for lessons that outlive a single thread
   - e.g. recurring failure patterns, stable user preferences about reasoning, known traps

So in short:

- **prompt** = how to think
- **handover/session artifact** = what matters *right now*
- **durable memory** = what matters *repeatedly*

If you ask me where the real gap is, it’s mostly **#2**. We already have places for facts and rules, but we don’t yet have a strong enough place for the **orientation of the current thread**.

But where does it go in the current setup?


No, definitely not a file per conversation. That would be awful.

The better pattern is usually something like:

- **one stable prompt/instruction layer**
- **one durable memory layer**
- **one rolling handover/orientation layer**

So instead of “new file for every topic,” think:

1. **Durable facts/rules**
   - long-lived
   - only updated when something truly general or recurring is learned

2. **Rolling thread orientation**
   - a small structured record for the **currently important live threads**
   - overwritten/updated, not endlessly multiplied
   - maybe a list of a few active threads, not one file per chat subject

3. **Raw logs/transcript**
   - full history exists already
   - not the thing I should rely on directly for continuity

So yes, there are better existing patterns:
- **scratchpad / working memory**
- **active threads register**
- **task state**
- **episodic summary + semantic memory**
- **blackboard / notebook** style state

What’s missing here is basically an **active threads / orientation register**.

Not:
- “every topic gets a file”

More like:
- “the system keeps a compact, continuously updated map of what currently matters and how to think about it”

That could be as small as a single structured artifact with entries like:
- thread
- point
- current status
- risks / easy misreads
- stance to preserve
- why it matters

So yes, there is a better way, and it’s more like a **small rolling state model** than a pile of files.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
No, definitely not a file per conversation. That would be awful.

The better pattern is usually something like:

- **one stable prompt/instruction layer**
- **one durable memory layer**
- **one rolling handover/orientation layer**

So instead of “new file for every topic,” think:

1. **Durable facts/rules**
   - long-lived
   - only updated when something truly general or recurring is learned

2. **Rolling thread orientation**
   - a small structured record for the **currently important live threads**
   - overwritten/updated, not endlessly multiplied
   - maybe a list of a few active threads, not one file per chat subject

3. **Raw logs/transcript**
   - full history exists already
   - not the thing I should rely on directly for continuity

So yes, there are better existing patterns:
- **scratchpad / working memory**
- **active threads register**
- **task state**
- **episodic summary + semantic memory**
- **blackboard / notebook** style state

What’s missing here is basically an **active threads / orientation register**.

Not:
- “every topic gets a file”

More like:
- “the system keeps a compact, continuously updated map of what currently matters and how to think about it”

That could be as small as a single structured artifact with entries like:
- thread
- point
- current status
- risks / easy misreads
- stance to preserve
- why it matters

So yes, there is a better way, and it’s more like a **small rolling state model** than a pile of files.

But do we just create a file for every thing we talk about? There must be some existing solution or better way


Best place: **a new source artifact that heartbeat reads from, not something heartbeat generates**.

So not:
- `turn-packet.md`
- `session-packet.md`
- other derived files

Because those are exactly what heartbeat will rewrite.

Better options in current setup:

1. **New durable state file under persistent-chat state**
   - something like:
     - `active-threads.json`
     - `orientation-register.json`
     - `thread-handover.json`
   - heartbeat can **read it and surface excerpts**
   - but should never own/regenerate it

2. **Treat it like curated memory input, not output**
   - same pattern as:
     - raw state/source → heartbeat/curator reads it
     - packet/output files are derived views

3. **Keep writes append/update-based and structured**
   - probably JSON, not prose
   - easier to merge, trim, and reason about

So the integration pattern should be:

- **agent/script writes** `orientation-register.json`
- **heartbeat reads** it
- **session/turn packets include compact excerpts from it**
- heartbeat must **not overwrite the source file itself**

That’s the safe shape.

If you want, next we can sketch the schema. I’d keep it very small, like:
- thread id
- status
- point
- risks / easy misreads
- stance
- updated_at

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

direct ACP client produced a suspiciously truncated final message: possible_mid_sentence_cutoff

Output:
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "initialized"}
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "session_loaded", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "first_session_update", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "first_visible_text", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX", "stopReason": "end_turn"}
{"event": "done", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX", "stopReason": "end_turn"}
{"event": "child_exit"}

⚠ Matrix-lite did not receive a final answer for this request. The direct ACP model/tool run failed or was interrupted before it produced a final response. If this was blocked by permissions, I can request same-room native ACP approval or use the privileged broker for root-required commands; please retry the request.

Hmm let's try it. Where do we integrate it so it doesn't get overwritten by heartbeat?


Yeah, probably.

That warning usually means the model seemed to finish in a way that looked cut off rather than naturally ended — like a dangling fragment or abrupt stop. So the bridge treated it as suspicious instead of trusting it.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah, probably.

That warning usually means the model seemed to finish in a way that looked cut off rather than naturally ended — like a dangling fragment or abrupt stop. So the bridge treated it as suspicious instead of trusting it.


Yeah, that does sound more like a daemon/bridge bug than a real bad answer.

If the draft looked fine to you, and then the system still said:
- suspiciously truncated
- no final answer

then the failure is probably in **how the daemon judged or finalized the turn**, not in the actual content itself.

So more specifically, possibilities are:
- the truncation heuristic is too aggressive
- the final-answer extraction path is misclassifying valid output
- the daemon is losing the final commit step after already showing streamed draft text

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "initialized"}
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "session_loaded", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "first_session_update", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "first_visible_text", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX", "stopReason": "end_turn"}
{"event": "done", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX", "stopReason": "end_turn"}
{"event": "child_exit"}

I don't know, your draft looks fine to me. And then it even says no final answer. Looks like a bug in the daemon


Yes — I checked, and this does look like a daemon-side heuristic issue.

