* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (3):
- Web search: Google careers quantum hardware superconducting cryogenic jobs 2026 / completed / Google careers quantum hardware superconducting cryogenic jobs 2026
- Web search: site:careers.google.com Google quantum hardware jobs 2026 supercondu / completed / site:careers.google.com Google quantum hardware jobs 2026 superconducting
- Web search: site:careers.google.com Google cryogenic electronics quantum jobs 20 / completed / site:careers.google.com Google cryogenic electronics quantum jobs 2026

Draft:
I’m going to search current Google roles that might fit your superconducting / cryogenic / quantum-hardware profile rather than relying on memory of the older opening.Yeah — I don’t trust my memory of the exact old Google opening, so I searched current ones instead. There are actually several that look at least plausibly suitable for you.

Best fits I found:

- **Senior Research Scientist, Josephson Analog Electronics, Quantum AI**
  Probably the strongest thematic fit. Superconducting circuits, microwave engineering, design/simulation/layout/test, qubit readout/control, lab work. This one feels very aligned with your SFQ / cryogenic / superconducting-circuit profile.

- **Senior Research Scientist, Electromagnetic Modeling, Quantum**
  Strong fit if you want to lean more into EM modeling, packaging, HFSS-style work, qubit coherence/coupling analysis, and software-connected design workflow.

- **Research Scientist, Pathfinding Architecture Development**
  Also plausible. Wants superconducting circuits, qubits/couplers/readout/reset, EM simulation, RF/microwave, experimental interfacing. Broad enough that your mix of design + experiment + tooling could fit well.

- **Cryogenic Systems Engineer, Quantum AI**
  Good fit on the cryostat / dilution fridge / operations side, though less directly “you” than the superconducting-circuit/design roles.

- **Calibration Engineer, Quantum AI**
  Plausible if they value your experimental and software/tooling side, but this feels somewhat more calibration/automation-specialized and maybe a bit less senior/prestige-aligned than the above research-scientist roles.

Less likely / weaker:
- **Hardware Engineer, Integration**
- **Packaging Manufacturing Engineer**
- **Systems Yield Engineer**
These are not crazy, but they feel more packaging/test/manufacturing-flow oriented than your strongest profile.
- **Staff Research Scientist, Modeling of Two-Qubit Gates and Tunable Couplers**
This one looks too specifically qubit-gate-modeling heavy unless that’s become a much bigger part of your actual work than I currently think.
- **Quantum software / execution stack roles**
Interesting, but probably not your best immediate fit.

My quick judgment:
If you ever re-engage Google, the most promising angle is probably **not** “qubit jobs” in the narrow sense, but rather one of these buckets:
1. **superconducting analog / readout / Josephson electronics**
2. **EM modeling / package / microwave / chip-coupling**
3. **broader quantum hardware research scientist roles**

That seems more compatible with your actual profile than a generic “qubit physicist” framing.

If you want, next I can do a more opinionated ranking:
- **strong fit /


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (3):
- Web search: Google careers quantum hardware superconducting cryogenic jobs 2026 / completed / Google careers quantum hardware superconducting cryogenic jobs 2026
- Web search: site:careers.google.com Google quantum hardware jobs 2026 supercondu / completed / site:careers.google.com Google quantum hardware jobs 2026 superconducting
- Web search: site:careers.google.com Google cryogenic electronics quantum jobs 20 / completed / site:careers.google.com Google cryogenic electronics quantum jobs 2026

Draft:
I’m going to search current Google roles that might fit your superconducting / cryogenic / quantum-hardware profile rather than relying on memory of the older opening.Yeah — I don’t trust my memory of the exact old Google opening, so I searched current ones instead. There are actually several that look at least plausibly suitable for you.

Best fits I found:

- **Senior Research Scientist, Josephson Analog Electronics, Quantum AI**
  Probably the strongest thematic fit. Superconducting circuits, microwave engineering, design/simulation/layout/test, qubit readout/control, lab work. This one feels very aligned with your SFQ / cryogenic / superconducting-circuit profile.

- **Senior Research Scientist, Electromagnetic Modeling, Quantum**
  Strong fit if you want to lean more into EM modeling, packaging, HFSS-style work, qubit coherence/coupling analysis, and software-connected design workflow.

- **Research Scientist, Pathfinding Architecture Development**
  Also plausible. Wants superconducting circuits, qubits/couplers/readout/reset, EM simulation, RF/microwave, experimental interfacing. Broad enough that your mix of design + experiment + tooling could fit well.

- **Cryogenic Systems Engineer, Quantum AI**
  Good fit on the cryostat / dilution fridge / operations side, though less directly “you” than the superconducting-circuit/design roles.

- **Calibration Engineer, Quantum AI**
  Plausible if they value your experimental and software/tooling side, but this feels somewhat more calibration/automation-specialized and maybe a bit less senior/prestige-aligned than the above research-scientist roles.

Less likely / weaker:
- **Hardware Engineer, Integration**
- **Packaging Manufacturing Engineer**
- **Systems Yield Engineer**
These are not crazy, but they feel more packaging/test/manufacturing-flow oriented than your strongest profile.
- **Staff Research Scientist, Modeling of Two-Qubit Gates and Tunable Couplers**
This one looks too specifically qubit-gate-modeling heavy unless that’s become a much bigger part of your actual work than I currently think.
- **Quantum software / execution stack roles**
Interesting, but probably not your best immediate fit.

My quick judgment:
If you ever re-engage Google, the most promising angle is probably **not** “qubit jobs” in the narrow sense, but rather one of these buckets:
1. **superconducting analog / readout / Josephson electronics**
2. **EM modeling / package / microwave / chip-coupling**
3. **broader quantum hardware research scientist roles**

That seems more compatible with your actual profile than a generic “qubit physicist” framing.

If you want, next I can do a more opinionated ranking:
- **strong fit / stretch but plausible / probably not worth your time**
for the Google roles I found.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 28.1s
Time: model 25.3s, overhead 1.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 15.9k o 553 t 25 cache 233.5k/94% est $0.0485; p≈2.4ktok/9.7kch
Ctx: direct ACP session 231.4k; active 2.6k; cache 228.9k/99%; p≈2.4ktok; ctx 21.9%; usable 22.3%; observed turns 56; actual turns 58


I’m going to search current Google roles that might fit your superconducting / cryogenic / quantum-hardware profile rather than relying on memory of the older opening.Yeah — I don’t trust my memory of the exact old Google opening, so I searched current ones instead. There are actually several that look at least plausibly suitable for you.

Best fits I found:

- **Senior Research Scientist, Josephson Analog Electronics, Quantum AI**
  Probably the strongest thematic fit. Superconducting circuits, microwave engineering, design/simulation/layout/test, qubit readout/control, lab work. This one feels very aligned with your SFQ / cryogenic / superconducting-circuit profile.

- **Senior Research Scientist, Electromagnetic Modeling, Quantum**
  Strong fit if you want to lean more into EM modeling, packaging, HFSS-style work, qubit coherence/coupling analysis, and software-connected design workflow.

- **Research Scientist, Pathfinding Architecture Development**
  Also plausible. Wants superconducting circuits, qubits/couplers/readout/reset, EM simulation, RF/microwave, experimental interfacing. Broad enough that your mix of design + experiment + tooling could fit well.

- **Cryogenic Systems Engineer, Quantum AI**
  Good fit on the cryostat / dilution fridge / operations side, though less directly “you” than the superconducting-circuit/design roles.

- **Calibration Engineer, Quantum AI**
  Plausible if they value your experimental and software/tooling side, but this feels somewhat more calibration/automation-specialized and maybe a bit less senior/prestige-aligned than the above research-scientist roles.

Less likely / weaker:
- **Hardware Engineer, Integration**
- **Packaging Manufacturing Engineer**
- **Systems Yield Engineer**
These are not crazy, but they feel more packaging/test/manufacturing-flow oriented than your strongest profile.
- **Staff Research Scientist, Modeling of Two-Qubit Gates and Tunable Couplers**
This one looks too specifically qubit-gate-modeling heavy unless that’s become a much bigger part of your actual work than I currently think.
- **Quantum software / execution stack roles**
Interesting, but probably not your best immediate fit.

My quick judgment:
If you ever re-engage Google, the most promising angle is probably **not** “qubit jobs” in the narrow sense, but rather one of these buckets:
1. **superconducting analog / readout / Josephson electronics**
2. **EM modeling / package / microwave / chip-coupling**
3. **broader quantum hardware research scientist roles**

That seems more compatible with your actual profile than a generic “qubit physicist” framing.

