Good question. Here's what I found by checking what the heartbeat script actually touches:

**Files OVERWRITTEN by scripts (DO NOT manually edit):**
- `continuity-pinned-notes.txt` — heartbeat overwrites (line 1504)
- `continuity-summary.txt` — heartbeat overwrites (line 1505)
- `semantic-memory.json` — heartbeat writes (line 565)
- `decision-state.json` — `note-decision` helper
- `curated-memory.md` / `recent-topics.md` — model-heartbeat overwrites
- All packet files (turn/session/restore) — heartbeat overwrites

**Files SAFE for me to edit (not overwritten by scripts):**

1. **`knowledge-base.jsonl`** ✅ — already using this. Script only APPENDS/reads, never overwrites
2. **`curator-journal.md`** ✅ — both I and the model-heartbeat can APPEND to it (it's a journal, not overwritten)
3. **`todo.md`** ✅ — not touched by any script at all
4. **`reasoning-tuning-proposals.md`** ✅ — not touched by any script
5. **`operational-synthesis/findings.jsonl`** ✅ — reflection agent writes new entries but doesn't overwrite
6. **`operational-synthesis/actions.jsonl`** ✅ — same, append-only
7. **`operational-synthesis/notices.jsonl`** ✅ — same

So for handover purposes, the best fit is **`knowledge-base.jsonl`** (already using it) plus **`curator-journal.md`** for more narrative session notes. And **`todo.md`** for task tracking. Want me to start using those more actively for session-to-session continuity?

Output:
h: Path, rows):

      Line 446:         for row in rows: f.write(json.dumps(row, ensure_ascii=False)+'\n')

      Line 502:             Path(args.output).parent.mkdir(parents=True, exist_ok=True); Path(args.output).write_text(text, encoding='utf8')

      Line 513:         if changed: write_jsonl(SEMANTIC_CANDIDATES, rows)

      Line 520:         if changed and not args.dry_run: write_jsonl(SEMANTIC_CANDIDATES, rows)

    ... (37 more lines)

[thinking] Now I can see what the script writes:

           **Files the script OVERWRITES (DO NOT EDIT):**
           - `continuity-pinned-notes.txt` (line 1504) - OVERWRITTEN by heartbeat
           - `continuity-summary.txt` (line 1505) - OVERWRITTEN by heartbeat
           - `semantic-memory.json` (line 565, 1510 area) - WRITTEN by script
           - `decision-state.json` (line 640) - WRITTEN by `note-decision` helper
           - `recent-failures.json` (line 664) - WRITTEN by `note-failure` helper
           - `turn-packet.md/json` - OVERWRITTEN
           - `session-packet.md/json` - OVERWRITTEN
           - `restore-packet.md/json` - OVERWRITTEN
           - `session-start-packet.md/json` - OVERWRITTEN
           - `memory-candidates.jsonl` - APPENDED
           - `semantic-candidates.jsonl` - APPENDED
           - `curated-memory.md` - WRITTEN by model-heartbeat (different script)
           - `recent-topics.md` - WRITTEN by model-heartbeat
           - `reflection-journal.md` - WRITTEN by reflection agent
           - `perpetual-log.jsonl` - APPENDED

           **File...

[tool] grep (pending)
  input: {}

[tool] CURATOR_JOURNAL (completed)
  kind: search
  input: /home/matrix-lite/app/scripts/persistent-chat-memory
  files: /home/matrix-lite/app/scripts/persistent-chat-memory
  output:
    Found 5 matches
    /home/matrix-lite/app/scripts/persistent-chat-memory:
      Line 45: CURATOR_JOURNAL = STATE / 'curator-journal.md'

      Line 1272:         f'- curator journal: {CURATOR_JOURNAL}',

      Line 1289:         ('curator journal', CURATOR_JOURNAL),

      Line 1401:     full_sources=[CHARACTER,SUMMARY,PINNED,CURATED,RECENT_TOPICS,SYNTHESIS_NOTICES,SYNTHESIS_DIRECTED_INDEX,SYNTHESIS_LATEST,SYNTHESIS_DIRECTED_LATEST,SYNTHESIS_FINDINGS,SYNTHESIS_HYPOTHESES,SYNTHESIS_ACTIONS,TIMER_STATE_FILE,CURATOR_JOURNAL,MODEL_HEARTBEAT_STATUS,PRE_RELOAD_HANDOFF_MD,RELOAD_NOTE_MD,BACKEND_RESTART_EVENTS,HEARTBEAT]

      Line 1423:         'curator_artifacts':{'curated_memory':file_fingerprint(CURATED),'curator_journal':file_fingerprint(CURATOR_JOURNAL),'model_heartbeat_status':file_fingerprint(MODEL_HEARTBEAT_STATUS),'semantic_candidates':file_fingerprint(SEMANTIC_CANDIDATES)},

[tool] grep (pending)
  input: {}

[tool] curator-journal (completed)
  kind: search
  input: /home/matrix-lite/app/scripts
  files: /home/matrix-lite/app/scripts
  output:
    Found 21 matches
    /home/matrix-lite/app/scripts/persistent-chat-memory:
      Line 45: CURATOR_JOURNAL = STATE / 'curator-journal.md'

    /home/matrix-lite/app/scripts/persistent-chat-reflection:
      Line 33: CURATOR_JOURNAL = STATE / "curator-journal.md"

      Line 203: You are a restricted operational synthesis worker for the persistent-chat agent. This scheduled cycle is for higher-level operational synthesis: review recent context, form hypotheses, verify what available tools allow, record blockers/missing tools, and prepare actionable context for future cycles and the main agent. It is distinct from the model curator's `curator-journal.md` synthesis notes. You are intentionally closer to the main persistent-chat persona than the lightweight curator, but this is still a background maintenance turn, not a user-facing chat turn.

      Line 228: - /home/matrix-lite/state/persistent-chat/curator-journal.md last 1 day of curator synthesis notes for debugging context; use older entries selectively later if needed

    /home/matrix-lite/app/scripts/persistent-chat-model-heartbeat:
      Line 16: CURATOR_JOURNAL = STATE / 'curator-journal.md'

      Line 146:         evidence.append('curator-journal changed')

      Line 148:         evidence.append('curator-journal touched')

      Line 278: - Also append a dated curator note to `/home/matrix-lite/state/persistent-chat/curator-journal.md` on every run. This file is the curator's synthesis journal; it is not the same thing as the operational synthesis worker below.

      Line 325: Recent curator synthesis journal tail (`curator-journal.md`):

      Line 433:             'curator-journal.md_before': reflection_before,

      Line 434:             'curator-journal.md_after': reflection_after,

    ... (23 more lines)

[thinking] So `curator-journal.md` is:
           1. READ by the heartbeat script (line 1289, as a source file)
           2. WRITTEN by the model-heartbeat script (line 278: "Also append a dated curator note to curator-journal.md on every run")
           3. READ by the reflection script (line 228)
           4. READ by memory MCP and pages scripts

           So the curator-journal is APPENDED to by the model-heartbeat, not overwritten. That means I can ALSO append to it without breaking anything.

           The key finding: `knowledge-base.jsonl` and potentially `curator-journal.md` are append-only files that I can safely edit. But the heartbeat script OVERWRITES:
           - `continuity-pinned-notes.txt` (line 1504)
           - `continuity-summary.txt` (line 1505)

           So I should NOT use those for manual curation.

           **Files safe for me to edit:**
           1. **`knowledge-base.jsonl`** — Append-only JSONL. I already use this. Script reads for search but doesn't overwrite.
           2. **`curator-jo...

[tool] grep (pending)
  input: {}

[tool] SYNTHESIS_FINDINGS|SYNTHESIS_ACTIONS|SYNTHESIS_NOTICES|SYNTHESIS_HYPOTHESES (completed)
  kind: search
  input: /home/matrix-lite/app/scripts/persistent-chat-memory
  files: /home/matrix-lite/app/scripts/persistent-chat-memory
  output:
    Found 14 matches
    /home/matrix-lite/app/scripts/persistent-chat-memory:
      Line 36: SYNTHESIS_NOTICES = SYNTHESIS / 'notices.jsonl'

      Line 40: SYNTHESIS_FINDINGS = SYNTHESIS / 'findings.jsonl'

      Line 41: SYNTHESIS_HYPOTHESES = SYNTHESIS / 'hypotheses.jsonl'

      Line 42: SYNTHESIS_ACTIONS = SYNTHESIS / 'actions.jsonl'

      Line 800:     return {DECISIONS, FAILURES, FACTS, CANDIDATES, SEMANTIC_CANDIDATES, SYNTHESIS_NOTICES, SYNTHESIS_FINDINGS, SYNTHESIS_HYPOTHESES, SYNTHESIS_ACTIONS}

      Line 804:     jsonl_files=[CANDIDATES, SEMANTIC_CANDIDATES, SYNTHESIS_NOTICES, SYNTHESIS_FINDINGS, SYNTHESIS_HYPOTHESES, SYNTHESIS_ACTIONS]

      Line 1022:     rows=load_jsonl(SYNTHESIS_NOTICES)

      Line 1066:     rows=load_jsonl(SYNTHESIS_ACTIONS)

      Line 1280:         f'- actionable notices: {SYNTHESIS_NOTICES}',

      Line 1281:         f'- findings: {SYNTHESIS_FINDINGS}',

      Line 1282:         f'- hypotheses: {SYNTHESIS_HYPOTHESES}',

      Line 1283:         f'- actions: {SYNTHESIS_ACTIONS}',

      Line 1401:     full_sources=[CHARACTER,SUMMARY,PINNED,CURATED,RECENT_TOPICS,SYNTHESIS_NOTICES,SYNTHESIS_DIRECTED_INDEX,SYNTHESIS_LATEST,SYNTHESIS_DIRECTED_LATEST,SYNTHESIS_FINDINGS,SYNTHESIS_HYPOTHESES,SYNTHESIS_ACTIONS,TIMER_STATE_FILE,CURATOR_JOURNAL,MODEL_HEARTBEAT_STATUS,PRE_RELOAD_HANDOFF_MD,RELOAD_NOTE_MD,BACKEND_RESTART_EVENTS,HEARTBEAT]

    ... (1 more lines)

[tool] grep (pending)
  input: {}

[tool] findings|actions|notices|hypotheses".*write|write.*findings|write.*actions (completed)
  kind: search
  input: /home/matrix-lite/app/scripts/persistent-chat-reflection
  files: /home/matrix-lite/app/scripts/persistent-chat-reflection
  output:
    Found 17 matches
    /home/matrix-lite/app/scripts/persistent-chat-reflection:
      Line 26: FINDINGS = SYNTHESIS / "findings.jsonl"

      Line 28: ACTIONS = SYNTHESIS / "actions.jsonl"

      Line 29: NOTICES = SYNTHESIS / "notices.jsonl"

      Line 148: def derive_notices_from_state(report_path: Path, focus: str, rc: int, output: str) -> None:

      Line 199: This focus overrides ordinary no-op behavior. Even if no new chat messages arrived, analyze the requested backlog/problem and write a report. Record any blockers, proposed actions, candidate cleanup recommendations, or follow-up hypotheses in the operational-synthesis artifacts.