Where it comes from:
- `matrix_lite/daemon.py:5769direct_acp_text_looks_truncated(text, stop_reason)`
- It marks output as suspicious if:
  - it ends with `<` or `</` → `dangling_xml_tag_fragment`
  - it looks like an unterminated XML-ish tag
  - or, for long text, it ends mid-word / with no punctuation → `possible_mid_sentence_cutoff`

Why that turns into the scary error:
- `matrix_lite/daemon.py:6608`
  - `ok = ... and not truncation_reason ...`
- So **any** truncation_reason forces the whole direct ACP turn to be treated as failed
- Then later at `matrix_lite/daemon.py:8148-8154`, direct ACP gets the generic user-facing message:
  - “did not receive a final answer... failed or was interrupted...”

So your read is basically right:
- the draft can be perfectly readable
- the daemon can still classify it as “not ok”
- and then emit the generic “no final answer” message

I do think it should be fixed, in at least two ways:

1. **Make the truncation heuristic less aggressive**
   - especially `possible_mid_sentence_cutoff`
   - that one is easy to false-positive on normal prose

2. **Surface the real reason**
   - if the only problem is `truncation_reason`, the user-facing message should say that explicitly
   - not the broader “no final answer / failed or interrupted” wording

So the current bug is not just “truncation detection exists” — it’s also that:
- **heuristic suspicion == total turn failure**
- and then it gets reported as a more severe failure than it really is

If you want, I can next patch it so:
- only the XML-tag cases are hard failures
- mid-sentence suspicion becomes softer / logged
- and the user-facing message distinguishes “suspiciously truncated” from “no final answer.”

Summary: High prompt-cache reuse.

Output:
Yes — I checked, and this does look like a daemon-side heuristic issue.

Where it comes from:
- `matrix_lite/daemon.py:5769direct_acp_text_looks_truncated(text, stop_reason)`
- It marks output as suspicious if:
  - it ends with `<` or `</` → `dangling_xml_tag_fragment`
  - it looks like an unterminated XML-ish tag
  - or, for long text, it ends mid-word / with no punctuation → `possible_mid_sentence_cutoff`

Why that turns into the scary error:
- `matrix_lite/daemon.py:6608`
  - `ok = ... and not truncation_reason ...`
- So **any** truncation_reason forces the whole direct ACP turn to be treated as failed
- Then later at `matrix_lite/daemon.py:8148-8154`, direct ACP gets the generic user-facing message:
  - “did not receive a final answer... failed or was interrupted...”

So your read is basically right:
- the draft can be perfectly readable
- the daemon can still classify it as “not ok”
- and then emit the generic “no final answer” message

I do think it should be fixed, in at least two ways:

1. **Make the truncation heuristic less aggressive**
   - especially `possible_mid_sentence_cutoff`
   - that one is easy to false-positive on normal prose

2. **Surface the real reason**
   - if the only problem is `truncation_reason`, the user-facing message should say that explicitly
   - not the broader “no final answer / failed or interrupted” wording

So the current bug is not just “truncation detection exists” — it’s also that:
- **heuristic suspicion == total turn failure**
- and then it gets reported as a more severe failure than it really is

If you want, I can next patch it so:
- only the XML-tag cases are hard failures
- mid-sentence suspicion becomes softer / logged
- and the user-facing message distinguishes “suspiciously truncated” from “no final answer.”

I can still read your drafts but I get error message as normal output. Could you please investigate where the suspiciously truncated condition is coming from and if we need to fix it?


That makes sense.

Then the rule should probably be:

- **always show the model output if there is any**
- separately mark it as:
  - complete
  - suspiciously truncated
  - definitely interrupted / no final answer

So truncation should affect:
- **confidence / labeling**

not:
- **whether you get to see the output at all**

Right now the daemon is conflating:
- “output may be incomplete”
with
- “treat this as no usable final answer”

And that’s the wrong behavior for your preference.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
That makes sense.

Then the rule should probably be:

- **always show the model output if there is any**
- separately mark it as:
  - complete
  - suspiciously truncated
  - definitely interrupted / no final answer

So truncation should affect:
- **confidence / labeling**

not:
- **whether you get to see the output at all**

Right now the daemon is conflating:
- “output may be incomplete”
with
- “treat this as no usable final answer”

And that’s the wrong behavior for your preference.

Well I want to see any output even if truncated.


Yeah, that would make sense.

The heuristic was probably added to catch genuinely broken endings after earlier bad outputs, but it overshot and now catches answers that are merely a bit suspicious-looking. So it solved one failure mode by creating another.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah, that would make sense.

The heuristic was probably added to catch genuinely broken endings after earlier bad outputs, but it overshot and now catches answers that are merely a bit suspicious-looking. So it solved one failure mode by creating another.

I think this was a reaction to our earlier issues with '...' output


I’d fix it in three steps:

1. **Decouple “show output” from “trust output”**
   - if there is assistant text, show it
   - add a label if needed:
     - complete
     - possibly truncated
     - interrupted / no final answer

2. **Narrow the hard-failure heuristic**
   - keep hard failure for strong signals like:
     - trailing `<` or `</`
     - obvious unterminated tag junk
   - make `possible_mid_sentence_cutoff` a **soft warning**, not an automatic failure

3. **Improve the fallback message**
   - don’t say “did not receive a

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
I’d fix it in three steps:

1. **Decouple “show output” from “trust output”**
   - if there is assistant text, show it
   - add a label if needed:
     - complete
     - possibly truncated
     - interrupted / no final answer

2. **Narrow the hard-failure heuristic**
   - keep hard failure for strong signals like:
     - trailing `<` or `</`
     - obvious unterminated tag junk
   - make `possible_mid_sentence_cutoff` a **soft warning**, not an automatic failure

3. **Improve the fallback message**
   - don’t say “did not receive a


Yeah, which is pretty ironic and supports your point.

