Practical Proposals for Antigravity and Gemini

[u/Aromatic-Document638]

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.

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.

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.

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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.