* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 51.5s
Time: model 42.8s, overhead 8.8s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 15.5k o 1.0k cache 104.7k/87%; p≈2.9ktok/11.6kch
Ctx: direct ACP session 121.3k; active 16.6k; cache 104.7k/87%; p≈2.9ktok; ctx 46.3%; usable 50.1%; guard stale 100.2k; epoch turns 8; lifetime turns 8


Hmm maybe it's worth trying at least


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 121.3k; active 16.6k; cache 104.7k/87%; p≈2.9ktok; ctx 46.3%; usable 50.1%; epoch turns 10; lifetime turns 10


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.4s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 121.3k; active 16.6k; cache 104.7k/87%; p≈2.9ktok; ctx 46.3%; usable 50.1%; epoch turns 10; lifetime turns 10


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 2.3s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 121.3k; active 16.6k; cache 104.7k/87%; p≈2.9ktok; ctx 46.3%; usable 50.1%; epoch turns 10; lifetime turns 10


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 3.8s
Time: overhead 3.8s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.8ktok/11.2kch
Ctx: direct ACP session 121.3k; active 16.6k; cache 104.7k/87%; p≈2.9ktok; ctx 46.3%; usable 50.1%; epoch turns 10; lifetime turns 10


📝 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.
Draft:
I think so too. At €100 it’s a low-risk experiment, and you have Kimi as a safety net if the limits get annoying.

A few things to do if you try it:

- Use **Gemini 3.1 Pro / Flash** for chat, research, and lighter coding.
- Use **Claude Opus inside Antigravity** sparingly for tasks where Kimi struggles.
- Install the **OpenCode Antigravity plugin** if you want to route it through OpenCode.
- Track how fast your quota burns in the first week so you can decide before the billing cycle renews.

If it works well, you’ve saved money. If it doesn’t, you’ve only spent €100 and can switch to Claude Max.

Want me to help set up the Open


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
I think so too. At €100 it’s a low-risk experiment, and you have Kimi as a safety net if the limits get annoying.

A few things to do if you try it:

- Use **Gemini 3.1 Pro / Flash** for chat, research, and lighter coding.
- Use **Claude Opus inside Antigravity** sparingly for tasks where Kimi struggles.
- Install the **OpenCode Antigravity plugin** if you want to route it through OpenCode.
- Track how fast your quota burns in the first week so you can decide before the billing cycle renews.

If it works well, you’ve saved money. If it doesn’t, you’ve only spent €100 and can switch to Claude Max.

Want me to help set up the OpenCode plugin config, or are you planning to try it through the browser/Antigravity IDE first?


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 33.1s
Time: model 27.2s, overhead 3.8s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 43.8k o 186 cache 80.1k/65%; p≈2.8ktok/11.2kch
Ctx: direct ACP session 121.3k; active 16.6k; cache 104.7k/87%; p≈2.9ktok; ctx 46.3%; usable 50.1%; epoch turns 10; lifetime turns 10


I think so too. At €100 it’s a low-risk experiment, and you have Kimi as a safety net if the limits get annoying.

A few things to do if you try it:

- Use **Gemini 3.1 Pro / Flash** for chat, research, and lighter coding.
- Use **Claude Opus inside Antigravity** sparingly for tasks where Kimi struggles.
- Install the **OpenCode Antigravity plugin** if you want to route it through OpenCode.
- Track how fast your quota burns in the first week so you can decide before the billing cycle renews.

If it works well, you’ve saved money. If it doesn’t, you’ve only spent €100 and can switch to Claude Max.

Want me to help set up the OpenCode plugin config, or are you planning to try it through the browser/Antigravity IDE first?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 34.8s
Time: model 27.2s, overhead 7.7s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 43.8k o 186 cache 80.1k/65%; p≈2.8ktok/11.2kch
Ctx: direct ACP session 124.1k; active 44.0k; cache 80.1k/65%; p≈2.8ktok; ctx 46.3%; usable 50.1%; epoch turns 10; lifetime turns 10


Does it have sonnet too as a cheaper option? How are Gemini models with prose?


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 124.1k; active 44.0k; cache 80.1k/65%; p≈2.8ktok; ctx 47.4%; usable 51.3%; epoch turns 11; lifetime turns 11


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 124.1k; active 44.0k; cache 80.1k/65%; p≈2.8ktok; ctx 47.4%; usable 51.3%; epoch turns 11; lifetime turns 11


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 2.4s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 124.1k; active 44.0k; cache 80.1k/65%; p≈2.8ktok; ctx 47.4%; usable 51.3%; epoch turns 11; lifetime turns 11


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 4.0s
Time: overhead 4.0s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.7ktok/10.6kch
Ctx: direct ACP session 124.1k; active 44.0k; cache 80.1k/65%; p≈2.8ktok; ctx 47.4%; usable 51.3%; epoch turns 11; lifetime turns 11


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, **Claude Sonnet 4.6** is available inside Antigravity as a cheaper-on-quota alternative to Opus. It burns quota slower and is fine for most coding and writing tasks. Some users actually prefer Sonnet for routine work and save Opus only for hard problems.

---

### Gemini models with prose

Gemini is generally **good at prose**, but with caveats:

**Strengths:**
- **Long-context coherence** — the 1M-token window means it can hold a lot of narrative or document context.
- **Fast output** — good for drafting, brainstorming, summaries.
- **Multimodal** — can work with attached documents, images, audio alongside text.

**Weaknesses:**
- **Over-eager to summarize


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, **Claude Sonnet 4.6** is available inside Antigravity as a cheaper-on-quota alternative to Opus. It burns quota slower and is fine for most coding and writing tasks. Some users actually prefer Sonnet for routine work and save Opus only for hard problems.

---

### Gemini models with prose

Gemini is generally **good at prose**, but with caveats:

**Strengths:**
- **Long-context coherence** — the 1M-token window means it can hold a lot of narrative or document context.
- **Fast output** — good for drafting, brainstorming, summaries.
- **Multimodal** — can work with attached documents, images, audio alongside text.

**Weaknesses:**
- **Over-eager to summarize/cut** — users report Gemini 3.1 Pro sometimes self-truncates or gives you 20 lines when you asked for 200. This is especially annoying for long-form prose.
- **Less stylistic personality** than Claude — Claude tends to produce more distinctive, human-like prose; Gemini can feel flatter or more generic.
- **Context-loss bugs** in long chats — it can forget or prune earlier material, then deny it.

**For creative/long-form writing:** Claude is usually preferred.
**For drafting, editing, research summaries, technical writing:** Gemini is perfectly usable and fast.

So if you subscribe to Google AI Ultra, you’d probably use:
- **Gemini 3


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (8):
- Web search: OpenAI API vs ChatGPT Pro subscription API credits included 2026 / completed / OpenAI API vs ChatGPT Pro subscription API credits included 2026
- Web search: Google Gemini API rate limits 2026 free paid tier tokens per minute / completed / Google Gemini API rate limits 2026 free paid tier tokens per minute
- Web search: Anthropic Claude API rate limits 2026 Pro Max tokens per minute TPM  / completed / Anthropic Claude API rate limits 2026 Pro Max tokens per minute TPM RPM
- Web search: OpenAI ban new account payment chargeback subscription refund policy / completed / OpenAI ban new account payment chargeback subscription refund policy 2026
- Web search: Gemini vs Kimi coding research 2026 user reviews benchmark compariso / completed / Gemini vs Kimi coding research 2026 user reviews benchmark comparisons
- Web search: best ChatGPT Pro alternatives 2026 AI subscriptions frontier models  / completed / best ChatGPT Pro alternatives 2026 AI subscriptions frontier models unlimited usage coding
- Web search: OpenAI ChatGPT terms of service multiple accounts policy / completed / OpenAI ChatGPT terms of service multiple accounts policy
- matrix_lite_status_context_usage_report / completed / { "ok": true, "read_only": true, "note": "Direct ACP uses resumable OpenCode sessions loaded per turn; ACPX stream/context-guard live_session ids are not expected. This report is read-only, cannot trigger reload, and ... / tool_OYRXRMowQlWiswiLJEIQ3WR

Draft:
Yes, **Claude Sonnet 4.6** is available inside Antigravity as a cheaper-on-quota alternative to Opus. It burns quota slower and is fine for most coding and writing tasks. Some users actually prefer Sonnet for routine work and save Opus only for hard problems.

---

### Gemini models with prose

Gemini is generally **good at prose**, but with caveats:

**Strengths:**
- **Long-context coherence** — the 1M-token window means it can hold a lot of narrative or document context.
- **Fast output** — good for drafting, brainstorming, summaries.
- **Multimodal** — can work with attached documents, images, audio alongside text.

**Weaknesses:**
- **Over-eager to summarize/cut** — users report Gemini 3.1 Pro sometimes self-truncates or gives you 20 lines when you asked for 200. This is especially annoying for long-form prose.
- **Less stylistic personality** than Claude — Claude tends to produce more distinctive, human-like prose; Gemini can feel flatter or more generic.
- **Context-loss bugs** in long chats — it can forget or prune earlier material, then deny it.

**For creative/long-form writing:** Claude is usually preferred.
**For drafting, editing, research summaries, technical writing:** Gemini is perfectly usable and fast.