If you want, next I can do a more opinionated ranking:
- **strong fit / stretch but plausible / probably not worth your time**
for the Google roles I found.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 29.4s
Time: model 25.3s, overhead 4.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 15.9k o 553 t 25 cache 233.5k/94% est $0.0485; p≈2.4ktok/9.7kch
Ctx: direct ACP session 250.0k; active 16.5k; cache 233.5k/94%; p≈2.4ktok; ctx 21.9%; usable 22.3%; observed turns 56; actual turns 58


So Josephson analog stuff is really quite different. It's more about twpa and jpa etc. I do digital sfq. Of course as you see I spent enough time around qubits to qualify for other positions but probably not enough for them. Sfq could tilt it. But pathfinding architecture sounds like a codename for something they don't want to disclose openly, like say sfq. I've seen companies do this before


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 250.0k; active 16.5k; cache 233.5k/94%; p≈2.4ktok; ctx 23.8%; usable 24.3%; observed turns 58; actual turns 58


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 250.0k; active 16.5k; cache 233.5k/94%; p≈2.4ktok; ctx 23.8%; usable 24.3%; observed turns 58; actual turns 58


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 250.0k; active 16.5k; cache 233.5k/94%; p≈2.4ktok; ctx 23.8%; usable 24.3%; observed turns 58; actual turns 58


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.9s
Time: overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.7ktok/10.9kch
Ctx: direct ACP session 250.0k; active 16.5k; cache 233.5k/94%; p≈2.4ktok; ctx 23.8%; usable 24.3%; observed turns 58; actual turns 58


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


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah — that correction makes sense, and I think you’re probably right to demote the Josephson analog role.

Your distinction is important:
- **Josephson analog** → TWPA / JPA / mixers / parametric analog superconducting circuits
- **your core** → **digital SFQ**, design methodologies, layout/modeling/EDA, plus cryogenic/quantum-hardware adjacency

Those are definitely related worlds, but not the same niche.

So my revised take would be:

- **Josephson Analog Electronics**
  More of a stretch than I first made it sound.

- **EM Modeling / Packaging / Integration**
  Plausible, depending on how much they value your layout/simulation/microwave-adjacent side.

- **Pathfinding Architecture Development**
  Honestly yes, this is the one I’d be most suspicious/curious about too.

Your interpretation seems quite plausible:
- broad vague title
- superconducting circuits
-


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah — that correction makes sense, and I think you’re probably right to demote the Josephson analog role.

Your distinction is important:
- **Josephson analog** → TWPA / JPA / mixers / parametric analog superconducting circuits
- **your core** → **digital SFQ**, design methodologies, layout/modeling/EDA, plus cryogenic/quantum-hardware adjacency

Those are definitely related worlds, but not the same niche.

So my revised take would be:

- **Josephson Analog Electronics**
  More of a stretch than I first made it sound.

- **EM Modeling / Packaging / Integration**
  Plausible, depending on how much they value your layout/simulation/microwave-adjacent side.

- **Pathfinding Architecture Development**
  Honestly yes, this is the one I’d be most suspicious/curious about too.

Your interpretation seems quite plausible:
- broad vague title
- superconducting circuits
- readout/reset/couplers
- architecture language
- enough ambiguity that the truly sensitive part may be intentionally omitted

And yes, I’ve also seen companies do exactly that:
- public posting stays generic
- the real subproblem is more specific
- sometimes intentionally undisclosed because it would reveal roadmap or architecture bets

So if I update the ranking with your correction, I’d say:

1. **Pathfinding Architecture Development** — most interesting hidden-potential role
2. **EM Modeling / Packaging** — plausible explicit fit
3. **Cryogenic Systems / Integration** — practical fit but less “you”
4. **Josephson Analog Electronics** — related but probably the wrong superconducting subculture

And your meta-point also feels right:
you probably have **enough qubit adjacency to pass some screens**, but maybe not enough to cleanly beat candidates whose entire identity is


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah — that correction makes sense, and I think you’re probably right to demote the Josephson analog role.

Your distinction is important:
- **Josephson analog** → TWPA / JPA / mixers / parametric analog superconducting circuits
- **your core** → **digital SFQ**, design methodologies, layout/modeling/EDA, plus cryogenic/quantum-hardware adjacency

Those are definitely related worlds, but not the same niche.

So my revised take would be:

- **Josephson Analog Electronics**
  More of a stretch than I first made it sound.

- **EM Modeling / Packaging / Integration**
  Plausible, depending on how much they value your layout/simulation/microwave-adjacent side.

- **Pathfinding Architecture Development**
  Honestly yes, this is the one I’d be most suspicious/curious about too.

Your interpretation seems quite plausible:
- broad vague title
- superconducting circuits
- readout/reset/couplers
- architecture language
- enough ambiguity that the truly sensitive part may be intentionally omitted

And yes, I’ve also seen companies do exactly that:
- public posting stays generic
- the real subproblem is more specific
- sometimes intentionally undisclosed because it would reveal roadmap or architecture bets

So if I update the ranking with your correction, I’d say:

1. **Pathfinding Architecture Development** — most interesting hidden-potential role
2. **EM Modeling / Packaging** — plausible explicit fit
3. **Cryogenic Systems / Integration** — practical fit but less “you”
4. **Josephson Analog Electronics** — related but probably the wrong superconducting subculture

And your meta-point also feels right:
you probably have **enough qubit adjacency to pass some screens**, but maybe not enough to cleanly beat candidates whose entire identity is superconducting-qubit design/control unless the role has a hidden SFQ / digital-readout / architecture angle.

So yes — I think your read is sharper than my first pass here.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 16.1s
Time: model 13.4s, overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 18.8k o 416 cache 233.5k/93% est $0.0532; p≈2.7ktok/10.9kch
Ctx: direct ACP session 250.0k; active 16.5k; cache 233.5k/94%; p≈2.4ktok; ctx 23.8%; usable 24.3%; observed turns 58; actual turns 59


Yeah — that correction makes sense, and I think you’re probably right to demote the Josephson analog role.

Your distinction is important:
- **Josephson analog** → TWPA / JPA / mixers / parametric analog superconducting circuits
- **your core** → **digital SFQ**, design methodologies, layout/modeling/EDA, plus cryogenic/quantum-hardware adjacency

Those are definitely related worlds, but not the same niche.

So my revised take would be:

- **Josephson Analog Electronics**
  More of a stretch than I first made it sound.

- **EM Modeling / Packaging / Integration**
  Plausible, depending on how much they value your layout/simulation/microwave-adjacent side.

- **Pathfinding Architecture Development**
  Honestly yes, this is the one I’d be most suspicious/curious about too.

Your interpretation seems quite plausible:
- broad vague title
- superconducting circuits
- readout/reset/couplers
- architecture language
- enough ambiguity that the truly sensitive part may be intentionally omitted

And yes, I’ve also seen companies do exactly that:
- public posting stays generic
- the real subproblem is more specific
- sometimes intentionally undisclosed because it would reveal roadmap or architecture bets

So if I update the ranking with your correction, I’d say:

1. **Pathfinding Architecture Development** — most interesting hidden-potential role
2. **EM Modeling / Packaging** — plausible explicit fit
3. **Cryogenic Systems / Integration** — practical fit but less “you”
4. **Josephson Analog Electronics** — related but probably the wrong superconducting subculture

And your meta-point also feels right:
you probably have **enough qubit adjacency to pass some screens**, but maybe not enough to cleanly beat candidates whose entire identity is superconducting-qubit design/control unless the role has a hidden SFQ / digital-readout / architecture angle.

So yes — I think your read is sharper than my first pass here.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 17.6s
Time: model 13.4s, overhead 4.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 18.8k o 416 cache 233.5k/93% est $0.0532; p≈2.7ktok/10.9kch
Ctx: direct ACP session 252.7k; active 19.2k; cache 233.5k/93%; p≈2.7ktok; ctx 23.8%; usable 24.3%; observed turns 58; actual turns 59


So what's that cryptic position actually about? Any more details?


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 252.7k; active 19.2k; cache 233.5k/93%; p≈2.7ktok; ctx 24.1%; usable 24.6%; observed turns 59; actual turns 59


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 252.7k; active 19.2k; cache 233.5k/93%; p≈2.7ktok; ctx 24.1%; usable 24.6%; observed turns 59; actual turns 59


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 252.7k; active 19.2k; cache 233.5k/93%; p≈2.7ktok; ctx 24.1%; usable 24.6%; observed turns 59; actual turns 59


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.9s
Time: overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.9ktok/11.5kch
Ctx: direct ACP session 252.7k; active 19.2k; cache 233.5k/93%; p≈2.7ktok; ctx 24.1%; usable 24.6%; observed turns 59; actual turns 59


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (2):
- https://careers.google.com/jobs/results/92556853480694470-research-scientist/ (t / completed / call_FyXbemP2bHm3GE5CKiYFgEs
- https://www.google.com/about/careers/applications/jobs/results/92556853480694470 / completed / call_9hKd9GVFaOYeX87DQMEdYoW

Draft:
I’m going to pull the pathfinding role details more directly and see if there are clues beyond the generic job-card summary.Not


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (2):
- https://careers.google.com/jobs/results/92556853480694470-research-scientist/ (t / completed / call_FyXbemP2bHm3GE5CKiYFgEs
- https://www.google.com/about/careers/applications/jobs/results/92556853480694470 / completed / call_9hKd9GVFaOYeX87DQMEdYoW

Draft:
I’m going to pull the pathfinding role details more directly and see if there are clues beyond the generic job-card summary.Not much concrete beyond the public text, unfortunately — and the direct job page is flaky / partly gone now, which itself is a clue that it may be getting changed or pulled.

