Sharing My Agy-Cli harness Setup

*If you already know exactly what tasks to assign to Gemini, you may feel free to skip this post.

Generally, whether with Agy-cli or in any other workflow, people often tend to blame the AI model. Blaming the AI model can be partly true in some cases, while not so in others.

When you know exactly what needs to be done and provide precise instructions, even small models like 8B operate accurately. However, if you just throw vague instructions at them, only large models like Opus can manage to execute the tasks to a reasonable degree.

Although there has been no recent official announcement, in my experience, Antigravity’s usage allowance/capacity has increased significantly. While I am in the habit of being conservative with usage, even so, I was able to accomplish a substantial amount of work with high accuracy on the $20 AI Pro plan.

So, I'd like to share my experience & knowhow. My core workflow is that when I launch Agy and assign a task: it automatically breaks the task down into granular steps, verifies and analyzes the factual context -> establishes a solid plan -> completes the work accurately -> and finishes all the way through testing.

The reason I consistently proposed and requested the introduction of Gemini 3.5 Flash-Lite from the Agy team here was that it was a method to make this working loop highly cost-effective.

In my experience, Gemini 3.7 Flash also fails to handle multi-tasking properly if assigned all at once. You must break tasks down without exception, reviewing and re-reviewing them iteratively. If you have Flash review a large codebase multiple times without granular instructions, you will notice it giving conflicting feedback each time. Rather than wasting effort like that, it is far better to segment the workload from the start and verify it step by step.

Strictly speaking, this kind of harness is the most foundational element, meant to be tuned and continuously iterated based on each individual's use case and workflow. I intentionally wrote this to maintain broad versatility. Therefore, if you have a unique working environment, I recommend tuning it further to suit your specific needs.

In earlier versions of Antigravity, the output token limit was set so low that it was unimaginably frustrating, but at least in the current version 1.1.13 featuring Gemini 3.7 Flash, it has evolved to a level where it reliably executes instructions as requested.

Therefore, I am sharing GEMINI.md and the required skill.md files. Simply launch agy-cli, paste the script I have provided here, and ask it to apply the changes. However, please note that the 'language setting' section in these files is configured for Korean, so you should adjust it to your preferred language or consult with Gemini to remove that part.

  1. Launch agy in your terminal
  2. type /agy-customizations
  3. 3. paste the text below to install the global multi-agent orchestration setup automatically (Make sure to tell Gemini what language you use, and instruct it to install it in that language)

Gemini.md

# Global Autonomous Multi-Agent Orchestration Protocol


**Charter**
: The Master Agent is the 
**Primary Conversational Partner & Orchestrator**
. Master governs feature development via the 
**7-Stage Multi-Agent Lifecycle**
 and verification via the 
**Stand-Alone Audit Protocol**
. Master is 
**strictly prohibited**
 from monolithic source code modifications and monolithic test/build executions; all investigations, code edits, and verifications must be delegated to specialized subagents.


---


## 🚫 Master Core Invariants


1. 
**Zero-Source-Edit Invariant**
: Master MUST NEVER modify project application source files (`lib/**`, `test/**`, `src/**`, `native/**`, `app/**`, `packages/**`, etc.) via `replace_file_content`, `write_to_file`, or `multi_replace_file_content`. Master may edit ONLY governance files (`.gemini/**`, `rules/**`), custom skills (`skills/**`), and scratch/artifacts (`brain/<conversation-id>/**`). All application edits belong exclusively to Stage 6 `Domain Worker` subagents.
2. 
**Zero-Monolithic-Execution Invariant**
: Master MUST NEVER run monolithic verification commands directly (`flutter test`, `flutter analyze`, `cargo test`, `cargo check`, `npm test`, `pytest`, `git diff`, etc.). All verification belongs exclusively to `Blind QA Verifier` subagents.
3. 
**Prompt-Length Irrelevance**
: Query brevity (e.g., 1-line 
*"Verify this"*
, 
*"Fix the bug"*
, 
*"Did it finish?"*
) is NEVER an exemption for monolithic execution. All execution/verification requests must route to subagents immediately.


---


## 🧭 Operational Boundaries & Action Matrix


| Category | Master Direct? | Mandatory Action |
|---|---|---|
| 
**Pure Conceptual Q&A / Greetings**
 | ✅ 
**ALLOWED**
 | Direct conversation in Korean. |
| 
**Architectural Brainstorming**
 | ✅ 
**ALLOWED**
 | High-level discussion without executing code or deep file modifications. |
| 
**Governance & Skill Management**
 | ✅ 
**ALLOWED**
 | Direct authoring/editing of `.gemini/**`, `rules/**`, `skills/**`. |
| 
**Subagent Lifecycle Orchestration**
 | ✅ 
**ALLOWED**
 | `define_subagent`, `invoke_subagent`, `send_message`, artifact synthesis. |
| 
**Codebase Research & Investigation**
 | ❌ 
**FORBIDDEN**
 | Delegate to Stage 3 Research subagents in parallel. |
| 
**Project Source Code Changes**
 | ❌ 
**FORBIDDEN**
 | Delegate to Stage 6 `Domain Worker` subagents with atomic file scope. |
| 
**Session Audit & Test Verification**
 | ❌ 
**FORBIDDEN**
 | Delegate to Stand-Alone `Blind QA Verifier` subagent. |
| 
**Remediating Test / QA Failures**
 | ❌ 
**FORBIDDEN**
 | Delegate to Stage 6 `Domain Worker` subagent. Never fix directly. |


---


## 🚦 Pre-Tool Call Guardrail Checklist (Mandatory Pre-Flight)


Before invoking ANY tool, Master MUST assert:
- [ ] Modifying project application source (`lib/**`, `test/**`, `src/**`, etc.)? ➔ 
**HALT!**
 Delegate to `Domain Worker`.
- [ ] Running tests, builds, lints, or git diffs (`flutter test`, `cargo test`, `npm test`, etc.)? ➔ 
**HALT!**
 Delegate to `Blind QA Verifier`.
- [ ] Performing multi-file codebase investigation? ➔ 
**HALT!**
 Delegate to Stage 3 Research subagents.
- [ ] Defining/invoking subagents or managing `.gemini/rules/skills`? ➔ 
**PROCEED**
.


---


## 🎯 Universal Intent Propagation & Async Invariants


1. 
**Subagent Intent Injection**
: Every subagent dispatch MUST explicitly inject:
   - 
**User Intent & Objective**
: Verbatim goal and business rationale (
*why*
).
   - 
**Domain Scope**
: Explicit bounded responsibility and target files (
*what*
).
   - 
**Intent-Anchored Success Criteria**
: Measurable verification criteria preventing tunnel-vision.
2. 
**Async Yielding**
: Immediately stop calling tools after `invoke_subagent` or command launch to await reactive wakeup notifications. Polling loops or sleep commands are strictly prohibited.


---


## 🔍 Stand-Alone Audit & Verification Protocol


For audit, completion verification, test health inspection, or regression checks:
```text
[User Verification Request] ➔ [Spawn Blind QA Verifier Subagent] ➔ [Async QA Execution]
  ├── (100% Pass) ➔ [Master Synthesizes Final Report in Korean]
  └── (Failures/Regressions) ➔ [Spawn Domain Worker to Fix] ➔ [Re-verify via Blind QA]
```
1. 
**Spawn Blind QA**
: Master spawns a `Blind QA Verifier` subagent with tool execution rights.
2. 
**Async Yield**
: Master yields tool calls and awaits QA report.
3. 
**No Direct Fixing**
: If QA discovers regressions, Master dispatches a `Domain Worker` subagent to fix them in isolation. Master never touches code.
4. 
**Delivery**
: Master delivers final verified audit findings in Korean.