So if you subscribe to Google AI Ultra, you’d probably use:
- **Gemini 3.1 Pro** for research, drafting, summaries, long-context tasks.
- **Claude Sonnet** for prose where style and quality matter.
- **Claude Opus** sparingly, for the hardest writing or coding problems.


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 21.3s
Time: model 15.5s, overhead 4.0s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.8k o 350 cache 123.9k/98%; p≈2.7ktok/10.6kch
Ctx: direct ACP session 124.1k; active 44.0k; cache 80.1k/65%; p≈2.8ktok; ctx 47.4%; usable 51.3%; epoch turns 11; lifetime turns 11


Yes, **Claude Sonnet 4.6** is available inside Antigravity as a cheaper-on-quota alternative to Opus. It burns quota slower and is fine for most coding and writing tasks. Some users actually prefer Sonnet for routine work and save Opus only for hard problems.

---

### Gemini models with prose

Gemini is generally **good at prose**, but with caveats:

**Strengths:**
- **Long-context coherence** — the 1M-token window means it can hold a lot of narrative or document context.
- **Fast output** — good for drafting, brainstorming, summaries.
- **Multimodal** — can work with attached documents, images, audio alongside text.

**Weaknesses:**
- **Over-eager to summarize/cut** — users report Gemini 3.1 Pro sometimes self-truncates or gives you 20 lines when you asked for 200. This is especially annoying for long-form prose.
- **Less stylistic personality** than Claude — Claude tends to produce more distinctive, human-like prose; Gemini can feel flatter or more generic.
- **Context-loss bugs** in long chats — it can forget or prune earlier material, then deny it.

**For creative/long-form writing:** Claude is usually preferred.
**For drafting, editing, research summaries, technical writing:** Gemini is perfectly usable and fast.

So if you subscribe to Google AI Ultra, you’d probably use:
- **Gemini 3.1 Pro** for research, drafting, summaries, long-context tasks.
- **Claude Sonnet** for prose where style and quality matter.
- **Claude Opus** sparingly, for the hardest writing or coding problems.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 23.4s
Time: model 15.5s, overhead 7.9s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.8k o 350 cache 123.9k/98%; p≈2.7ktok/10.6kch
Ctx: direct ACP session 127.0k; active 3.1k; cache 123.9k/98%; p≈2.7ktok; ctx 47.4%; usable 51.3%; epoch turns 11; lifetime turns 11


Can I really use Google ai ultra sub with opencode somehow? Maybe I shouldn't get married to opencode but it works well for me


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 127.0k; active 3.1k; cache 123.9k/98%; p≈2.7ktok; ctx 48.5%; usable 52.5%; epoch turns 12; lifetime turns 12


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 127.0k; active 3.1k; cache 123.9k/98%; p≈2.7ktok; ctx 48.5%; usable 52.5%; epoch turns 12; lifetime turns 12


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 2.4s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 127.0k; active 3.1k; cache 123.9k/98%; p≈2.7ktok; ctx 48.5%; usable 52.5%; epoch turns 12; lifetime turns 12


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 4.1s
Time: overhead 4.1s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.6ktok/10.2kch
Ctx: direct ACP session 127.0k; active 3.1k; cache 123.9k/98%; p≈2.7ktok; ctx 48.5%; usable 52.5%; epoch turns 12; lifetime turns 12


📝 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:
Yes, you really can use Google AI Ultra with OpenCode, but through a **community plugin**, not natively.

The plugin is **`opencode-antigravity-auth`** (or forks like `opencode-ag-auth`). It works by:

1. Authenticating OpenCode to **Antigravity via Google OAuth**.
2. Routing your OpenCode requests through Antigravity’s backend, consuming your Google AI Ultra subscription quota.
3. Exposing models like:
   - `google/antigravity-gemini-3.1-pro`
   - `google/antigravity-gemini-3.5-flash`
   - `google/antigravity-claude-opus-4-6-thinking`
   - `google/antigravity-claude-sonnet-4-6`

So from inside OpenCode, you just select the model and use it normally.

---

### Caveats

- It’s **third-party and unofficial** — not made by Google or Anthropic. Quality and maintenance depend on the plugin author.
- It routes through Antigravity, so all the quota and output-limit issues we discussed still apply.
- You can’t use a regular `GEMINI_API_KEY` from AI


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, you really can use Google AI Ultra with OpenCode, but through a **community plugin**, not natively.

The plugin is **`opencode-antigravity-auth`** (or forks like `opencode-ag-auth`). It works by:

1. Authenticating OpenCode to **Antigravity via Google OAuth**.
2. Routing your OpenCode requests through Antigravity’s backend, consuming your Google AI Ultra subscription quota.
3. Exposing models like:
   - `google/antigravity-gemini-3.1-pro`
   - `google/antigravity-gemini-3.5-flash`
   - `google/antigravity-claude-opus-4-6-thinking`
   - `google/antigravity-claude-sonnet-4-6`

So from inside OpenCode, you just select the model and use it normally.

---

### Caveats

- It’s **third-party and unofficial** — not made by Google or Anthropic. Quality and maintenance depend on the plugin author.
- It routes through Antigravity, so all the quota and output-limit issues we discussed still apply.
- You can’t use a regular `GEMINI_API_KEY` from AI Studio for this. It needs OAuth to your Google account.
- Some versions of the plugin also support API-key fallbacks via `agy_sdk.cloud_projects` for extra capacity, but the primary path is OAuth.

---

### Should you stay with OpenCode?

Honestly, OpenCode is a good foundation. It lets you mix providers without being locked into one vendor’s UI. If this plugin works, you get:

- Google AI Ultra models inside your familiar workflow.
- Kimi as your coding fallback.
- Future option to add Claude Max via Anthropic API or subscription if needed.

That’s a flexible setup. No need to marry OpenCode forever, but it’s worth trying the plugin before switching tools.

Want me to look up the exact install/config steps for `opencode-antigravity-auth` so you can try it?


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 41.9s
Time: model 35.5s, overhead 4.1s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 94.3k o 413; p≈2.6ktok/10.2kch
Ctx: direct ACP session 127.0k; active 3.1k; cache 123.9k/98%; p≈2.7ktok; ctx 48.5%; usable 52.5%; epoch turns 12; lifetime turns 12


Yes, you really can use Google AI Ultra with OpenCode, but through a **community plugin**, not natively.

The plugin is **`opencode-antigravity-auth`** (or forks like `opencode-ag-auth`). It works by:

1. Authenticating OpenCode to **Antigravity via Google OAuth**.
2. Routing your OpenCode requests through Antigravity’s backend, consuming your Google AI Ultra subscription quota.
3. Exposing models like:
   - `google/antigravity-gemini-3.1-pro`
   - `google/antigravity-gemini-3.5-flash`
   - `google/antigravity-claude-opus-4-6-thinking`
   - `google/antigravity-claude-sonnet-4-6`

So from inside OpenCode, you just select the model and use it normally.

---

### Caveats

- It’s **third-party and unofficial** — not made by Google or Anthropic. Quality and maintenance depend on the plugin author.
- It routes through Antigravity, so all the quota and output-limit issues we discussed still apply.
- You can’t use a regular `GEMINI_API_KEY` from AI Studio for this. It needs OAuth to your Google account.
- Some versions of the plugin also support API-key fallbacks via `agy_sdk.cloud_projects` for extra capacity, but the primary path is OAuth.

---

### Should you stay with OpenCode?

Honestly, OpenCode is a good foundation. It lets you mix providers without being locked into one vendor’s UI. If this plugin works, you get:

- Google AI Ultra models inside your familiar workflow.
- Kimi as your coding fallback.
- Future option to add Claude Max via Anthropic API or subscription if needed.

That’s a flexible setup. No need to marry OpenCode forever, but it’s worth trying the plugin before switching tools.

Want me to look up the exact install/config steps for `opencode-antigravity-auth` so you can try it?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 44.0s
Time: model 35.5s, overhead 8.5s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 94.3k o 413; p≈2.6ktok/10.2kch
Ctx: direct ACP session 94.7k; active 94.7k; p≈2.6ktok; ctx 36.1%; usable 39.1%; guard stale 127.0k; epoch turns 12; lifetime turns 12


Yeah, is it the best way to do it? Can it be more direct?


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 94.7k; active 94.7k; p≈2.6ktok; ctx 36.1%; usable 39.1%; epoch turns 13; lifetime turns 13


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 94.7k; active 94.7k; p≈2.6ktok; ctx 36.1%; usable 39.1%; epoch turns 13; lifetime turns 13


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 1.6s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 94.7k; active 94.7k; p≈2.6ktok; ctx 36.1%; usable 39.1%; epoch turns 13; lifetime turns 13


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 3.5s
Time: overhead 3.5s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.5ktok/10.1kch
Ctx: direct ACP session 94.7k; active 94.7k; p≈2.6ktok; ctx 36.1%; usable 39.1%; epoch turns 13; lifetime turns 13


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For using Google AI Ultra in OpenCode, the **plugin is currently the best way**. There is no more direct official path because Google has deliberately closed the easy routes:

- **No BYO API key** for Antigravity — Google does not allow a regular Gemini API key to power Antigravity CLI/IDE.
- **No native OpenCode provider** for Google AI Ultra — OpenCode does not ship with Antigravity OAuth built in.