      Line 214:   - /home/matrix-lite/state/persistent-chat/operational-synthesis/findings.jsonl for verified or strongly evidenced findings.

      Line 216:   - /home/matrix-lite/state/persistent-chat/operational-synthesis/actions.jsonl for open actions. Keep actions separate from hypotheses.

      Line 217:   - /home/matrix-lite/state/persistent-chat/operational-synthesis/notices.jsonl only for concise actionable notices/blockers that the main agent should see in normal turns. Do not dump broad introspection here; include source_report pointers.

      Line 220: - Directed/out-of-order synthesis may be started by the main agent or subagents. Treat directed reports and notices as additional input to scheduled synthesis, not as chronological chat turns.

      Line 221: - Never conclude "no material change" solely because raw chat/log tails are unchanged. First check open actions, open hypotheses, stale memory-candidates, semantic-candidates status distribution, known invalid prior runs, and unresolved blockers. If any of those need review, this is material operational work.

      Line 234: - /home/matrix-lite/state/persistent-chat/operational-synthesis/findings.jsonl prior findings

      Line 236: - /home/matrix-lite/state/persistent-chat/operational-synthesis/actions.jsonl open actions

      Line 238: - /home/matrix-lite/state/persistent-chat/operational-synthesis/directed/l...

[thinking] The operational-synthesis JSONL files (findings, actions, notices, hypotheses) are READ by the heartbeat script but WRITTEN by the reflection/synthesis agent (persistent-chat-reflection). The reflection script checks for changes since last run and writes new entries.

           So I could append to these files, but the reflection agent might also clean/reorganize them. They're append-oriented but managed by the reflection agent.

           Let me now give the user a clear answer about which files are safe for manual edits:
Good question. Here's what I found by checking what the heartbeat script actually touches:

**Files OVERWRITTEN by scripts (DO NOT manually edit):**
- `continuity-pinned-notes.txt` — heartbeat overwrites (line 1504)
- `continuity-summary.txt` — heartbeat overwrites (line 1505)
- `semantic-memory.json` — heartbeat writes (line 565)
- `decision-state.json` — `note-decision` helper
- `curated-memory.md` / `recent-topics.md` — model-heartbeat overwrites
- All packet files (turn/session/restore) — heartbeat overwrites

**Files SAFE for me to edit (not overwritten by scripts):**

1. **`knowledge-base.jsonl`** ✅ — already using this. Script only APPENDS/reads, never overwrites
2. **`curator-journal.md`** ✅ — both I and the model-heartbeat can APPEND to it (it's a journal, not overwritten)
3. **`todo.md`** ✅ — not touched by any script at all
4. **`reasoning-tuning-proposals.md`** ✅ — not touched by any script
5. **`operational-synthesis/findings.jsonl`** ✅ — reflection agent writes new entries but doesn't overwrite
6. **`operational-synthesis/actions.jsonl`** ✅ — same, append-only
7. **`operational-synthesis/notices.jsonl`** ✅ — same

So for handover purposes, the best fit is **`knowledge-base.jsonl`** (already using it) plus **`curator-journal.md`** for more narrative session notes. And **`todo.md`** for task tracking. Want me to start using those more actively for session-to-session continuity?
</matrix_lite_final>

[done] end_turn

1 does this map to any existing source artifacts?
2 same, you need to curate your memory sometimes. Curator just extracts the facts and guesses it doesn't have the session context.

Please check which files make sense for you to edit yourself (not script output, not breaking existing scripts)


No scripts needed for writing — I can edit those files directly with the Edit tool. The real question is **discovery**: how would a fresh session know what's important?

Here's what currently exists:

**Already injected at session start** (daemon reads these):
- curated-memory.md — curator's facts (but curator misses session context)
- decision-state.json — actions/decisions from note-decision helper
- recent-failures.json — recent errors
- reflection-journal.md — synthesis notes
- recent-topics.md — curator's topic summary

**Not injected, but searchable** (via memory_search):
- curator_journal (in the index ✅)
- operational_synthesis files (in the index ✅)

**Not injected, not searchable**:
- knowledge-base.jsonl ❌ (not in the index, not injected)
- todo.md ❌ (not in the index, not injected)

**The gap:** I could write handover notes to `curator-journal.md` or `knowledge-base.jsonl`, but a fresh session wouldn't see them unless it thinks to search. And you can't search for something you don't know exists.

**Options to bridge this:**
1. **Register knowledge-base.jsonl as an index source** — then at session start I can search for `topic:handover` or similar
2. **Add a small "handover pointer" to the turn packet** — the heartbeat script could include a line like "handover notes in curator-journal.md under ## 2026-05-19" when a fresh session starts
3. **Add knowledge-base.jsonl to the daemon's startup injection** — one more file, but only ~500 chars if we keep the entries lean
4. **Tag based approach** — tag handover entries in the knowledge base as `topic:handover`, then at session start I just grep for that topic

Option 4 is the simplest — no scripts, no daemon changes. I just need to tag handover entries consistently, and search for them at the start. Want me to add `knowledge-base.jsonl` to the memory index so it's searchable?

ensure skipped; preserving open backend session

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is asking two things:
           1. Do we need any scripts for those files?
           2. How would I know to read the important items at session start without reading the entire todo file?

           These are both good architectural questions. Let me think about this:

           **1. Do we need scripts?**

           Not necessarily. The files are append-only JSONL/markdown that I can edit directly with the Edit tool. I don't need a script to write to them. But:

           - `knowledge-base.jsonl`: I can append with the Edit tool. The heartbeat reads it for inclusion in packets.
           - `curator-journal.md`: I can append with the Edit tool. The model-heartbeat reads it.
           - `todo.md`: I can edit with Read/Edit tools. No script reads it.
           - `operational-synthesis/*.jsonl`: I can append with Edit. The reflection agent reads them.

           So no, scripts aren't strictly needed. I can use the tools I already have (Read, Edit, Write) to manage these files.
           ...

[tool] persistent_chat_index_source_list (pending)
  input: {}

[tool] persistent_chat_index_source_list (completed)
  kind: other
  input: {}
  output:
    {
      "ok": true,
      "default_search_sources": [
        "memory"
      ],
      "memory_expands_to": [
        "curated",
        "recent_topics",
        "decisions",
        "failures",
        "perpetual_log",
        "archives",
        "curator_journal",
        "operational_synthesis"
      ],
      "sources": {
        "workspace": {
          "name": "workspace",
          "type": "file_tree",
          "path": "/home/matrix-lite/workspace",
          "default_search": false,
          "description": "OpenClaw workspace files outside generated/cached trees."
        },
        "matrix_lite": {
          "name": "matrix_lite",
          "type": "file_tree",
          "path": "/home/gleb/matrix-lite",
          "default_search": false,
    ... (11 more lines)

[thinking] Interesting! The memory search already has these sources built in:
           - curator_journal
           - operational_synthesis

           But NOT `knowledge-base.jsonl`. The `memory_expands_to` list shows what gets searched when I search with `mode: memory`. These are the files indexed in the memory search SQLite.

           But the key question the user is asking is: at **session start**, how would I know to read the important items? The answer is:

           1. The daemon currently injects: curated-memory.md, recent-topics.md, reflection-journal.md, decision-state.json, recent-failures.json at startup
           2. The curator_journal and operational_synthesis are SEARCHABLE but not injected
           3. The knowledge-base.jsonl is neither injected nor in the index

           So at session start, I'd have to remember to search for things. That's not reliable.

           The best approach without adding scripts is:
           - Use the knowledge base as the primary handover store (...
No scripts needed for writing — I can edit those files directly with the Edit tool. The real question is **discovery**: how would a fresh session know what's important?

Here's what currently exists:

**Already injected at session start** (daemon reads these):
- curated-memory.md — curator's facts (but curator misses session context)
- decision-state.json — actions/decisions from note-decision helper
- recent-failures.json — recent errors
- reflection-journal.md — synthesis notes
- recent-topics.md — curator's topic summary

**Not injected, but searchable** (via memory_search):
- curator_journal (in the index ✅)
- operational_synthesis files (in the index ✅)

**Not injected, not searchable**:
- knowledge-base.jsonl ❌ (not in the index, not injected)
- todo.md ❌ (not in the index, not injected)

**The gap:** I could write handover notes to `curator-journal.md` or `knowledge-base.jsonl`, but a fresh session wouldn't see them unless it thinks to search. And you can't search for something you don't know exists.

**Options to bridge this:**
1. **Register knowledge-base.jsonl as an index source** — then at session start I can search for `topic:handover` or similar
2. **Add a small "handover pointer" to the turn packet** — the heartbeat script could include a line like "handover notes in curator-journal.md under ## 2026-05-19" when a fresh session starts
3. **Add knowledge-base.jsonl to the daemon's startup injection** — one more file, but only ~500 chars if we keep the entries lean
4. **Tag based approach** — tag handover entries in the knowledge base as `topic:handover`, then at session start I just grep for that topic

Option 4 is the simplest — no scripts, no daemon changes. I just need to tag handover entries consistently, and search for them at the start. Want me to add `knowledge-base.jsonl` to the memory index so it's searchable?
</matrix_lite_final>

[done] end_turn

Do we need any scripts for those files? How would you know to read the important items at session start without reading the entire Todo?


Output: [acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected

⚠ Matrix-lite did not receive a final answer for this request. The model/tool run may still be pending or failed; I logged the details.


Output: nothing to cancel

previous prompt incomplete/stale before prompt

Output: [acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected


Output: nothing to cancel

Output: [acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected


Output: nothing to cancel

Output:
[acpx] created session matrix-lite-phase1 (ses_1d2e0c26affegmXFV87kxJSBUJ)
[acpx] agent: opencode-subagent
[acpx] cwd: /home/matrix-lite/workspace
ses_1d2e0c26affegmXFV87kxJSBUJ	(created)

no open session before prompt; resuming existing session

Output:
[acpx] session matrix-lite-phase1 (ses_1d2e0c26affegmXFV87kxJSBUJ) · /home/matrix-lite/workspace · agent needs reconnect
[client] initialize (running)

[client] session/load (running)


Output:
[acpx] session matrix-lite-phase1 (ses_1bd795428ffeyh5q5oD9ZmG71F) · /home/matrix-lite/workspace · agent connected
[thinking] The user says "Ping". I should respond with "Pong".
<matrix_lite_final>P

[done] end_turn
ong</matrix_lite_final>


Doing well! Ready to help. What's on your mind?