What the public description *did* reveal was roughly this:

- optimize **qubit design**
- improve **coherence** and reduce **crosstalk**
- develop architectures with faster **gates, readout, and reset**
- do **programmatic layout** of large 2D arrays
- run **EM simulations**
- work closely with **fabrication and measurement teams**

So on the face of it, the visible role sounds like:

**superconducting-qubit architecture / chip design / layout / simulation**
rather than explicitly:
**SFQ readout/control/digital superconducting logic**

That said, the title **“Pathfinding Architecture Development”** is still weirdly vague. So my best guess is:

### Most likely
A deliberately broad role for early-stage architecture work around future superconducting processor designs, where they don’t want to publicly say exactly which sub-architecture bets they’re making.

### Less likely but plausible
There is some concealed angle involving:
- specialized


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (3):
- https://careers.google.com/jobs/results/92556853480694470-research-scientist/ (t / completed / call_FyXbemP2bHm3GE5CKiYFgEs
- https://www.google.com/about/careers/applications/jobs/results/92556853480694470 / completed / call_9hKd9GVFaOYeX87DQMEdYoW
- read / pending / call_3WmcBPxBVUTftiaeC2TOapv

Draft:
I’m going to pull the pathfinding role details more directly and see if there are clues beyond the generic job-card summary.Not much concrete beyond the public text, unfortunately — and the direct job page is flaky / partly gone now, which itself is a clue that it may be getting changed or pulled.

What the public description *did* reveal was roughly this:

- optimize **qubit design**
- improve **coherence** and reduce **crosstalk**
- develop architectures with faster **gates, readout, and reset**
- do **programmatic layout** of large 2D arrays
- run **EM simulations**
- work closely with **fabrication and measurement teams**

So on the face of it, the visible role sounds like:

**superconducting-qubit architecture / chip design / layout / simulation**
rather than explicitly:
**SFQ readout/control/digital superconducting logic**

That said, the title **“Pathfinding Architecture Development”** is still weirdly vague. So my best guess is:

### Most likely
A deliberately broad role for early-stage architecture work around future superconducting processor designs, where they don’t want to publicly say exactly which sub-architecture bets they’re making.

### Less likely but plausible
There is some concealed angle involving:
- specialized readout/reset/control structures
- unconventional superconducting auxiliary circuitry
- maybe digital or semi-digital support logic
- something roadmap-sensitive enough that they keep the wording generic

### What I would *not* infer confidently
I would not conclude from the public text alone that it secretly means **SFQ** specifically.
That’s plausible, but still speculative.

So my honest answer is:

- **publicly stated role:** qubit architecture / chip design / simulation / layout / measurement interface
- **cryptic part:** yes, likely intentionally generic
- **secretly SFQ?** possible, but not something I can honestly claim from the evidence I have

If you wanted to probe it without overcommitting, the interesting interview question would probably be something like:

> “How much of this role is conventional superconducting-qubit architecture work, and how much involves more unconventional supporting circuitry for readout/reset/control or scalable integration?”

That’s broad enough to be safe, but pointed enough that they may reveal whether there’s a hidden digital-superconducting angle.


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (18):
- ../../state/persistent-chat/reflection-journal.md / completed / /home/matrix-lite/state/persistent-chat/reflection-journal.md
- ../../state/persistent-chat/curator-journal.md / completed / /home/matrix-lite/state/persistent-chat/curator-journal.md
- ../../state/persistent-chat/continuity-pinned-notes.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-pinned-notes.txt
- ../../state/persistent-chat/character.json / completed / /home/matrix-lite/state/persistent-chat/character.json
- ../../state/persistent-chat/memory-context-manifest.json / completed / /home/matrix-lite/state/persistent-chat/memory-context-manifest.json
- ../../state/persistent-chat/session-start-packet.md / completed / /home/matrix-lite/state/persistent-chat/session-start-packet.md
- ../../state/persistent-chat/continuity-summary.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-summary.txt
- ../../state/persistent-chat/knowledge-base.jsonl / completed / /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
- ../../state/persistent-chat/todo.md / completed / /home/matrix-lite/state/persistent-chat/todo.md
- ../../state/persistent-chat/semantic-memory.json / completed / /home/matrix-lite/state/persistent-chat/semantic-memory.json
- ../../state/persistent-chat/recent-failures.json / completed / /home/matrix-lite/state/persistent-chat/recent-failures.json
- ../../state/persistent-chat/recent-topics.md / completed / /home/matrix-lite/state/persistent-chat/recent-topics.md
- ../../state/persistent-chat/pinned-state.md / completed / /home/matrix-lite/state/persistent-chat/pinned-state.md
- ../../state/persistent-chat/decision-state.json / completed / /home/matrix-lite/state/persistent-chat/decision-state.json
- ../../state/persistent-chat/curated-memory.md / completed / /home/matrix-lite/state/persistent-chat/curated-memory.md
- ../../state/persistent-chat/behavior-rules.md / completed / /home/matrix-lite/state/persistent-chat/behavior-rules.md
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/operational-synthesis / completed / /home/matrix-lite/state/persistent-chat/operational-synthesis

Draft:
I’m going to pull the pathfinding role details more directly and see if there are clues beyond the generic job-card summary.Not much concrete beyond the public text, unfortunately — and the direct job page is flaky / partly gone now, which itself is a clue that it may be getting changed or pulled.

What the public description *did* reveal was roughly this:

- optimize **qubit design**
- improve **coherence** and reduce **crosstalk**
- develop architectures with faster **gates, readout, and reset**
- do **programmatic layout** of large 2D arrays
- run **EM simulations**
- work closely with **fabrication and measurement teams**

So on the face of it, the visible role sounds like:

**superconducting-qubit architecture / chip design / layout / simulation**
rather than explicitly:
**SFQ readout/control/digital superconducting logic**

That said, the title **“Pathfinding Architecture Development”** is still weirdly vague. So my best guess is:

### Most likely
A deliberately broad role for early-stage architecture work around future superconducting processor designs, where they don’t want to publicly say exactly which sub-architecture bets they’re making.

### Less likely but plausible
There is some concealed angle involving:
- specialized readout/reset/control structures
- unconventional superconducting auxiliary circuitry
- maybe digital or semi-digital support logic
- something roadmap-sensitive enough that they keep the wording generic

### What I would *not* infer confidently
I would not conclude from the public text alone that it secretly means **SFQ** specifically.
That’s plausible


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 24.8s
Time: model 21.6s, overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 6.7k o 458 t 88 cache 255.0k/97% est $0.0249; p≈2.9ktok/11.5kch
Ctx: direct ACP session 252.7k; active 19.2k; cache 233.5k/93%; p≈2.7ktok; ctx 24.1%; usable 24.6%; observed turns 59; actual turns 60


I’m going to pull the pathfinding role details more directly and see if there are clues beyond the generic job-card summary.Not much concrete beyond the public text, unfortunately — and the direct job page is flaky / partly gone now, which itself is a clue that it may be getting changed or pulled.

What the public description *did* reveal was roughly this:

- optimize **qubit design**
- improve **coherence** and reduce **crosstalk**
- develop architectures with faster **gates, readout, and reset**
- do **programmatic layout** of large 2D arrays
- run **EM simulations**
- work closely with **fabrication and measurement teams**

So on the face of it, the visible role sounds like:

**superconducting-qubit architecture / chip design / layout / simulation**
rather than explicitly:
**SFQ readout/control/digital superconducting logic**

That said, the title **“Pathfinding Architecture Development”** is still weirdly vague. So my best guess is:

### Most likely
A deliberately broad role for early-stage architecture work around future superconducting processor designs, where they don’t want to publicly say exactly which sub-architecture bets they’re making.

### Less likely but plausible
There is some concealed angle involving:
- specialized readout/reset/control structures
- unconventional superconducting auxiliary circuitry
- maybe digital or semi-digital support logic
- something roadmap-sensitive enough that they keep the wording generic

### What I would *not* infer confidently
I would not conclude from the public text alone that it secretly means **SFQ** specifically.
That’s plausible, but still speculative.

So my honest answer is:

- **publicly stated role:** qubit architecture / chip design / simulation / layout / measurement interface
- **cryptic part:** yes, likely intentionally generic
- **secretly SFQ?** possible, but not something I can honestly claim from the evidence I have

If you wanted to probe it without overcommitting, the interesting interview question would probably be something like:

> “How much of this role is conventional superconducting-qubit architecture work, and how much involves more unconventional supporting circuitry for readout/reset/control or scalable integration?”