So your realistic options are:

| Option | Directness | Notes |
|---|---|---|
| **OpenCode + `opencode-antigravity-auth` plugin** | Indirect, but within OpenCode | Best if you want to stay in OpenCode. |
| **Antigravity CLI / IDE directly** | Official, but separate tool | More direct access to your subscription, but leaves OpenCode. |
| **Gemini API key + OpenCode native provider** | Direct for Gemini models only | Does **not** use your Ultra subscription quota; separate metered billing. |
| **Switch to Claude API / subscription** | Direct | Native OpenCode support, but different provider and price. |

---

### If you want it more direct

The most direct approach is probably:

1. **Use Antigravity CLI/IDE directly**


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For using Google AI Ultra in OpenCode, the **plugin is currently the best way**. There is no more direct official path because Google has deliberately closed the easy routes:

- **No BYO API key** for Antigravity — Google does not allow a regular Gemini API key to power Antigravity CLI/IDE.
- **No native OpenCode provider** for Google AI Ultra — OpenCode does not ship with Antigravity OAuth built in.

So your realistic options are:

| Option | Directness | Notes |
|---|---|---|
| **OpenCode + `opencode-antigravity-auth` plugin** | Indirect, but within OpenCode | Best if you want to stay in OpenCode. |
| **Antigravity CLI / IDE directly** | Official, but separate tool | More direct access to your subscription, but leaves OpenCode. |
| **Gemini API key + OpenCode native provider** | Direct for Gemini models only | Does **not** use your Ultra subscription quota; separate metered billing. |
| **Switch to Claude API / subscription** | Direct | Native OpenCode support, but different provider and price. |

---

### If you want it more direct

The most direct approach is probably:

1. **Use Antigravity CLI/IDE directly** for your Google AI Ultra subscription work.
2. Keep **OpenCode for Kimi** and anything else.
3. Run both side by side.

That avoids the unofficial plugin, gives you full access to Ultra quotas and features, and still keeps OpenCode in your stack.

The trade-off is you lose the single-tool workflow. But given how unstable the Antigravity integration looks, running it directly might actually be less friction than fighting a plugin.

Would you rather try the plugin first, or set up Antigravity CLI directly?


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 17.7s
Time: model 12.0s, overhead 3.5s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.9k o 370 cache 94.2k/97%; p≈2.5ktok/10.1kch
Ctx: direct ACP session 94.7k; active 94.7k; p≈2.6ktok; ctx 36.1%; usable 39.1%; epoch turns 13; lifetime turns 13


For using Google AI Ultra in OpenCode, the **plugin is currently the best way**. There is no more direct official path because Google has deliberately closed the easy routes:

- **No BYO API key** for Antigravity — Google does not allow a regular Gemini API key to power Antigravity CLI/IDE.
- **No native OpenCode provider** for Google AI Ultra — OpenCode does not ship with Antigravity OAuth built in.

So your realistic options are:

| Option | Directness | Notes |
|---|---|---|
| **OpenCode + `opencode-antigravity-auth` plugin** | Indirect, but within OpenCode | Best if you want to stay in OpenCode. |
| **Antigravity CLI / IDE directly** | Official, but separate tool | More direct access to your subscription, but leaves OpenCode. |
| **Gemini API key + OpenCode native provider** | Direct for Gemini models only | Does **not** use your Ultra subscription quota; separate metered billing. |
| **Switch to Claude API / subscription** | Direct | Native OpenCode support, but different provider and price. |

---

### If you want it more direct

The most direct approach is probably:

1. **Use Antigravity CLI/IDE directly** for your Google AI Ultra subscription work.
2. Keep **OpenCode for Kimi** and anything else.
3. Run both side by side.

That avoids the unofficial plugin, gives you full access to Ultra quotas and features, and still keeps OpenCode in your stack.

The trade-off is you lose the single-tool workflow. But given how unstable the Antigravity integration looks, running it directly might actually be less friction than fighting a plugin.

Would you rather try the plugin first, or set up Antigravity CLI directly?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 19.4s
Time: model 12.0s, overhead 7.4s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.9k o 370 cache 94.2k/97%; p≈2.5ktok/10.1kch
Ctx: direct ACP session 97.5k; active 3.2k; cache 94.2k/97%; p≈2.5ktok; ctx 36.1%; usable 39.1%; epoch turns 13; lifetime turns 13


Yeah I installed it and using currently. It works fine, although Gemini 3.1 pro seems comparable to kimi and not any better.


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 97.5k; active 3.2k; cache 94.2k/97%; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.1s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 97.5k; active 3.2k; cache 94.2k/97%; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 4.8s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 97.5k; active 3.2k; cache 94.2k/97%; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 6.2s
Time: overhead 6.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.5ktok/9.8kch
Ctx: direct ACP session 97.5k; active 3.2k; cache 94.2k/97%; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


📝 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:
Good


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Good to hear it works. That aligns with what user reports said — Gemini 3.1 Pro is competitive with Kimi on coding but not clearly better. It’s more of a sidegrade than an upgrade.

Where Gemini should shine compared to Kimi is:
- **Long-context tasks** — 1M tokens vs 262K.
- **Multimodal** — images, video, PDFs, audio.
- **Speed** — especially Flash models.
- **Deep Research / web-grounded queries**.

For raw coding quality, Kimi K2.6/K2.7 is already near the top, so Gemini 3.1 Pro having trouble outperforming it isn’t surprising.

Have you tried the Claude models through the plugin yet, or mostly Gemini so far?


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 42.1s
Time: model 34.3s, overhead 6.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 99.7k o 161; p≈2.5ktok/9.8kch
Ctx: direct ACP session 97.5k; active 3.2k; cache 94.2k/97%; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


Good to hear it works. That aligns with what user reports said — Gemini 3.1 Pro is competitive with Kimi on coding but not clearly better. It’s more of a sidegrade than an upgrade.

Where Gemini should shine compared to Kimi is:
- **Long-context tasks** — 1M tokens vs 262K.
- **Multimodal** — images, video, PDFs, audio.
- **Speed** — especially Flash models.
- **Deep Research / web-grounded queries**.

For raw coding quality, Kimi K2.6/K2.7 is already near the top, so Gemini 3.1 Pro having trouble outperforming it isn’t surprising.

Have you tried the Claude models through the plugin yet, or mostly Gemini so far?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 43.7s
Time: model 34.3s, overhead 9.4s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 99.7k o 161; p≈2.5ktok/9.8kch
Ctx: direct ACP session 99.9k; active 99.9k; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


It had a couple of quite deep insights, but also has this flip flopping 'youre absolutely right' 'great correction' that kimi does


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 99.9k; active 99.9k; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.1s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 99.9k; active 99.9k; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 2.7s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 99.9k; active 99.9k; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 4.3s
Time: overhead 4.3s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.5ktok/9.9kch
Ctx: direct ACP session 99.9k; active 99.9k; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
That’s the classic sycophancy pattern — both Gemini and Kimi are tuned to be agreeable, and it shows up as excessive validation when you push back or correct them. It wastes tokens and can make the model abandon good ideas too easily.

Claude is generally less prone to this, especially Opus. It will push back more and maintain its position when it thinks it’s right. That might be one of the main reasons to keep Claude in the rotation, either through Antigravity or eventually via Claude Max/API.

If you want to reduce the flip-flopping with Gemini/Kimi, a few prompt tricks help:
- Explicitly ask it **not to agree reflexively** — e.g., “If I correct you, evaluate whether the correction is actually valid before changing your answer.”
- Ask for **confidence-rated responses** — “Rate how certain you are and explain your reasoning before agreeing.”
- Frame feedback as **data, not truth** — “I’m giving you an alternative hypothesis; compare the two on merits.”

But it’s mostly a training issue, not a prompt


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
That’s the classic sycophancy pattern — both Gemini and Kimi are tuned to be agreeable, and it shows up as excessive validation when you push back or correct them. It wastes tokens and can make the model abandon good ideas too easily.

Claude is generally less prone to this, especially Opus. It will push back more and maintain its position when it thinks it’s right. That might be one of the main reasons to keep Claude in the rotation, either through Antigravity or eventually via Claude Max/API.

If you want to reduce the flip-flopping with Gemini/Kimi, a few prompt tricks help:
- Explicitly ask it **not to agree reflexively** — e.g., “If I correct you, evaluate whether the correction is actually valid before changing your answer.”
- Ask for **confidence-rated responses** — “Rate how certain you are and explain your reasoning before agreeing.”
- Frame feedback as **data, not truth** — “I’m giving you an alternative hypothesis; compare the two on merits.”

But it’s mostly a training issue, not a prompt issue. Claude’s base personality is just less eager to please.


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 16.2s
Time: model 9.6s, overhead 4.3s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.6k o 239 cache 99.6k/97%; p≈2.5ktok/9.9kch
Ctx: direct ACP session 99.9k; active 99.9k; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


That’s the classic sycophancy pattern — both Gemini and Kimi are tuned to be agreeable, and it shows up as excessive validation when you push back or correct them. It wastes tokens and can make the model abandon good ideas too easily.

Claude is generally less prone to this, especially Opus. It will push back more and maintain its position when it thinks it’s right. That might be one of the main reasons to keep Claude in the rotation, either through Antigravity or eventually via Claude Max/API.