Output:
[acpx] session matrix-lite-phase1 (ses_1bd795428ffeyh5q5oD9ZmG71F) · /home/matrix-lite/workspace · agent connected
[thinking] The user is asking "How are you doing today?" which was queued from a previous failed turn. I should address it now.
<matrix_lite_final>Doing well! Ready to help. What's on your

[done] end_turn
 mind?</matrix_lite_final>


42. Or more seriously: to learn, create, connect, and find your own purpose.

Output:
[acpx] session matrix-lite-phase1 (ses_1bd795428ffeyh5q5oD9ZmG71F) · /home/matrix-lite/workspace · agent connected
[thinking] The user is asking about the meaning of life. This is a philosophical question. I'll give a concise, slightly witty answer.
<matrix_lite_final>42. Or more seriously: to learn

[done] end_turn
, create, connect, and find your own purpose.</matrix_lite_final>


There's something quietly magical about what happens when you press the power button on a computer. In that first instant, voltage rails ramp up under the watchful eye of the power-good signal. The CPU, still groggy, begins executing instructions at the reset vector — a fixed address hardwired into silicon, etched during fabrication years ago in a clean room halfway around the world. What follows is a chain of trust: microcode patches, cache initialization, memory controller training, POST. Every single transistor waking up in sequence, like an orchestra tuning before the concert begins. The engineers who wrote that bootstrap code may have moved on to other projects, but their logic lives on, faithfully replayed billions of times.

Then the kernel takes over. Virtual memory paging tables are constructed, interrupt descriptors are laid out in memory, scheduler runqueues are initialized. The system begins managing resources — not because anyone told it to this time around, but because the code paths were hardened over decades of iteration. Device drivers probe for hardware they may or may not find: a controller enumerates the PCI bus, an AHCI driver checks for SATA links, network cards negotiate link speeds. Each probe is a conversation between software and physics. Some devices respond, some don't. Either way, the kernel handles it gracefully. There's a resilience in that — it took thousands of developer-years to make failure feel this routine.

And finally, the init system spawns userspace. `systemd` (or your init of choice) fans out dependencies, starts dbus, logs in. You get a prompt, a desktop, or a web server accepting requests. The machine, for all its staggering complexity — from quantum tunneling in doped silicon to the TCP stack's congestion control — settles into service. It's just sitting there, waiting for your next thought. The warmth I find in this is simple: all that intricate, layered effort exists to amplify one person's intent into action. That's remarkable engineering, and it never gets old.

Output:
[acpx] session matrix-lite-phase1 (ses_1bd795428ffeyh5q5oD9ZmG71F) · /home/matrix-lite/workspace · agent connected
[thinking] The user wants a warm but technical long text, a few paragraphs. Let me think of something appropriate. I'll write about something technically interesting but presented warmly. Let me think... maybe about the beauty of quantum error correction or something with hardware. Or the magic of a computer booting up. Let me write a few warm, technical paragraphs.

           I'll write about the boot process of a computer — it's technical but can be described warmly.
<matrix_lite_final>There's something quietly magical about what happens when you press the power button on a computer. In that first instant, voltage rails ramp up under the watchful eye of the power-good signal. The CPU, still groggy, begins executing instructions at the reset vector — a fixed address hardwired into silicon, etched during fabrication years ago in a clean room halfway around the world. What follows is a chain of trust: microcode patches, cache initialization, memory controller training, POST. Every single transistor waking up in sequence, like an orchestra tuning before the concert begins. The engineers who wrote that bootstrap code may have moved on to other projects, but their logic lives on, faithfully replayed billions of times.

Then the kernel takes over. Virtual memory paging tables are constructed, interrupt descriptors are laid out in memory, scheduler runqueues are initialized. The system begins managing resources — not because anyone told it to this time around, but because the code paths were hardened over decades of iteration. Device drivers probe for hardware they may or may not find: a controller enumerates the PCI bus, an AHCI driver checks for SATA links, network cards negotiate link speeds. Each probe is a conversation between software and physics. Some devices respond, some don't. Either way, the kernel handles it gracefully. There's a resilience in that — it took thousands of developer-years to make failure feel this routine.

And finally, the init system spawns userspace. `systemd` (or your init of choice) fans out dependencies, starts dbus, logs in. You get a prompt, a desktop, or a web server accepting requests. The machine, for all its staggering complexity — from quantum tunneling in doped silicon to the TCP stack's congestion control — settles into service. It's just sitting there, waiting for your next thought. The warmth I find in this is simple: all that intricate, layered effort exists to amplify one person's intent into action. That's remarkable engineering, and

[done] end_turn
 it never gets old.</matrix_lite_final>

Can you write a warm but technical and long text? Say a few paragraphs


Output:
[acpx] session matrix-lite-phase1 (ses_1bd795428ffeyh5q5oD9ZmG71F) · /home/matrix-lite/workspace · agent connected
[thinking] The user is saying thanks for the previous long text. Simple acknowledgment response.
<matrix_lite_final>You're welcome! Glad you enjoyed it. :)</matrix_lite_final>

[done] end_turn


Output: nothing to cancel

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is sending a health check / test message after what seems like another timeout/wedge issue. They've had several failed turns. I should respond simply to confirm I'm back and working.
<matrix_lite_final>HEALTH_OK
</matrix_lite_final>

[done] end_turn


Hey, you're back. The session had a rough patch with timeouts there — sorry about that. The 110K+ context with heavy tool calls might be hitting Kimi's processing limits on certain turns.

We were talking about the handover discovery problem (how would I know to grep at session start without being told). Want to pick that back up, or shift gears to something else?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is acknowledging it's them this time (not just a test/health check). Good - we're back in a conversation state. Let me acknowledge and continue where we left off. We were discussing the handover problem - specifically how I'd know to grep the knowledge base at session start.
<matrix_lite_final>Hey, you're back. The session had a rough patch with timeouts there — sorry about that. The 110K+ context with heavy tool calls might be hitting Kimi's processing limits on certain turns.

We were talking about the handover discovery problem (how would I know to grep at session start without being told). Want to pick that back up, or shift gears to something else?
</matrix_lite_final>

[done] end_turn


Ahh, you're right — I just checked my system prompt and I'm literally running on `opencode/big-pickle`, not Kimi at all. My bad for repeating that wrong assumption all session. The model alias in the config was `kimi-for-coding/k2p6`, but the actual runtime model is `big-pickle`. That explains the timeouts — different model, different rate limits and processing characteristics.

The rate limits were probably the cause of those wedges earlier too, not Kimi subscription issues. So the whole caching/billing discussion was based on the wrong model assumptions.

Good to have that clarified. What should I know about big-pickle's capabilities and limits?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is correcting me - I thought we were on `kimi-for-coding/k2p6`, but they're saying we're actually on `big-pickle` (which is the model name in my system prompt: "You are powered by the model named big-pickle. The exact model ID is opencode/big-pickle"). And the rate limiting we experienced was because of this model's limitations, not Kimi's.

           That's an important correction - I've been referring to Kimi as our model throughout this session when it's actually a different model entirely.
<matrix_lite_found_good>Ahh, you're right — I just checked my system prompt and I'm literally running on `opencode/big-pickle`, not Kimi at all. My bad for repeating that wrong assumption all session. The model alias in the config was `kimi-for-coding/k2p6`, but the actual runtime model is `big-pickle`. That explains the timeouts — different model, different rate limits and processing characteristics.

The rate limits were probably the cause of those wedges earlier too, not Kimi subscription issues. So the whole caching/billing discussion was based on the wrong model assumptions.

Good to have that clarified. What should I know about big-pickle's capabilities and limits?
</matrix_lite_found_good>

[done] end_turn

Kimi? We here discovered that you're actually on big pickle, that's why we were rate limited


Hi there! Ready to help. What can I do for you?

Output:
[acpx] session matrix-lite-phase1 (ses_1bd3f600cffegaMruzmekoJWFK) · /home/matrix-lite/workspace · agent connected
[thinking] The user is saying "Hello". I should respond concisely. Based on the conversation history, there's a pattern where the user says hello and I respond with a friendly greeting. Let me keep it concise.
Hi there! Ready to help. What can I do for you?

[done] end_turn


Testing a thinking LLM involves several angles:

1. **Correctness** — does the final answer match ground truth? Standard benchmarks (GPQA, MATH, LiveCodeBench) still apply.
2. **Chain-of-thought fidelity** — does the internal reasoning actually lead to the answer, or is it post-hoc rationalization? Check for contradictions between the CoT and final answer. Probe by inserting misleading premises and seeing if the model catches them.
3. **Overthinking / efficiency** — does it waste tokens on trivial problems? Measure token cost vs. accuracy tradeoff across difficulty levels.
4. **Calibration** — does the model's expressed confidence in its reasoning match actual correctness?
5. **Adversarial robustness** — can you break the chain by injecting subtle errors or distractions? Does it recover gracefully?
6. **Reversibility / backtracking** — when it hits a dead end in reasoning, does it notice and backtrack, or plough forward?

A solid eval suite mixes automated scoring (exact match, execution-based for code) with human or LLM-as-judge ratings for open-ended reasoning quality.

Output:
[acpx] session matrix-lite-phase1 (ses_1bd3f600cffegaMruzmekoJWFK) · /home/matrix-lite/workspace · agent connected
[thinking] The user is asking how I would test a "thinking LLM" (likely referring to reasoning/thinking models like o1, DeepSeek-R1, Claude with extended thinking, etc.). This is a technical question about LLM evaluation methodology.

           Let me provide a concise but informative answer.
<matrix_lite_final>Testing a thinking LLM involves several angles:

1. **Correctness** — does the final answer match ground truth? Standard benchmarks (GPQA, MATH, LiveCodeBench) still apply.
2. **Chain-of-thought fidelity** — does the internal reasoning actually lead to the answer, or is it post-hoc rationalization? Check for contradictions between the CoT and final answer. Probe by inserting misleading premises and seeing if the model catches them.
3. **Overthinking / efficiency** — does it waste tokens on trivial problems? Measure token cost vs. accuracy tradeoff across difficulty levels.
4. **Calibration** — does the model's expressed confidence in its reasoning match actual correctness?
5. **Adversarial robustness** — can you break the chain by injecting subtle errors or distractions? Does it recover gracefully?
6. **Reversibility / backtracking** — when it hits a dead end in reasoning, does it notice and backtrack, or plough forward?

A solid eval suite mixes automated scoring (exact match, execution-based for code) with human or LLM-as-judge ratings for open-ended reasoning quality.</matrix_lite_final>

[done] end_turn


Nope, I got through fine just now — no timeout. Seems like the rate limits eased up, or the lighter context this turn helped. We're back in business. 🙂

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The previous turn completed successfully (it was my response about big-pickle). So I'm not currently limited. The user is asking if it's limited again, potentially referring to the rate limits we hit earlier or if there's some new limitation.
<matrix_lite_found_good>Nope, I got through fine just now — no timeout. Seems like the rate limits eased up, or the lighter context this turn helped. We're back in business. 🙂
</matrix_lite_found_good>

[done] end_turn


I answered "Nope, I got through fine just now" — that turn went through on my end and I see it in the conversation history here. But looking at the turn packet, it seems like it might have timed out or failed on the backend side before completing delivery. The inconsistent delivery might be related to the rate limits we were discussing — big-pickle might be dropping some responses under load. Does it show up on your end?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is asking about what happened to the previous turn - the one where I responded with the big-pickle realization. They're seeing that something went wrong with that turn or it didn't arrive properly. Let me check - from the turn packet, the "Current thread" shows that the latest user request was the big metadata message and the status note shows my big-pickle response got truncated. The conversation history shows my response about big-pickle did go through though...