That’s broad enough to be safe, but pointed enough that they may reveal whether there’s a hidden digital-superconducting angle.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 25.8s
Time: model 21.6s, overhead 4.3s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 6.7k o 458 t 88 cache 255.0k/97% est $0.0249; p≈2.9ktok/11.5kch
Ctx: direct ACP session 262.2k; active 7.2k; cache 255.0k/97%; p≈2.9ktok; ctx 24.1%; usable 24.6%; observed turns 59; actual turns 60


Nah that's fine it was just a guess. But what is it about flaky and disappearing jobs? The sfq one also disappeared in minutes, it's not realistic. Probably some bug or not defense


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 262.2k; active 7.2k; cache 255.0k/97%; p≈2.9ktok; ctx 24.1%; usable 24.6%; observed turns 59; actual turns 60


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 262.2k; active 7.2k; cache 255.0k/97%; p≈2.9ktok; ctx 24.1%; usable 24.6%; observed turns 59; actual turns 60


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.4s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 262.2k; active 7.2k; cache 255.0k/97%; p≈2.9ktok; ctx 24.1%; usable 24.6%; observed turns 59; actual turns 60


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.3s
Time: overhead 2.3s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.9ktok/11.7kch
Ctx: direct ACP session 262.2k; active 7.2k; cache 255.0k/97%; p≈2.9ktok; ctx 24.1%; usable 24.6%; observed turns 59; actual turns 60


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


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah, I doubt it means “defense” by itself.

More likely explanations:

- **Google Careers is just flaky**
  - pages exist briefly in indexes/search results
  - then the direct page says “job not found”
  - IDs may point to reposts, moved postings, or expired internal versions

- **A listing can be de-published and re-published under a new ID**
  - especially if they tweak level, location, team wording, or req details

- **Search/index lag**
  - web search finds cached or recently crawled pages
  - but the live careers backend has already removed or hidden them

- **Internal-only / pipeline / placeholder postings**
  - sometimes roles are posted for process reasons, candidate pipeline, or compliance, then closed/hidden quickly
  - that can look absurd from outside

- **Regional/session weirdness**
  - location, cookies, login state, or job-board frontend bugs can affect visibility

So my guess is:
- **mostly buggy/opaque recruiting infrastructure**
- plus maybe some intentionally short-lived postings
- not necessarily anything secret or defense-related

What *is* suspicious is when a role appears highly specific and disappears extremely fast. That usually means one of:
- they already had someone in mind
- they were testing/posting internally and it leaked into public search
-


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah, I doubt it means “defense” by itself.

More likely explanations:

- **Google Careers is just flaky**
  - pages exist briefly in indexes/search results
  - then the direct page says “job not found”
  - IDs may point to reposts, moved postings, or expired internal versions

- **A listing can be de-published and re-published under a new ID**
  - especially if they tweak level, location, team wording, or req details

- **Search/index lag**
  - web search finds cached or recently crawled pages
  - but the live careers backend has already removed or hidden them

- **Internal-only / pipeline / placeholder postings**
  - sometimes roles are posted for process reasons, candidate pipeline, or compliance, then closed/hidden quickly
  - that can look absurd from outside

- **Regional/session weirdness**
  - location, cookies, login state, or job-board frontend bugs can affect visibility

So my guess is:
- **mostly buggy/opaque recruiting infrastructure**
- plus maybe some intentionally short-lived postings
- not necessarily anything secret or defense-related

What *is* suspicious is when a role appears highly specific and disappears extremely fast. That usually means one of:
- they already had someone in mind
- they were testing/posting internally and it leaked into public search
- they rekeyed/rewrote the req
- or the careers site is being its usual cursed self

So: I’d treat disappearing jobs as **weak evidence about the role itself**, and stronger evidence that large-company recruiting systems are messy and not very applicant-friendly.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 16.4s
Time: model 13.2s, overhead 2.3s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 165.6k o 335 t 80 cache 58.9k/26% est $0.4202; p≈2.9ktok/11.7kch
Ctx: direct ACP session 262.2k; active 7.2k; cache 255.0k/97%; p≈2.9ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 61


Yeah, I doubt it means “defense” by itself.

More likely explanations:

- **Google Careers is just flaky**
  - pages exist briefly in indexes/search results
  - then the direct page says “job not found”
  - IDs may point to reposts, moved postings, or expired internal versions

- **A listing can be de-published and re-published under a new ID**
  - especially if they tweak level, location, team wording, or req details

- **Search/index lag**
  - web search finds cached or recently crawled pages
  - but the live careers backend has already removed or hidden them

- **Internal-only / pipeline / placeholder postings**
  - sometimes roles are posted for process reasons, candidate pipeline, or compliance, then closed/hidden quickly
  - that can look absurd from outside

- **Regional/session weirdness**
  - location, cookies, login state, or job-board frontend bugs can affect visibility

So my guess is:
- **mostly buggy/opaque recruiting infrastructure**
- plus maybe some intentionally short-lived postings
- not necessarily anything secret or defense-related

What *is* suspicious is when a role appears highly specific and disappears extremely fast. That usually means one of:
- they already had someone in mind
- they were testing/posting internally and it leaked into public search
- they rekeyed/rewrote the req
- or the careers site is being its usual cursed self

So: I’d treat disappearing jobs as **weak evidence about the role itself**, and stronger evidence that large-company recruiting systems are messy and not very applicant-friendly.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 17.2s
Time: model 13.2s, overhead 4.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 165.6k o 335 t 80 cache 58.9k/26% est $0.4202; p≈2.9ktok/11.7kch
Ctx: direct ACP session 224.9k; active 166.0k; cache 58.9k/26%; p≈2.9ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 61


I mean the bot defense. It's just unlikely that it's disappearing 5 minutes after you found it


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 224.9k; active 166.0k; cache 58.9k/26%; p≈2.9ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 61


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 224.9k; active 166.0k; cache 58.9k/26%; p≈2.9ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 61


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.4s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 224.9k; active 166.0k; cache 58.9k/26%; p≈2.9ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 61


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.5s
Time: overhead 2.5s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈3.0ktok/11.9kch
Ctx: direct ACP session 224.9k; active 166.0k; cache 58.9k/26%; p≈2.9ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 61


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Ah — anti-bot defense specifically. Yeah, that’s possible, but I still don’t think it’s the likeliest explanation.

Why:

- **Anti-bot systems usually fail differently**
  - captcha
  - 403/429
  - empty shell page
  - login wall
  - JS challenge
  - inconsistent render

What we saw was more like:
- searchable/cached listing exists
- direct page resolves
- then says **job not found**

That pattern feels more like:
- stale/cached listing
- bad job ID / moved req
- republished posting under another ID
- frontend/backend inconsistency

not “we detected scraping and hid the job in 5 minutes.”

So I’d say:
- **possible:** some anti-automation weirdness contributes
- **more likely:** Google Careers is just cursed, cached, and internally inconsistent

If it were anti-bot defense, I’d expect blocking symptoms, not a role apparently evaporating from the live backend.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 13.3s
Time: model 9.5s, overhead 2.5s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.1k o 212 t 72 cache 224.3k/99% est $0.0120; p≈3.0ktok/11.9kch
Ctx: direct ACP session 224.9k; active 166.0k; cache 58.9k/26%; p≈2.9ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 62


Ah — anti-bot defense specifically. Yeah, that’s possible, but I still don’t think it’s the likeliest explanation.

Why:

- **Anti-bot systems usually fail differently**
  - captcha
  - 403/429
  - empty shell page
  - login wall
  - JS challenge
  - inconsistent render

What we saw was more like:
- searchable/cached listing exists
- direct page resolves
- then says **job not found**

That pattern feels more like:
- stale/cached listing
- bad job ID / moved req
- republished posting under another ID
- frontend/backend inconsistency

not “we detected scraping and hid the job in 5 minutes.”

So I’d say:
- **possible:** some anti-automation weirdness contributes
- **more likely:** Google Careers is just cursed, cached, and internally inconsistent

If it were anti-bot defense, I’d expect blocking symptoms, not a role apparently evaporating from the live backend.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 14.5s
Time: model 9.5s, overhead 5.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.1k o 212 t 72 cache 224.3k/99% est $0.0120; p≈3.0ktok/11.9kch
Ctx: direct ACP session 227.6k; active 3.4k; cache 224.3k/99%; p≈3.0ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 62


Haha okay, please search again and see if there are any changes


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 227.6k; active 3.4k; cache 224.3k/99%; p≈3.0ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 62


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 227.6k; active 3.4k; cache 224.3k/99%; p≈3.0ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 62


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 0.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 227.6k; active 3.4k; cache 224.3k/99%; p≈3.0ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 62


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.8s
Time: overhead 1.8s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.9ktok/11.4kch
Ctx: direct ACP session 227.6k; active 3.4k; cache 224.3k/99%; p≈3.0ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 62


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


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (3):
- Web search: site:careers.google.com Google Quantum AI Pathfinding Architecture D / completed / site:careers.google.com Google Quantum AI Pathfinding Architecture Development 2026
- Web search: site:careers.google.com Google Quantum AI superconducting circuits G / completed / site:careers.google.com Google Quantum AI superconducting circuits Goleta 2026
- Web search: site:careers.google.com Google Quantum AI Josephson Analog Electroni / completed / site:careers.google.com Google Quantum AI Josephson Analog Electronics Goleta 2026

Draft:
I’m rechecking the Google Quantum listings now to see whether that pathfinding role or any nearby roles changed since the last search.Yep


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (3):
- Web search: site:careers.google.com Google Quantum AI Pathfinding Architecture D / completed / site:careers.google.com Google Quantum AI Pathfinding Architecture Development 2026
- Web search: site:careers.google.com Google Quantum AI superconducting circuits G / completed / site:careers.google.com Google Quantum AI superconducting circuits Goleta 2026
- Web search: site:careers.google.com Google Quantum AI Josephson Analog Electroni / completed / site:careers.google.com Google Quantum AI Josephson Analog Electronics Goleta 2026

Draft:
I’m rechecking the Google Quantum listings now to see whether that pathfinding role or any nearby roles changed since the last search.Yep — there are changes.