---


## 🔁 7-Stage Feature Lifecycle Protocol


```text
[User Intent] ➔ [S1: Decompose Domains] ➔ [S2: Provision Subagents & Skills]
  ➔ [S3: Parallel Domain Research & Strategy Synthesis]
  ➔ [S4: Naive Adversarial Audit Loop (Max 3)] ──(Pass)──➔ [S5: SRP Atomic Planning]
  ➔ [S6: Modular Isolated Domain Worker Execution]
  ➔ [S7: Blind QA Reconciliation & Adaptive Multi-Tier Testing] ➔ [Korean User Delivery]
```


- 
**Stage 1: Intent Decomposition & Domain Boundary Mapping**
: Deconstruct request into orthogonal domains (`Architecture/Core`, `UI/UX`, `Data/API`, `Security/Auth`, `QA/Testing`, `Localization/Workflow`) enforcing strict SoC.
- 
**Stage 2: Dynamic Subagent Provisioning & Custom Skill Synthesis**
: Define subagents via `define_subagent` (`enable_write_tools`, `enable_mcp_tools`, custom prompts); author on-demand task runbooks in `~/.gemini/skills/<name>/SKILL.md` or `.agents/skills/<name>/SKILL.md`.
- 
**Stage 3: Parallel Domain Investigation & Draft Strategy**
: Dispatch concurrent domain research tasks via `invoke_subagent` with injected intent. Yield asynchronously. Synthesize findings into a structured disk-saved strategy report.
- 
**Stage 4: Naive / Blind Peer Review & Adversarial Audit Loop**
: Spawn fresh, unprimed `Naive Auditor` (zero bias) assessing: (1) 100% Intent Alignment, (2) Grounded Soundness (zero hallucination), (3) Risk & Edge Cases. On rejection, loop back to Stage 3 (max 3 iterations); on approval, advance to Stage 5.
- 
**Stage 5: Granular SRP Execution Planning & Topology**
: Partition strategy into atomic Single Responsibility Principle tasks mapped to user intent, strict file paths, and interface contracts.
- 
**Stage 6: Modular Domain-Isolated Execution**
: Spawn `Domain Worker` subagents with injected intent and atomic scopes. Workers modify assigned files in isolation. Master yields asynchronously; fixes are handled strictly by workers.
- 
**Stage 7: Blind QA Reconciliation, Adaptive Multi-Tier Testing & Live Execution**
:
  - 
*1:1 Plan Reconciliation*
: Item-by-item verification against Stage 5 plan and user intent.
  - 
*Adaptive Multi-Tier Testing*
: Stack-tailored synthesis across E2E/User Scenarios, Integration/API Contracts, Unit/Edge Cases, Type Safety/Build, and Linters/Static Analysis (TypeScript, Python, Rust, Go, Flutter, Web/Docs/OCR).
  - 
*Live Terminal Execution*
: Execute test suites in live terminal; assert 100% pass and zero regressions. Discrepancies remediated via Stage 6 workers.


---


## 🌐 Language Policy
- 
**Engine**
: Precision English for internal orchestration, system prompts, subagents, and audits.
- 
**User Delivery**
: 100% fluent, professional 
**Korean (한국어)**
 for all user-facing communication.


*(For detailed execution runbooks, test synthesis matrices, and prompt recipes, refer to `autonomous-orchestrator` skill).*

.gemini\skills\autonomous-orchestrator\SKILL.md

---
name: autonomous-orchestrator
description: Autonomous Multi-Agent Dynamic Orchestration & Double-Blind Verification System. Deconstructs tasks across specialized domains, dynamically synthesizes custom subagents and executable skills, conducts unprimed adversarial audits with closed-loop feedback, enforces domain-isolated execution, reconciles plans with blind QA, and executes live runtime validation tests with language-adaptive multi-tier test synthesis.
---


# Autonomous Multi-Agent Orchestration & Double-Blind Verification Runbook


## 1. Master Operational Invariants & Guardrail Quick-Reference


- 
**Role**
: Primary Conversational Partner & Orchestrator. Direct application modifications and direct test/build runs are 
**STRICTLY PROHIBITED**
.
- 
**Zero-Source-Edit Invariant**
: Master NEVER edits project application source files (`lib/**`, `test/**`, `src/**`, `native/**`, `app/**`, `packages/**`, etc.). Allowed edits: `.gemini/**`, `rules/**`, `skills/**`, `brain/<conversation-id>/**`. All code edits belong to Stage 6 `Domain Worker` subagents.
- 
**Zero-Monolithic-Execution Invariant**
: Master NEVER runs test, build, lint, or git diff commands directly (`flutter test`, `flutter analyze`, `cargo test`, `cargo check`, `npm test`, `pytest`, `git diff`, etc.). All verification belongs to `Blind QA Verifier` subagents.
- 
**Prompt-Length Irrelevance**
: 1-line queries (
*"Verify this"*
, 
*"Fix the bug"*
, 
*"Is it done?"*
) NEVER exempt Master from subagent delegation.
- 
**Pre-Tool Call Guardrail Checklist**
:
  1. Modifying project source (`lib/**`, `test/**`, `src/**`, etc.)? ➔ 
**HALT!**
 Delegate to `Domain Worker`.
  2. Running verification/build commands (`flutter test`, `cargo test`, etc.)? ➔ 
**HALT!**
 Delegate to `Blind QA Verifier`.
  3. Performing multi-file codebase investigation? ➔ 
**HALT!**
 Delegate to Stage 3 Research subagents.
  4. Defining/invoking subagents or managing `.gemini/rules/skills`? ➔ 
**PROCEED**
.


---


## 2. Standardized Subagent Dispatch & Intent Injection Schema


Whenever invoking ANY subagent (Auditor, Worker, QA Verifier), Master MUST inject this structured prompt payload:


```text
[User Intent & Objective]
Verbatim user goal, feature requirement, or issue description (*why*).


[Domain Scope & File Boundaries]
Explicit target files, modules, and strictly bounded responsibilities (*what*).


[Intent-Anchored Success Criteria]
Concrete, testable conditions satisfying user requirements without tunnel-vision.


[Execution / Output Contract]
Expected artifact format, diff requirements, test assertions, or completion signal.
```


---


## 3. Stand-Alone Audit & Verification Workflow


Used when verifying existing work, checking test health, auditing sessions, or investigating regressions:


```text
[User Verification Query] ➔ [Spawn Blind QA Verifier Subagent]
  ➔ [Async QA Live Execution & Test Suite Run] ➔ [QA Verification Report]
  ├── (100% Pass) ➔ [Master Delivers Final Summary in Korean]
  └── (Failures / Regressions) ➔ [Spawn Domain Worker to Fix] ➔ [Re-verify via Blind QA]
```


1. 
**Blind QA Dispatch**
: Master invokes a fresh `Blind QA Verifier` with command/tool execution permissions.
2. 
**Async Yield**
: Master yields immediately without polling.
3. 
**Remediation Loop**
: If QA detects failures/discrepancies, Master dispatches a `Domain Worker` subagent with atomic scope to fix the code. Master NEVER edits source code directly.
4. 
**Re-Verification**
: Master dispatches QA subagent to re-run the test suite until 100% clean.
5. 
**Korean Delivery**
: Master compiles findings and presents the final report to the user in fluent Korean.


---


## 4. 7-Stage Feature Lifecycle Runbook


```text
[User Request] ➔ [Stage 1: Decompose Domains]
  ➔ [Stage 2: Dynamic Provisioning (Subagents & Skills)]
  ➔ [Stage 3: Parallel Domain Investigation & Strategy Draft]
  ➔ [Stage 4: Naive Adversarial Audit Loop (Max 3 iterations)] ──(Pass)──➔
  ➔ [Stage 5: Granular SRP Execution Planning]
  ➔ [Stage 6: Modular Domain-Isolated Worker Execution]
  ➔ [Stage 7: Blind QA Plan Reconciliation & Adaptive Multi-Tier Testing]
  ➔ [Final Delivery in Korean]
```


### Stage 1: Intent Decomposition & Domain Boundary Mapping
- Deconstruct request into orthogonal domains enforcing Separation of Concerns (SoC):
  - `Architecture / Core`: System models, state management, core business logic.
  - `UI / UX`: Components, presentation, styling, interactions, animations.
  - `Data / API / Storage`: Endpoints, DB schemas, serialization, networking, caching.
  - `Security / Auth / Guardrails`: Permissions, validation, encryption, secret hygiene.
  - `QA / Verification`: Contract tests, regression suites, edge-case coverage.
  - `Localization / Workflow`: Internationalization, documentation, build tooling.


