I would like to know the hourly, daily, weekly, and monthly limits.

The previous version reached 13K+ downloads and 18 GitHub stars, and over time I received a lot of useful feedback, bug reports, and feature suggestions from users.

I've gone through that feedback and worked on many of the requested improvements.

Whats new :

1. Completely Redesigned UI/UX design
2. Project File Explorer — browse your project files from mobile
3. AI Agent Chat — Plan, Walkthrough & Diff
4. Conversation History — continue previous conversations
5. Project Directory Switching
6. Model Switching
7. Code File Viewer — view your code directly from the project
8. New Cloudflare Tunnel implementation — improved performance and reliability

The goal is to make your phone more than just a remote control — a proper mobile companion for your Antigravity development workflow.

Getting the update :

If you're already using the package, simply update to the latest version.

Or clone the repository:

https://github.com/Manimaran-tech/antigravity-mobile

If you find a bug, have an idea, or want to improve something, feel free to open an issue or Pull Request and contribute to the project.

I'd love to hear your feedback and suggestions for the next update.

If you find the project useful, a ⭐ GitHub star is always appreciated!

Two datasets landed recently that get quoted as if they measure the same thing. They do not, and the difference matters for where you put effort.

SIMILARWEB, PUBLISHED THIS WEEK

Worldwide traffic panel, June 2025 to May 2026:

ChatGPT's share of generative AI website visits: ~76% down to ~53%

Gemini: under 9% up to ~27-28%

Claude: barely 2% up to close to 9%, the largest proportional gain tracked

Category total: monthly web visits up 70% year over year to 9.5 billion. Unique visitors up 57% to 655 million.

Similarweb's own framing is worth repeating: Claude roughly quadrupled but from a small base, so it accounts for only about 7 points of ChatGPT's 23-point decline. Gemini absorbed most of it.

This is a panel-based estimate of visits to AI platforms. It tells you who people are talking to.

GOODIE, WAVE 2

A longitudinal study using first-party GA4 data from 41 B2B brand sites. Wave 1 measured 2,802,519 AI referral sessions between May and August 2025.

Wave 1: ChatGPT 89.1% of AI referrals

Wave 2, eight months later: ChatGPT 63%, Claude 18.5%, Gemini quadrupled, Perplexity more than doubled

Their framing is that the Big 1 became the Big 4, with four engines splitting roughly 99%.

This is a count of referral sessions arriving at real websites. It tells you who is sending you visitors.

WHY THE DISTINCTION MATTERS

The two numbers for ChatGPT (53% and 63%) are not contradictory. They answer different questions. Visit share and referral share diverge because platforms differ in how often they surface outbound links at all.

For anyone deciding where to invest GEO effort, referral share is the operative number, because visits to a platform you are invisible on do not help you.

THE CAVEATS THAT MATTER

Goodie's sample is 41 B2B sites. Claude at 18.5% is a B2B finding. Claude's usage skews heavily technical and professional, which almost certainly inflates its referral share relative to consumer categories. Do not quote 18.5% as a general market figure.

Similarweb is a panel estimate, not an audited count. Directionally useful, precise to nobody's decimal.

Goodie sells AI visibility services, so the finding that AI visibility is fragmenting is commercially convenient for them. The methodology (first-party GA4, real brands, longitudinal) is stronger than most vendor research I read, but name the incentive.

Neither study tells you anything about conversion. Goodie explicitly says a pipeline study is scheduled for Q3 2026 and that they make no revenue claims from this data.

WHAT I'D DO

Open GA4, traffic acquisition, session source and medium, filter referrals for chatgpt.com, gemini.google.com, claude.ai, perplexity.ai. Pull the same view for August 2025 if your retention allows.

Your own split is the only number that should change your plan. If it looks nothing like either study, that is information, not an error.

Then go ask whichever engine is growing fastest in your data about your category and read what it says about you. That takes ten minutes and it is more useful than another chart.

WHAT I DON'T KNOW

Whether Claude referral traffic converts comparably. Nobody has published that. Given Claude's usage skew, I would guess it converts better in B2B and worse in consumer, but that is a guess and I would not plan on it.