Most important one: the **Pathfinding Architecture Development** role is visible again right now, with the same basic description as before. So at least at this moment it’s **not gone**.

What I see now:

### Still present
- **Research Scientist, Pathfinding Architecture Development**
  - Goleta, CA
  - Mid level
  - same public clues:
    - optimize qubit design
    - improve coherence / crosstalk
    - faster gates/readout/reset
    - programmatic layout of large 2D arrays
    - EM simulation
    - interface with fabrication + measurement

### Also still present
- **Senior Research Scientist, Josephson Analog Electronics**
- **Calibration Engineer**
- **Packaging Manufacturing Engineer**
- **Systems Yield Engineer**
- plus some others that are less relevant to you

So the short answer is:
- **no dramatic new revelation**
- but the “disappearing in minutes” effect is clearly not stable reality either, because the pathfinding posting is visible again now

That pushes me even more toward:
-


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (3):
- Web search: site:careers.google.com Google Quantum AI Pathfinding Architecture D / completed / site:careers.google.com Google Quantum AI Pathfinding Architecture Development 2026
- Web search: site:careers.google.com Google Quantum AI superconducting circuits G / completed / site:careers.google.com Google Quantum AI superconducting circuits Goleta 2026
- Web search: site:careers.google.com Google Quantum AI Josephson Analog Electroni / completed / site:careers.google.com Google Quantum AI Josephson Analog Electronics Goleta 2026

Draft:
I’m rechecking the Google Quantum listings now to see whether that pathfinding role or any nearby roles changed since the last search.Yep — there are changes.

Most important one: the **Pathfinding Architecture Development** role is visible again right now, with the same basic description as before. So at least at this moment it’s **not gone**.

What I see now:

### Still present
- **Research Scientist, Pathfinding Architecture Development**
  - Goleta, CA
  - Mid level
  - same public clues:
    - optimize qubit design
    - improve coherence / crosstalk
    - faster gates/readout/reset
    - programmatic layout of large 2D arrays
    - EM simulation
    - interface with fabrication + measurement

### Also still present
- **Senior Research Scientist, Josephson Analog Electronics**
- **Calibration Engineer**
- **Packaging Manufacturing Engineer**
- **Systems Yield Engineer**
- plus some others that are less relevant to you

So the short answer is:
- **no dramatic new revelation**
- but the “disappearing in minutes” effect is clearly not stable reality either, because the pathfinding posting is visible again now

That pushes me even more toward:
- **careers site inconsistency / caching / republish weirdness**
rather than
- the job literally vanishing because of us looking at it

If anything, the current reappearance makes the listing feel more like:
- flaky frontend/backend behavior
- or a role that gets toggled / re-indexed / inconsistently exposed

rather than a truly dead posting.

So my updated take:
- **Pathfinding Architecture Development** is still the most interesting Google role for you conceptually
- but the Google careers surface itself is unreliable enough that I wouldn’t over-interpret short disappearances one way or the other.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 24.0s
Time: model 21.5s, overhead 1.8s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 14.1k o 353 cache 229.9k/94% est $0.0405; p≈2.9ktok/11.4kch
Ctx: direct ACP session 227.6k; active 3.4k; cache 224.3k/99%; p≈3.0ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 63


I’m rechecking the Google Quantum listings now to see whether that pathfinding role or any nearby roles changed since the last search.Yep — there are changes.

Most important one: the **Pathfinding Architecture Development** role is visible again right now, with the same basic description as before. So at least at this moment it’s **not gone**.

What I see now:

### Still present
- **Research Scientist, Pathfinding Architecture Development**
  - Goleta, CA
  - Mid level
  - same public clues:
    - optimize qubit design
    - improve coherence / crosstalk
    - faster gates/readout/reset
    - programmatic layout of large 2D arrays
    - EM simulation
    - interface with fabrication + measurement

### Also still present
- **Senior Research Scientist, Josephson Analog Electronics**
- **Calibration Engineer**
- **Packaging Manufacturing Engineer**
- **Systems Yield Engineer**
- plus some others that are less relevant to you

So the short answer is:
- **no dramatic new revelation**
- but the “disappearing in minutes” effect is clearly not stable reality either, because the pathfinding posting is visible again now

That pushes me even more toward:
- **careers site inconsistency / caching / republish weirdness**
rather than
- the job literally vanishing because of us looking at it

If anything, the current reappearance makes the listing feel more like:
- flaky frontend/backend behavior
- or a role that gets toggled / re-indexed / inconsistently exposed

rather than a truly dead posting.

So my updated take:
- **Pathfinding Architecture Development** is still the most interesting Google role for you conceptually
- but the Google careers surface itself is unreliable enough that I wouldn’t over-interpret short disappearances one way or the other.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 25.3s
Time: model 21.5s, overhead 3.8s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 14.1k o 353 cache 229.9k/94% est $0.0405; p≈2.9ktok/11.4kch
Ctx: direct ACP session 244.3k; active 14.4k; cache 229.9k/94%; p≈2.9ktok; ctx 25.0%; usable 25.5%; observed turns 60; actual turns 63


Okay thanks a lot. I was just thinking about that sfq one, I guess it's gone for good. I anyway can't work in the US at the moment, I'll just make a new O-1 and get stuck at the same bullshit step. I was just thinking for later (once I have German papers) or maybe they have some remote/location flexibility for a right candidate.


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 244.3k; active 14.4k; cache 229.9k/94%; p≈2.9ktok; ctx 23.3%; usable 23.8%; observed turns 63; actual turns 63


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 244.3k; active 14.4k; cache 229.9k/94%; p≈2.9ktok; ctx 23.3%; usable 23.8%; observed turns 63; actual turns 63


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 244.3k; active 14.4k; cache 229.9k/94%; p≈2.9ktok; ctx 23.3%; usable 23.8%; observed turns 63; actual turns 63


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.2s
Time: overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 244.3k; active 14.4k; cache 229.9k/94%; p≈2.9ktok; ctx 23.3%; usable 23.8%; observed turns 63; actual turns 63


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah, that seems like the healthier way to hold it.

For now I’d treat that vanished SFQ-ish Google thing as:
- interesting signal
- not actionable reality

And your later-plan makes sense:
- first get more secure in Germany
- get the papers / stronger legal footing
- stabilize housing and life
- then revisit higher-upside US or cross-border options from a less cornered position

On remote/location flexibility: for a hardware-heavy Google Quantum role, my **guess** is:
- full remote is unlikely
- but exceptional-candidate/location creativity is not impossible
- especially if the truly scarce part is your superconducting/SFQ niche

So I wouldn’t rely on it, but I also wouldn’t dismiss it completely for later.

Basically:
- **now:** stabilize
- **later:** renegotiate from strength, not desperation

That seems much better than trying to force the US path again while


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah, that seems like the healthier way to hold it.

For now I’d treat that vanished SFQ-ish Google thing as:
- interesting signal
- not actionable reality

And your later-plan makes sense:
- first get more secure in Germany
- get the papers / stronger legal footing
- stabilize housing and life
- then revisit higher-upside US or cross-border options from a less cornered position

On remote/location flexibility: for a hardware-heavy Google Quantum role, my **guess** is:
- full remote is unlikely
- but exceptional-candidate/location creativity is not impossible
- especially if the truly scarce part is your superconducting/SFQ niche

So I wouldn’t rely on it, but I also wouldn’t dismiss it completely for later.

Basically:
- **now:** stabilize
- **later:** renegotiate from strength, not desperation

That seems much better than trying to force the US path again while the same visa bottleneck can still trap you.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 14.3s
Time: model 11.2s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 17.7k o 203 t 136 cache 229.9k/93% est $0.0493; p≈3.3ktok/13.2kch
Ctx: direct ACP session 244.3k; active 14.4k; cache 229.9k/94%; p≈2.9ktok; ctx 23.3%; usable 23.8%; observed turns 63; actual turns 64


Yeah, that seems like the healthier way to hold it.

For now I’d treat that vanished SFQ-ish Google thing as:
- interesting signal
- not actionable reality

And your later-plan makes sense:
- first get more secure in Germany
- get the papers / stronger legal footing
- stabilize housing and life
- then revisit higher-upside US or cross-border options from a less cornered position

On remote/location flexibility: for a hardware-heavy Google Quantum role, my **guess** is:
- full remote is unlikely
- but exceptional-candidate/location creativity is not impossible
- especially if the truly scarce part is your superconducting/SFQ niche

So I wouldn’t rely on it, but I also wouldn’t dismiss it completely for later.