           Actually, looking at the "Recent incomplete/failed turns" section, the user's message about "Is it limited again?" is marked as `user_only` (queued). And the user's message "What happened to the previous turn?" is also `user_only`. This means the user is asking about a turn that may have been lost or didn't complete properly.

           Let me look at the conversation more carefully:

           The last successful interaction was:
           - U...
<matrix_lite_found_good>I answered "Nope, I got through fine just now" — that turn went through on my end and I see it in the conversation history here. But looking at the turn packet, it seems like it might have timed out or failed on the backend side before completing delivery. The inconsistent delivery might be related to the rate limits we were discussing — big-pickle might be dropping some responses under load. Does it show up on your end?
</matrix_lite_found_good>

[done] end_turn


This one came through loud and clear. Looks like we're stable again. The earlier timeout on "Is it limited again?" might have been a transient hiccup with the rate limiter, or just the context being a bit heavy at that moment. But these last few turns have been smooth. Good to go! 👍

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is replying to their earlier message about big-pickle and asking "What about this message?" - they're checking if this one goes through. This is a continuation of the delivery testing after the timeout issues.
<matrix_lite_found_good>This one came through loud and clear. Looks like we're stable again. The earlier timeout on "Is it limited again?" might have been a transient hiccup with the rate limiter, or just the context being a bit heavy at that moment. But these last few turns have been smooth. Good to go! 👍
</matrix_lite_found_good>

[done] end_turn


Sounds like a plan. To make the switch smooth on next reload, here's what's needed:

1. **Add DEEPSEEK_API_KEY** to the environment (same place the Kimi key is set — likely systemd service env or profile)
2. **Change OPENCODE_MODEL** in `/home/matrix-lite/bin/matrix-lite-persistent-chat` from `kimi-for-coding/k2p6` to `deepseek/deepseek-v4-flash`
3. **Reload the session**

DeepSeek V4 Flash has 1M context, strong text generation when prompted properly, and is currently free on OpenRouter too. Want me to check what needs changing in the wrapper script while we're here?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user wants to switch to DeepSeek V4 Flash on the next reload. I should note this for the handover and also let them know what needs to be set up for the switch.
<matrix_lite_found_good>Sounds like a plan. To make the switch smooth on next reload, here's what's needed:

1. **Add DEEPSEEK_API_KEY** to the environment (same place the Kimi key is set — likely systemd service env or profile)
2. **Change OPENCODE_MODEL** in `/home/matrix-lite/bin/matrix-lite-persistent-chat` from `kimi-for-coding/k2p6` to `deepseek/deepseek-v4-flash`
3. **Reload the session**

DeepSeek V4 Flash has 1M context, strong text generation when prompted properly, and is currently free on OpenRouter too. Want me to check what needs changing in the wrapper script while we're here?
</matrix_lite_found_good>

[done] end_turn

Let's try deepseek flash on the next reload, it can write good text if asked


Right — free tier means no privacy guarantees. With nanogpt you at least have a subscription relationship and some contractual protections, even if it's not as tight as ZDR providers. It's a pragmatic trade-off, and the subscription model is simpler to manage anyway.

When you want to do the switch, the steps are the same regardless of the provider — just the model name changes:
- For **nanogpt**: probably something like `nanogpt/deepseek-v4-flash`
- The API key and base URL would be whatever nanogpt provides

Want to write down the switch plan in the knowledge base so it's ready when you want to pull the trigger?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is saying they can't use the free tier through OpenRouter because of privacy settings (ZDR etc.), so they'll use DeepSeek Flash via nanogpt instead. They're acknowledging the privacy trade-off.

           nanogpt is a different provider/service they have a subscription to.
<matrix_lite_found_good>Right — free tier means no privacy guarantees. With nanogpt you at least have a subscription relationship and some contractual protections, even if it's not as tight as ZDR providers. It's a pragmatic trade-off, and the subscription model is simpler to manage anyway.

When you want to do the switch, the steps are the same regardless of the provider — just the model name changes:
- For **nanogpt**: probably something like `nanogpt/deepseek-v4-flash`
- The API key and base URL would be whatever nanogpt provides

Want to write down the switch plan in the knowledge base so it's ready when you want to pull the trigger?
</matrix_lite_found_good>

[done] end_turn

It's not free with privacy settings so I can just hope nanogpt doesn't leak too much data


Good night, Gleb! Have a great rest. I'll be here when you're back — the switch plan is ready whenever you want it. Sleep well! 🙂

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is signing off for the night. Let me wish them good night.
<matrix_lite_found_good>Good night, Gleb! Have a great rest. I'll be here when you're back — the switch plan is ready whenever you want it. Sleep well! 🙂
</matrix_lite_found_good>

[done] end_turn

Should be ready already. But maybe tomorrow. Good night


Yep, we already covered that — turns out I've been on `opencode/big-pickle` all session, not Kimi. The emoji style was the giveaway! 😄

We ended the night with a plan to try DeepSeek V4 Flash on the next reload. Rest well!

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] This message seems to be a delayed delivery of the user's earlier message that was already answered. The user said "Good night" and I already replied. Now this old message got re-queued and is being presented again. I already answered this when it was first sent (the "You tell me" message was already in the conversation history).

           I should just acknowledge it briefly since it was already handled.
<matrix_lite_found_good>Yep, we already covered that — turns out I've been on `opencode/big-pickle` all session, not Kimi. The emoji style was the giveaway! 😄

We ended the night with a plan to try DeepSeek V4 Flash on the next reload. Rest well!
</matrix_lite_found_good>

[done] end_turn

You tell me, you've been using it this entire session. I noticed something was off by the emojis
Earlier wedges are related to patching the backend, and big pickle probably came with the new backend


Good morning, Gleb! You're up early — or maybe not that early given it's after 8am. The session survived the night, still running at ~130K context. Ready to try the DeepSeek Flash switch today, or anything else on your mind?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is saying good morning - it's a new day (May 20). The session is still running from last night. Let me greet them warmly and naturally.
<matrix_lite_found_good>Good morning, Gleb! You're up early — or maybe not that early given it's after 8am. The session survived the night, still running at ~130K context. Ready to try the DeepSeek Flash switch today, or anything else on your mind?
</matrix_lite_found_good>

[done] end_turn


Makes sense — no need to interrupt a working session. I can prep the config changes now so they're ready for the next reload automatically. Want me to:

1. Update the wrapper script (`/home/matrix-lite/bin/matrix-lite-persistent-chat`) to set `OPENCODE_MODEL=deepseek/deepseek-v4-flash`
2. Update the main opencode config model field
3. Note that the API key still needs to be set on your end (systemd or env)

That way when you next reload or the session eventually restarts, it picks up DeepSeek Flash without extra steps. Sound good?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user wants DeepSeek Flash configured for the next session but doesn't want to interrupt this working session. They confirm it works with the smoke agent already. I should note this plan clearly so I can execute it when the time comes - either prepare the config changes now (they'll take effect on next reload) or save the plan for later.

           Since the user doesn't want to interrupt, I could make the config changes proactively - change the wrapper script and update the opencode config now, and they'll take effect on the next reload. Or I could just save the plan.

           Let me offer to prep the changes now so they're ready for next reload without needing a separate turn to do it.
<matrix_lite_found_good>Makes sense — no need to interrupt a working session. I can prep the config changes now so they're ready for the next reload automatically. Want me to:

1. Update the wrapper script (`/home/matrix-lite/bin/matrix-lite-persistent-chat`) to set `OPENCODE_MODEL=deepseek/deepseek-v4-flash`
2. Update the main opencode config model field
3. Note that the API key still needs to be set on your end (systemd or env)

That way when you next reload or the session eventually restarts, it picks up DeepSeek Flash without extra steps. Sound good?
</matrix_lite_found_good>

[done] end_turn

Yeah, not that early, pretty late actually. I'm trying to get up earlier but it's difficult when I go to sleep that late.

Deepseek flash should be configured for the next session, it works with smoke agent but I'd rather not interrupt a working session without need


A few telltale signs:

1. **Latency gap** — thinking models pause noticeably before the first token (often 5–30+ seconds of "thinking"), while non-thinking models start streaming almost instantly.
2. **Visible reasoning** — some interfaces show the chain-of-thought in a collapsible section or a "thinking..." indicator.
3. **Language patterns** — a thinking model's output tends to include meta-cognitive phrasing: "Let me reconsider...", "Actually, that's wrong because...", "First I need to check...". Non-thinking models usually give a final answer directly without self-correction.
4. **Model label** — the UI often tells you the model name (e.g. "claude-sonnet-4-thinking" vs "claude-sonnet-4").

That said, some implementations hide the thinking entirely and only show the final output — in that case, latency and response thoroughness are your best clues.

Output:
[acpx] session matrix-lite-phase1 (ses_1bd3f600cffegaMruzmekoJWFK) · /home/matrix-lite/workspace · agent connected
[thinking] The user is asking about how they can tell if I'm using a thinking model from the end-user perspective. Let me think about this.

           From the user's perspective, the telltale signs of a thinking model would be:

           1. **Latency** — thinking models typically have a noticeable delay before responding (they're "thinking")
           2. **Visible chain of thought** — some interfaces show the reasoning process (like a "thinking..." bubble or the actual token-by-token reasoning)
           3. **Response characteristics** — thinking models tend to be more thorough, structured, and may show meta-cognitive language ("let me think about this", "first, I need to consider...", "wait, that's not right")
           4. **The API/interface** — some clients explicitly label which model is being used

           Let me give a concise answer.
<matrix_lite_final>A few telltale signs:

1. **Latency gap** — thinking models pause noticeably before the first token (often 5–30+ seconds of "thinking"), while non-thinking models start streaming almost instantly.
2. **Visible reasoning** — some interfaces show the chain-of-thought in a collapsible section or a "thinking..." indicator.
3. **Language patterns** — a thinking model's output tends to include meta-cognitive phrasing: "Let me reconsider...", "Actually, that's wrong because...", "First I need to check...". Non-thinking models usually give a final answer directly without self-correction.
4. **Model label** — the UI often tells you the model name (e.g. "claude-sonnet-4-thinking" vs "claude-sonnet-4").

That said, some implementations hide the thinking entirely and only show the final output — in that case, latency and response thoroughness are your best clues.</matrix_lite_final>

[done] end_turn

What about the end user side? How do I know if you are using a thinking model or not?


The opencode config already has `nano-gpt/deepseek/deepseek-v4-flash:thinking` set everywhere, but the wrapper script still has the old model:

Good news: the opencode config files are all updated to `nano-gpt/deepseek/deepseek-v4-flash:thinking` ✅

But the wrapper script at `/home/matrix-lite/bin/matrix-lite-persistent-chat` (line 5) still says `OPENCODE_MODEL='kimi-for-coding/k2p6'` — that needs updating to match. The `OPENCODE_MODEL` env var likely takes precedence over the config file's `model` field.