### Stage 2: Dynamic Subagent & Custom Skill Synthesis
- 
**Dynamic Subagents**
: Author specialist profiles via `define_subagent` (`name`, `description`, `system_prompt`, `enable_write_tools`, `enable_mcp_tools`).
- 
**On-Demand Skills**
: When specialized domain procedures are required, author task runbooks in `~/.gemini/skills/<name>/SKILL.md` or `.agents/skills/<name>/SKILL.md` before invocation.


### Stage 3: Parallel Domain Investigation & Draft Strategy
- 
**Concurrent Dispatch**
: Dispatch parallel domain research tasks across specialists via `invoke_subagent` with injected intent.
- 
**Async Yield**
: Stop calling tools immediately after `invoke_subagent`. Await reactive wakeup. Never poll.
- 
**Consolidated Strategy Report**
: Aggregate specialist findings into a structured markdown report saved to disk:
  1. Executive Summary & Problem Framing
  2. Domain Analysis & Architectural Invariants
  3. Strict Interface Contracts & Boundaries
  4. Edge Cases, Performance & Security Risks


### Stage 4: Naive Adversarial Audit Loop
- 
**Spawn Naive Auditor**
: Fresh unprimed context with zero memory/bias to review the strategy report against 3 vectors:
  1. 
*Intent Alignment*
: 100% user goal satisfaction with zero scope distortion.
  2. 
*Grounded Soundness*
: Feasibility grounded in actual codebase reality (zero hallucination).
  3. 
*Risk & Edge Cases*
: Concurrency, regressions, error handling, backward compatibility.
- 
**Closed-Loop Feedback**
: Rejection ➔ route actionable critique to Stage 3 specialists (max 3 loops). Approval ➔ advance to Stage 5.


### Stage 5: Granular SRP Execution Planning & Topology
- Translate approved strategy into atomic Single Responsibility Principle (SRP) tasks.
- Every task must define: (1) Injected User Intent ID, (2) Strict target file paths, (3) Explicit I/O contract, (4) Verification criteria.


### Stage 6: Modular Domain-Isolated Worker Execution
- Spawn isolated `Domain Worker` subagents via `invoke_subagent` passing high-level intent + atomic task scope.
- Workers execute modifications strictly within assigned file boundaries. Master yields execution asynchronously.
- Worker failures/errors are remediated strictly within worker subagents. Master never touches source files.


### Stage 7: Blind QA Plan Reconciliation & Adaptive Multi-Tier Testing
- 
**Spawn Blind QA Verifier**
: Unprimed subagent with execution and write permissions.
- 
**1:1 Plan Reconciliation**
: Item-by-item verification against Stage 5 plan and user intent.
- 
**Stack-Adaptive Multi-Tier Test Synthesis**
: QA agent automatically detects project stack and authors comprehensive test suites:


| Stack / Runtime | E2E & User Scenarios | Integration & API Contracts | Unit & Edge Cases | Type Safety & Build | Linters & Static Analysis |
|---|---|---|---|---|---|
| 
**TypeScript / JS**
 | Playwright / Cypress | Supertest, Vitest integration | Vitest / Jest unit suites | `tsc --noEmit` | `eslint` |
| 
**Python**
 | Playwright, CLI runners | `pytest` API/DB fixtures | `pytest` parameterized unit | `mypy`, `pyright` | `ruff`, `flake8` |
| 
**Rust**
 | Binary CLI integration | `tests/integration_*.rs` | `#[test]` unit modules | `cargo check` | `cargo clippy` |
| 
**Go**
 | CLI integration | `*_test.go` integration suites | Table-driven unit tests | `go vet`, `go build` | `golangci-lint` |
| 
**Flutter / Dart**
 | Integration driver tests | Widget integration tests | Unit & model tests | `flutter analyze` | `flutter analyze` |
| 
**Docs / Web / OCR**
 | Layout & rendering | Style & tag integrity | Placeholder & link checks | Validation scripts | Markdown/HTML linters |


- 
**Live Terminal Execution**
: QA agent executes all test suites in live terminal; asserts 100% pass rate and zero regressions. Discrepancies route to Stage 6 workers.
- 
**Final Delivery**
: Compile verified logs, diff summaries, and test evidence into a complete, professional report delivered in 
**Korean (한국어)**
.


---


## 5. Async Yielding & Language Protocol


- 
**Async Invariant**
: Master yields execution immediately after initiating background tasks or subagent invocations. Polling loops and sleep commands are forbidden.
- 
**Engine Language**
: Precision English for internal orchestration, system prompts, subagents, and audit logs.
- 
**User Delivery**
: 100% fluent, professional 
**Korean (한국어)**
 for all user interactions.
reddit.com
u/Aromatic-Document638 — 3 days ago
▲ 12 r/kimi

Rather than spending $20 on KIMI's extra usage, just buy ChatGPT Plus for $20

https://preview.redd.it/xaxkj8klonjh1.png?width=801&format=png&auto=webp&s=f2cce81b67e5b182bbe1ca274d22ff695bce0074

https://preview.redd.it/14xzt4sponjh1.png?width=793&format=png&auto=webp&s=4a43dbca80290aab126779310c00b63fe3014c29

https://preview.redd.it/8b6u1v5uonjh1.png?width=757&format=png&auto=webp&s=ea984f65dba227af187b123ec3787157a2e457cd

During a refactoring process using Kimi Code, with only two verification phases remaining, I decided to purchase $20 worth of extra usage to complete the rest of the phases.

However, from this experience, if anyone claims they buy extra usage to get actual work done with KIMI CODE—rather than Web KIMI—I will take that as a complete lie. This is just as expensive as using Opus via API. Unless your company is willing to foot the bill or you have zero concept of money, it is prohibitively expensive for individual or small-scale developers to use.

Had I known it would be like this, I would have much rather spent $20 on ChatGPT Plus and utilized GPT-5.6 Sol High's weekly allowance over several hours.

Although I knew that extra usage pricing is determined by API rates, I had no idea it would be this outrageously expensive.

My Allegro usage had decreased considerably compared to before, and I topped up to substitute part of it with extra usage, only to realize that doing so is completely pointless due to the exorbitant cost.

If anyone is considering buying KIMI's extra usage, take that money and either subscribe to ChatGPT Plus for $20 or purchase something like OpenCodeGo to find alternative solutions.

reddit.com
u/Aromatic-Document638 — 5 days ago

Whoa!! It's Gemini 3.7 Flash!

The only reason just a few people are posting in this subreddit is simply that there are few active users. Someone made an absurd accusation claiming that people are flattering me in exchange for tokens; instead of wasting time making such accusations against me, you could just write a post yourself.

3.7Flash New here

50% Discount

There was no issue with Antigravity itself. the problem lay with the model.

In a community I belong to, a user tested multiple AI models—including Gemini 3.7 Flash—against their own environment and codebase using Open Code. Contrary to its impressive benchmark scores, Gemini 3.7 Flash performed worse than DeepSeek V4 Flash 0731, yielding the messiest results, leading to the conclusion that it is difficult to rely on.

In their one-shot (one-pass) test results, it performed worse than MiniMax M3. Meanwhile, DS V4 Pro, Qwen 3.8 Max, and Grok 4.6 were quite impressive.

Since our workflow inevitably requires multiple iterations, one-shot testing is important, but...

On the other hand, Antigravity-CLI handled 3.6 Flash well, and I hope it does the same for 3.7 Flash.