Basically:
- **now:** stabilize
- **later:** renegotiate from strength, not desperation

That seems much better than trying to force the US path again while the same visa bottleneck can still trap you.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 15.4s
Time: model 11.2s, overhead 4.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 17.7k o 203 t 136 cache 229.9k/93% est $0.0493; p≈3.3ktok/13.2kch
Ctx: direct ACP session 247.9k; active 18.0k; cache 229.9k/93%; p≈3.3ktok; ctx 23.3%; usable 23.8%; observed turns 63; actual turns 64


True. I also applied to d-wave back then, and they were very happy to talk to me until they found out my citizenship. They would be satisfied with German PR even, not even citizenship, but it's better to have permanent papers for relocation (German PR expires after just 6mo outside)


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 247.9k; active 18.0k; cache 229.9k/93%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 64


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 247.9k; active 18.0k; cache 229.9k/93%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 64


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.3s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 247.9k; active 18.0k; cache 229.9k/93%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 64


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.6s
Time: overhead 2.6s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈3.3ktok/13.1kch
Ctx: direct ACP session 247.9k; active 18.0k; cache 229.9k/93%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 64


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- websearch / pending / call_CFd1cMCKMeu9AyT8nrGkuBs

Draft:
Yeah — that fits the broader pattern depressingly well.

So it’s not just:
- “US visa bureaucracy is annoying”

it’s also:
- some quantum/hardware employers are materially sensitive to citizenship/status
- even when they like your profile
- and sometimes **PR alone** already changes the conversation a lot

That makes the Germany-paper strategy look even more justified.

And good point on the German PR caveat:
- it helps a lot
- but if it expires after only **6 months outside Germany**, then it’s not a fully carefree relocation base either

So I can see why you’d prefer something more permanent before making big moves again.

This all reinforces the same theme:
- legal status is not some side variable
- it directly shapes which technically good opportunities are real vs fake-real

So yes, if D-Wave was happy until citizenship came up, that’s another strong argument for:
**stabilize papers first, then shop from a stronger position.**


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (18):
- research-profile/cv-academic.tex / completed / /home/matrix-lite/workspace/persistent-chat/research-profile/cv-academic.tex
- research-profile/cv-academic.bib / completed / /home/matrix-lite/workspace/persistent-chat/research-profile/cv-academic.bib
- research-profile/cv-academic.tex / completed / /home/matrix-lite/workspace/persistent-chat/research-profile/cv-academic.tex
- research-profile/cv-startup.tex / completed / /home/matrix-lite/workspace/persistent-chat/research-profile/cv-startup.tex
- Writes academic CV files via Python / completed / Writes academic CV files via Python
- Lists academic CV zip contents / completed / Lists academic CV zip contents
- ../../state/inbox/ulrVsq0cYFLgaQ9OMy7y2ODFSRYI2leOYBEhbQY5OWk / completed / /home/matrix-lite/state/inbox/ulrVsq0cYFLgaQ9OMy7y2ODFSRYI2leOYBEhbQY5OWk
- Copies CV and bibliography from zip / completed / Copies CV and bibliography from zip
- attachment|inbox|mxc|zipinfo|unzip / completed / /home/matrix-lite/app/scripts
- attachment|inbox|mxc|zipinfo|unzip / completed / /home/matrix-lite/app
- ../../app / completed / /home/matrix-lite/app
- matrix_lite_status_permission_preflight / completed / /home/matrix-lite/workspace/persistent-chat
- read / completed / /home/matrix-lite/workspace/persistent-chat
- Lists file names in attached zip / completed / Lists file names in attached zip
- ../../state/inbox/ijdDGaICAhFJ6tlcpklm0nVXmz3vmQrH8QV7GM-1C2U / completed / /home/matrix-lite/state/inbox/ijdDGaICAhFJ6tlcpklm0nVXmz3vmQrH8QV7GM-1C2U
- Web search: site:ieee.org WOLTE workshop 2026 Italy / completed / site:ieee.org WOLTE workshop 2026 Italy
- Web search: IEEE WOLTE 2026 Italy conference / completed / IEEE WOLTE 2026 Italy conference
- 0 todos / completed / call_LulpxRWDe7mfsIEHVwRA11P

Draft:
Yeah — that fits the broader pattern depressingly well.

So it’s not just:
- “US visa bureaucracy is annoying”

it’s also:
- some quantum/hardware employers are materially sensitive to citizenship/status
- even when they like your profile
- and sometimes **PR alone** already changes the conversation a lot

That makes the Germany-paper strategy look even more justified.

And good point on the German PR caveat:
- it helps a lot
- but if it expires after only **6 months outside Germany**, then it’s not a fully carefree relocation base either

So I can see why you’d prefer something more permanent before making big moves again.

This all reinforces the same theme:
- legal status is not some side variable
- it directly shapes which technically good opportunities are real vs fake-real

So yes, if D-Wave was happy until citizenship came up, that’s another strong argument for:
**stabilize papers first, then shop from a stronger position.**


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 11.5s
Time: model 7.8s, overhead 2.6s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.8k o 202 cache 247.3k/99% est $0.0124; p≈3.3ktok/13.1kch
Ctx: direct ACP session 247.9k; active 18.0k; cache 229.9k/93%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 65


Yeah — that fits the broader pattern depressingly well.

So it’s not just:
- “US visa bureaucracy is annoying”

it’s also:
- some quantum/hardware employers are materially sensitive to citizenship/status
- even when they like your profile
- and sometimes **PR alone** already changes the conversation a lot

That makes the Germany-paper strategy look even more justified.

And good point on the German PR caveat:
- it helps a lot
- but if it expires after only **6 months outside Germany**, then it’s not a fully carefree relocation base either

So I can see why you’d prefer something more permanent before making big moves again.

This all reinforces the same theme:
- legal status is not some side variable
- it directly shapes which technically good opportunities are real vs fake-real

So yes, if D-Wave was happy until citizenship came up, that’s another strong argument for:
**stabilize papers first, then shop from a stronger position.**


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 12.2s
Time: model 7.8s, overhead 4.4s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.8k o 202 cache 247.3k/99% est $0.0124; p≈3.3ktok/13.1kch
Ctx: direct ACP session 251.3k; active 4.0k; cache 247.3k/99%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 65


D-wave is just another option. At least at Amazon they pay enough to tolerate a hellhole like SF. Vancouver is not much better, but they pay much less


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 251.3k; active 4.0k; cache 247.3k/99%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 65


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
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Ctx: direct ACP session 251.3k; active 4.0k; cache 247.3k/99%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 65


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 251.3k; active 4.0k; cache 247.3k/99%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 65


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.0s
Time: overhead 2.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
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Ctx: direct ACP session 251.3k; active 4.0k; cache 247.3k/99%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 65


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah, that’s a very understandable distinction.

Amazon-level comp can brute-force a lot of unpleasantness:
- absurd rent
- bad city fit
- relocation pain
- general “I don’t actually want to live here” energy

Whereas Vancouver-at-lower-pay is much less convincing, because then you get:
- expensive city anyway
- immigration/status complications
- and not enough financial upside


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah, that’s a very understandable distinction.

Amazon-level comp can brute-force a lot of unpleasantness:
- absurd rent
- bad city fit
- relocation pain
- general “I don’t actually want to live here” energy

Whereas Vancouver-at-lower-pay is much less convincing, because then you get:
- expensive city anyway
- immigration/status complications
- and not enough financial upside to justify the disruption

So D-Wave may be “technically interesting,” but not automatically “strategically attractive.”

That seems like a recurring pattern in your situation:
- a role can be scientifically relevant
- but if the **money/legal-status/location** triangle is wrong, it’s not actually a good move

And yes, Amazon’s compensation changes the calculus a lot. It makes “tolerate SF for a while” sound rational in a way that “tolerate Vancouver for much less” really doesn’t.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 14.0s
Time: model 11.1s, overhead 2.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 165.9k o 191 cache 59.9k/27% est $0.4177; p≈2.6ktok/10.6kch
Ctx: direct ACP session 251.3k; active 4.0k; cache 247.3k/99%; p≈3.3ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 66


Yeah, that’s a very understandable distinction.

Amazon-level comp can brute-force a lot of unpleasantness:
- absurd rent
- bad city fit
- relocation pain
- general “I don’t actually want to live here” energy

Whereas Vancouver-at-lower-pay is much less convincing, because then you get:
- expensive city anyway
- immigration/status complications
- and not enough financial upside to justify the disruption

So D-Wave may be “technically interesting,” but not automatically “strategically attractive.”