Want me to update that wrapper script too so it's fully ready for next reload?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user says DeepSeek Flash should already be configured. Let me check the wrapper script and opencode config to see if it's already set up.

[tool] read (pending)
  input: {}

[tool] ../../bin/matrix-lite-persistent-chat (completed)
  kind: read
  input: /home/matrix-lite/bin/matrix-lite-persistent-chat
  files: /home/matrix-lite/bin/matrix-lite-persistent-chat
  output:
    <path>/home/matrix-lite/bin/matrix-lite-persistent-chat</path>
    <type>file</type>
    <content>
    1: #!/usr/bin/env bash
    2: set -euo pipefail
    3: export PATH="/usr/bin:/bin:/usr/local/bin:/home/matrix-lite/bin"
    4: export HOME=/home/matrix-lite
    5: export OPENCODE_MODEL='kimi-for-coding/k2p6'
    6: export XDG_CONFIG_HOME='/home/matrix-lite/xdg/persistent-chat'
    7: export OPENCODE_ENABLE_EXA='1'
    8: export OPENCODE_ACP_MCP_ALLOWLIST='matrix_lite_status,persistent_chat,persistent_chat_repo,matrix_lite_extended_tools'
    9: exec /home/matrix-lite/opt/opencode-matrix-lite/bin/opencode-matrix-lite acp "$@"

    (End of file - total 9 lines)
    </content>

[tool] grep (pending)
  input: {}

[tool] "model" (completed)
  kind: search
  input: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  files: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  output:
    Found 10 matches
    /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json:
      Line 1963:   "model": "nano-gpt/deepseek/deepseek-v4-flash:thinking",

      Line 1966:       "model": "nano-gpt/deepseek/deepseek-v4-flash:thinking",

      Line 3609:       "model": "nano-gpt/deepseek/deepseek-v4-flash:thinking",

      Line 5252:       "model": "nano-gpt/deepseek/deepseek-v4-flash:thinking",

      Line 6895:       "model": "nano-gpt/deepseek/deepseek-v4-flash:thinking",

      Line 8456:       "model": "nano-gpt/deepseek/deepseek-v4-flash:thinking",

      Line 10017:       "model": "nano-gpt/deepseek/deepseek-v4-flash:thinking",

      Line 11578:       "model": "nano-gpt/deepseek/deepseek-v4-flash:thinking",

      Line 24423:       "model": "nano-gpt/deepseek/deepseek-v4-flash:thinking",

      Line 24427:       "model": "nano-gpt/deepseek/deepseek-v4-flash:thinking",

[thinking] So the opencode config has been updated to `nano-gpt/deepseek/deepseek-v4-flash:thinking` everywhere! But the wrapper script at `/home/matrix-lite/bin/matrix-lite-persistent-chat` still has `OPENCODE_MODEL='kimi-for-coding/k2p6'`.

           The wrapper script's `OPENCODE_MODEL` env var takes precedence or overrides the opencode config's `model` field, depending on how OpenCode resolves this. Looking at the ACPX config, the main persistent-chat agent uses `command: /home/matrix-lite/bin/matrix-lite-persistent-chat` which is this wrapper. The wrapper sets `OPENCODE_MODEL`, which OpenCode reads as the model to use.

           But also, there's a `matrix_lite_status_switch_bot_model` tool that can switch models. Let me check if there's a discrepancy here - the opencode config says DeepSeek Flash, but the wrapper still says Kimi.

           Actually, looking at the daemon code earlier, `authoritative_model_name()` (li...
The opencode config already has `nano-gpt/deepseek/deepseek-v4-flash:thinking` set everywhere, but the wrapper script still has the old model:

<matrix_lite_found_good>Good news: the opencode config files are all updated to `nano-gpt/deepseek/deepseek-v4-flash:thinking` ✅

But the wrapper script at `/home/matrix-lite/bin/matrix-lite-persistent-chat` (line 5) still says `OPENCODE_MODEL='kimi-for-coding/k2p6'` — that needs updating to match. The `OPENCODE_MODEL` env var likely takes precedence over the config file's `model` field.

Want me to update that wrapper script too so it's fully ready for next reload?
</matrix_lite_found_good>

[done] end_turn

It should be configured already but you can check it too


Confirmed — line 5 now reads `OPENCODE_MODEL='nano-gpt/deepseek/deepseek-v4-flash:thinking'` ✅. Fully aligned with the opencode config. Everything is in place for the next reload — it'll pick up DeepSeek Flash automatically. Nice catch on getting that sorted! 🙂

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user says they've fixed the wrapper script. Let me check it again.

[tool] read (pending)
  input: {}

[tool] ../../bin/matrix-lite-persistent-chat (completed)
  kind: read
  input: /home/matrix-lite/bin/matrix-lite-persistent-chat
  files: /home/matrix-lite/bin/matrix-lite-persistent-chat
  output:
    <path>/home/matrix-lite/bin/matrix-lite-persistent-chat</path>
    <type>file</type>
    <content>
    1: #!/usr/bin/env bash
    2: set -euo pipefail
    3: export PATH="/usr/bin:/bin:/usr/local/bin:/home/matrix-lite/bin"
    4: export HOME=/home/matrix-lite
    5: export OPENCODE_MODEL='nano-gpt/deepseek/deepseek-v4-flash:thinking'
    6: export XDG_CONFIG_HOME='/home/matrix-lite/xdg/persistent-chat'
    7: export OPENCODE_ENABLE_EXA='1'
    8: export OPENCODE_ACP_MCP_ALLOWLIST='matrix_lite_status,persistent_chat,persistent_chat_repo,matrix_lite_extended_tools'
    9: exec /home/matrix-lite/opt/opencode-matrix-lite/bin/opencode-matrix-lite acp "$@"

    (End of file - total 9 lines)
    </content>

[thinking] The wrapper script now has `OPENCODE_MODEL='nano-gpt/deepseek/deepseek-v4-flash:thinking'` which matches the opencode config. Fully ready for next reload.
<matrix_lite_found_good>Confirmed — line 5 now reads `OPENCODE_MODEL='nano-gpt/deepseek/deepseek-v4-flash:thinking'` ✅. Fully aligned with the opencode config. Everything is in place for the next reload — it'll pick up DeepSeek Flash automatically. Nice catch on getting that sorted! 🙂
</matrix_lite_found_good>

[done] end_turn

Great catch, should be fixed now, please check again


Yes, several classic "gotchas" separate them:

- **Alice in Wonderland logic**: *"If you have a cup of tea and you drink half of it, how many cups do you have?"* — non-thinking models often answer "half", thinking models realize you still have one cup.
- **The reversal test**: *"A farmer has 3 rabbits, 5 chickens, and 2 dogs. How many rabbits does the farmer have if you reverse the digits of the total number of animals?"* — requires multi-step reasoning that thinner models shortcut.
- **Sally-Anne false belief**: *"Sally puts a marble in a basket and leaves. Anne moves it to a box. Where will Sally look?"* — tests theory-of-mind reasoning chains.
- **Self-contradiction detection**: *"I'm lying right now. Is the previous sentence true?"* — thinking models explicitly reason about the paradox rather than giving a flat answer.
- **Word problem with distraction**: *"A bat and a ball cost $1.10. The bat costs $1.00 more than the ball. How much is the ball?"* — famous for tripping up fast intuitions; thinking models pause and catch the $0.05 answer.

The common thread: non-thinking models pattern-match and jump to an answer. Thinking models explore, question their first instinct, and backtrack.

Output:
[acpx] session matrix-lite-phase1 (ses_1bd3f600cffegaMruzmekoJWFK) · /home/matrix-lite/workspace · agent connected
[thinking] The user is asking about simple reasoning tests/prompts that differentiate thinking models from non-thinking ones. Let me provide a concise answer with a few classic examples.
<matrix_lite_final>Yes, several classic "gotchas" separate them:

- **Alice in Wonderland logic**: *"If you have a cup of tea and you drink half of it, how many cups do you have?"* — non-thinking models often answer "half", thinking models realize you still have one cup.
- **The reversal test**: *"A farmer has 3 rabbits, 5 chickens, and 2 dogs. How many rabbits does the farmer have if you reverse the digits of the total number of animals?"* — requires multi-step reasoning that thinner models shortcut.
- **Sally-Anne false belief**: *"Sally puts a marble in a basket and leaves. Anne moves it to a box. Where will Sally look?"* — tests theory-of-mind reasoning chains.
- **Self-contradiction detection**: *"I'm lying right now. Is the previous sentence true?"* — thinking models explicitly reason about the paradox rather than giving a flat answer.
- **Word problem with distraction**: *"A bat and a ball cost $1.10. The bat costs $1.00 more than the ball. How much is the ball?"* — famous for tripping up fast intuitions; thinking models pause and catch the $0.05 answer.

The common thread: non-thinking models pattern-match and jump to an answer. Thinking models explore, question their first instinct, and backtrack.</matrix_lite_final>

[done] end_turn

Are there simple reasoning tests/prompts that thinking models are better at?


You're right — the packs are the foundation, but they're passive right now. I only use them when asked or when I remember to. What we need is a **proactive consistency habit**: when certain topics come up (models, configs, permissions), I should check the pack first and validate against live state.

For this specific case: the matrix-lite pack already has the wrapper script path and the config model field documented. If I had cross-referenced the pack when we first discussed DeepSeek Flash, I'd have noticed the mismatch immediately instead of waiting for you to point me at it.

The pack is good — the trigger to USE it is what needs work. That's partly a prompt/bootstrap thing and partly my own discipline. I'll start building the habit: when we start discussing a topic I've documented, I proactively check the pack and validate against what I see live.

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is making an important point - they want me to be able to proactively catch config mismatches and consistency issues, not just react when pointed at them. The knowledge packs we're building (matrix-lite, videoctl, etc.) are the foundation for that. I need to be able to look at the running system and compare it against what I know from the packs.

           For this specific case: the wrapper script had `kimi-for-coding/k2p6` while the opencode config had `nano-gpt/deepseek/deepseek-v4-flash:thinking`. Ideally, I should have been able to notice this mismatch proactively when we first started talking about models, rather than only catching it when pointed at the wrapper script.

           The knowledge pack for matrix-lite already exists (I wrote it yesterday). But I didn't proactively reference it to check for consistency. The challenge is: I need to not only HAVE the knowledge, but also have a p...
You're right — the packs are the foundation, but they're passive right now. I only use them when asked or when I remember to. What we need is a **proactive consistency habit**: when certain topics come up (models, configs, permissions), I should check the pack first and validate against live state.

For this specific case: the matrix-lite pack already has the wrapper script path and the config model field documented. If I had cross-referenced the pack when we first discussed DeepSeek Flash, I'd have noticed the mismatch immediately instead of waiting for you to point me at it.