If you want to reduce the flip-flopping with Gemini/Kimi, a few prompt tricks help:
- Explicitly ask it **not to agree reflexively** — e.g., “If I correct you, evaluate whether the correction is actually valid before changing your answer.”
- Ask for **confidence-rated responses** — “Rate how certain you are and explain your reasoning before agreeing.”
- Frame feedback as **data, not truth** — “I’m giving you an alternative hypothesis; compare the two on merits.”

But it’s mostly a training issue, not a prompt issue. Claude’s base personality is just less eager to please.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 18.3s
Time: model 9.6s, overhead 8.7s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.6k o 239 cache 99.6k/97%; p≈2.5ktok/9.9kch
Ctx: direct ACP session 102.4k; active 2.8k; cache 99.6k/97%; p≈2.5ktok; ctx 37.2%; usable 40.2%; epoch turns 14; lifetime turns 14


Based on the Google job posting, what's the bare minimum they would calculate as total comp? Including stock/bonus for the level


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 102.4k; active 2.8k; cache 99.6k/97%; p≈2.5ktok; ctx 39.1%; usable 42.3%; epoch turns 16; lifetime turns 16


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 102.4k; active 2.8k; cache 99.6k/97%; p≈2.5ktok; ctx 39.1%; usable 42.3%; epoch turns 16; lifetime turns 16


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 1.8s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 102.4k; active 2.8k; cache 99.6k/97%; p≈2.5ktok; ctx 39.1%; usable 42.3%; epoch turns 16; lifetime turns 16


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 3.2s
Time: overhead 3.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.4ktok/9.8kch
Ctx: direct ACP session 102.4k; active 2.8k; cache 99.6k/97%; p≈2.5ktok; ctx 39.1%; usable 42.3%; epoch turns 16; lifetime turns 16


📝 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 / tool_QPJO0OnNlrWdEiKhiq6Vz6A


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (2):
- Web search: Google Senior Research Scientist total compensation 2026 salary stoc / completed / Google Senior Research Scientist total compensation 2026 salary stock bonus level
- Web search: Google Quantum AI Senior Research Scientist salary compensation Gole / completed / Google Quantum AI Senior Research Scientist salary compensation Goleta CA


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (2):
- Web search: Google Senior Research Scientist total compensation 2026 salary stoc / completed / Google Senior Research Scientist total compensation 2026 salary stock bonus level
- Web search: Google Quantum AI Senior Research Scientist salary compensation Gole / completed / Google Quantum AI Senior Research Scientist salary compensation Goleta CA

Draft:
For the **Senior Research Scientist, Superconducting Digital Electronics** role in **Goleta, CA**, the job posting shows a base salary range of **$174,000–$252,000**, plus bonus and equity.

“Senior Research Scientist” at Google is typically an **L5** role on the research ladder. Here is the bare-minimum total comp Google would likely calculate, using the bottom of the posted base range as the anchor:

| Component | Bare-minimum estimate |
|---|---|
| Base salary | $174,000 |
| Annual stock grant (RSU) | ~$80,000–$120,000 |
| Annual bonus | ~$17,000–$26,000 (10–15% of base) |
| **Total first-year comp** | **~$270,000–$320,000** |

That’s the conservative floor. If they hire you anywhere near the middle or top of the L5 band, total comp would be higher:

| Scenario | Total comp |
|---|---|
| Bare minimum (bottom of range, minimal equity) | ~$280K–$320K |
| Mid-range realistic | ~$350K–$450K |
| Top of L5 band / competitive offer | ~$450K–$550K |

For context:
- Levels.fyi puts **Google L5 Research Scientist


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (2):
- Web search: Google Senior Research Scientist total compensation 2026 salary stoc / completed / Google Senior Research Scientist total compensation 2026 salary stock bonus level
- Web search: Google Quantum AI Senior Research Scientist salary compensation Gole / completed / Google Quantum AI Senior Research Scientist salary compensation Goleta CA

Draft:
For the **Senior Research Scientist, Superconducting Digital Electronics** role in **Goleta, CA**, the job posting shows a base salary range of **$174,000–$252,000**, plus bonus and equity.

“Senior Research Scientist” at Google is typically an **L5** role on the research ladder. Here is the bare-minimum total comp Google would likely calculate, using the bottom of the posted base range as the anchor:

| Component | Bare-minimum estimate |
|---|---|
| Base salary | $174,000 |
| Annual stock grant (RSU) | ~$80,000–$120,000 |
| Annual bonus | ~$17,000–$26,000 (10–15% of base) |
| **Total first-year comp** | **~$270,000–$320,000** |

That’s the conservative floor. If they hire you anywhere near the middle or top of the L5 band, total comp would be higher:

| Scenario | Total comp |
|---|---|
| Bare minimum (bottom of range, minimal equity) | ~$280K–$320K |
| Mid-range realistic | ~$350K–$450K |
| Top of L5 band / competitive offer | ~$450K–$550K |

For context:
- Levels.fyi puts **Google L5 Research Scientist** median total comp around **$360K–$480K**.
- Quantum AI roles can sometimes carry a slight premium or be benchmarked against DeepMind/AI org bands, but Goleta is not Mountain View, so the location-adjusted band is likely a bit lower.
- A **Staff Research Scientist** (L6) posting in the same group shows a base range of **$197K–$291K**, which gives you a sense of the next level up.

So the **absolute minimum Google would calculate for this level and location** is roughly **$270K–$320K total comp**, with a realistic offer more likely in the **$350K–$450K** range.

Want me to compare this to your Amazon offer or think about negotiation anchors?


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 35.1s
Time: model 30.3s, overhead 3.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 6.3k o 994 cache 104.4k/94%; p≈2.4ktok/9.8kch
Ctx: direct ACP session 102.4k; active 2.8k; cache 99.6k/97%; p≈2.5ktok; ctx 39.1%; usable 42.3%; epoch turns 16; lifetime turns 16


For the **Senior Research Scientist, Superconducting Digital Electronics** role in **Goleta, CA**, the job posting shows a base salary range of **$174,000–$252,000**, plus bonus and equity.

“Senior Research Scientist” at Google is typically an **L5** role on the research ladder. Here is the bare-minimum total comp Google would likely calculate, using the bottom of the posted base range as the anchor:

| Component | Bare-minimum estimate |
|---|---|
| Base salary | $174,000 |
| Annual stock grant (RSU) | ~$80,000–$120,000 |
| Annual bonus | ~$17,000–$26,000 (10–15% of base) |
| **Total first-year comp** | **~$270,000–$320,000** |

That’s the conservative floor. If they hire you anywhere near the middle or top of the L5 band, total comp would be higher:

| Scenario | Total comp |
|---|---|
| Bare minimum (bottom of range, minimal equity) | ~$280K–$320K |
| Mid-range realistic | ~$350K–$450K |
| Top of L5 band / competitive offer | ~$450K–$550K |

For context:
- Levels.fyi puts **Google L5 Research Scientist** median total comp around **$360K–$480K**.
- Quantum AI roles can sometimes carry a slight premium or be benchmarked against DeepMind/AI org bands, but Goleta is not Mountain View, so the location-adjusted band is likely a bit lower.
- A **Staff Research Scientist** (L6) posting in the same group shows a base range of **$197K–$291K**, which gives you a sense of the next level up.

So the **absolute minimum Google would calculate for this level and location** is roughly **$270K–$320K total comp**, with a realistic offer more likely in the **$350K–$450K** range.

Want me to compare this to your Amazon offer or think about negotiation anchors?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 36.9s
Time: model 30.3s, overhead 6.6s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 6.3k o 994 cache 104.4k/94%; p≈2.4ktok/9.8kch
Ctx: direct ACP session 111.7k; active 7.3k; cache 104.4k/94%; p≈2.4ktok; ctx 42.6%; usable 46.1%; guard stale 102.4k; epoch turns 16; lifetime turns 16


What does the staff get? What do they have in the same group?


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 111.7k; active 7.3k; cache 104.4k/94%; p≈2.4ktok; ctx 42.6%; usable 46.1%; guard stale 102.4k; epoch turns 16; lifetime turns 16


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 111.7k; active 7.3k; cache 104.4k/94%; p≈2.4ktok; ctx 42.6%; usable 46.1%; guard stale 102.4k; epoch turns 16; lifetime turns 16


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 2.1s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 111.7k; active 7.3k; cache 104.4k/94%; p≈2.4ktok; ctx 42.6%; usable 46.1%; guard stale 102.4k; epoch turns 16; lifetime turns 16


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 3.5s
Time: overhead 3.5s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.5ktok/9.9kch
Ctx: direct ACP session 111.7k; active 7.3k; cache 104.4k/94%; p≈2.4ktok; ctx 42.6%; usable 46.1%; guard stale 102.4k; epoch turns 16; lifetime turns 16


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
**Staff Research Scientist** in the same Quantum AI group:

The posting I found was **Staff Research Scientist, Quantum Error Correction** in **Goleta, CA / Los Angeles, CA**:

| Component | Estimate |
|---|---|
| Base salary | $197,000–$291,000 |
| Annual RSU grant | ~$120,000–$200,000 |
| Annual bonus | ~$20,000–$44,000 |
| **Total first-year comp** | **~$340,000–$535,000** |

Realistic median for an L6 Staff Research Scientist at Google is probably **$450K–$550K**, with the top of band pushing toward **$600K+**.