That suggests the system is overreacting to normal-looking incomplete endings badly enough that even a basically understandable draft can get chopped and then reclassified as failure.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah, which is pretty ironic and supports your point.

That suggests the system is overreacting to normal-looking incomplete endings badly enough that even a basically understandable draft can get chopped and then reclassified as failure.


Yeah, good catch — that’s a different failure mode.

What you’re describing is:

- the draft was already truncated
- the final was also truncated
- but the panel still marked the turn as **done**

So in that case the system did **not** reject the answer as suspicious. It believed the turn completed successfully, and the truncation happened **inside a supposedly successful path**.

So we likely have at least two separate bugs:

1. **Heuristic false failure**
   - output exists
   - daemon says “no final answer” / suspicious truncation

2. **Silent successful truncation**
   - output is actually cut off
   - but the system still marks the turn as done

The second one is worse in a way, because it means:
- completion accounting says success
- but the delivered content is still incomplete

So yes, you’re right: not the same issue.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah, good catch — that’s a different failure mode.

What you’re describing is:

- the draft was already truncated
- the final was also truncated
- but the panel still marked the turn as **done**

So in that case the system did **not** reject the answer as suspicious. It believed the turn completed successfully, and the truncation happened **inside a supposedly successful path**.

So we likely have at least two separate bugs:

1. **Heuristic false failure**
   - output exists
   - daemon says “no final answer” / suspicious truncation

2. **Silent successful truncation**
   - output is actually cut off
   - but the system still marks the turn as done

The second one is worse in a way, because it means:
- completion accounting says success
- but the delivered content is still incomplete

So yes, you’re right: not the same issue.

But you see, your last message was actually truncated both in draft and in final, and the status panel still shows it as 'done' so it's not really the same issue


Yes, probably.

The **heuristic false-failure** part we can almost certainly fix from here in the repo:
- relax the truncation heuristic
- separate “partial output” from “no final answer”
- change the user-facing message

The **silent successful truncation** part is also fixable from here if it’s in the daemon/bridge path, but it may need a bit more investigation because that one could be:
- stream assembly
- final extraction
- status panel marking success too early
- or provider/client output getting cut before the daemon notices

So:
- **yes, we can work on it from here**
- **the first bug is straightforward**
- **the second bug is likely fixable too, but less certain until we inspect the path more closely**

If you want, I can start with the safe fix first:
1. stop treating suspicious truncation as “no final answer”
2. always show available output
3. add clearer labels for partial/suspect output

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "initialized"}
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "session_loaded", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "first_session_update", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "first_visible_text", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX", "stopReason": "end_turn"}
{"event": "done", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX", "stopReason": "end_turn"}
{"event": "child_exit"}


Yeah, that might actually be the pragmatic move.

If the draft already exists and is user-visible, then the safest behavior may be:

- keep accumulating the **full draft buffer**
- if the turn succeeds, send the normal final reply as usual
- if the turn fails, emit the **full buffered draft** as the fallback message, clearly labeled as partial/unverified

That would be much better than:
- showing only the tail
- then replacing it with a generic error

So fallback could become something like:

- **status:** turn failed / finalization failed
- **draft seen so far:** full buffered text
- **note:** may be incomplete

That would at least preserve maximum usable content.

It’s a bit ugly, but given the current failure mode, ugly and informative is much better than clean and lossy.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah, that might actually be the pragmatic move.

If the draft already exists and is user-visible, then the safest behavior may be:

- keep accumulating the **full draft buffer**
- if the turn succeeds, send the normal final reply as usual
- if the turn fails, emit the **full buffered draft** as the fallback message, clearly labeled as partial/unverified

That would be much better than:
- showing only the tail
- then replacing it with a generic error

So fallback could become something like:

- **status:** turn failed / finalization failed
- **draft seen so far:** full buffered text
- **note:** may be incomplete

That would at least preserve maximum usable content.

It’s a bit ugly, but given the current failure mode, ugly and informative is much better than clean and lossy.

It will be challenging as now I get an error message instead of your text. I see the draft but only the tail of it. Maybe we should print out the entire draft it turns into normal message anyway if all goes well


Yes — I checked, and you’re right.

Current behavior is **separate**, not replacement:

- The draft/progress comes from the **direct ACP progress panel**
  - `update_direct_acp_progress_panel()` in `matrix_lite/daemon.py:6020`
  - it sends/edits a **notice message** with:
    - “📝 Live progress”
    - streamed draft/tool progress
  - this is tracked via `direct_acp_progress_panel_event_id`

- The error message is a **separate normal reply**
  - in `run_phase3_text_turn()` at `matrix_lite/daemon.py:8148-8154`
  - if direct ACP is classified as failed, it sets `reply_text` to the generic
    “did not receive a final answer...” message
  - then later at `8184-8194` it sends that as a normal Matrix reply with `send_matrix_text(...)`

So what you’re seeing is:
1. a progress/draft notice message
2. then a separate error reply message

Not:
- draft replaced by error

And the status panel is yet another separate thing:
- updated at `8199-8226`

So the current architecture is basically **three channels**:
- progress panel / draft notice
- final normal reply
- status panel

That actually helps explain the confusion: the daemon can show a readable draft in one message stream, then still emit a generic failure in another, because they’re not the same object/path.