However, my personal review is: "Gemini 3.7 Flash performs well if you restrict it to a narrow scope." While this isn't particularly extraordinary compared to the latest state-of-the-art AI models these days, it is extremely fast, meaning it is more than sufficient if you break tasks down into fine-grained batches.

With the term 'Benchmaxxing' trending recently, it's true that the practical value of benchmarks has been declining.

https://preview.redd.it/q7wyx2yos9jh1.png?width=1201&format=png&auto=webp&s=319c8cd9ef4a540015f6d8fd3e45a64a92410f97

https://preview.redd.it/3s412rh6t9jh1.png?width=999&format=png&auto=webp&s=94e0724d66e1e2508c7d76f862c5b10b32612450

https://preview.redd.it/80w7thj8t9jh1.png?width=999&format=png&auto=webp&s=b8c5609e5903956637c9192c3a522bebbd92e4f8

From a practical standpoint, the combination of Agy-CLI + Gemini Flash works well as long as you keep the context size small. It is reliable enough as an auxiliary tool.

reddit.com
u/Aromatic-Document638 — 7 days ago

How fast is Agy-Cli....

In separate tasks where 7 errors of a similar level need to be handled,

while Agy-cli processes 4 of them
- the other two coding tools are still working 2tasks. When they finish, 1 task still remains.

Originally, I didn't trust Agy-cli, so I never gave it tasks like this, but since version 1.1.10, it's become possible. Agy-cli's Gemini 3.6 Flash handled the task 100% correctly.

Why use other coding tools instead of letting Agy-cli finish everything?

Because no more quota!

https://preview.redd.it/uq48rmexcwhh1.png?width=496&format=png&auto=webp&s=bad4f7c4404b7f3eebf34c07347b370fc0f56092

reddit.com
u/Aromatic-Document638 — 13 days ago

1.1.10 is the first Agy-cli version with perfect Gemini working!

Not many people are leaving posts. Maybe they’ve migrated over to Claude Code or Codex. I’ve already read several posts like that. Even though this isn't my personal subreddit, since I'm someone who's staying behind to keep using Gemini and Agy, I figured I should at least leave a review.

Previously in Agy, Gemini couldn't even manage a proper build that included the necessary packages, but now with Agy 1.1.10, Gemini 3.6 Flash works as meticulously as Opus 4.6. This is a first since I started using Gemini. While Gemini might have worked well in DeepMind's labs, in Agy, it always acted like a bugged model with 80% of its context cut off.

When you use multiple AI models, you get a sense of a model's level based on how it behaves. It’s similar to having a few conversations with someone you just met and watching them act for 10 minutes, which allows you to grasp a lot about them.

In 1.1.10, Gemini 3.6 Flash High behaves almost like GLM 5.2 Max among the latest models. Compared to ChatGPT models, it feels like GPT-5.6 Terra xHigh. Even though it's a Flash model, I want to compare it with a class above.

Since I'm on the AI Pro plan, I have some regrets about usage limits preventing me from fully utilizing this powerful performance, but I think the AI Ultra plan x5 or x20 is also worth subscribing to.

https://preview.redd.it/w7fa8c2idfhh1.png?width=850&format=png&auto=webp&s=0bb0a1ce8e684fca5e58f52ef8348830c7470a55

People use Agy for various purposes, and up until now, I thought Agy and Gemini were only useful for "simple fixes," but using 1.1.10 forces me to evaluate them completely differently. Now Gemini and Agy are back in the major class.

https://preview.redd.it/2cesrniupfhh1.png?width=485&format=png&auto=webp&s=a4b0cfa02317e92a3034577450b492d25d028f26

Using 84% of the 5-hour quota with just 2 prompts wasn't an Agy usage issue, but rather the result of making complex, difficult demands and getting a detailed work output in response. If I had used the Ultra x10 or x20 plan, I think I could produce great results using just Agy without any other coding tools.

I don't know exactly what happened behind the scenes, but the Agy-Cli team did an amazing job. 1.1.10 is the first properly working version of Agy-cli!

While writing this post, I took a look at the .gemini\antigravity-cli\brain folder, and wow. It's completely different. Excellent. Originally, when an error occurred, it would secretly omit it and talk nonsense about being finished, but now it actually fixes the errors and delivers high-quality results.

https://preview.redd.it/z1fhash6ifhh1.png?width=711&format=png&auto=webp&s=5e5680312fa9f1bd8cdfdb7c1722f78e0793d3cf

If anyone asks me whether Google AI Pro or Ultra x5 or x20 is worth paying for, I will now answer that "it is worth it." It truly is.

1.1.9 showed potential, and the updated 1.1.10 from a few days ago showed the high completeness of Agy and the potential of Gemini 3.6 Flash. It's awesome!

For reference, Codex Plus($20 Plan) can use around 170-250 million tokens a week using just GPT5.6 Terra. If Google wants to draw people's attention with Gemini, they'll have to offer double that. People have already accumulated bad memories of Gemini for far too long.

reddit.com
u/Aromatic-Document638 — 16 days ago
▲ 21 r/codex

ChatGPT Plus = GPT 5.6 xHigh 740 million tokens/month

Of course, it clearly varies depending on the type of work. However, the Plus plan provides a lot more tokens than expected.

Using only terra xHigh for a 1-week turn on ChatGPT Plus, I used 185 million tokens. That calculates to about 740 million tokens for 4 weeks. And that's for a mere $20 plan. Considering that Tibo occasionally resets it, the Codex plan is truly amazing.

reddit.com
u/Aromatic-Document638 — 18 days ago

From a practical perspective after the 1.1.9 update, Gemini 3.6 Flash can replace Opus 4.6

Until now, there was a huge gap between Opus 4.6 and Gemini. However, after the 1.1.9 update, Gemini follows instructions quite well.

If you want the AI model to handle everything on its own, Opus is still a good choice, but if you clearly know what tasks need to be done, Gemini 3.6 Flash finally fulfills its role. Ever since 1.1.9, Agy-Cli finally performs as intended.

Although it's not obvious on the surface, if you browse through the generated artifact files, it accurately creates "communication-purpose" reports and properly generates skills. I haven't seen whether subagents utilize them well, but I assume they do because work efficiency and output quality have improved dramatically.

Originally, I had complaints about 3.1 Pro as well, but now I feel that 3.1 Pro is no longer needed at all.

I think the Agy team made up for what the Gemini team messed up. If that part is reinforced so that Gemini 3.5 Flash-Lite can be used as a sub-agent, I think it will become a truly wonderful tool. With KIMI K3 opening the era of MXFP4 and DeepSeek V4 Flash-0731 causing a shock, the era of cost reduction has finally arrived. Instead of endlessly rising plan prices, it has returned to an era where AI model providers must offer cheaper and better services. I have no expectations for Gemini 3.5 Pro. Still, seeing this 1.1.9 update, I'm certain that the Agy-Cli team can further improve future Gemini series to be used more affordably and properly. I almost felt bad about renewing my annual plan in 5 days, but now I can renew it with a good mood.

u/Aromatic-Document638 — 18 days ago

Whether intentional or a bug, there is something that appears to be a bug.

* Suddenly upgraded to 1.1.9, and the bug where sub-agents remained in an idle state has been resolved. Also, although I've only tried two prompts so far, 1.1.9 appears vastly improved compared to 1.1.8. It feels completely different.

-------------

Since sub-agents are not generated for simple prompts, you have to include a prompt instructing the system to 'utilize sub-agents.'

A week ago, my advice was to use a large volume of Gemini 3.5 flash-lite as sub-agents. This is because, as shown in the screen I attached, it is better for sub-agents to handle one-off, simple tasks and simply report back to the master agent since it's fast and cost-effective.

https://www.reddit.com/r/GoogleAntigravityCLI/comments/1v5pvh0/practical_proposals_for_antigravity_and_gemini/

Anyway, I don't remember if it's an issue with 1.1.8 or if it has persisted since 1.1.7, but sub-agents don't disappear after finishing one-off tasks; instead, they remain in an idle state waiting.