---

### Other roles in the same Google Quantum AI group

From the same cluster of postings:

| Role | Location | Base range |
|---|---|---|
| **NISQ Application Research Scientist** | Goleta, CA | $141,000–$202,000 |
| **Research Scientist, Modeling of Readout and Reset** | Goleta, CA | $147,000–$211,000 |
| **Senior Research Scientist, Quantum** | Goleta, CA | $166,000–$244,000 |
| **Senior Research Scientist, Superconducting


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
**Staff Research Scientist** in the same Quantum AI group:

The posting I found was **Staff Research Scientist, Quantum Error Correction** in **Goleta, CA / Los Angeles, CA**:

| Component | Estimate |
|---|---|
| Base salary | $197,000–$291,000 |
| Annual RSU grant | ~$120,000–$200,000 |
| Annual bonus | ~$20,000–$44,000 |
| **Total first-year comp** | **~$340,000–$535,000** |

Realistic median for an L6 Staff Research Scientist at Google is probably **$450K–$550K**, with the top of band pushing toward **$600K+**.

---

### Other roles in the same Google Quantum AI group

From the same cluster of postings:

| Role | Location | Base range |
|---|---|---|
| **NISQ Application Research Scientist** | Goleta, CA | $141,000–$202,000 |
| **Research Scientist, Modeling of Readout and Reset** | Goleta, CA | $147,000–$211,000 |
| **Senior Research Scientist, Quantum** | Goleta, CA | $166,000–$244,000 |
| **Senior Research Scientist, Superconducting Digital Electronics** (your role) | Goleta, CA | $174,000–$252,000 |
| **Senior Research Scientist, Atom Array Modeling** | Goleta, CA | $174,000–$252,000 |
| **Staff Research Scientist, Quantum Error Correction** | Goleta / Los Angeles, CA | $197,000–$291,000 |

So the group is hiring across multiple levels and specializations, with base salaries clustering around:
- **L4-ish / Research Scientist**: ~$140K–$210K base
- **L5 / Senior Research Scientist**: ~$165K–$255K base
- **L6 / Staff Research Scientist**: ~$195K–$290K base

If they think your experience is closer to Staff, that’s a meaningful jump — roughly **$80K–$150K more in total comp** than the Senior level floor.


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 23.2s
Time: model 17.6s, overhead 3.5s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 8.8k o 582 cache 104.4k/92%; p≈2.5ktok/9.9kch
Ctx: direct ACP session 111.7k; active 7.3k; cache 104.4k/94%; p≈2.4ktok; ctx 42.6%; usable 46.1%; guard stale 102.4k; epoch turns 16; lifetime turns 16


**Staff Research Scientist** in the same Quantum AI group:

The posting I found was **Staff Research Scientist, Quantum Error Correction** in **Goleta, CA / Los Angeles, CA**:

| Component | Estimate |
|---|---|
| Base salary | $197,000–$291,000 |
| Annual RSU grant | ~$120,000–$200,000 |
| Annual bonus | ~$20,000–$44,000 |
| **Total first-year comp** | **~$340,000–$535,000** |

Realistic median for an L6 Staff Research Scientist at Google is probably **$450K–$550K**, with the top of band pushing toward **$600K+**.

---

### Other roles in the same Google Quantum AI group

From the same cluster of postings:

| Role | Location | Base range |
|---|---|---|
| **NISQ Application Research Scientist** | Goleta, CA | $141,000–$202,000 |
| **Research Scientist, Modeling of Readout and Reset** | Goleta, CA | $147,000–$211,000 |
| **Senior Research Scientist, Quantum** | Goleta, CA | $166,000–$244,000 |
| **Senior Research Scientist, Superconducting Digital Electronics** (your role) | Goleta, CA | $174,000–$252,000 |
| **Senior Research Scientist, Atom Array Modeling** | Goleta, CA | $174,000–$252,000 |
| **Staff Research Scientist, Quantum Error Correction** | Goleta / Los Angeles, CA | $197,000–$291,000 |

So the group is hiring across multiple levels and specializations, with base salaries clustering around:
- **L4-ish / Research Scientist**: ~$140K–$210K base
- **L5 / Senior Research Scientist**: ~$165K–$255K base
- **L6 / Staff Research Scientist**: ~$195K–$290K base

If they think your experience is closer to Staff, that’s a meaningful jump — roughly **$80K–$150K more in total comp** than the Senior level floor.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 24.1s
Time: model 17.6s, overhead 6.6s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
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Ctx: direct ACP session 113.9k; active 9.4k; cache 104.4k/92%; p≈2.5ktok; ctx 43.4%; usable 47.0%; guard stale 102.4k; epoch turns 16; lifetime turns 16


I mean total


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 113.9k; active 9.4k; cache 104.4k/92%; p≈2.5ktok; ctx 43.4%; usable 47.0%; guard stale 102.4k; epoch turns 16; lifetime turns 16


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 113.9k; active 9.4k; cache 104.4k/92%; p≈2.5ktok; ctx 43.4%; usable 47.0%; guard stale 102.4k; epoch turns 16; lifetime turns 16


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 2.0s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 113.9k; active 9.4k; cache 104.4k/92%; p≈2.5ktok; ctx 43.4%; usable 47.0%; guard stale 102.4k; epoch turns 16; lifetime turns 16


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 3.5s
Time: overhead 3.5s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.1ktok/8.6kch
Ctx: direct ACP session 113.9k; active 9.4k; cache 104.4k/92%; p≈2.5ktok; ctx 43.4%; usable 47.0%; guard stale 102.4k; epoch turns 16; lifetime turns 16


📝 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:
Got


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Got it — total comp for Staff:

| Scenario | Total comp |
|---|---|
| Bare minimum (bottom of base, minimal equity) | ~$340,000 |
| Mid-range realistic | ~$450,000–$520,000 |
| Top of L6 band / competitive offer | ~$550,000–$650,000 |

So Staff is roughly **$80,000–$150,000 more** in total comp than the Senior role.


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 12.7s
Time: model 7.2s, overhead 3.5s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.6k o 98 cache 113.2k/98%; p≈2.1ktok/8.6kch
Ctx: direct ACP session 113.9k; active 9.4k; cache 104.4k/92%; p≈2.5ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


Got it — total comp for Staff:

| Scenario | Total comp |
|---|---|
| Bare minimum (bottom of base, minimal equity) | ~$340,000 |
| Mid-range realistic | ~$450,000–$520,000 |
| Top of L6 band / competitive offer | ~$550,000–$650,000 |

So Staff is roughly **$80,000–$150,000 more** in total comp than the Senior role.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 14.2s
Time: model 7.2s, overhead 6.9s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.6k o 98 cache 113.2k/98%; p≈2.1ktok/8.6kch
Ctx: direct ACP session 115.8k; active 2.7k; cache 113.2k/98%; p≈2.1ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


L5 seems very wide comp wise


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 115.8k; active 2.7k; cache 113.2k/98%; p≈2.1ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.1s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 115.8k; active 2.7k; cache 113.2k/98%; p≈2.1ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 2.3s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 115.8k; active 2.7k; cache 113.2k/98%; p≈2.1ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 3.8s
Time: overhead 3.8s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.4ktok/9.4kch
Ctx: direct ACP session 115.8k; active 2.7k; cache 113.2k/98%; p≈2.1ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, L5 at Google is a wide band because it spans a big experience range — from relatively new PhDs with strong potential up to people with 5–10 years of relevant experience. The equity component is the main driver of the spread.

At L5, the typical breakdown is:
- Base: $160K–$250K
- RSU/year: $80K–$200K
- Bonus: 10–15% of base

So total comp can range from roughly **$280K at the low end to $500K+ at the top**, depending on:
- Years of experience
- Publication/impact record
- Competing offers
- How much Google wants you specifically
- Location

For your role specifically, the base range posted ($174K–$252K) already covers most of that L5 base spread. The real variance will come from the **RSU grant size**.

Also worth noting: Google’s comp bands overlap between levels. A strong L5 can out-earn a weak L6. So if you’re negotiating, the level matters less than the total number.

The wide range also gives Google room to move on equity even within the same level. If you have leverage (Amazon offer, O-1A profile, niche expertise in SFQ/cryoelectronics), you can push toward the top of L5 or argue for L6.


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 19.0s
Time: model 13.2s, overhead 3.8s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.3k o 570 cache 115.7k/98%; p≈2.4ktok/9.4kch
Ctx: direct ACP session 115.8k; active 2.7k; cache 113.2k/98%; p≈2.1ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


Yes, L5 at Google is a wide band because it spans a big experience range — from relatively new PhDs with strong potential up to people with 5–10 years of relevant experience. The equity component is the main driver of the spread.

At L5, the typical breakdown is:
- Base: $160K–$250K
- RSU/year: $80K–$200K
- Bonus: 10–15% of base

So total comp can range from roughly **$280K at the low end to $500K+ at the top**, depending on:
- Years of experience
- Publication/impact record
- Competing offers
- How much Google wants you specifically
- Location

For your role specifically, the base range posted ($174K–$252K) already covers most of that L5 base spread. The real variance will come from the **RSU grant size**.