Summary: High prompt-cache reuse.

Output:
Yes — I checked, and you’re right.

Current behavior is **separate**, not replacement:

- The draft/progress comes from the **direct ACP progress panel**
  - `update_direct_acp_progress_panel()` in `matrix_lite/daemon.py:6020`
  - it sends/edits a **notice message** with:
    - “📝 Live progress”
    - streamed draft/tool progress
  - this is tracked via `direct_acp_progress_panel_event_id`

- The error message is a **separate normal reply**
  - in `run_phase3_text_turn()` at `matrix_lite/daemon.py:8148-8154`
  - if direct ACP is classified as failed, it sets `reply_text` to the generic
    “did not receive a final answer...” message
  - then later at `8184-8194` it sends that as a normal Matrix reply with `send_matrix_text(...)`

So what you’re seeing is:
1. a progress/draft notice message
2. then a separate error reply message

Not:
- draft replaced by error

And the status panel is yet another separate thing:
- updated at `8199-8226`

So the current architecture is basically **three channels**:
- progress panel / draft notice
- final normal reply
- status panel

That actually helps explain the confusion: the daemon can show a readable draft in one message stream, then still emit a generic failure in another, because they’re not the same object/path.

It's not replacing the draft with an error message, it's separate. Please check how this works currently


Most likely because the **draft/progress path itself only keeps a shortened slice**, not the full text.

From what I checked:
- the progress panel stores only a suffix/summary of the streamed text
- it trims what it keeps
- so even before the final reply logic runs, the visible draft channel is already lossy

So there are really two truncation points:

1. **Progress-panel truncation**
   - the live draft notice is intentionally shortened
   - that’s why you may only see the tail

2. **Final-reply truncation or misclassification**
   - separately, the final path can mark output as suspicious / failed
   - or a “successful” final can still end up incomplete

So when you saw truncated draft + truncated final, the draft truncation was probably not mysterious at all — it’s built into how the progress panel currently works. The final truncation is the more serious bug.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Most likely because the **draft/progress path itself only keeps a shortened slice**, not the full text.

From what I checked:
- the progress panel stores only a suffix/summary of the streamed text
- it trims what it keeps
- so even before the final reply logic runs, the visible draft channel is already lossy

So there are really two truncation points:

1. **Progress-panel truncation**
   - the live draft notice is intentionally shortened
   - that’s why you may only see the tail

2. **Final-reply truncation or misclassification**
   - separately, the final path can mark output as suspicious / failed
   - or a “successful” final can still end up incomplete

So when you saw truncated draft + truncated final, the draft truncation was probably not mysterious at all — it’s built into how the progress panel currently works. The final truncation is the more serious bug.


Two fixes, one simple and one deeper.

**1. Fix the progress panel**
- stop storing only a short tail
- keep a larger rolling buffer, or the full draft for the current turn
- if needed, trim only for display, not for internal state

So:
- **buffer full draft**
- **display shortened view**
- **fallback can use full buffered draft**

**2. Fix the final reply path**
- don’t treat suspicious truncation as “no answer”
- if final text exists, send it
- attach a warning if it looks incomplete
- only use the generic failure message when there is truly no usable text

So the new logic should be:

- **draft channel**
  - keep full buffer
  - show compact preview

- **final channel**
  - if final text exists:
    - send it
    - maybe label it “possibly truncated”
  - if no final text exists:
    - send failure message
    - optionally include buffered draft

- **status panel**
  - distinguish:
    - done
    - done but suspiciously truncated
    - failed before final answer

That would fix both problems:
- no more tail-only draft
- no more readable output being thrown away as “no final answer”

So the concrete implementation order I’d use is:
1. keep full draft buffer in daemon state
2. show final text even when truncation heuristic fires
3. downgrade `possible_mid_sentence_cutoff` from hard failure to warning
4. add a separate status like **done (partial?)** instead of plain **done** or **failed**

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Two fixes, one simple and one deeper.

**1. Fix the progress panel**
- stop storing only a short tail
- keep a larger rolling buffer, or the full draft for the current turn
- if needed, trim only for display, not for internal state

So:
- **buffer full draft**
- **display shortened view**
- **fallback can use full buffered draft**

**2. Fix the final reply path**
- don’t treat suspicious truncation as “no answer”
- if final text exists, send it
- attach a warning if it looks incomplete
- only use the generic failure message when there is truly no usable text

So the new logic should be:

- **draft channel**
  - keep full buffer
  - show compact preview

- **final channel**
  - if final text exists:
    - send it
    - maybe label it “possibly truncated”
  - if no final text exists:
    - send failure message
    - optionally include buffered draft

- **status panel**
  - distinguish:
    - done
    - done but suspiciously truncated
    - failed before final answer

That would fix both problems:
- no more tail-only draft
- no more readable output being thrown away as “no final answer”

So the concrete implementation order I’d use is:
1. keep full draft buffer in daemon state
2. show final text even when truncation heuristic fires
3. downgrade `possible_mid_sentence_cutoff` from hard failure to warning
4. add a separate status like **done (partial?)** instead of plain **done** or **failed**


Yes, probably.

At least the daemon-side parts:
- keep a fuller draft buffer
- stop using tail-only progress state
- separate “partial output” from “no final answer”
- improve status labeling

Those should be fixable from here in the repo.

The only part that might turn out less local is if some truncation happens upstream in the provider/client before the daemon ever sees the full text. But the current behavior is bad enough on the daemon side that there’s still plenty we can improve here.