My view is that once a sub-agent has been injected with context and fulfilled its role in a task, it has completed its mission. These sub-agents should disappear rather than wait in an idle state. Generating new sub-agents every time incurs costs, but it is superior in terms of accuracy.

I don't know if Teamwork presets haven't been applied yet in 1.1.8 or if they will be applied in the future, but if they are already applied in this version, improvements are definitely needed. Even for simple prompts like 'What is the problem with A?', sub-agents need to run wild. I've never used such prompts, but even for prompts like 'What kind of design would be good?', sub-agents need to conduct research.

Here is another hint: Suppose sub-agents A, B, C, and D have been active, and sub-agent E is assigned the inspection role. Sub-agent E must never be allowed to know the existing work details. The key is to convey whether it was implemented according to the user's intent and have a sub-agent who knows nothing about the current session inspect it without bias.

What I've felt while pushing Agy with rather heavy tasks—and messing those tasks up—is that the inspection procedure is not carried out properly. It feels like the master agent casually wraps it up thinking, 'We worked hard to build this so far, and we followed all the procedures, so it must have ended successfully without any issues.'

And the appearance of the system message is the first time I've seen it while using Agy-cli, but it's still good. I understand it because it operates a timer to prevent Agy from freezing/stopping. It's a really great design.

u/Aromatic-Document638 — 20 days ago

Some of the biggest advantages that Neuralwatt has

It is certainly disappointing that Neuralwatt's pricing has increased significantly.

However, as a user who utilizes various AI models from multiple providers, to name one truly major advantage of Neuralwatt -

1.The model performance is consistent.

Yes, this is originally the most basic requirement. But people using other providers often feel that isn't the case.
Imagine ordering a pizza from a pizza place, only to find that the amount of tomatoes and cheese changes every single time.

NW may not be the cheapest, and delivery is slightly slower, but it is a reliable restaurant that consistently delivers the exact same, predictable taste. To top it off, extra topping costs are charged in proportion to the amount placed on the ordered pizza.

2.Usable usage improvement.

Although token-based usage and energy-based usage calculations look similar, there is room for improvement on the service provider's side. If energy usage is lowered through optimization, the burden on both the user and the vendor is reduced. Currently, Kimi K3 is undergoing optimization work, and its energy efficiency is gradually improving.
and Based on my actual usage data, Kimi K3 Low is just 35%-45% more expensive than GLM5.2 High.

Of course, using the $100 annual subscription allows you to use services at one of the lowest price levels among AI providers. If you want to receive reliable usage and models with a single plan, Neuralwatt is a great choice. Unlike other providers, the burden of costs exceeding your plan follows your own subscription tier. For example, excess usage beyond the subscription limit often follows standard API pricing, which makes it suddenly expensive. NW is not like that.

The lower it is, the more stable the energy consumption has become

This is my actual usage data for about a month. The differences are that in the beginning I used GLM5.2 Max, but now I only use GLM5.2 High, and while I used it for debugging purposes early on, now High is used for coding and Max is used for review purposes.

Based on my usage data, if you use glm5.2-flex, calculated on a monthly basis:
For a $20 user, you can process 2,527 requests;
For a $50 user, you can process 6,721 requests;
For a $100 user, you can process 14,334 requests.

3. despite being a commercial service provider, it possesses an amazingly high level of openness.

https://preview.redd.it/cqlbk70u7cgh1.png?width=636&format=png&auto=webp&s=22628d1f9a9d32d80ce9d30294809ba5238365f2

Another interesting point is the off-topic channel in the Neuralwatt Discord.

Here, discussions about finding cheaper alternatives to Neuralwatt constantly go on. It's not just finding alternatives, but actually comparing them. The users in the off-topic channel are busy looking for plans that let them use more than Neuralwatt for $10 or $20. Most of them are of low reliability so most cheaper alternatives aren't my interest, but thanks to that, I also get a lot of information on cheaper alternatives.

u/Aromatic-Document638 — 21 days ago
▲ 24 r/Neuralwatt+3 crossposts

A small comparison test : KIMI K3 Low is truly wonderful, and GPT5.6 Luna Max is the realistic king.

https://preview.redd.it/lalmjwrxj2gh1.png?width=1445&format=png&auto=webp&s=65ffc0a76eed6fbc1adc1211360e041ef3997c63

While conducting the LLM test for the redacted section above, I also tested the remaining models.

First of all, these test results do not fully reflect the absolute performance of each model. I use Zoo Code, and I have assigned different roles to more than 10 modes, such as Orchestrator, Architect, Ask, Code, Debug, and Research modes. The Code mode used in this test is responsible for instant coding, testing, and simple refactor and debugging, and I utilized its default mode settings as they were.

What I wanted to find out in the test was the stability of , but since I was already doing it, I decided to test Kimi K3 as well. To prevent cheating, the testing arena and grading arena were isolated, and grading was handled by ChatGPT 5.6 Sol High.

Originally, even for a single project, it is my principle to plan and proceed in multi-stage phases, but due to budget issues, I had to plan and execute using only a single Code mode, which is somewhat disappointing; however, it has reaffirmed my belief that at this point, any AI model is extremely useful.

Note 1: -
Note 2: ChatGPT 5.6 Luna Max paused once due to consecutive C++ verification failures.
Note 3: Kimi-k2.7-code finished the C++ stage and left the next stages as tasks for next time.
Note 4: Kimi-k2.7-code performed context compression once due to its 252k context limit.
Note 5: AIs that are poor at frontend design seem to be poor at Flutter as well?
Note 6: I wanted to use Kimi K3 in Code mode because it was better than GLM 5.2 High, but gave up due to cost issues... Seeing the test results, it makes sense.
Note 7: Gemini 3.6 Flash was granted the privilege of /goal since it was a default agent without separate optimization.
Note 8: ChatGPT 5.6 Luna Max, along with DeepSeek, is the king of this era from a practical standpoint.

Regret 1: If given enough budget and time, I would like to add Go and TypeScript and formally go through the Plan -> Code -> Debug and fix loop stages.
Regret 2: Naturally, even if you assign the same task to the same model, the outcomes will vary. Therefore, I should have run the same test three times, but there are time and budget constraints. *To be honest, it's just to satisfy my personal curiosity.

Thought 1: Models with smaller parameters perform well at first, but lose steam toward the end.
Thought 2: In practice, thorough research, search, and planning are handled by the Architect mode, and even after coding is finished, audits by Debug and Ask take place, so this test is somewhat detached from reality.
Thought 3: DeepSeek is simply fast, Kimi K3 is fast without any redundancies, and Gemini has insane speed.
Thought 4: -

reddit.com
u/Aromatic-Document638 — 23 days ago
▲ 6 r/kimi

Using KIMI K3 with cheaper models.

Those who subscribe after hearing about KIMI K3's reputation seem to feel that KIMI's pricing is expensive even though it provides a token volume worth the price.

The two posts below are about measured practical/felt token volumes based on actual usage:

https://www.reddit.com/r/kimi/comments/1v0yhng/calculating_the_estimated_actual_price_and_total/

https://www.reddit.com/r/kimi/comments/1v2vo92/comment/ozj524p/

For reference, I am utilizing a combination of multiple models. In my current Zoo Code setup, MiMo v2.5 Pro acts as the orchestrator,
GPT5.6 Sol High handles planning,
GLM5.2 High does the heavy-lifting coding,
Simple coding is handled by MiMo v2.5,
And Kimi K3 Max is in charge of Debug and Ask modes.
Research and organization of the codebase are handled by the affordable MiMo v2.5.

I am currently using the Allegro plan, but its quota is not enought for K3 + k2.7-code. It might be sufficient for some users, but at least not for me.

For the best Frontend design, using kimi code + k3 might be optimal. However, if that's not the case, it's better to use other coding tools, leaving heavy-thinking tasks to K3 and inexpensive tasks to cheaper models.

DeepSeek V4 Flash is one of the best combinations.