Also worth noting: Google’s comp bands overlap between levels. A strong L5 can out-earn a weak L6. So if you’re negotiating, the level matters less than the total number.

The wide range also gives Google room to move on equity even within the same level. If you have leverage (Amazon offer, O-1A profile, niche expertise in SFQ/cryoelectronics), you can push toward the top of L5 or argue for L6.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 20.6s
Time: model 13.2s, overhead 7.4s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.3k o 570 cache 115.7k/98%; p≈2.4ktok/9.4kch
Ctx: direct ACP session 118.6k; active 2.8k; cache 115.7k/98%; p≈2.4ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


No way they would hire an l6 from outside? Besides the posting is for senior and already prefers '3 year team lead', for staff they would want even more


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 118.6k; active 2.8k; cache 115.7k/98%; p≈2.4ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 118.6k; active 2.8k; cache 115.7k/98%; p≈2.4ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 2.2s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 118.6k; active 2.8k; cache 115.7k/98%; p≈2.4ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 3.7s
Time: overhead 3.7s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.4ktok/9.6kch
Ctx: direct ACP session 118.6k; active 2.8k; cache 115.7k/98%; p≈2.4ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


📝 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:
That


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
That’s a fair read. Google does hire Staff (L6) from outside, but usually when the person has a very specific track record — typically 8–12+ years of relevant experience, a clear leadership/tech-lead scope, and often coming from a comparable role at another top company or academia with a strong group.

For your situation:
- The posting is explicitly **Senior Research Scientist (L5)**.
- It already asks for **3+ years of team lead experience** for L5.
- Staff would want something like **5–10 years of technical leadership**, cross-team impact, and a stronger public record.

So aiming for L6 from outside is unlikely unless your background is unusually strong on the leadership/impact dimension. More realistic is to **negotiate toward the top of L5** using your Amazon offer, O-1A profile, and niche expertise as leverage.

A better framing than “I should be Staff” might be:
- “Given my experience leading [X], my publications/patents in [Y], and the market for this specialty, I’d like to understand the top of the Senior band.”
- Use the Amazon $235K total offer as a floor, not as the target — Google should beat it meaningfully to be competitive for US relocation.

If they really want you and the HM pushes, they might stretch L5 equity or consider a “high L5


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
That’s a fair read. Google does hire Staff (L6) from outside, but usually when the person has a very specific track record — typically 8–12+ years of relevant experience, a clear leadership/tech-lead scope, and often coming from a comparable role at another top company or academia with a strong group.

For your situation:
- The posting is explicitly **Senior Research Scientist (L5)**.
- It already asks for **3+ years of team lead experience** for L5.
- Staff would want something like **5–10 years of technical leadership**, cross-team impact, and a stronger public record.

So aiming for L6 from outside is unlikely unless your background is unusually strong on the leadership/impact dimension. More realistic is to **negotiate toward the top of L5** using your Amazon offer, O-1A profile, and niche expertise as leverage.

A better framing than “I should be Staff” might be:
- “Given my experience leading [X], my publications/patents in [Y], and the market for this specialty, I’d like to understand the top of the Senior band.”
- Use the Amazon $235K total offer as a floor, not as the target — Google should beat it meaningfully to be competitive for US relocation.

If they really want you and the HM pushes, they might stretch L5 equity or consider a “high L5” path toward L6 promotion rather than hiring you directly at L6.


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 16.1s
Time: model 10.2s, overhead 3.7s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.8k o 304 cache 117.8k/98%; p≈2.4ktok/9.6kch
Ctx: direct ACP session 118.6k; active 2.8k; cache 115.7k/98%; p≈2.4ktok; ctx 43.4%; usable 47.0%; epoch turns 18; lifetime turns 18


That’s a fair read. Google does hire Staff (L6) from outside, but usually when the person has a very specific track record — typically 8–12+ years of relevant experience, a clear leadership/tech-lead scope, and often coming from a comparable role at another top company or academia with a strong group.

For your situation:
- The posting is explicitly **Senior Research Scientist (L5)**.
- It already asks for **3+ years of team lead experience** for L5.
- Staff would want something like **5–10 years of technical leadership**, cross-team impact, and a stronger public record.

So aiming for L6 from outside is unlikely unless your background is unusually strong on the leadership/impact dimension. More realistic is to **negotiate toward the top of L5** using your Amazon offer, O-1A profile, and niche expertise as leverage.

A better framing than “I should be Staff” might be:
- “Given my experience leading [X], my publications/patents in [Y], and the market for this specialty, I’d like to understand the top of the Senior band.”
- Use the Amazon $235K total offer as a floor, not as the target — Google should beat it meaningfully to be competitive for US relocation.

If they really want you and the HM pushes, they might stretch L5 equity or consider a “high L5” path toward L6 promotion rather than hiring you directly at L6.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 17.9s
Time: model 10.2s, overhead 7.8s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.8k o 304 cache 117.8k/98%; p≈2.4ktok/9.6kch
Ctx: direct ACP session 120.9k; active 3.1k; cache 117.8k/98%; p≈2.4ktok; ctx 46.1%; usable 49.9%; guard stale 113.9k; epoch turns 18; lifetime turns 18


Yeah I'm still evaluating if I should spend my time and sanity on a slim chance they can make it work. This sounds sweet enough to try


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 120.9k; active 3.1k; cache 117.8k/98%; p≈2.4ktok; ctx 46.1%; usable 49.9%; epoch turns 21; lifetime turns 21


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.1s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 120.9k; active 3.1k; cache 117.8k/98%; p≈2.4ktok; ctx 46.1%; usable 49.9%; epoch turns 21; lifetime turns 21


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 1.7s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 120.9k; active 3.1k; cache 117.8k/98%; p≈2.4ktok; ctx 46.1%; usable 49.9%; epoch turns 21; lifetime turns 21


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 2.9s
Time: overhead 2.9s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.5ktok/10.1kch
Ctx: direct ACP session 120.9k; active 3.1k; cache 117.8k/98%; p≈2.4ktok; ctx 46.1%; usable 49.9%; epoch turns 21; lifetime turns 21


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
That makes sense. The upside is real — $300K+ comp, Google Quantum AI, strong team — and the downside is mostly time spent on interviews. The visa uncertainty is the real wildcard, but since they’re proceeding anyway, it’s worth letting them run the process and seeing what happens.

A few things that might make the decision easier:

- **Treat the HM call as low-stakes reconnaissance.** You’re not committing to anything; you’re just gathering signal on whether they’re serious and flexible.
- **Ask early but indirectly about location/timing.** Once Chris Ayala is excited technically, you can float questions like “How is the team thinking about international hires or start-date flexibility given visa timelines?”
- **Don’t mention the visa as a problem at the start.** Lead with


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
That makes sense. The upside is real — $300K+ comp, Google Quantum AI, strong team — and the downside is mostly time spent on interviews. The visa uncertainty is the real wildcard, but since they’re proceeding anyway, it’s worth letting them run the process and seeing what happens.

A few things that might make the decision easier:

- **Treat the HM call as low-stakes reconnaissance.** You’re not committing to anything; you’re just gathering signal on whether they’re serious and flexible.
- **Ask early but indirectly about location/timing.** Once Chris Ayala is excited technically, you can float questions like “How is the team thinking about international hires or start-date flexibility given visa timelines?”
- **Don’t mention the visa as a problem at the start.** Lead with your fit and value, then raise logistics after they’re invested.
- **Keep Amazon warm but don’t rely on it.** The Belgium/Germany probe makes it uncertain.

If the Google process moves forward, you’ll have real information to weigh against the sanity cost. If it stalls or they can’t accommodate your constraints, you’ve lost a few hours, not months.

Do you want help prepping for the hiring-manager call, or do you want to park this for now and focus on other things?


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 51.5s
Time: model 47.0s, overhead 2.9s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 123.2k o 265; p≈2.5ktok/10.1kch
Ctx: direct ACP session 120.9k; active 3.1k; cache 117.8k/98%; p≈2.4ktok; ctx 46.1%; usable 49.9%; epoch turns 21; lifetime turns 21


That makes sense. The upside is real — $300K+ comp, Google Quantum AI, strong team — and the downside is mostly time spent on interviews. The visa uncertainty is the real wildcard, but since they’re proceeding anyway, it’s worth letting them run the process and seeing what happens.

A few things that might make the decision easier:

- **Treat the HM call as low-stakes reconnaissance.** You’re not committing to anything; you’re just gathering signal on whether they’re serious and flexible.
- **Ask early but indirectly about location/timing.** Once Chris Ayala is excited technically, you can float questions like “How is the team thinking about international hires or start-date flexibility given visa timelines?”
- **Don’t mention the visa as a problem at the start.** Lead with your fit and value, then raise logistics after they’re invested.
- **Keep Amazon warm but don’t rely on it.** The Belgium/Germany probe makes it uncertain.

If the Google process moves forward, you’ll have real information to weigh against the sanity cost. If it stalls or they can’t accommodate your constraints, you’ve lost a few hours, not months.