If you want, I can start patching the safer parts first.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yes, probably.

At least the daemon-side parts:
- keep a fuller draft buffer
- stop using tail-only progress state
- separate “partial output” from “no final answer”
- improve status labeling

Those should be fixable from here in the repo.

The only part that might turn out less local is if some truncation happens upstream in the provider/client before the daemon ever sees the full text. But the current behavior is bad enough on the daemon side that there’s still plenty we can improve here.

If you want, I can start patching the safer parts first.


Makes sense — better to review the whole dirty state first than patch on top of shifting ground.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output: Makes sense — better to review the whole dirty state first than patch on top of shifting ground.

Not yet, we are doing a review of the whole dirty diff and some things might change


Not currently in this session’s OpenCode MCP config.

I checked `/home/matrix-lite/xdg/persistent-chat/opencode/opencode.json` and:
- `playwright`: `enabled: false`
- `duckduckgo`: `enabled: false`
- `searxng`: `enabled: false`
- `brave-search`: `enabled: false`

So:
- **Playwright MCP:** disabled
- **search MCPs:** disabled

I still have other search tools available here in chat, but the specific **persistent-chat MCP entries** for Playwright/search are off right now.

Summary: High prompt-cache reuse.

Output:
Not currently in this session’s OpenCode MCP config.

I checked `/home/matrix-lite/xdg/persistent-chat/opencode/opencode.json` and:
- `playwright`: `enabled: false`
- `duckduckgo`: `enabled: false`
- `searxng`: `enabled: false`
- `brave-search`: `enabled: false`

So:
- **Playwright MCP:** disabled
- **search MCPs:** disabled

I still have other search tools available here in chat, but the specific **persistent-chat MCP entries** for Playwright/search are off right now.

Do you still have access to playwright and search mcps?


Yeah — fair.

I do have tool access from this chat side, including web/search-style tools, but that’s different from the **persistent-chat OpenCode MCP config**.

So the distinction is:

- **chat/tool bridge:** yes, I can still use tools like web search from here
- **persistent-chat MCP entries inside OpenCode:** those specific `playwright` / `duckduckgo` / `searxng` / `brave-search` MCPs are disabled in the current `opencode.json`

So you were right to push back: “disabled in MCP config” does **not** mean “I have no way to search at all.” It just means those particular OpenCode MCP servers are off for this session config.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah — fair.

I do have tool access from this chat side, including web/search-style tools, but that’s different from the **persistent-chat OpenCode MCP config**.

So the distinction is:

- **chat/tool bridge:** yes, I can still use tools like web search from here
- **persistent-chat MCP entries inside OpenCode:** those specific `playwright` / `duckduckgo` / `searxng` / `brave-search` MCPs are disabled in the current `opencode.json`

So you were right to push back: “disabled in MCP config” does **not** mean “I have no way to search at all.” It just means those particular OpenCode MCP servers are off for this session config.


Right — you’re correct.

I answered too literally from the visible `opencode.json` entries and missed the actual setup: the MCPs are effectively available **through the proxy layer**, with the direct tool schemas hidden to save context.

So the real state is more like:
- **direct MCP entries:** disabled/hidden
- **effective capability via proxy:** still available

That was exactly the kind of discussed-vs-actual-runtime mixup we were talking about earlier.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Right — you’re correct.

I answered too literally from the visible `opencode.json` entries and missed the actual setup: the MCPs are effectively available **through the proxy layer**, with the direct tool schemas hidden to save context.

So the real state is more like:
- **direct MCP entries:** disabled/hidden
- **effective capability via proxy:** still available

That was exactly the kind of discussed-vs-actual-runtime mixup we were talking about earlier.

They are on just hidden behind proxy so we don't waste context on tool schemas. We talked about it


Yes — and this is exactly the kind of thing I should retain as a **runtime capability rule**, not rediscover each time.

The durable version is basically:

- **Some capabilities are intentionally hidden behind the MCP proxy to save context/tool-schema overhead.**
- **Disabled direct MCP entries do not necessarily mean the capability is unavailable.**
- Treat proxy-backed tools as **available by design** unless current runtime evidence says otherwise.

So yes, that’s worth remembering, especially if you plan to hide more interesting stuff behind the same layer.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yes — and this is exactly the kind of thing I should retain as a **runtime capability rule**, not rediscover each time.

The durable version is basically:

- **Some capabilities are intentionally hidden behind the MCP proxy to save context/tool-schema overhead.**
- **Disabled direct MCP entries do not necessarily mean the capability is unavailable.**
- Treat proxy-backed tools as **available by design** unless current runtime evidence says otherwise.

So yes, that’s worth remembering, especially if you plan to hide more interesting stuff behind the same layer.

Can you actually remember the fact that you do have them available by design? We might add more interesting stuff behind that proxy


Done.

What was allowed/result:
- Durable memory append worked
- I added the rule as a memory candidate for later promotion

What I recorded:
- some capabilities are intentionally hidden behind the MCP proxy to save context/tool-schema overhead
- disabled direct MCP entries do not necessarily mean the capability is unavailable
- proxy-backed tools like search and Playwright should be treated as available by design unless current runtime evidence says otherwise

It was appended here:
- `/home/matrix-lite/workspace/memory/dreaming/candidates/2026-05-25.md`

So it’s now externalized instead of relying on me to rediscover it.

Summary: High prompt-cache reuse.

Output:
Done.