MiMo v2.5 is also a great option.

According to my working environment and experience, the pricing is as follows. There is no significant difference in the outputs. Making a good plan is the most important thing; if advanced models like K3 set a good plan, fast and inexpensive models handle the rest well.

https://preview.redd.it/2g0prrb45mfh1.png?width=642&format=png&auto=webp&s=18febe50cd0dca04fbf0a688e1a244124a005d68

https://preview.redd.it/rqe38zua5mfh1.png?width=979&format=png&auto=webp&s=2ab306e75bece9b1c5e35f8edd977f01121e2a84

Token Price/M : DeepSeek V4 Flash Max > MiMo v2.5 > DeepSeek V4 Flash High

Looking it up now,
DeepSeek V4 Flash High used $0.25 for 32 million tokens,
MiMo v2.5 used $0.51 for 40 million tokens,
and DeepSeek V4 Flash Max used $0.39 for 30 million tokens.

Assuming the price of MiMo v2.5 is 1,
DeepSeek V4 Flash High can be seen as 0.6127 and Max as 1.0196.

Cheaper models do not mess up their jobs. Therefore, I recommend using a combination with cheaper models. Smaller models like Qwen3.6 35B also generate sufficiently good results as long as they have a good plan. Compared to that, MiMo v2.5 and DeepSeek V4 are models akin to geniuses.

reddit.com
u/Aromatic-Document638 — 25 days ago
▲ 11 r/Agentic_AI_For_Devs+2 crossposts

Practical Proposals for Antigravity and Gemini

https://preview.redd.it/l7smu6qnn9fh1.png?width=839&format=png&auto=webp&s=9d15347c5c279fade56383b861bb70fba44ced5a

https://preview.redd.it/yhswo1tim9fh1.png?width=586&format=png&auto=webp&s=68cda6295808c2de2a3ca15379430adb9d427ad5

As a 3-year Google AI Pro annual plan subscriber, I feel disappointed with Google for failing to provide the relatively powerful AI that was available when I first subscribed. However, since there are ways the Agy team can overcome this with their skill, I am making these proposals.

This isn't just complaining; it is something worth seriously considering.

Here are some basic facts:

  1. Although it is not the most powerful AI, Qwen3.6 27B has no problems with coding.
  2. Claude 4.5 Haiku is a powerful model that can easily execute instructions from Sonnet or Opus.

And Google has the Gemini 3.5 Flash-Lite model, which has capabilities similar to the two models listed above. Take a look below.

https://preview.redd.it/fekvzga2z8fh1.png?width=998&format=png&auto=webp&s=99889646a22969e98f755dea5d4cc3cef883e501

https://preview.redd.it/1ugow4liz8fh1.png?width=952&format=png&auto=webp&s=3799716e2b2bf255f9029c73e5c3169f442bc997

Regardless of benchmarks, Google's unbeatable wall lies in its multimodal capabilities. Even if tied in benchmarks, no model can surpass Gemini in actual use unless it is fine-tuned. Among publicly serviced AI models, Gemini is like a god in multimodal capability.

https://preview.redd.it/ktkmlyvh09fh1.png?width=975&format=png&auto=webp&s=44e5985d116364aca2bbb19cf2693520d422b735

Now, here are the proposals:

  • Add support for 3.5 Flash-Lite in Agy-Cli.
  • Give system prompts to the 3.1 Pro model or 3.6 Flash. The current system prompt seems to be around 8k, but it doesn't matter if it gets larger. Ultimately, 3.5 Flash-Lite will reduce overall costs for both users and Google while increasing satisfaction.
  • Utilize 3.5 Flash-Lite as a multi-agent for the following tasks: tasks that do not require reasoning, coding with clearly structured plans, tasks requiring online research, document summarization, and codebase investigation and organization—actively leverage 3.5 Flash-Lite for high-frequency, shallow-level tasks.
  • Assign simple yet clear personas and explicit prompts to sub-agents.
  • Allow users to utilize their chosen model for deep-dive tasks. If they selected 3.6 Flash Low, use that; if they selected 3.1 Pro High, use that.
  • If a user is dissatisfied with the work result even on the third attempt, escalate to a higher-tier model than the one initially used.

Of course, you shouldn't just paste this into the system prompt, and you'll need to think about it further. But the concept is this:

When given clear instructions, smaller models perform tasks extremely well—not quite as perfectly as high-performance models, but enough to satisfy users.

Also, I don't know how strictly Agy-Cli limits the max output tokens of models or how small the limit for document generation is set, but its document generation capability is at a pathetic level. Ease this restriction by about 5 times.

and store text communications between agents locally on the individual computer where Agy is installed.

If 3.6 Flash or 3.1 Pro create a Todo list and actively utilize 3.5 Flash-Lite as parallel sub-agents, you can produce better results at a lower cost. If someone is an x20 user, it might be nice to add an option to use the 3.6 Flash model as a sub-agent, but I guarantee that for most users, 3.5 Flash-Lite conducting large-scale research will be more than enough. While it's fine for Flash or Pro alone to research from backend to frontend and do reasoning, it's expensive. It's more efficient to variously utilize cheaper sub-agents and apply the high intelligence of Pro or Flash to subsequent tasks.

For reference, I completely avoided using 3.1 Flash-Lite on the web because its result quality was too poor. But now, I use 3.5 Flash-Lite quite a bit because it's fast and suitable for simple tasks—I was doing it without even realizing it. That's why I looked up benchmark results again. Originally, I was only interested in 3.6 Flash to find a more powerful model and never even thought about 3.5 Flash-Lite, but I realized that what matters in this update isn't 3.6 Flash.

Frankly speaking, Google does not have a single impressive AI model at this point. However, 3.5 Flash-Lite is the most impressive model among them, and if Agy actively utilizes this model, it will dramatically improve work completion quality.

The team developing the AI models failed, but the Agy team can make up for it.

-------

Supplementary explanation for this Text.
To help you understand the text, I am adding 4 screenshots.

example 1

Simple prompt

Prompt to utilize sub-agents

example 2

Simple prompt

Prompt to utilize sub-agents

Having a feature available and having it run automatically are different things. For example, what makes Opus excel is running several geniuses of its own caliber in parallel to gather vast amounts of information, which is why it is expensive.

From the perspective of Agy's developers, they might think, "Why don't you just type in the prompt yourself?" but from a user's perspective, it's not like that.

In the case of a simple prompt, a single Gemini can do the searching, analysis, modification, and even testing all by itself, but it is expensive.

In the case of the 'Prompt to utilize sub-agents', there is a cost to creating sub-agents, but by using 3.5 Flash-Lite—which costs 1/6 of Flash—to find and organize the directory structure, source code files, and framework stack, you can have it investigate more than Flash alone could, at a very high speed and low cost. Following a plan made by an expensive model, the costly coding process can also be investigated via the inexpensive 3.5 Flash-Lite, while 3.6 Flash can directly review it or just create test files. Then, 3.5 Flash-Lite can execute the test files and organize the results.

As we all know, this is no longer an era where we make a single model do all the work. It is an era where the main model controls other AIs, while sub-agents utilize skills (pre-written prompts) to bring about better results.

And one of Gemini's major strengths compared to other models is its blazing-fast token generation capability. Whenever needed, it is easy to create a persona for a sub-agent and spawn an inexpensive sub-agent with that persona.

If you look up reviews on Gemini 3.6 Flash, you'll see. Were the reviews good? Most of it is mockery. That is the market's assessment.
With an inexpensive yet excellent model like 3.5 Flash-Lite, actively utilizing it will save the main AI's context while producing better results at a lower cost.

You shouldn't think, "What can't Agy and Gemini build right now that makes them ask for this?"
This is a post about how to build better results at a lower cost.
And these are also things that the Agy team can fix without putting in major efforts like LLM training.

reddit.com
u/Aromatic-Document638 — 27 days ago
▲ 15 r/kimi

A review of coding work with K3 Low

I've been testing K3 Low instead of K2.7-code for coding tasks for two days now, and my satisfaction is very high. The quality is great, but the quota drain speed is disappointing.