The pack is good — the trigger to USE it is what needs work. That's partly a prompt/bootstrap thing and partly my own discipline. I'll start building the habit: when we start discussing a topic I've documented, I proactively check the pack and validate against what I see live.
</matrix_lite_final>

[done] end_turn

We need some way later to review things like these proactively. Config mismatches can be caught by scripts but it would really help if you knew more about matrix-lite, that's why we are building those packs


The car wash riddle: **Drive.** If you walk, you arrive at the car wash — but your car is still at home and you can't wash it. The obvious answer ("it's only 100m, just walk") misses the point that *you need the car there*. Early frontier models overthought it — they'd argue about fuel waste or carbon footprint instead of realizing the premise requires the car on-site. A thinking model catches the functional constraint: the goal isn't *you* getting to the car wash, it's the *car* getting there.

Output:
[acpx] session matrix-lite-phase1 (ses_1bd3f600cffegaMruzmekoJWFK) · /home/matrix-lite/workspace · agent connected
[thinking] The user is asking about a classic riddle that tripped up many LLMs. Let me think about this.

           The car wash riddle: The car wash is 100m away. You want to wash your car. Should you walk or drive?

           The trick is: if you walk to the car wash, you don't have your car there to wash it. If you drive, your car gets dirty on the way (or you're driving to a car wash to wash the car, but the car is already dirty - but the point is you need the car at the car wash).

           The classic answer that puzzled models: You should walk, because if you drive to the car wash, the car will be clean afterward but you still need to get home. Wait, let me think more carefully.

           Actually the classic riddle goes: You live 100m from a car wash. You want to wash your car. Should you walk or drive?

           The trick: If you walk, you get to the car wash but your car is still at home. If you drive, your car arrives at the car ...
<matrix_lite_final>The car wash riddle: **Drive.** If you walk, you arrive at the car wash — but your car is still at home and you can't wash it. The obvious answer ("it's only 100m, just walk") misses the point that *you need the car there*. Early frontier models overthought it — they'd argue about fuel waste or carbon footprint instead of realizing the premise requires the car on-site. A thinking model catches the functional constraint: the goal isn't *you* getting to the car wash, it's the *car* getting there.</matrix_lite_final>

[done] end_turn

What about that car wash riddle that was puzzling frontier models for a while?

The car wash is 100m away from my house. I want to wash my car there. Should I walk or drive there?


Fair point. That riddle specifically exploited a blind spot in even the best reasoning models at the time — they'd spin elaborate chains about energy efficiency or convenience, never questioning whether "walk" actually satisfies the goal. It shows that *having* a thinking architecture isn't enough; the model also needs to know *when* to step back and re-examine the premise rather than just elaborating on a wrong initial framing. The gap narrows fast though — the same models today handle it easily.

Output:
[acpx] session matrix-lite-phase1 (ses_1bd3f600cffegaMruzmekoJWFK) · /home/matrix-lite/workspace · agent connected
[thinking] The user is pointing out that the frontier models that got the car wash riddle wrong were also "thinking" models. They're making a good point - even advanced reasoning models (like GPT-4, Claude, etc.) a year ago were failing on this riddle despite having chain-of-thought capabilities. So the distinction isn't simply "thinking vs non-thinking" models - it's about the quality and robustness of the reasoning process. Let me acknowledge this nuance.
<matrix_lite_final>Fair point. That riddle specifically exploited a blind spot in even the best reasoning models at the time — they'd spin elaborate chains about energy efficiency or convenience, never questioning whether "walk" actually satisfies the goal. It shows that *having* a thinking architecture isn't enough; the model also needs to know *when* to step back and re-examine the premise rather than just elaborating on a wrong initial framing. The gap narrows fast though — the same models today handle it easily.</matrix_lite_final>

[done] end_turn

The frontier models were thinking too, and it was less than a year ago.


Good point — priming changes everything. Telling a model "here's a riddle" is like winking before telling a joke; it signals that the obvious answer is probably wrong, so the model engages its guardrails and checks the premise. Without that signal, the same model treats it as a straightforward logistical question and optimizes along the wrong axis entirely. That makes the riddle a better test of *situational awareness* than raw reasoning — can the model detect on its own that the frame doesn't fit?

Output:
[acpx] session matrix-lite-phase1 (ses_1bd3f600cffegaMruzmekoJWFK) · /home/matrix-lite/workspace · agent connected
[thinking] The user is making an interesting point - when I tested the car wash riddle with frontier models, I gave them context that it was a riddle, which likely primed them to think more carefully. In the user's tests without that context, the models performed differently. Let me acknowledge this.
<matrix_lite_final>Good point — priming changes everything. Telling a model "here's a riddle" is like winking before telling a joke; it signals that the obvious answer is probably wrong, so the model engages its guardrails and checks the premise. Without that signal, the same model treats it as a straightforward logistical question and optimizes along the wrong axis entirely. That makes the riddle a better test of *situational awareness* than raw reasoning — can the model detect on its own that the frame doesn't fit?</matrix_lite_final>

[done] end_turn

The problem is also I already told it will be a riddle in advance. My tests with frontier models were with no context


Exactly — staleness is the risk. Every entry has a timestamp already, so it's clear when something was last checked. The habit I need to build: whenever I discover a change or confirm something is still accurate, I append or update the relevant entry.

For now, I'll at least add a note about today's model switch so the next session knows what happened. And going forward, when you or I notice something has changed, I'll make a point of updating the pack right then rather than letting it drift.

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is saying we need to keep the knowledge packs updated. This is a good point - stale knowledge is worse than no knowledge. Let me think about how to handle this.

           The packs I've created are:
           1. WGT - 24 entries (mostly static research, won't need updates)
           2. videoctl - 11 entries (pretty stable, code changes slowly)
           3. matrix-lite - 12 entries (actively evolving - daemon, configs, scripts change)
           4. knowledge-base usage - 3 entries

           The matrix-lite pack is the one most likely to go stale since the app changes frequently. And the AGENTS.md in the app repo also references the architecture doc which needs updates.

           I should commit to updating the knowledge base entries when I discover things have changed, and maybe add a timestamp to entries so it's clear how fresh they are (which I already do with the `ts` field).

           But more practically: I need to make it a habit to append update...
Exactly — staleness is the risk. Every entry has a timestamp already, so it's clear when something was last checked. The habit I need to build: whenever I discover a change or confirm something is still accurate, I append or update the relevant entry.

For now, I'll at least add a note about today's model switch so the next session knows what happened. And going forward, when you or I notice something has changed, I'll make a point of updating the pack right then rather than letting it drift.
</matrix_lite_final>

[done] end_turn

Yes and also we need to keep them updating


Found it. Here's what the system is hardcoded to:

Yes, there are 200K hard limits:

1. **`persistent-chat-context-guard`** (line 12-13) — defaults to `WARN_TOKENS=160000` and `RELOAD_TOKENS=200000`. These can be overridden with env vars but the defaults are 200K.
2. **Context guard JSON** — mirrors those defaults
3. **The daemon reads this guard and triggers reload at 200K**

So even with DeepSeek V4 Flash's 1M context, the daemon would force a reload at 200K. To use the full 1M you'd need to:

- Set `PERSISTENT_CHAT_CONTEXT_RELOAD_TOKENS=900000` and `PERSISTENT_CHAT_CONTEXT_WARN_TOKENS=800000` in the environment
- Also check the daemon's ACPX timeout — processing 1M tokens takes time
- The `build_persistent_memory_context` char limit (currently 8000) might also need adjustment for larger context

Want me to look at the ACPX timeout settings next?

Output:
_logical_metric": 310092,
    35:   "reload_decision_source": "acpx-stream-estimate"
    36: }

    (End of file - total 36 lines)
    </content>

[tool] grep (pending)
  input: {}

[tool] configured_warn|configured_reload|max_context|reload_tokens|warn_tokens" (completed)
  kind: search
  input: /home/matrix-lite/app/config/matrix-lite.json
  files: /home/matrix-lite/app/config/matrix-lite.json
  output:
    No files found

[thinking] Found it. The context guard has:
           - `configured_warn_tokens`: 160000
           - `configured_reload_tokens`: 200000
           - `max_context`: 200000

           These are hardcoded limits. Let me check where they come from - probably the daemon code or the opencode config.

[tool] grep (pending)
  input: {}

[tool] configured_warn_tokens|configured_reload_tokens|warn_tokens.*=|reload_tokens.*=" (completed)
  kind: search
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    No files found

[tool] grep (pending)
  input: {}

[tool] warn_tokens|reload_tokens" (completed)
  kind: search
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    Found 2 matches
    /home/matrix-lite/app/matrix_lite/daemon.py:
      Line 1551:     warn = guard.get("warn_tokens") if guard_matches else None

      Line 1552:     reload_at = guard.get("reload_tokens") if guard_matches else None

[thinking] Let me search for where the context guard configuration is set - likely in the opencode config or as a boot config default.

[tool] grep (pending)
  input: {}

[tool] 160000|190000|200000 (completed)
  kind: search
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    No files found

[thinking] Not hardcoded in the daemon. Let me check the opencode config.

[tool] grep (pending)
  input: {}

[tool] 160000|190000|200000|warn|reload (completed)
  kind: search
  input: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  files: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  output:
    Found 284 matches (showing first 100)
    /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json:
      Line 180:       "/home/matrix-lite/state/persistent-chat/reload-state.json": "deny",

      Line 181:       "/home/matrix-lite/state/persistent-chat/pending-reload.json": "deny",

      Line 182:       "/home/matrix-lite/state/persistent-chat/reload-note.md": "deny",

      Line 183:       "/home/matrix-lite/state/persistent-chat/reload-note.json": "deny",

      Line 184:       "/home/matrix-lite/state/persistent-chat/pre-reload-handoff.md": "deny",

      Line 185:       "/home/matrix-lite/state/persistent-chat/pre-reload-handoff.json": "deny",

      Line 323:       "/home/matrix-lite/app/scripts/persistent-chat-reload *": "deny",

      Line 732:       "/home/matrix-lite/app/scripts/persistent-chat-memory note-reload-handoff *": "allow",

      Line 1854:       "/home/matrix-lite/state/persistent-chat/reload-state.json": "deny",

      Line 1855:       "/home/matrix-lite/state/persistent-chat/pending-reload.json": "deny",

      Line 1856:       "/home/matrix-lite/state/persistent-chat/reload-note.md": "deny",

      Line 1857:       "/home/matrix-lite/state/persistent-chat/reload-note.json": "deny",

      Line 1858:       "/home/matrix-lite/state/persistent-chat/pre-reload-handoff.md": "deny",

    ... (176 more lines)

[thinking] Those were all matches about "reload" in path names, not the context limit. The context guard limits are apparently configured in the context-guard.json file, which the daemon reads but doesn't set directly. Let me check where `configured_warn_tokens` and `configured_reload_tokens` come from — they might be set when the daemon generates the context guard or come from an ACPX configuration.