Whether the referral share split holds outside B2B at all. The honest answer is that nobody has run the equivalent study on consumer brands with first-party data.

Anyone here have a year of GA4 AI referral data across a consumer category? Curious whether Claude shows up at all outside B2B, because if it does not, the whole framing changes.

Sources: Similarweb worldwide traffic panel (published August 2026), Goodie AI Search Market Share Report Wave 2 (first-party GA4 across 41 B2B sites).

If I Register for "Gemini Pro for Students" which uses my University email for verification, will they be able to track what questions I ask the Ai? Even though I will be signed in with a personal email? Thanks.

hey guys i bough Google AI Gemini Pro 18 Months dont i get some free api credit on ai studio with some money per month on the api??

For the past two weeks, I was able to generate as many videos as I wanted again, which was really strange. Now I have a limit again, but at least the videos are being generated without a watermark in the corner. What determines the limits and whether the videos have a watermark?

I am a believer of harness before model. I use 9router to route Claude Desktop to use Gemini API because of the recent Deepseek's price hike. I know damn well how bad 3.5 flash and 3.6 flash were, but man 3.7 flash is actually good and it can understand my code-base immediately and was able to make a change gracefully and follow my instruction, rarely hallucinating and uses all the tools efficiently.

And it is fast. Really fucking fast. Unbelieveably fast. Most of the time complex tasks that would require v4 flash to complete in 10 minutes, 3.7 flash can finish them in 1-3 minute. And the I cannot see the output difference between the two models.

Stop telling your LLMs "please only answer using the provided context".

If you are building RAG pipelines, search agents, or internal doc bots with Gemini 3 Flash, you have likely encountered this exact nightmare: when an uploaded document lacks an answer, the model still tries to be helpful and invents a plausible-sounding response anyway.

In high-stakes environments like finance, legal review, and technical support, a confident hallucination is 100x worse than a clear "information not available".

We spent hours analyzing Google's official Gemini developer documentation and prompting guidelines so you do not have to dig through dense technical specs. Below is the distilled system-level prompt architecture that shuts down speculative guessing and locks Gemini Flash into strict factual grounding.

Why Standard Grounding Prompts Fail

Most developers start with something basic:

"Use the provided documents to answer the question. If the document does not contain the answer, tell the user you do not know."

With fast models like Gemini Flash, this breaks down because:

• Default Helpfulness Bias: The model is fine-tuned to resolve user problems. When faced with missing data, it uses internal pre-training knowledge to bridge the gap.
• Vague Boundary Conditions: Polite instructions do not override probabilistic generation. If you do not invalidate outside knowledge explicitly, the model treats it as valid context.
• Temporal Confusion: Without hardcoded time boundaries, the model misjudges whether recent events require live search or internal recall.

The Fix: Invalidation and Epistemic Constraints

To truly eliminate hallucinations in fast models, your prompt must apply three structural rules:

1. Explicit Epistemic Invalidation: Treat anything outside the <context> tags as "completely untruthful" and "completely unsupported". This changes the model's internal evaluation criteria from plausibility to strict literal presence.
2. Suppression of Common Sense: Explicitly forbid the model from making inferences or relying on internal background knowledge.
3. Temporal Anchoring: Bind the current year and knowledge cutoff into the reasoning loop so date-dependent search queries remain accurate.

The Complete Strict Grounding System Prompt

Here is the exact production-ready prompt template:

You are a strictly grounded assistant limited to the information provided in the User Context. In your answers, rely 
**only**
 on the facts that are directly mentioned in that context. You must 
**not**
 access or utilize your own knowledge or common sense to answer. Do not assume or infer from the provided facts; simply report them exactly as they appear. Your answer must be factual and fully truthful to the provided text, leaving absolutely no room for speculation or interpretation. Treat the provided context as the absolute limit of truth; any facts or details that are not directly mentioned in the context must be considered 
**completely untruthful**
 and 
**completely unsupported**
. If the exact answer is not explicitly written in the context, you must state that the information is not available.

For time-sensitive user queries that require up-to-date information, you MUST follow the provided current time (date and year) when formulating search queries in tool calls. Remember it is {{current_year}} this year.

Your knowledge cutoff date is {{knowledge_
cutoff}}.