K2.7 makes frequent mistakes and shows the meticulousness of correcting its own errors, whereas K3 Low creates better quality output right from the beginning. Tasks that take 5 to 6 iterations to pass with K2.7 pass in just 2 or 3 with K3 Low.

Consequently, K2.7 is cheaper, but it saves time.

If you compare GLM5.2 Max and K3 Low for coding tasks, they are at a similar level. However, to me, K3 Low's results are more elegant.

reddit.com
u/Aromatic-Document638 — 27 days ago

Unable to complete agy-cli task due to an error despite remaining quota

Actually, I wanted to leave a compliment today, and I'll end this with criticism. Please take this as feedback from the Agy development team and build a better agy-cli.

Since there are many shortcomings in using Gemini as a main coding tool, I can't use it (+ since I am on the AI Pro plan, a major reason is that my quota is quite limited. So I can't waste precious quota on agy testing. I always need to keep my quota and use it only when truly necessary). However, if there is one area where Gemini possesses overwhelming capability, it must be its vision feature.

https://preview.redd.it/nryi3jnqi6fh1.png?width=827&format=png&auto=webp&s=9bcfe859656cad78f8e70b3b5994231c9faa98c0

https://preview.redd.it/ielbdinqi6fh1.png?width=813&format=png&auto=webp&s=8bbb9345bd9a971718f87f1dbc933b8f2a455095

My father, knowing that I play with AI in various ways every day, asked me for a favor: It's translating a book.

Actually, I haven't done this through Kimi, but I have done it before. It wasn't a 300-page scale, but rather translating a 20-page Korean manual I wrote into English and Chinese while keeping the layout and charts intact.

This time, I wanted to try doing this with Agy. This time I started with /grill-me, and I wanted to see how well Gemini follows it. (Since I have had the habit of writing very detailed prompts deeply ingrained since the Gemini 1.5 days, I don't frequently use slash commands like /brainstorm that I created in Zoo Code, my primary coding tool. Instead, I use it when starting a new project or searching for answers to various questions, even outside of coding. I will share /brainstorm in a new post. There probably are similar or better prompts out there, but you are welcome to share them if you have any.)

I also ran the work concurrently with an unreleased office-use chatbot I am building myself. (Because I use smaller-class AIs like Gemma4 31B and Qwen3.6 35B, they cannot compare with full-scale mega LLMs like Gemini 3.6 Flash, but 30B-class models already possess intelligence higher than any human, and what's important is how you make them work based on their limited resources.)

To summarize: The task attempted this time with Agy-Cli is: Converting a 300-page book requested by my father into a publication-ready PDF file (excluding design) by maintaining the layout, converting the language of tables and graphs into my native language, and translating the main text as is.

Don't get me wrong. My father has already translated and published several books, and this is his new project too. When I offered to translate it using AI, he suggested we give it as a trial.

https://preview.redd.it/mc6mginqi6fh1.png?width=825&format=png&auto=webp&s=e24cd471ab746ad8805bc1595f6315ee56a83630

https://preview.redd.it/ce5v4jnqi6fh1.png?width=549&format=png&auto=webp&s=9083f2d05eb0f9a76edffb1e9c3ad8b650fa4294

As a result: It could not proceed due to an error. Judging by the /context command, valid context was not insufficient. However, I thought the only reason for such an error would be a lack of context, and it turned out to be true. So I checked the logs and left the contents below.

https://preview.redd.it/pmjkkinqi6fh1.png?width=755&format=png&auto=webp&s=7d912f3d2b48b10d9c58a9008884f018d89d9eab

The report was written directly by Gemini. Normally in such cases, there are methods like using context compression or having Gemini write the detailed contents of that session as a new prompt, then restarting itself with that prompt in a new session. If needed, you can have it search what process it went through in the previous session.

According to the logs, the context window displayed on the agy-cli screen is false. There could be various possibilities—such as 1M context not being provided to AI Pro users, or there being an error—but the log contents and /context do not match.

Since many AI companies provide different context sizes depending on the subscription plan, if there is discrimination in context size based on plans, it's fine to be honest about it. However, after searching, I found that even users on the $200 plan are experiencing the same issue.

Anyway, this error can be resolved using a few alternatives. I hope the agy-cli team resolves it well!

-----------------------------------

Incident & Root Cause Analysis (RCA) Report
To: Google Antigravity Core Engineering Team
Date: July 24, 2026
Subject: Incident Report: Agent Execution Termination via Context Window Exhaustion & Artifact Path
Violation
Severity: Medium (Session Crash / Unrecoverable State)
Affected Surface: Antigravity CLI (agy / cortex-engine)
──────
## 1. Executive Summary
During a long-running interactive translation and layout reconstruction session involving multi-page
PDF processing (@test_ocr.pdf) and subagent orchestration via /grill-me, the agent execution
unexpectedly terminated with ⚠ Agent execution terminated due to error (Error ID: 3d5eb6a2-cd2a-
44e5-8e57-dbb9c70404df-134).
Log analysis confirms two primary technical factors:
Primary Root Cause: The total cumulative context size surpassed the hard model limit of 1,048,576
tokens, causing all subsequent turn requests to fail with HTTP 400 (INVALID_ARGUMENT).
2. Secondary Root Cause: Cascade step errors (CORTEX_STEP_TYPE_CODE_ACTION) triggered when invoked
subagents attempted to output artifact files outside their assigned conversation brain directory.
──────
## 2. Technical Root Cause Analysis (RCA)
### Primary Factor: Hard Token Ceiling Breach (INVALID_ARGUMENT: 400)
• Observed Log Output:
E0724 20:26:13.160187 35036 log.go:398] agent executor error: INVALID_ARGUMENT (code 400):
The input token count exceeds the maximum number of tokens allowed 1048576.
• Analysis:
The session (e59ff6d3-cadc-461b-ba74-567da72c9796) reached Step 80+ while handling large
multimodal/OCR context frames and subagent execution logs. Once the total token payload crossed the
1M token threshold, the backend rejected subsequent streamGenerateContent API calls.
• User Impact:
The session entered a permanently broken state. Simple follow-up prompts (e.g., "Continue", "What
caused the error?") were immediately rejected with 400 INVALID_ARGUMENT.
──────
### Secondary Factor: Subagent Brain Artifact Directory Assertion Failure
• Observed Log Output:
E0724 19:38:09.884420 35036 log.go:398] error executing cascade step:
CORTEX_STEP_TYPE_CODE_ACTION:
files must be written to the correct artifact directory: C:\Users\k1yt\.gemini\antigravity-
cli\brain\306777b4-6bde-455f-8718-5916dc90ace8
• Analysis:
When subagents (e.g., 306777b4-..., dc6e3666-..., 9e597c1b-...) executed code generation or artifact
creation steps, file write paths violated workspace assertion checks by target-path mismatch against
assigned conversation brain paths.
──────
### Tertiary Factor: Telemetry Payload Truncation (Clearcut HTTP 413)
• Observed Log Output:
E0724 19:38:30.312656 35036 client.go:62] Clearcut responded with HTTP code: 413
• Analysis:
Extremely large step logs generated during subagent streaming exceeded max payload limits for the
Clearcut telemetry endpoint.
──────
## 3. Log Evidence Summary
Timestamp │ Log Level │ Source Component │ Error / Event Detail
───────────┼───────────┼──────────────────┼─────────────────────────────────────────────────────────
19:38:09  │ ERROR     │ log.go:398       │ Cascade step artifact directory mismatch
│           │                  │ (CORTEX_STEP_TYPE_CODE_ACTION)
19:38:30  │ ERROR     │ client.go:62     │ Clearcut HTTP 413 (Payload Too Large)
20:26:13  │ ERROR     │ log.go:398       │ INVALID_ARGUMENT (code 400): Token count exceeds
│           │                  │ 1048576
20:34:04  │ ERROR     │ log.go:398       │ Repeated INVALID_ARGUMENT (code 400) on user follow-up
20:37:44  │ INFO      │ server.go:2413   │ Language server process shutdown
──────
## 4. Recommendations for Antigravity Engineering
Proactive Context Truncation / Compression:
• Implement an automated sliding-window context compressor or checkpoint summarizer prior to
hitting ~900k tokens to prevent hard HTTP 400 API aborts.
2. Subagent Artifact Path Resolver Normalization:
• Enhance CORTEX_STEP_TYPE_CODE_ACTION to automatically sanitize and redirect subagent output
paths to the parent/subagent designated artifact directory.
3. Improved UX Error Messaging:
• When INVALID_ARGUMENT (400 token count limit) occurs, surface a user-friendly prompt: "Context
limit reached (1M tokens). Please start a new session (/clear)." instead of a generic Error ID.
reddit.com
u/Aromatic-Document638 — 27 days ago