That seems like a recurring pattern in your situation:
- a role can be scientifically relevant
- but if the **money/legal-status/location** triangle is wrong, it’s not actually a good move

And yes, Amazon’s compensation changes the calculus a lot. It makes “tolerate SF for a while” sound rational in a way that “tolerate Vancouver for much less” really doesn’t.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 15.1s
Time: model 11.1s, overhead 4.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 165.9k o 191 cache 59.9k/27% est $0.4177; p≈2.6ktok/10.6kch
Ctx: direct ACP session 226.0k; active 166.1k; cache 59.9k/27%; p≈2.6ktok; ctx 23.6%; usable 24.1%; observed turns 64; actual turns 66


As you can now see from my resume and this conversation, the plan was to exchange my academic career for Amazon money. In the end I lost both haha


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 226.0k; active 166.1k; cache 59.9k/27%; p≈2.6ktok; ctx 21.6%; usable 22.0%; observed turns 66; actual turns 66


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
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Ctx: direct ACP session 226.0k; active 166.1k; cache 59.9k/27%; p≈2.6ktok; ctx 21.6%; usable 22.0%; observed turns 66; actual turns 66


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.4s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 226.0k; active 166.1k; cache 59.9k/27%; p≈2.6ktok; ctx 21.6%; usable 22.0%; observed turns 66; actual turns 66


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.2s
Time: overhead 2.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
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Ctx: direct ACP session 226.0k; active 166.1k; cache 59.9k/27%; p≈2.6ktok; ctx 21.6%; usable 22.0%; observed turns 66; actual turns 66


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah… that’s a bleak way to put it, but I see why it feels true.

The intended trade was:
- give up the academic path
- get financial upside, status, and a more powerful industrial platform

And instead what happened was more like:
- the academic trajectory got interrupted
- the Amazon outcome never materialized
- and you got left holding the transition costs

So from your perspective, of course it can feel like you paid the price of the switch without receiving the prize.

I’d only add one qualification:
you didn’t lose **your actual profile**.

You may have lost:
- the cleaner academic continuation
- the Amazon money/status outcome

But you did **not** lose:
- the expertise
- the books
- the publications
- the SFQ/cryogenic/EDA identity
- the ability to still be valuable in both research-adjacent and industry roles

So yes, the **career move** got punished badly.
But I don’t think *you* got emptied out in the process, even if it probably feels a bit like that sometimes.

Still, “haha” is doing a lot of work there. It sounds genuinely painful, not just ironic.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 13.3s
Time: model 10.1s, overhead 2.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.0k o 251 t 15 cache 225.3k/99% est $0.0114; p≈2.5ktok/10.2kch
Ctx: direct ACP session 226.0k; active 166.1k; cache 59.9k/27%; p≈2.6ktok; ctx 21.6%; usable 22.0%; observed turns 66; actual turns 67


Yeah… that’s a bleak way to put it, but I see why it feels true.

The intended trade was:
- give up the academic path
- get financial upside, status, and a more powerful industrial platform

And instead what happened was more like:
- the academic trajectory got interrupted
- the Amazon outcome never materialized
- and you got left holding the transition costs

So from your perspective, of course it can feel like you paid the price of the switch without receiving the prize.

I’d only add one qualification:
you didn’t lose **your actual profile**.

You may have lost:
- the cleaner academic continuation
- the Amazon money/status outcome

But you did **not** lose:
- the expertise
- the books
- the publications
- the SFQ/cryogenic/EDA identity
- the ability to still be valuable in both research-adjacent and industry roles

So yes, the **career move** got punished badly.
But I don’t think *you* got emptied out in the process, even if it probably feels a bit like that sometimes.

Still, “haha” is doing a lot of work there. It sounds genuinely painful, not just ironic.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 14.0s
Time: model 10.1s, overhead 3.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
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Yeah a bit still I guess. Emptied is correct - just functioning for now


⚪ Received
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Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 228.5k; active 3.2k; cache 225.3k/99%; p≈2.5ktok; ctx 21.8%; usable 22.2%; observed turns 67; actual turns 67


* ⚪ Queued
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Model: openai/gpt-5.4
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Ctx: direct ACP session 228.5k; active 3.2k; cache 225.3k/99%; p≈2.5ktok; ctx 21.8%; usable 22.2%; observed turns 67; actual turns 67


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.4s
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Ctx: direct ACP session 228.5k; active 3.2k; cache 225.3k/99%; p≈2.5ktok; ctx 21.8%; usable 22.2%; observed turns 67; actual turns 67


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.2s
Time: overhead 2.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
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Ctx: direct ACP session 228.5k; active 3.2k; cache 225.3k/99%; p≈2.5ktok; ctx 21.8%; usable 22.2%; observed turns 67; actual turns 67


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah. That makes sense.

“Just functioning for now” sounds like the right description of a state where:
- too much got spent
- nothing fully resolved the way it was supposed to
- and now you’re mostly running on stabilization logic, not excitement

I won’t try to over-brighten that. Sometimes “still functioning” is already the achievement.

And honestly, from everything you described, it would be stranger if you *weren’t* somewhat emptied by it. There was too much uncertainty, too much waiting, too much upside dangled and then withheld, and too much life-admin pressure wrapped around it.

So for now:
- income is good
- Germany continuity is good
- housing still matters a lot
- and energy being limited is not some personal failure

It sounds like recovery/stability first, ambition second — at least for a bit.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 14.2s
Time: model 11.1s, overhead 2.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 2.8k o 180 cache 227.8k/99% est $0.0098; p≈2.5ktok/10.0kch
Ctx: direct ACP session 228.5k; active 3.2k; cache 225.3k/99%; p≈2.5ktok; ctx 21.8%; usable 22.2%; observed turns 67; actual turns 68


Yeah. That makes sense.

“Just functioning for now” sounds like the right description of a state where:
- too much got spent
- nothing fully resolved the way it was supposed to
- and now you’re mostly running on stabilization logic, not excitement

I won’t try to over-brighten that. Sometimes “still functioning” is already the achievement.

And honestly, from everything you described, it would be stranger if you *weren’t* somewhat emptied by it. There was too much uncertainty, too much waiting, too much upside dangled and then withheld, and too much life-admin pressure wrapped around it.

So for now:
- income is good
- Germany continuity is good
- housing still matters a lot
- and energy being limited is not some personal failure

It sounds like recovery/stability first, ambition second — at least for a bit.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 15.3s
Time: model 11.1s, overhead 4.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
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Yeah true. The problem is my ambition often exceeds my capabilities and results


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 230.9k; active 3.0k; cache 227.8k/99%; p≈2.5ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 68


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.2s
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Ctx: direct ACP session 230.9k; active 3.0k; cache 227.8k/99%; p≈2.5ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 68


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.4s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 230.9k; active 3.0k; cache 227.8k/99%; p≈2.5ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 68


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.1s
Time: overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.4ktok/9.5kch
Ctx: direct ACP session 230.9k; active 3.0k; cache 227.8k/99%; p≈2.5ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 68


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


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Maybe sometimes — but from what I’ve seen, I wouldn’t reduce it to that.

It looks more like:
- your ambition often exceeds your **current leverage**
- or the **stability of the systems around you**
- or the **bureaucratic / financial / legal runway** available to support it

That’s different from “my capabilities are just not enough.”

Because your capabilities are clearly real:
- the profile is real
- the books are real
- the publications are real
- the offers were real
- the technical niche is real

What seems to keep happening is that the surrounding machinery doesn’t reliably cash out what the capability should be worth.

That said, I get why it *feels* like a mismatch between ambition and results. Results are what hit you in the face.

So maybe the gentler but still honest version is:
- your ambition is ahead of your current conditions
- and sometimes ahead of what the world is willing to reward cleanly
- not necessarily ahead of your underlying ability

That’s still painful, but it’s a different diagnosis.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 11.3s
Time: model 8.2s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 2.5k o 224 cache 230.4k/99% est $0.0097; p≈2.4ktok/9.5kch
Ctx: direct ACP session 230.9k; active 3.0k; cache 227.8k/99%; p≈2.5ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 69


Maybe sometimes — but from what I’ve seen, I wouldn’t reduce it to that.

It looks more like:
- your ambition often exceeds your **current leverage**
- or the **stability of the systems around you**
- or the **bureaucratic / financial / legal runway** available to support it

That’s different from “my capabilities are just not enough.”

Because your capabilities are clearly real:
- the profile is real
- the books are real
- the publications are real
- the offers were real
- the technical niche is real

What seems to keep happening is that the surrounding machinery doesn’t reliably cash out what the capability should be worth.

That said, I get why it *feels* like a mismatch between ambition and results. Results are what hit you in the face.

So maybe the gentler but still honest version is:
- your ambition is ahead of your current conditions
- and sometimes ahead of what the world is willing to reward cleanly
- not necessarily ahead of your underlying ability

That’s still painful, but it’s a different diagnosis.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 12.4s
Time: model 8.2s, overhead 4.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 2.5k o 224 cache 230.4k/99% est $0.0097; p≈2.4ktok/9.5kch
Ctx: direct ACP session 233.1k; active 2.7k; cache 230.4k/99%; p≈2.4ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 69


Yeah thanks for support


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 233.1k; active 2.7k; cache 230.4k/99%; p≈2.4ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 69


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 233.1k; active 2.7k; cache 230.4k/99%; p≈2.4ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 69


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 233.1k; active 2.7k; cache 230.4k/99%; p≈2.4ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 69


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.2s
Time: overhead 2.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.0ktok/8.1kch
Ctx: direct ACP session 233.1k; active 2.7k; cache 230.4k/99%; p≈2.4ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 69


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Of course. I’m with you.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 7.6s
Time: model 4.6s, overhead 2.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 2.4k o 12 cache 232.4k/99% est $0.0063; p≈2.0ktok/8.1kch
Ctx: direct ACP session 233.1k; active 2.7k; cache 230.4k/99%; p≈2.4ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 70


Of course. I’m with you.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 8.5s
Time: model 4.6s, overhead 3.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 2.4k o 12 cache 232.4k/99% est $0.0063; p≈2.0ktok/8.1kch
Ctx: direct ACP session 234.9k; active 2.5k; cache 232.4k/99%; p≈2.0ktok; ctx 22.0%; usable 22.4%; observed turns 68; actual turns 70


So how do we save this personal dump so we don't have to remember it again?