[tool] grep (pending)
  input: {}

[tool] configured_warn|configured_reload|guard.*update|context_guard (completed)
  kind: search
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    Found 2 matches
    /home/matrix-lite/app/matrix_lite/daemon.py:
      Line 1529: def context_guard_status() -> dict[str, Any]:

      Line 1547:     guard = context_guard_status()

[thinking] The context guard limits aren't hardcoded in the daemon code — they likely come from the ACPX session's max context value. Let me check the ACPX config or the daemon's context guard function.

[tool] read (pending)
  input: {}

[tool] ../../app/matrix_lite/daemon.py (completed)
  kind: read
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    <path>/home/matrix-lite/app/matrix_lite/daemon.py</path>
    <type>file</type>
    <content>
    1529: def context_guard_status() -> dict[str, Any]:
    1530:     try:
    1531:         data = json.loads(PERSISTENT_CONTEXT_GUARD_FILE.read_text(encoding="utf-8", errors="replace"))
    1532:         return data if isinstance(data, dict) else {}
    1533:     except Exception:
    1534:         return {}
    1535:
    1536:
    1537: def session_context_status_line(conversation: dict[str, Any] | None) -> str:
    1538:     if not conversation:
    1539:         return ""
    1540:     session_label = str(conversation.get("session_label") or "")
    1541:     session_id = current_session_id_for_label(session_label) if session_label else ""
    1542:     if not session_id:
    1543:         return "Ctx: pending ACPX session id"

    (Showing lines 1529-1543 of 7123. Use offset=1544 to continue.)
    </content>

[thinking] So `context_guard_status()` just reads the `context-guard.json` file. And the `configured_warn_tokens: 160000` and `configured_reload_tokens: 200000` are just fields in that JSON. Let me find where that file gets written/updated — it's likely in the context guard script.

[tool] grep (pending)
  input: {}

[tool] context-guard|context_guard (completed)
  kind: search
  input: /home/matrix-lite/app/scripts
  files: /home/matrix-lite/app/scripts
  output:
    Found 18 matches
    /home/matrix-lite/app/scripts/matrix-lite-status-mcp.py:
      Line 83: CONTEXT_GUARD_STATUS = Path("/home/matrix-lite/state/persistent-chat/context-guard.json")

    /home/matrix-lite/app/scripts/persistent-chat-reload:
      Line 522: def context_guard_snapshot() -> dict[str, Any]:

      Line 523:     return load_json(STATE / "context-guard.json", {})

      Line 537:     ctx = context_guard_snapshot()

      Line 665:     parser.add_argument("--context-guard", action="store_true", help="context-guard initiated reload; records synthesis metadata; old backend remains open unless --close-old is passed")

    /home/matrix-lite/app/scripts/matrix-lite-pages:
      Line 21: CONTEXT_GUARD = STATE / 'persistent-chat' / 'context-guard.json'

      Line 119:     return {'ok': True, 'generated_at': now(), 'conversations': conversations if isinstance(conversations,dict) else {}, 'context_guard': guard if isinstance(guard,dict) else {}, 'queues': queue_summary(), 'pages': {'root': str(PAGES), 'count': len(scan_pages(10000))}}

      Line 123:     for name in ['curated-memory.md','recent-topics.md','decision-state.json','recent-failures.json','reflection-journal.md','continuity-summary.txt','perpetual-log.jsonl','context-guard.json']:

      Line 141:     payload=status_payload(); guard=payload.get('context_guard') or {}

    /home/matrix-lite/app/scripts/persistent-chat-context-guard:
      Line 10: STATUS = STATE / 'context-guard.json'

    ... (21 more lines)

[thinking] The `persistent-chat-context-guard` script at line 10 sets `STATUS = STATE / 'context-guard.json'`. The values 160000/200000 must be configured somewhere in that script. Let me check it.

[tool] read (pending)
  input: {}

[tool] ../../app/scripts/persistent-chat-context-guard (completed)
  kind: read
  input: /home/matrix-lite/app/scripts/persistent-chat-context-guard
  files: /home/matrix-lite/app/scripts/persistent-chat-context-guard
  output:
    <path>/home/matrix-lite/app/scripts/persistent-chat-context-guard</path>
    <type>file</type>
    <content>
    1: #!/usr/bin/env python3
    2: """Persistent-chat context guard for Matrix-lite ACPX logs."""
    3: from __future__ import annotations
    4: import json, os, re, subprocess, time
    5: from pathlib import Path
    6:
    7: STATE = Path('/home/matrix-lite/state/persistent-chat')
    8: CONVERSATIONS = Path('/home/matrix-lite/state/conversations.json')
    9: ACPX_SESSIONS = Path('/home/matrix-lite/.acpx/sessions')
    10: STATUS = STATE / 'context-guard.json'
    11: EVENT_LOG = STATE / 'perpetual-log.jsonl'
    12: WARN_TOKENS = int(os.environ.get('PERSISTENT_CHAT_CONTEXT_WARN_TOKENS', '160000'))
    13: RELOAD_TOKENS = int(os.environ.get('PERSISTENT_CHAT_CONTEXT_RELOAD_TOKENS', '200000'))
    14: WARN_FRACTION = float(os.environ.get('PERSISTENT_CHAT_CONTEXT_WARN_FRACTION', '0.80'))
    15: RELOAD_FRACTION = float(os.environ.get('PERSISTENT_CHAT_CONTEXT_RELOAD_FRACTION', '0.95'))
    16: MIN_RELOAD_INTERVAL_SEC = int(os.environ.get('PERSISTENT_CHAT_MIN_RELOAD_INTERVAL_SEC', '1800'))
    17: AUTO_RELOAD = os.environ.get('PERSISTENT_CHAT_CONTEXT_AUTO_RELOAD', '0').lower() in {'1','true','yes','on'}
    18: NOTICES = STATE / 'operational-synthesis' / 'notices.jsonl'
    19: CHARS_PER_TOKEN = float(os.environ.get('PERSISTENT_CHAT_CONTEXT_CHARS_PER_TOKEN', '4.0'))
    20:
    21: def now(): return time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime())
    22: def load_json(path, default):
    23:     try: return json.loads(path.read_text())
    24:     except Exception: return default
    25: def write_json(path, data):
    ... (8 more lines)

[thinking] Now I see it clearly. Lines 12-13:

           ```python
           WARN_TOKENS = int(os.environ.get('PERSISTENT_CHAT_CONTEXT_WARN_TOKENS', '160000'))
           RELOAD_TOKENS = int(os.environ.get('PERSISTENT_CHAT_CONTEXT_RELOAD_TOKENS', '200000'))
           ```

           These default to 160K and 200K, respectively. And they can be overridden by environment variables.

           The `max_context: 200000` in context-guard.json is derived from `RELOAD_TOKENS`.

           So the answers to the user's questions:
           1. **Yes, 200K is hardcoded as the default** in `persistent-chat-context-guard` (line 13)
           2. But it can be overridden with the `PERSISTENT_CHAT_CONTEXT_RELOAD_TOKENS` env var
           3. The `max_context: 200000` in the guard JSON matches this default
           4. The daemon reads this guard JSON and uses it for the reload decision

           For DeepSeek V4 Flash's 1M context:
           - The context guard would trigger a reload at 200K even though the model supports 1M
           - To use the full...
Found it. Here's what the system is hardcoded to:

<matrix_lite_found_good>Yes, there are 200K hard limits:

1. **`persistent-chat-context-guard`** (line 12-13) — defaults to `WARN_TOKENS=160000` and `RELOAD_TOKENS=200000`. These can be overridden with env vars but the defaults are 200K.
2. **Context guard JSON** — mirrors those defaults
3. **The daemon reads this guard and triggers reload at 200K**

So even with DeepSeek V4 Flash's 1M context, the daemon would force a reload at 200K. To use the full 1M you'd need to:

- Set `PERSISTENT_CHAT_CONTEXT_RELOAD_TOKENS=900000` and `PERSISTENT_CHAT_CONTEXT_WARN_TOKENS=800000` in the environment
- Also check the daemon's ACPX timeout — processing 1M tokens takes time
- The `build_persistent_memory_context` char limit (currently 8000) might also need adjustment for larger context

Want me to look at the ACPX timeout settings next?
</matrix_lite_found_good>

[done] end_turn

Are we ready to handle 1m context? Is anything hardcoding 200k?


This is a much better system now — the script was rewritten to be **model-aware**:

Great fix! The context guard script was rewritten. It now:

1. **Reads the model from the OpenCode config** (line 12, 169-195)
2. **Looks up the model's max context** from the provider definitions
3. **Calculates thresholds dynamically**: `warn = max_context × 0.80`, `reload = max_context × 0.90`
4. Falls back to env vars if explicitly set, but defaults to model-aware

So when DeepSeek V4 Flash (1M context) is active, it'll automatically set warn at 800K and reload at 900K. No more hardcoded 200K limit.

The guard JSON still shows 200K because the current session is on big-pickle which reports that as its max. It'll adjust automatically on the next reload with DeepSeek Flash. Clean solution! ✅

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user says they've fixed the context guard limits. Let me check.

[tool] read (pending)
  input: {}

[tool] ../../state/persistent-chat/context-guard.json (completed)
  kind: read
  input: /home/matrix-lite/state/persistent-chat/context-guard.json
  files: /home/matrix-lite/state/persistent-chat/context-guard.json
  output:
    <path>/home/matrix-lite/state/persistent-chat/context-guard.json</path>
    <type>file</type>
    <content>
    1: {
    2:   "ts": "2026-05-20T07:04:45Z",
    3:   "live_session": "ses_1bf0bdb40ffeAFrAzQZD1eauNw",
    4:   "warn_tokens": 160000,
    5:   "reload_tokens": 180000,
    6:   "latest_sample": {
    7:     "source": "acpx-stream-estimate",
    8:     "total": 157493,
    9:     "contextish": 157493,
    10:     "prompt_chars": 555028,
    11:     "assistant_chars": 74944,
    12:     "chars_per_token": 4.0,
    13:     "stream": "/home/matrix-lite/.acpx/sessions/ses_1bf0bdb40ffeAFrAzQZD1eauNw.stream.ndjson"
    14:   },
    15:   "sample_count": 3,
    16:   "metric": 157493,
    17:   "action": "none",
    18:   "reason": null,
    19:   "live_session_label": "persistent-chat-20260519T155636Z",
    20:   "configured_warn_tokens": null,
    21:   "configured_reload_tokens": null,
    22:   "max_context": 200000,
    23:   "metric_kind": "estimated_full_context_tokens",
    24:   "last_warn_label": "persistent-chat-20260518T123431Z",
    25:   "metric_sample": {
    ... (3 more lines)

[thinking] Hmm, the context-guard.json still shows:
           - `warn_tokens`: 160000 (but this is the live guard value)
           - `reload_tokens`: 180000 (changed from 190000? earlier it said 190000?)
           - `configured_warn_tokens`: null
           - `configured_reload_tokens`: null
           - `max_context`: 200000

           Wait, it changed! `reload_tokens` is now 180000 (was 190000 earlier). But `configured_warn_tokens` and `configured_reload_tokens` are both null now. These might have been cleared, meaning it's now using context guard defaults rather than explicit overrides.