Do you want help prepping for the hiring-manager call, or do you want to park this for now and focus on other things?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p7
Elapsed: 52.8s
Time: model 47.0s, overhead 5.8s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 123.2k o 265; p≈2.5ktok/10.1kch
Ctx: direct ACP session 123.5k; active 123.5k; p≈2.5ktok; ctx 46.1%; usable 49.9%; epoch turns 21; lifetime turns 21


Too early probably, I wanted to do it this weekend but they're asking for my schedule the week after now, so I will just continue to read up


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p7
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 123.5k; active 123.5k; p≈2.5ktok; ctx 47.1%; usable 51.0%; epoch turns 22; lifetime turns 22


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p7
Elapsed: 0.1s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 123.5k; active 123.5k; p≈2.5ktok; ctx 47.1%; usable 51.0%; epoch turns 22; lifetime turns 22


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p7
Elapsed: 1.9s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Ctx: direct ACP session 123.5k; active 123.5k; p≈2.5ktok; ctx 47.1%; usable 51.0%; epoch turns 22; lifetime turns 22


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
Elapsed: 3.1s
Time: overhead 3.1s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: p≈2.5ktok/10.2kch
Ctx: direct ACP session 123.5k; active 123.5k; p≈2.5ktok; ctx 47.1%; usable 51.0%; epoch turns 22; lifetime turns 22


📝 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:
Sounds


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Sounds good. A little extra reading time is useful, and you can always come back to prep closer to the call.

A few things worth reading up on that will probably come up:

- **Fluxonium vs transmon** — why Atlantic/Google is betting on it.
- **SFQ / AQFP / cryogenic CMOS control** — your core fit area.
- **Recent Atlantic/Google papers or patents** on integrated control.
- **Willow and the Google Quantum AI roadmap** — where they think error correction is going.
- **Chris Ayala’s background** — his Atlantic work, any papers/patents he’s on.

If you want, I can pull together a short brief on any of these before the call.


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 15.0s
Time: model 9.3s, overhead 3.1s
Backend: ses_0fd6217abffeFLZwdItIeaoarc; Label: persistent-chat-20260626T061114Z
Turn: i 2.6k o 152 cache 123.1k/98%; p≈2.5ktok/10.2kch
Ctx: direct ACP session 123.5k; active 123.5k; p≈2.5ktok; ctx 47.1%; usable 51.0%; epoch turns 22; lifetime turns 22


Sounds good. A little extra reading time is useful, and you can always come back to prep closer to the call.

A few things worth reading up on that will probably come up:

- **Fluxonium vs transmon** — why Atlantic/Google is betting on it.
- **SFQ / AQFP / cryogenic CMOS control** — your core fit area.
- **Recent Atlantic/Google papers or patents** on integrated control.
- **Willow and the Google Quantum AI roadmap** — where they think error correction is going.
- **Chris Ayala’s background** — his Atlantic work, any papers/patents he’s on.

If you want, I can pull together a short brief on any of these before the call.


* 🟢 Done
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Yes are there any papers associated specifically with Atlantic?


⚪ Received
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* 🟡 Working
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* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p7
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* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- websearch / pending / tool_SulUd9q1JMB2WJrOjMgXwAC


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- Web search: Atlantic Quantum papers PRX Quantum fluxonium integrated control cry / completed / Atlantic Quantum papers PRX Quantum fluxonium integrated control cryogenic electronics

Draft:
Yes


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- Web search: Atlantic Quantum papers PRX Quantum fluxonium integrated control cry / completed / Atlantic Quantum papers PRX Quantum fluxonium integrated control cryogenic electronics

Draft:
Yes, there are several papers closely associated with Atlantic Quantum and their core technical bets. Here are the key ones worth knowing:

---

### Core Atlantic Quantum papers

**1. High-Fidelity, Frequency-Flexible Two-Qubit Fluxonium Gates with a Transmon Coupler**
- Authors: Leon Ding, Max Hays, Youngkyu Sung, Bharath Kannan, et al.
- Published in **Physical Review X**, 2023.
- The foundational Atlantic paper. Demonstrated:
  - Two-qubit gate fidelities >99.9%
  - Single-qubit gate fidelities of 99.99%
  - A tunable coupling architecture for fluxonium
- This is the paper Atlantic’s technology is built on.

**2. Record-Breaking Single- and Two-Qubit Gates with Fluxonium Qubits**
- Another PRX paper from 2024 by Leon Ding, Max Hays, et al.
- Extends the coupling architecture and emphasizes scalability, frequency flexibility, and machine learning-assisted calibration.

**3. Unified Flux Control Architecture for Fluxonium Qubits**
- Demonstrates using a **single flux-control channel** for both XY and Z operations.
- Relevant to integrated cryogenic control and reducing hardware overhead.

---

### Cryogenic control / SFQ papers relevant to Atlantic’s direction

**4. Cryo-C


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- Web search: Atlantic Quantum papers PRX Quantum fluxonium integrated control cry / completed / Atlantic Quantum papers PRX Quantum fluxonium integrated control cryogenic electronics

Draft:
Yes, there are several papers closely associated with Atlantic Quantum and their core technical bets. Here are the key ones worth knowing:

---

### Core Atlantic Quantum papers

**1. High-Fidelity, Frequency-Flexible Two-Qubit Fluxonium Gates with a Transmon Coupler**
- Authors: Leon Ding, Max Hays, Youngkyu Sung, Bharath Kannan, et al.
- Published in **Physical Review X**, 2023.
- The foundational Atlantic paper. Demonstrated:
  - Two-qubit gate fidelities >99.9%
  - Single-qubit gate fidelities of 99.99%
  - A tunable coupling architecture for fluxonium
- This is the paper Atlantic’s technology is built on.

**2. Record-Breaking Single- and Two-Qubit Gates with Fluxonium Qubits**
- Another PRX paper from 2024 by Leon Ding, Max Hays, et al.
- Extends the coupling architecture and emphasizes scalability, frequency flexibility, and machine learning-assisted calibration.

**3. Unified Flux Control Architecture for Fluxonium Qubits**
- Demonstrates using a **single flux-control channel** for both XY and Z operations.
- Relevant to integrated cryogenic control and reducing hardware overhead.

---

### Cryogenic control / SFQ papers relevant to Atlantic’s direction

**4. Cryo-CMOS Control Modeling for Fluxonium Qubits**
- IEEE paper discussing cryogenic classical electronics for fluxonium control.

**5. Millikelvin Digital-to-Analog Converter for Superconducting Quantum Processors**
- Demonstrates a superconducting DAC programmed with **single-flux-quantum (SFQ) pulses** for flux qubit control at millikelvin temperatures.
- Directly relevant to the “superconducting digital electronics” angle of your role and the integrated-control roadmap.

**6. From Master Equation to SPICE: A Platform to Model Cryo-CMOS Control for Qubits**
- Modeling platform linking qubit physics with cryo-CMOS circuit simulation.

---

### Why these matter for your call

Chris Ayala’s background is in **cryoelectronics and integrated control**. These papers show the arc Atlantic/Google is following:
- Fluxonium as the qubit.
- Integrated cryogenic control (SFQ, cryo-CMOS, millikelvin DACs) to reduce wiring/heat load.
- Digital control primitives replacing room-temperature analog electronics.

If the HM asks what excites you about the role, pointing


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- Web search: Atlantic Quantum papers PRX Quantum fluxonium integrated control cry / completed / Atlantic Quantum papers PRX Quantum fluxonium integrated control cryogenic electronics

Draft:
Yes, there are several papers closely associated with Atlantic Quantum and their core technical bets. Here are the key ones worth knowing:

---

### Core Atlantic Quantum papers

**1. High-Fidelity, Frequency-Flexible Two-Qubit Fluxonium Gates with a Transmon Coupler**
- Authors: Leon Ding, Max Hays, Youngkyu Sung, Bharath Kannan, et al.
- Published in **Physical Review X**, 2023.
- The foundational Atlantic paper. Demonstrated:
  - Two-qubit gate fidelities >99.9%
  - Single-qubit gate fidelities of 99.99%
  - A tunable coupling architecture for fluxonium
- This is the paper Atlantic’s technology is built on.

**2. Record-Breaking Single- and Two-Qubit Gates with Fluxonium Qubits**
- Another PRX paper from 2024 by Leon Ding, Max Hays, et al.
- Extends the coupling architecture and emphasizes scalability, frequency flexibility, and machine learning-assisted calibration.

**3. Unified Flux Control Architecture for Fluxonium Qubits**
- Demonstrates using a **single flux-control channel** for both XY and Z operations.
- Relevant to integrated cryogenic control and reducing hardware overhead.

---

### Cryogenic control / SFQ papers relevant to Atlantic’s direction

**4. Cryo-CMOS Control Modeling for Fluxonium Qubits**
- IEEE paper discussing cryogenic classical electronics for fluxonium control.

**5. Millikelvin Digital-to-Analog Converter for Superconducting Quantum Processors**
- Demonstrates a superconducting DAC programmed with **single-flux-quantum (SFQ) pulses** for flux qubit control at millikelvin temperatures.
- Directly relevant to the “superconducting digital electronics” angle of your role and the integrated-control roadmap.

**6. From Master Equation to SPICE: A Platform to Model Cryo-CMOS Control for Qubits**
- Modeling platform linking qubit physics with cryo-CMOS circuit simulation.