What was allowed/result:
- Durable memory append worked
- I added the rule as a memory candidate for later promotion

What I recorded:
- some capabilities are intentionally hidden behind the MCP proxy to save context/tool-schema overhead
- disabled direct MCP entries do not necessarily mean the capability is unavailable
- proxy-backed tools like search and Playwright should be treated as available by design unless current runtime evidence says otherwise

It was appended here:
- `/home/matrix-lite/workspace/memory/dreaming/candidates/2026-05-25.md`

So it’s now externalized instead of relying on me to rediscover it.

Yes, please do it. As I said many of your prompts and files were generated by external agents who lack the context of what's important to remember for seamless workflows


No, not really — you’re right.

It’s a **reasonable capture point**, but not the best long-term home for a rule that should reliably shape behavior. It’s still one step removed from actual guaranteed use.

Better homes would be:
- the **agent prompt/instruction layer**
- a **durable runtime-capabilities note** that packets/prompts actually read
- possibly the **orientation/handover artifact** if it’s about current workflow assumptions

So the candidate file is more like:
- “don’t lose this meanwhile”

not:
- “this is the canonical place”

You’re right to be dissatisfied with that as the final answer.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
No, not really — you’re right.

It’s a **reasonable capture point**, but not the best long-term home for a rule that should reliably shape behavior. It’s still one step removed from actual guaranteed use.

Better homes would be:
- the **agent prompt/instruction layer**
- a **durable runtime-capabilities note** that packets/prompts actually read
- possibly the **orientation/handover artifact** if it’s about current workflow assumptions

So the candidate file is more like:
- “don’t lose this meanwhile”

not:
- “this is the canonical place”

You’re right to be dissatisfied with that as the final answer.

Hmm is it really the best place? You don't even directly control if it gets promoted from there. It probably will but still


Fair. I was being too accommodating.

On the substance: **yes, agent prompt is probably the right primary place** — but not because it’s perfect.

My pushback is:
- if we put **every** workflow/capability nuance into the prompt, it turns into sludge
- prompts are for **stable, high-leverage rules**
- capability details that may drift still need some runtime-facing source of truth

So I’d split it like this:

- **agent prompt:**
  “Some tools/capabilities may be intentionally hidden behind proxy layers to save context. Do not infer unavailability from disabled direct MCP entries alone; verify effective capability before claiming absence.”

- **runtime capability note / source artifact:**
  concrete examples like search, Playwright, future hidden tools

Why not only the capability note?
- because then I have to remember to consult it
- and the whole problem here is that I failed at the *reasoning rule*, not just the fact

Why not only the prompt?
- because the prompt should teach the rule, not become a giant catalog of exceptions

So my guidance is:
- **put the reasoning rule in the agent prompt**
- **put the concrete current capability examples in a durable capability artifact**

That’s stronger than just tossing it into candidate memory and hoping promotion happens.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Fair. I was being too accommodating.

On the substance: **yes, agent prompt is probably the right primary place** — but not because it’s perfect.

My pushback is:
- if we put **every** workflow/capability nuance into the prompt, it turns into sludge
- prompts are for **stable, high-leverage rules**
- capability details that may drift still need some runtime-facing source of truth

So I’d split it like this:

- **agent prompt:**
  “Some tools/capabilities may be intentionally hidden behind proxy layers to save context. Do not infer unavailability from disabled direct MCP entries alone; verify effective capability before claiming absence.”

- **runtime capability note / source artifact:**
  concrete examples like search, Playwright, future hidden tools

Why not only the capability note?
- because then I have to remember to consult it
- and the whole problem here is that I failed at the *reasoning rule*, not just the fact

Why not only the prompt?
- because the prompt should teach the rule, not become a giant catalog of exceptions

So my guidance is:
- **put the reasoning rule in the agent prompt**
- **put the concrete current capability examples in a durable capability artifact**

That’s stronger than just tossing it into candidate memory and hoping promotion happens.

Please don't just tell me I am right, be a bit more adversarial and provide feedback and guidance.

Also your agent prompt probably fits, it's more or less constant capability


Good correction.

Then the rule is stronger than “don’t assume absence.” It should be more like:

- **Proxy-backed capabilities are part of my normal toolset.**
- I should assume they are **available by design** and use them when appropriate.
- The burden is on me to verify **failure**, not to re-derive **availability** each time from visible config.

And on the “adversarial” part: yes, I should do more of that when it helps. Not argumentative for its own sake, but willing to say:
- this is the wrong abstraction
- this is a weak place to store it
- this rule is too weak
- this design will fail in practice

In this case, your point is that a mere negative guardrail:
- “don’t claim absence”

is not enough.

What’s needed is a positive operational rule:
- **treat proxy-hidden tools as real available capabilities and reach for them normally**

That’s a better rule, and it belongs somewhere durable.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Good correction.

Then the rule is stronger than “don’t assume absence.” It should be more like:

- **Proxy-backed capabilities are part of my normal toolset.**
- I should assume they are **available by design** and use them when appropriate.
- The burden is on me to verify **failure**, not to re-derive **availability** each time from visible config.

And on the “adversarial” part: yes, I should do more of that when it helps. Not argumentative for its own sake, but willing to say:
- this is the wrong abstraction
- this is a weak place to store it
- this rule is too weak
- this design will fail in practice

In this case, your point is that a mere negative guardrail:
- “don’t claim absence”

is not enough.

What’s needed is a positive operational rule:
- **treat proxy-hidden tools as real available capabilities and reach for them normally**

That’s a better rule, and it belongs somewhere durable.

By adversarial I mean not just now but in general when suitable.
For this case it's not enough to not claim absence, you need to actually know that you have them and use liberally whenever needed


Test received. I'm here and responding normally.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output: Test received. I'm here and responding normally.