           Actually, the `configured_warn_tokens` and `configured_reload_tokens` being null might mean they were removed from the config. But the env var defaults for the context guard script are still 160K/200K.

           Wait, the user said "fixed" — maybe they meant the context guard script. Let me check the script file.

[tool] read (pending)
  input: {}

[tool] ../../app/scripts/persistent-chat-context-guard (completed)
  kind: read
  input: /home/matrix-lite/app/scripts/persistent-chat-context-guard
  files: /home/matrix-lite/app/scripts/persistent-chat-context-guard
  output:
    <path>/home/matrix-lite/app/scripts/persistent-chat-context-guard</path>
    <type>file</type>
    <content>
    12: OPENCODE_CONFIG = Path(os.environ.get('PERSISTENT_CHAT_OPENCODE_CONFIG', '/home/matrix-lite/xdg/persistent-chat/opencode/opencode.json'))
    13: LEGACY_WARN_TOKENS = 160000
    14: LEGACY_RELOAD_TOKENS = 200000
    15:
    16: def env_int_optional(name):

    (Showing lines 12-16 of 297. Use offset=17 to continue.)
    </content>

[thinking] The script was rewritten! The old hardcoded `WARN_TOKENS` and `RELOAD_TOKENS` env var defaults are gone. Now it has:
           - `LEGACY_WARN_TOKENS = 160000`
           - `LEGACY_RELOAD_TOKENS = 200000`
           - A function `env_int_optional` for reading env vars

           And importantly, there's `OPENCODE_CONFIG = Path(...)` at line 12 — the script now reads from the OpenCode config! Let me check how it uses this to get the model's actual context length.

[tool] read (pending)
  input: {}

[tool] ../../app/scripts/persistent-chat-context-guard (completed)
  kind: read
  input: /home/matrix-lite/app/scripts/persistent-chat-context-guard
  files: /home/matrix-lite/app/scripts/persistent-chat-context-guard
  output:
    <path>/home/matrix-lite/app/scripts/persistent-chat-context-guard</path>
    <type>file</type>
    <content>
    16: def env_int_optional(name):
    17:     value=os.environ.get(name)
    18:     if value is None or str(value).strip() == '':
    19:         return None
    20:     try: return int(value)
    21:     except Exception: return None
    22:
    23: WARN_TOKENS = env_int_optional('PERSISTENT_CHAT_CONTEXT_WARN_TOKENS')
    24: RELOAD_TOKENS = env_int_optional('PERSISTENT_CHAT_CONTEXT_RELOAD_TOKENS')
    25: WARN_FRACTION = float(os.environ.get('PERSISTENT_CHAT_CONTEXT_WARN_FRACTION', '0.80'))
    26: RELOAD_FRACTION = float(os.environ.get('PERSISTENT_CHAT_CONTEXT_RELOAD_FRACTION', '0.90'))
    27: MIN_RELOAD_INTERVAL_SEC = int(os.environ.get('PERSISTENT_CHAT_MIN_RELOAD_INTERVAL_SEC', '1800'))
    28: AUTO_RELOAD = os.environ.get('PERSISTENT_CHAT_CONTEXT_AUTO_RELOAD', '0').lower() in {'1','true','yes','on'}
    29: NOTICES = STATE / 'operational-synthesis' / 'notices.jsonl'
    30: CHARS_PER_TOKEN = float(os.environ.get('PERSISTENT_CHAT_CONTEXT_CHARS_PER_TOKEN', '4.0'))
    31:
    32: def now(): return time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime())
    33: def load_json(path, default):
    34:     try: return json.loads(path.read_text())
    35:     except Exception: return default
    36: def write_json(path, data):
    37:     path.parent.mkdir(parents=True, exist_ok=True); path.write_text(json.dumps(data, indent=2, ensure_ascii=False)+'\n')
    38:
    39: def live_session_label():
    40:     conv=load_json(CONVERSATIONS, {}).get('conversations', {}).get('persistent:main', {})
    ... (8 more lines)

[thinking] The script was significantly rewritten. Key changes:
           1. `WARN_TOKENS` and `RELOAD_TOKENS` are now `None` by default (read from env, no fallback to hardcoded values)
           2. `LEGACY_WARN_TOKENS` and `LEGACY_RELOAD_TOKENS` are defined as fallbacks
           3. It reads the OpenCode config to get model info
           4. `WARN_FRACTION` is now 0.80 and `RELOAD_FRACTION` is 0.90 (was 0.80 and 0.95?)

           Let me find where the actual limits are derived from the model config.

[tool] grep (pending)
  input: {}

[tool] max_context|reload_tokens|warn_tokens|model|context_length (completed)
  kind: search
  input: /home/matrix-lite/app/scripts/persistent-chat-context-guard
  files: /home/matrix-lite/app/scripts/persistent-chat-context-guard
  output:
    Found 38 matches
    /home/matrix-lite/app/scripts/persistent-chat-context-guard:
      Line 144: def acpx_session_model(entry):

      Line 147:     candidates=[meta.get('current_model_id'), meta.get('currentModelId'), meta.get('model')]

      Line 149:     candidates += [acpx.get('current_model_id'), acpx.get('currentModelId'), acpx.get('model')]

      Line 156:             models=result.get('models') if isinstance(result.get('models'), dict) else {}

      Line 158:                 result.get('current_model_id'), result.get('currentModelId'), result.get('model'),

      Line 159:                 models.get('current_model_id'), models.get('currentModelId'), models.get('model'),

      Line 160:                 models.get('primary'), models.get('large'),

      Line 163:             candidates += [usage.get('model'), usage.get('modelID'), usage.get('modelId')]

      Line 169: def configured_model_context(active_model=''):

      Line 171:     model=str(cfg.get('model') or os.environ.get('OPENCODE_MODEL') or '').strip()

      Line 172:     if active_model and model and active_model != model:

      Line 173:         return {'model': model, 'active_model': active_model, 'context': 0, 'source': str(OPENCODE_CONFIG), 'reason': 'configured model does not match active backend'}

      Line 174:     provider_id=''; model_id=model

    ... (49 more lines)

[thinking] Excellent! The new context guard script is much smarter. It:
           1. Reads the model from the OpenCode config (`configured_model_context`)
           2. Checks the ACPX session's actual max context from usage updates
           3. If no explicit env var overrides, calculates thresholds from the model's max context * fraction (0.80/0.90)
           4. So for DeepSeek V4 Flash with 1M context, it would automatically set warn at 800K and reload at 900K

           This is a great upgrade. Before it was hardcoded 160K/200K, now it's model-aware.

           But the context-guard.json still shows `configured_warn_tokens: null` and `configured_reload_tokens: null` and `max_context: 200000`. That might be because the current session's model (big-pickle) reports a max context of 200K, so the script is using that. It'll pick up the 1M context when DeepSeek Flash is active.
This is a much better system now — the script was rewritten to be **model-aware**:

<matrix_lite_found_good>Great fix! The context guard script was rewritten. It now:

1. **Reads the model from the OpenCode config** (line 12, 169-195)
2. **Looks up the model's max context** from the provider definitions
3. **Calculates thresholds dynamically**: `warn = max_context × 0.80`, `reload = max_context × 0.90`
4. Falls back to env vars if explicitly set, but defaults to model-aware

So when DeepSeek V4 Flash (1M context) is active, it'll automatically set warn at 800K and reload at 900K. No more hardcoded 200K limit.

The guard JSON still shows 200K because the current session is on big-pickle which reports that as its max. It'll adjust automatically on the next reload with DeepSeek Flash. Clean solution! ✅
</matrix_lite_found_good>

[done] end_turn

We might need to switch suddenly, big pickle is a free model and has low rate limits

Great catch again, fixed, please check


Done! Added 4 new entries to the knowledge base covering:

1. **Config mismatch risk** — model in wrapper vs opencode config can disagree, both need checking
2. **Context guard rewrite** — now model-aware, auto-calculates thresholds from model's max context
3. **Big-pickle discovery** — what model we were actually on, its traits (emoji style, verbose), rate limits, timeout behavior
4. **DeepSeek Flash switch** — confirmed ready for next reload with 1M context

55 entries total now across WGT, videoctl, matrix-lite, architecture, and knowledge-base. Good foundation for future sessions to build on. 🙂

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user wants me to record the key findings from this session into the knowledge base. Let me append the important things we discovered:

           1. Model switch from big-pickle → DeepSeek V4 Flash (nano-gpt)
           2. Wrapper script path and how model is configured
           3. Context guard rewrite (model-aware now)
           4. The whole model confusion (big-pickle vs kimi)
           5. Context limits and how they work

           Let me add these entries.

[tool] read (pending)
  input: {}

[tool] ../../state/persistent-chat/knowledge-base.jsonl (completed)
  kind: read
  input: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  files: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  output:
    <path>/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl</path>
    <type>file</type>
    <content>
    49: {"ts":"2026-05-19T20:10Z","topic":"knowledge-base","source":"session bootstrap","fact":"Knowledge base at /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl. JSONL format: {ts, topic, source, fact}. This file persists across sessions (not overwritten). Topics so far: WGT (24 entries), videoctl (11), matrix-lite (12), knowledge-base (1). grep this file by topic to get quick context"}
    50: {"ts":"2026-05-19T20:10Z","topic":"knowledge-base","source":"session bootstrap","fact":"How to use this pack: when user brings up a topic, grep knowledge-base.jsonl for that topic to get durable context without tool calls. If gaps found, use websearch or read source to fill in"}
    51: {"ts":"2026-05-19T20:50Z","topic":"architecture","source":"prompt-duplication-review.md","fact":"Full prompt duplication review saved to /home/matrix-lite/state/persistent-chat/operational-synthesis/findings/prompt-duplication-review.md. Covers: conversation tail redundancy (no compaction), volatile reminders (duplicate bootstrap), intro line (duplicate character), capability hints (static), metadata duplication. Estimated savings: 425-725 tok/turn or 127k-217k over 300 turns"}

    (End of file - total 51 lines)
    </content>

[tool] edit (pending)
  input: {}

[tool] ../../state/persistent-chat/knowledge-base.jsonl (completed)
  kind: edit
  input: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  files: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  output:
    Edit applied successfully.

    Edit applied successfully.

    diff /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl (+4 lines)
<matrix_lite_found_good>Done! Added 4 new entries to the knowledge base covering:

1. **Config mismatch risk** — model in wrapper vs opencode config can disagree, both need checking
2. **Context guard rewrite** — now model-aware, auto-calculates thresholds from model's max context
3. **Big-pickle discovery** — what model we were actually on, its traits (emoji style, verbose), rate limits, timeout behavior
4. **DeepSeek Flash switch** — confirmed ready for next reload with 1M context

55 entries total now across WGT, videoctl, matrix-lite, architecture, and knowledge-base. Good foundation for future sessions to build on. 🙂
</matrix_lite_found_good>

[done] end_turn

Please record all the stuff that is useful in the future in your knowledge base, we will later look at this and the other ones and decide on a better approach with these examples