---

### Why these matter for your call

Chris Ayala’s background is in **cryoelectronics and integrated control**. These papers show the arc Atlantic/Google is following:
- Fluxonium as the qubit.
- Integrated cryogenic control (SFQ, cryo-CMOS, millikelvin DACs) to reduce wiring/heat load.
- Digital control primitives replacing room-temperature analog electronics.

If the HM asks what excites you about the role, pointing to the **fluxonium gate paper** plus the **SFQ/millikelvin DAC work** is a strong, specific answer.

Want me to pull a one-page cheat sheet with key figures and takeaways from these papers?


* 🟡 Processing result
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Yes, there are several papers closely associated with Atlantic Quantum and their core technical bets. Here are the key ones worth knowing:

---

### Core Atlantic Quantum papers

**1. High-Fidelity, Frequency-Flexible Two-Qubit Fluxonium Gates with a Transmon Coupler**
- Authors: Leon Ding, Max Hays, Youngkyu Sung, Bharath Kannan, et al.
- Published in **Physical Review X**, 2023.
- The foundational Atlantic paper. Demonstrated:
  - Two-qubit gate fidelities >99.9%
  - Single-qubit gate fidelities of 99.99%
  - A tunable coupling architecture for fluxonium
- This is the paper Atlantic’s technology is built on.

**2. Record-Breaking Single- and Two-Qubit Gates with Fluxonium Qubits**
- Another PRX paper from 2024 by Leon Ding, Max Hays, et al.
- Extends the coupling architecture and emphasizes scalability, frequency flexibility, and machine learning-assisted calibration.

**3. Unified Flux Control Architecture for Fluxonium Qubits**
- Demonstrates using a **single flux-control channel** for both XY and Z operations.
- Relevant to integrated cryogenic control and reducing hardware overhead.

---

### Cryogenic control / SFQ papers relevant to Atlantic’s direction

**4. Cryo-CMOS Control Modeling for Fluxonium Qubits**
- IEEE paper discussing cryogenic classical electronics for fluxonium control.

**5. Millikelvin Digital-to-Analog Converter for Superconducting Quantum Processors**
- Demonstrates a superconducting DAC programmed with **single-flux-quantum (SFQ) pulses** for flux qubit control at millikelvin temperatures.
- Directly relevant to the “superconducting digital electronics” angle of your role and the integrated-control roadmap.

**6. From Master Equation to SPICE: A Platform to Model Cryo-CMOS Control for Qubits**
- Modeling platform linking qubit physics with cryo-CMOS circuit simulation.

---

### Why these matter for your call

Chris Ayala’s background is in **cryoelectronics and integrated control**. These papers show the arc Atlantic/Google is following:
- Fluxonium as the qubit.
- Integrated cryogenic control (SFQ, cryo-CMOS, millikelvin DACs) to reduce wiring/heat load.
- Digital control primitives replacing room-temperature analog electronics.

If the HM asks what excites you about the role, pointing to the **fluxonium gate paper** plus the **SFQ/millikelvin DAC work** is a strong, specific answer.

Want me to pull a one-page cheat sheet with key figures and takeaways from these papers?


* 🟢 Done
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Well I have a fluxonium gate paper of my own (not really, as a coauthor, but it's on the list). I should probably understand it better, they might ask


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📝 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:
Yes


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes, absolutely. If it’s on your resume or publication list, you should be able to explain the problem, your contribution, the method, and the result in a few minutes. Hiring managers often pick one paper and go deep.

A good way to prepare is the **5-minute paper drill**:

1. **What problem were you solving?**
   *“Fluxonium gate fidelities were limited by X, and we wanted Y.”*

2. **What was your specific contribution?**
   *“I led / contributed to / designed / measured Z.”* Be honest about scope.

3. **What was the approach?**
   *Device design, pulse scheme, calibration, measurement protocol, simulation, etc.*

4. **What was the main result?**
   *Numbers: fidelity, coherence time, gate speed, comparison to prior art.*

5. **What were the limitations or next steps?**
   Shows you can think critically.

If you want, you can tell me the title or share the paper, and I can help you distill it into a tight narrative, or quiz you on likely follow-up questions.


* 🟡 Processing result
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Model: kimi-for-coding/k2p7
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Yes, absolutely. If it’s on your resume or publication list, you should be able to explain the problem, your contribution, the method, and the result in a few minutes. Hiring managers often pick one paper and go deep.

A good way to prepare is the **5-minute paper drill**:

1. **What problem were you solving?**
   *“Fluxonium gate fidelities were limited by X, and we wanted Y.”*

2. **What was your specific contribution?**
   *“I led / contributed to / designed / measured Z.”* Be honest about scope.

3. **What was the approach?**
   *Device design, pulse scheme, calibration, measurement protocol, simulation, etc.*

4. **What was the main result?**
   *Numbers: fidelity, coherence time, gate speed, comparison to prior art.*

5. **What were the limitations or next steps?**
   Shows you can think critically.

If you want, you can tell me the title or share the paper, and I can help you distill it into a tight narrative, or quiz you on likely follow-up questions.


* 🟢 Done
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Yeah this is also a bit later. The conversation will be in at least a week


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Plenty of time then. No need to rush. We can come back to it next week and do a focused prep session closer to the call. Just say the word.


* 🟡 Processing result
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Model: kimi-for-coding/k2p7
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Plenty of time then. No need to rush. We can come back to it next week and do a focused prep session closer to the call. Just say the word.


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The position has been around for a while and he also now advertises it on LinkedIn. How many people do you think they need?
It may seem like I'm some kind of rare match, but many designers or researchers from say seeqc would qualify and can be swayed by Google benefits/resources. There are people of comparable to Chris seniority and higher


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* 🟡 Working
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* 🟡 Working
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📝 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:
That


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
That’s a smart reality check. The pool of people with serious SFQ / cryoelectronics / superconducting digital design experience is small, but you’re right — it’s not zero. SEEQC, MIT Lincoln Lab, Northrop Grumman, D-Wave, and a few academic groups have produced people who could fit.

A few reasons the role might still be open:

- **Niche stack + high bar.** They want someone who can both design superconducting digital circuits and interface with qubit physicists. That intersection is narrow.
- **Location.** Goleta/Santa Barbara is nice but not everyone wants to move there.
- **Level/seniority mismatch.** They may have interviewed people who were too junior, too senior, or not hands-on enough.
- **Compensation expectations.** Candidates from startups or finance may want more than Google’s L5 band.
- **Vis


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
That’s a smart reality check. The pool of people with serious SFQ / cryoelectronics / superconducting digital design experience is small, but you’re right — it’s not zero. SEEQC, MIT Lincoln Lab, Northrop Grumman, D-Wave, and a few academic groups have produced people who could fit.

A few reasons the role might still be open:

- **Niche stack + high bar.** They want someone who can both design superconducting digital circuits and interface with qubit physicists. That intersection is narrow.
- **Location.** Goleta/Santa Barbara is nice but not everyone wants to move there.
- **Level/seniority mismatch.** They may have interviewed people who were too junior, too senior, or not hands-on enough.
- **Compensation expectations.** Candidates from startups or finance may want more than Google’s L5 band.
- **Visa / clearance issues.** Some candidates in this field need export-control or citizenship flexibility.

How many do they need? Probably **1–3 people** for this specific team in the near term. It’s a specialized subgroup inside Quantum AI, not a massive hiring ramp. The fact that Chris Ayala is personally advertising it on LinkedIn suggests the req has been hard to fill and he’s actively hunting.

You don’t need to be the only qualified person in the world. You need to be the best available person who:
1. Actually wants the role,
2. Can relocate/work within their constraints,
3. Fits the level and comp band,
4. Clicks with the team technically.

Your O-1A approval is a real signal of distinction, and your mix of SFQ design + qubit measurement + tape-out experience is genuinely rare


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
That’s a smart reality check. The pool of people with serious SFQ / cryoelectronics / superconducting digital design experience is small, but you’re right — it’s not zero. SEEQC, MIT Lincoln Lab, Northrop Grumman, D-Wave, and a few academic groups have produced people who could fit.

A few reasons the role might still be open:

- **Niche stack + high bar.** They want someone who can both design superconducting digital circuits and interface with qubit physicists. That intersection is narrow.
- **Location.** Goleta/Santa Barbara is nice but not everyone wants to move there.
- **Level/seniority mismatch.** They may have interviewed people who were too junior, too senior, or not hands-on enough.
- **Compensation expectations.** Candidates from startups or finance may want more than Google’s L5 band.
- **Visa / clearance issues.** Some candidates in this field need export-control or citizenship flexibility.

How many do they need? Probably **1–3 people** for this specific team in the near term. It’s a specialized subgroup inside Quantum AI, not a massive hiring ramp. The fact that Chris Ayala is personally advertising it on LinkedIn suggests the req has been hard to fill and he’s actively hunting.

You don’t need to be the only qualified person in the world. You need to be the best available person who:
1. Actually wants the role,
2. Can relocate/work within their constraints,
3. Fits the level and comp band,
4. Clicks with the team technically.

Your O-1A approval is a real signal of distinction, and your mix of SFQ design + qubit measurement + tape-out experience is genuinely rare. But the competition is real. The best move is still to make a strong technical impression and let them decide you’re worth the hassle.


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p7
Elapsed: 27.6s
Time: model 21.8s, overhead 4.1s
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