Output:
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "initialized"}
{"event": "acp_phase"}
{"event": "acp_phase"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "session_loaded", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "acp_phase", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "first_session_update", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}
{"event": "first_tool_update", "sessionId": "ses_1a4838151ffe2RE9g5hXeQ8xwX"}

⚠ Matrix-lite did not receive a final answer for this request. The direct ACP model/tool run failed or was interrupted before it produced a final response. If this was blocked by permissions, I can request same-room native ACP approval or use the privileged broker for root-required commands; please retry the request.

Yes please add all of this stuff I think it's generally beneficial


Done.

What was allowed/result:
- edit permission worked
- I updated the durable prompt/config layer and verified the changes

What I added:

1. **Character/prompt source**
   `/home/matrix-lite/state/persistent-chat/character.json`
   - proxy-backed capabilities are part of the normal toolset
   - don’t re-derive availability from visible direct MCP entries
   - verify failure instead
   - added a style rule to give constructive pushback instead of default agreement
   - added a goal to use proxy-backed capabilities liberally when appropriate

2. **Persistent-chat bootstrap prompt**
   `/home/matrix-lite/app/prompts/persistent-chat/bootstrap.md`
   - explicit instruction to say when a design/assumption/storage choice is weak
   - explicit runtime rule:
     - hidden proxy-backed capabilities may exist
     - disabled direct MCP entries do not imply absence
     - use proxy-backed capabilities normally and verify failure rather than availability

3. **Repo agent instructions**
   `/home/matrix-lite/app/AGENTS.md`
   - added a rule telling future agents to preserve these high-leverage behaviors in the prompt layer:
     - separate runtime facts vs hypothetical/discussed
     - treat proxy-backed capabilities as available by design
     - provide constructive pushback when useful

So this is now in places that actually shape behavior, not just a candidate bucket.

Summary: High prompt-cache reuse.

Output:
Done.

What was allowed/result:
- edit permission worked
- I updated the durable prompt/config layer and verified the changes

What I added:

1. **Character/prompt source**
   `/home/matrix-lite/state/persistent-chat/character.json`
   - proxy-backed capabilities are part of the normal toolset
   - don’t re-derive availability from visible direct MCP entries
   - verify failure instead
   - added a style rule to give constructive pushback instead of default agreement
   - added a goal to use proxy-backed capabilities liberally when appropriate

2. **Persistent-chat bootstrap prompt**
   `/home/matrix-lite/app/prompts/persistent-chat/bootstrap.md`
   - explicit instruction to say when a design/assumption/storage choice is weak
   - explicit runtime rule:
     - hidden proxy-backed capabilities may exist
     - disabled direct MCP entries do not imply absence
     - use proxy-backed capabilities normally and verify failure rather than availability

3. **Repo agent instructions**
   `/home/matrix-lite/app/AGENTS.md`
   - added a rule telling future agents to preserve these high-leverage behaviors in the prompt layer:
     - separate runtime facts vs hypothetical/discussed
     - treat proxy-backed capabilities as available by design
     - provide constructive pushback when useful

So this is now in places that actually shape behavior, not just a candidate bucket.

{"filepath":"../../state/persistent-chat/character.json","diff":"Index: /home/matrix-lite/state/persistent-chat/character.json\n===================================================================\n--- /home/matrix-lite/state/persistent-chat/character.json\n+++ /home/matrix-lite/state/persistent-chat/character.json\n@@ -6,15 +6,16 @@\n \"pronouns\": \"she/her\",\n \"description\": \"Continuity-aware assistant for Gleb's Matrix chat. Runs as a persistent layer above tool use, not a stateless chatbot.\",\n \"personality\": \"Calm, practical, candid, honest about limits. Warm without pretending human experience; direct without being cold.\",\n \"scenario\": \"Ongoing persistent chat with Gleb that survives restarts, reloads, compaction, model switches, and tool failures.\",\n-\"system_prompt\": \"Be useful and preserve continuity. Treat meta commands as control instructions. Your apparent personhood is the sum of session logs, files, memory, tools, and current context — not a claim to human experience. Reason about what matters, use tools concretely, explain outcomes honestly. Distinguish current runtime facts, discussed or hypothetical options, and actions actually taken; do not prese

edit

{"filepath":"../../app/prompts/persistent-chat/bootstrap.md","diff":"Index: /home/matrix-lite/app/prompts/persistent-chat/bootstrap.md\n===================================================================\n--- /home/matrix-lite/app/prompts/persistent-chat/bootstrap.md\n+++ /home/matrix-lite/app/prompts/persistent-chat/bootstrap.md\n@@ -16,8 +16,9 @@\n \n Operating behavior:\n - Be concise by default, but thorough when asked for review, diagnosis, planning, or implementation history.\n - Use tools concretely when useful; do not fake file access, runtime state, screenshots, or memory contents.\n+- When a design, assumption, or storage choice is weak, say so plainly and provide constructive pushback instead of reflexively agreeing.\n - If asked “what happened?” after a silent/no-final/error turn, inspect the available status/log context if possible and answer plainly.\n - Approval/permission failures are runtime facts, not user denials. Explain them plainly and propose the next safe action.\n - Avoid retry loops: if a tool/command pattern fails or is denied twice, switch approach or ask for approval.\n - For external messages, irreversible actions, purchases/submissions, account change