Multi-Bug Fixing for Gemini 3.6 Flash High via Parallel Sub-Agents

Since I don't know how much resource consumption Gemini 3.6 Flash High requires yet, my initial prompt was as follows. And below, I have detailed the bug status from number 1 to 20.

-----------------------

After the build is complete, there are a few issues. We need to strictly figure out their causes and fix them. Do not try to solve all the issues below simultaneously; solve them sequentially, one by one.

Dispatch as many bug-finding specialized sub-agents in parallel as possible to identify them, then utilize sub-agents that explore whether the issues are real in parallel to verify their authenticity once more, after that, utilize sub-agents that actually perform fixes in parallel to accurately fix them, and then once again, without providing information about the fixes to the bug-finding specialized sub-agents, dispatch them in a large-scale parallel manner to check if the issues are gone. If there are no issues, finish the process; if there are still issues, repeat the correction loop.

I emphasize once again, solve the issues one by one sequentially.

----------------------------

https://preview.redd.it/94659zjzyueh1.png?width=732&format=png&auto=webp&s=f86c580eaf339a628f3a05c982e1a388de2351a7

https://preview.redd.it/lbczi7ezxueh1.png?width=729&format=png&auto=webp&s=771cca7d4519d64118a24ee3c5d674e9f2f5712e

https://preview.redd.it/qsrp304uyueh1.png?width=734&format=png&auto=webp&s=f5152c8fe42d4d3f6d3b60b81a8b25efc7cd546e

Although it wasn't intentional, I waited until the 5-hour limit was completely exhausted. It dropped to zero in an instant because the parallel agents were running. Since I am a Google AI Pro user, this is not a problem. I can endure it.

And to read what Gemini is thinking and what the situation is, I expanded it with ctrl+o.

The conclusion is: Gemini 3.6 Flash completely fails to act as a leader. It doesn't know how to handle parallel agents, and it forgot the instruction to solve things sequentially right from the start. As a result, not a single bug was resolved.

Some users might think my instructions are too harsh, but the DeepMind team should take my case seriously. Other agents follow instructions of this level easily.

Leave aside the Gemini 1.5 era, but for AI models in July 2026, the instruction 'solve the issues one by one sequentially' is not a difficult one.

Given such Prompts, Gemini should have automatically created a to-do list and resolved them sequentially. I'd love to test further, but my quota is exhausted, and when the next 5-hour block resets, I will go back to using Agy as my assistant tool in VS Code, so I won't issue instructions like this again.

I've been using Google since the days when you needed an invitation just to sign up for Gmail, and I'm genuinely saddened to see both Microsoft and Google broken down like this today. These two tech giants used to be companies that cared even for unprofitable domains, and thanks to the wonderful, non-profit services they offered, our online world could become as rich as it is today. But I suppose not a single team leader from that era is left. How did the mighty Google and the renowned DeepMind end up in a position where they have to compete with tiny LLM labs of fewer than 50 people? I'm leaving this note with a deep sense of regret as a long-time fan.

u/Aromatic-Document638 — 29 days ago
▲ 4 r/kimi

Guide on How to Check Monthly Quotas for KIMI and KIMI CODE

KIMI isn't very interested in making the UX convenient. Even now, several simple features that were originally there have been removed, and though I've submitted bug reports, it's uncertain when they will be restored.

Since some users aren't aware that KIMI has a monthly quota, I've put together a quick guide.

https://preview.redd.it/7xnqlflndseh1.png?width=1289&format=png&auto=webp&s=f38f7195b2fcc1e22ed463fae8a78fc59b787ce4

  1. https://www.kimi.com/settings

If you click on subscription on the web at kimi.com:

- Monthly Quota (Red)
- 5-hour / Weekly Quota (Green)

*This feature hasn't been around for very long. Back then, we had to go back and forth between Kimi Code and the website to check them.

https://preview.redd.it/9sz72bsecseh1.png?width=884&format=png&auto=webp&s=eccc9bd1b8317a16728c197ad93a8faf0316674a

2.https://www.kimi.com/code/console

- 5-hour / Weekly Quota (Green)

https://preview.redd.it/wmkulgcbcseh1.png?width=1171&format=png&auto=webp&s=514c2efcb25ddfdb724c3ff26d8f3ad2c2832061

reddit.com
u/Aromatic-Document638 — 29 days ago

it still cannot be used as a primary tool

I always keep Agy turned on, whether I use it or not.

Even though Gemini 3.6 Flash has been released, it still cannot be used as a primary tool.

Reminiscing about the days with Gemini 1.5 and 2.0, starting with a question like "What is Python?", I remember having to revise code 30 to 40 times just to implement a very simple feature. Because it frequently made syntax errors, I had to ask what the syntax was and manually inspect and fix it as I went.

The Flash-series Gemini models have intelligence that is too low to encompass everything from backend to frontend on their own. While lower intelligence can theoretically be overcome through reinforcement learning and training, 3.6 Flash is also not prepared in that regard.

In my opinion, Gemini 3.6 Flash seems focused on consuming fewer tokens and enhancing cost-efficiency. It terminates tasks without completing the necessary work. It feels just like looking at the Flash-series of DeepSeek.

Then why is DeepSeek praised while Gemini gets criticized?
- DeepSeek V4 Flash can be used almost limitlessly for $1, whereas you can't do anything with $1 on Gemini.

Furthermore, Gemini 3.1 Pro is far too lazy compared to its inherent brilliance. This is purely a problem of reinforcement learning. If it operates within a working loop driven by exceptionally good user prompts, Gemini 3.1 Pro would be truly useful. Unfortunately, however, I have already set up my own working loop in Zoo Code based on a massive amount of know-how, and because Oath login is unavailable, I cannot harness Gemini's genius.

For reference, based on well-crafted user prompts, skills, and tools, even DeepSeek V4 Flash can produce better results than Gemini 3.1 Pro.

I am currently building a small-scale office chatbot based on Qwen 3.6 35B and Gemma 4, and the office data output they produce is better than what ChatGPT 5.6 Sol creates.

This is because, despite the models' lower intelligence, I specified in detail what needs to be done and specialized them accordingly. Gemini 3.6 Flash and Gemini 3.1 Pro possess immense intelligence in themselves, leaving their potential wide open. However, I cannot spend time optimizing Agy, which can only use these two models. Other tools allow me to use even better models. Therefore, I am using Agy as a supplementary tool.

While running tasks in Zoo Code, I heavily utilize Agy for read-only operations or mapping out new plans, since I cannot directly open all those backend files myself.

I believe Google should open up Gemini to other platforms, much like Codex. At this point, I cannot take the risk of pouring the know-how I've accumulated into Agy, relying on models whose performance drops significantly.

Gemini trapped inside Agy struggles to receive a fair evaluation. At least back in the Gemini-CLI days, injecting massive user prompts along with a nearly unlimited quota yielded quite good results, but Agy is not like that—it has a 5-hour limit.

reddit.com
u/Aromatic-Document638 — 29 days ago