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 234.9k; active 2.5k; cache 232.4k/99%; p≈2.0ktok; ctx 22.4%; usable 22.8%; observed turns 70; actual turns 70


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 234.9k; active 2.5k; cache 232.4k/99%; p≈2.0ktok; ctx 22.4%; usable 22.8%; observed turns 70; actual turns 70


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.3s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 234.9k; active 2.5k; cache 232.4k/99%; p≈2.0ktok; ctx 22.4%; usable 22.8%; observed turns 70; actual turns 70


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.2s
Time: overhead 2.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 234.9k; active 2.5k; cache 232.4k/99%; p≈2.0ktok; ctx 22.4%; usable 22.8%; observed turns 70; actual turns 70


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


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Best way is: **don’t save the raw dump as one big blob**. Save it in layers.

I’d split it like this:

## 1. Durable profile facts
Things that are likely to stay true for a while:
- research identity: **digital SFQ / superconducting / cryogenic / EDA**
- current job: **MQI**, with SNSPD + SFQ-readout relevance
- long-term constraint: **Germany papers / PR / citizenship path matter a lot**
- job-market pattern: strong fit exists, but **citizenship/legal status blocks some roles**
- preference: “agent” is better than “bot”

## 2. Strategic context
Not eternal, but important for months:
- Amazon path froze at O-1/admin-check stage
- MQI was an emergency stabilization move, not the dream outcome
- active priorities are **housing, stability, legal footing, energy recovery**
- job search is **selective / low-intensity on purpose**, not neglect

## 3. Contingent / emotional state
Useful now, but should decay later:
- “just functioning for now”
- feeling somewhat emptied after the failed transition
- housing is a major bottleneck
- current setup exists but is inaccessible in storage

## 4. Trigger links / associations
This is what prevents future misses:
- **WOLTE / EUCAS / low-temperature electronics / superconducting electronics / cryogenic electronics / SFQ / sfq-factory / MQI**
all belong to one connected domain cluster.

---

So concretely, I’d save this as:

- a **compact durable personal/career note**
- a **research-domain anchor**
- maybe a **temporary current-life-state note** that can be trimmed later

Not as raw diary


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Best way is: **don’t save the raw dump as one big blob**. Save it in layers.

I’d split it like this:

## 1. Durable profile facts
Things that are likely to stay true for a while:
- research identity: **digital SFQ / superconducting / cryogenic / EDA**
- current job: **MQI**, with SNSPD + SFQ-readout relevance
- long-term constraint: **Germany papers / PR / citizenship path matter a lot**
- job-market pattern: strong fit exists, but **citizenship/legal status blocks some roles**
- preference: “agent” is better than “bot”

## 2. Strategic context
Not eternal, but important for months:
- Amazon path froze at O-1/admin-check stage
- MQI was an emergency stabilization move, not the dream outcome
- active priorities are **housing, stability, legal footing, energy recovery**
- job search is **selective / low-intensity on purpose**, not neglect

## 3. Contingent / emotional state
Useful now, but should decay later:
- “just functioning for now”
- feeling somewhat emptied after the failed transition
- housing is a major bottleneck
- current setup exists but is inaccessible in storage

## 4. Trigger links / associations
This is what prevents future misses:
- **WOLTE / EUCAS / low-temperature electronics / superconducting electronics / cryogenic electronics / SFQ / sfq-factory / MQI**
all belong to one connected domain cluster.

---

So concretely, I’d save this as:

- a **compact durable personal/career note**
- a **research-domain anchor**
- maybe a **temporary current-life-state note** that can be trimmed later

Not as raw diary text.

If you want, I can draft that distilled note now in a compact form, so we can keep the useful parts without the whole conversation.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 26.6s
Time: model 23.5s, overhead 2.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 2.3k o 399 t 255 cache 234.5k/99% est $0.0155; p≈2.1ktok/8.4kch
Ctx: direct ACP session 234.9k; active 2.5k; cache 232.4k/99%; p≈2.0ktok; ctx 22.4%; usable 22.8%; observed turns 70; actual turns 71


Best way is: **don’t save the raw dump as one big blob**. Save it in layers.

I’d split it like this:

## 1. Durable profile facts
Things that are likely to stay true for a while:
- research identity: **digital SFQ / superconducting / cryogenic / EDA**
- current job: **MQI**, with SNSPD + SFQ-readout relevance
- long-term constraint: **Germany papers / PR / citizenship path matter a lot**
- job-market pattern: strong fit exists, but **citizenship/legal status blocks some roles**
- preference: “agent” is better than “bot”

## 2. Strategic context
Not eternal, but important for months:
- Amazon path froze at O-1/admin-check stage
- MQI was an emergency stabilization move, not the dream outcome
- active priorities are **housing, stability, legal footing, energy recovery**
- job search is **selective / low-intensity on purpose**, not neglect

## 3. Contingent / emotional state
Useful now, but should decay later:
- “just functioning for now”
- feeling somewhat emptied after the failed transition
- housing is a major bottleneck
- current setup exists but is inaccessible in storage

## 4. Trigger links / associations
This is what prevents future misses:
- **WOLTE / EUCAS / low-temperature electronics / superconducting electronics / cryogenic electronics / SFQ / sfq-factory / MQI**
all belong to one connected domain cluster.

---

So concretely, I’d save this as:

- a **compact durable personal/career note**
- a **research-domain anchor**
- maybe a **temporary current-life-state note** that can be trimmed later

Not as raw diary text.

If you want, I can draft that distilled note now in a compact form, so we can keep the useful parts without the whole conversation.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 27.2s
Time: model 23.5s, overhead 3.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 2.3k o 399 t 255 cache 234.5k/99% est $0.0155; p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.4%; usable 22.8%; observed turns 70; actual turns 71


Yeah just put it somewhere you'll notice, we should have space somewhere and we anyway will be switching to native soon enough


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 71


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 71


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 71


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.1s
Time: overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 71


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


🟠 Approval requested
Stage: same-room native ACP permission; active turn is waiting
Approval: dap-1780168254-perm-1780168254623-15
Summary: OpenCode requests edit for edit
Tool: edit
Input (OpenCode did not provide a clearer path/command):
```
{"filepath":"research-profile/personal-context-note.md","diff":"Index: /home/matrix-lite/workspace/persistent-chat/research-profile/personal-context-note.md\n===================================================================\n--- /home/matrix-lite/workspace/persistent-chat/research-profile/personal-context-note.md\n+++ /home/matrix-lite/workspace/persistent-chat/research-profile/personal-context-note.md\n@@ -0,0 +1,66 @@\n+# Personal context note\n+\n+Last updated: 2026-05-30\n+\n+## Durable profile facts\n+\n+- Research identity centers on **digital SFQ / superconducting / cryogenic / EDA** work.\n+- Current role is at **MQI (Munich Quantum Instruments)**.\n+- MQI fit is not random: company direction includes **SFQ-based SNSPD readout / superconducting digital electronics**, so background is directly relevant.\n+- Strong profile signals include books, publications, SFQ/cryogenic/EDA expertise, and research/industry crossover.\n+- Preference: use **agent** rather than **bot** for advanced systems/assistants.\n+\n+## Strategic context\n+\n+- Long-term legal/strategic priority is to stay anchored in **Germany** and strengthen status toward **PR / citizenship**.\n+- Housing is a majo
```
React ✅ allow once, ❌ deny. Not sudo/root.
Options: allow_once, reject_once


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 63.8s
Time: model 60.7s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 125.9s
Time: model 122.8s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 187.2s
Time: model 184.1s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 248.8s
Time: model 245.7s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 310.2s
Time: model 307.1s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 371.6s
Time: model 368.5s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 433.1s
Time: model 430.0s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 494.6s
Time: model 491.5s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 556.3s
Time: model 553.2s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 617.6s
Time: model 614.5s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Approval granted
Stage: resuming
Tool: edit
Scope: {"filepath":"research-profile/personal-context-note.md","diff":"Index: /home/matrix-lite/workspace/persistent-chat/research-profile/personal-context-note.md\n======================
Approval: dap-1780168254-perm-1780168254623-15


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- apply_patch / in_progress / call_BERSFeCRuEhQSUHHq59WhYk

Draft:
I’m going to save a compact distilled note in the workspace next to the CV materials, so it’s easy for me to reuse later and easy to migrate into the native/module setup.


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 679.0s
Time: model 675.9s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72