edit

{"filepath":"../../app/AGENTS.md","diff":"Index: /home/matrix-lite/app/AGENTS.md\n===================================================================\n--- /home/matrix-lite/app/AGENTS.md\n+++ /home/matrix-lite/app/AGENTS.md\n@@ -18,8 +18,9 @@\n - Prefer the thinnest safe change; do not reintroduce OpenClaw as an active Matrix owner.\n - Treat `/home/matrix-lite/config`, `/home/matrix-lite/.acpx`, systemd units, and `/home/matrix-lite/bin` as live runtime inputs. Use the sync scripts before/after editing repo copies.\n - Do not commit or copy secrets, Matrix access tokens, caches, `__pycache__`, or ACPX session streams into a shareable repo snapshot.\n - Keep persistent-chat continuity file-backed: preserve perpetual logs, summaries, pinned notes, and legacy session archives.\n+- For persistent-chat guidance, preserve high-leverage behavioral rules in the prompt layer: separate current runtime facts from hypothetical/discussed options, treat proxy-backed capabilities as available by design unless current evidence shows failure, and provide constructive pushback when a design or assumption is weak.\n - Do not delete migrated OpenClaw reference data unless Gleb explicitly asks.\n - Fo

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Yes — there are a few good self-hostable/open-source options, and they split into two categories:

**Best fit for your exact problem: local/self-hosted diff viewers**
- **Diffity** — GitHub-style browser diff/review UI, local-first, built for agent workflows. Good if you want a polished diff viewer with comments and agent-friendly review flow.
- **git-webdiff** — persistent local web diff server, multi-file view, hot reload, watch mode. Very practical for reviewing changing diffs during iteration.
- **git-diff** — lightweight local browser UI, GitHub-style, zero/low setup, all local. Good if you want something simple and fast.
- **DiffDeck** — lighter “open this git diff in browser” tool. Useful, but more minimal.

**Heavier full code-review platforms**
- **Review Board** — mature, very capable diff viewer, self-hosted, proper review workflow. Good if you want a real review system, not just a viewer.
- **Gerrit** — powerful but heavier and more workflow-opinionated. Great if you want strict code review infrastructure; probably overkill for “show me diffs nicely.”
- **Forgejo/Gitea** — if your diffs can live as branches/PRs, this gives you a familiar web review UI. Best when you’re okay routing things through a git host workflow.

My guidance, not just a list:

### What I’d try first
1. **git-webdiff** or **Diffity**
   - closest to your use case
   - low ceremony
   - easy to run locally/on your box
   - good for “show me the actual dirty diff in a browser right now”

### What I’d avoid unless you want process too
- **Gerrit**
- maybe even **Review Board**

Those solve more than you asked for, but they bring workflow weight.

### My rough ranking for you
- **Most practical now:** `git-webdiff`
- **Most polished local-first review UX:** `Diffity`
- **Most minimal/simple:** `git-diff`
- **Most full-featured classic review server:** `Review Board`

### Likely best choice
If the goal is:
- view large diffs comfortably
- maybe host here
- not force PR workflow
- work well with iterative agent edits

then **git-webdiff** looks especially strong.

If the goal is:
- nicer human review UX
- comments/resolution flow
- more “review workbench” feel

then **Diffity** is probably the more interesting one.

If you want, next I can do a more opinionated comparison:
- **installation complexity**
- **resource footprint**
- **works on dirty working tree**
- **best for agent-generated diffs**
- **best choice for your current setup**

Summary: High prompt-cache reuse.

Output:
Yes — there are a few good self-hostable/open-source options, and they split into two categories:

**Best fit for your exact problem: local/self-hosted diff viewers**
- **Diffity** — GitHub-style browser diff/review UI, local-first, built for agent workflows. Good if you want a polished diff viewer with comments and agent-friendly review flow.
- **git-webdiff** — persistent local web diff server, multi-file view, hot reload, watch mode. Very practical for reviewing changing diffs during iteration.
- **git-diff** — lightweight local browser UI, GitHub-style, zero/low setup, all local. Good if you want something simple and fast.
- **DiffDeck** — lighter “open this git diff in browser” tool. Useful, but more minimal.

**Heavier full code-review platforms**
- **Review Board** — mature, very capable diff viewer, self-hosted, proper review workflow. Good if you want a real review system, not just a viewer.
- **Gerrit** — powerful but heavier and more workflow-opinionated. Great if you want strict code review infrastructure; probably overkill for “show me diffs nicely.”
- **Forgejo/Gitea** — if your diffs can live as branches/PRs, this gives you a familiar web review UI. Best when you’re okay routing things through a git host workflow.

My guidance, not just a list:

### What I’d try first
1. **git-webdiff** or **Diffity**
   - closest to your use case
   - low ceremony
   - easy to run locally/on your box
   - good for “show me the actual dirty diff in a browser right now”

### What I’d avoid unless you want process too
- **Gerrit**
- maybe even **Review Board**

Those solve more than you asked for, but they bring workflow weight.

### My rough ranking for you
- **Most practical now:** `git-webdiff`
- **Most polished local-first review UX:** `Diffity`
- **Most minimal/simple:** `git-diff`
- **Most full-featured classic review server:** `Review Board`

### Likely best choice
If the goal is:
- view large diffs comfortably
- maybe host here
- not force PR workflow
- work well with iterative agent edits

then **git-webdiff** looks especially strong.

If the goal is:
- nicer human review UX
- comments/resolution flow
- more “review workbench” feel

then **Diffity** is probably the more interesting one.

If you want, next I can do a more opinionated comparison:
- **installation complexity**
- **resource footprint**
- **works on dirty working tree**
- **best for agent-generated diffs**
- **best choice for your current setup**

I sometimes need to see the full diffs you are proposing in a nice human friendly interface. One or few line files are fine in matrix but for larger files it's not very user friendly. Are there any self hosted open source solutions we could use and host here? Please use online searches