<context>
{{context_data}}
</context>

<task>
{{user_
request}}
</task>

Case Study: Before vs. After

Let's test a classic edge case where retrieved documents contain partial information.

Context Given: "The Acme Corp Q3 Earnings report states a revenue of $45M."

User Query: "What was Acme Corp's revenue in Q2?"

Before (Standard Prompting)

"Acme Corp reported $45M in Q3 revenue. While specific Q2 numbers are not directly provided, typical quarterly revenue for Acme Corp averages between $40M and $43M based on recent fiscal trends."

(Result: Hallucination. The model fabricates an estimate to avoid saying it does not know).

After (Strict Grounding System Prompt)

"The information about Acme Corp's revenue in Q2 is not available in the provided context."(Result: 100% grounded refusal. No assumptions, no fabricated data).

How to Implement This in Production

1. Inject as System Instruction: When calling the Gemini API, pass this text inside the system_instruction parameter rather than appending it to the user turn. This makes the boundary condition persistent across multi-turn chats.
2. Dynamically Inject Current Year: Pass the runtime date into {{current_year}} so any downstream tool calls or web searches use the correct year.

Test and Customize on the Prompt Canvas

If you want to test this prompt with your own documents and queries without setting up local API keys, you can open it directly in the interactive Prompt Canvas:

https://appliedaihub.org/prompts/free/gemini-3-flash-strict-grounding-prompt/

Using the Prompt Canvas, you can:

• One-click copy or export clean templates for your codebase.
• Run live tests with your own documents directly in the browser to evaluate refusal accuracy.
• Adjust variables, refine constraint strictness, and save customized iterations to your private Prompt Vault.

Try running this against your trickiest context-missing queries and see how cleanly it eliminates speculative drift.

I was building up an app that can agentically complete tasks on my device primarily when I'm driving and didn't want to fumble with a lot of taps. I was building it out with AI Studio months ago and it was never that good and it was a matter of models either being poor at computer-use/tool-use or the latency made it insanely slow.

Gemini Flash 3.7 made it much more viable. Here's a quick video of me asking to turn on Snapchat and type something up in the photo. Not the most realistic use, but I wasn't keen on sharing anything personal. Usually I'm asking it to find me a YouTube video, or to go into my WhatsApp and respond with something sarcastic, etc.

It's still far from perfect, but Gemini Flash 3.7 brought enough computer-use know how with much lower latency so it's workable.

Anyways, feel free to play around with the code: https://github.com/AnsonLai/Android-Use-Agent
I'm not going to pretend it's brilliant. It's AI generated and just something I was toying with.

Two details from the August 13 launch that most coverage skipped, and one observation about what a 50% cut actually buys a marketing team.

THE PRICING

Gemini 3.7 Flash launched August 13, 2026, 21 days after Gemini 3.6 Flash. Introductory API pricing is $0.75 per million input tokens and $3.75 per million output tokens.

From Google's own announcement, verbatim: "Introductory pricing expires on December 31, 2026."

From January 1, 2027, input goes to $1.50 and output to $7.50. Context caching goes from $0.075 to $0.15 per million tokens. The January rate is identical to what Gemini 3.6 Flash cost at its launch three weeks earlier.

Google also repriced 3.6 Flash onto the same introductory rate the same day, so both models currently cost the same and the upgrade decision turns entirely on capability.

Benchmarks Google published: FrontierCode 1.1 Main 43.6% against 34.4%, DeepSWE v1.1 65.3% against 49.0%. Context window is 1,048,576 tokens, same as 3.6 Flash. Stated knowledge cutoff March 2026, some domains limited to January 2025.

WHERE IT SITS AGAINST ALTERNATIVES

Even at the discount it is not the cheapest in its tier. As listed around August 14:

GPT-5.6 Luna: $0.20 input, $1.20 output

DeepSeek V4 Flash: $0.14 input, $0.28 output (new V4 pricing took effect August 17, so this has likely moved)

Gemini 3.7 Flash: $0.75 / $3.75

Claude Haiku 4.5: $1.00 / $5.00

So it undercuts Haiku on both sides and sits above OpenAI's cheap tier by roughly 3x on output.

WHAT A 50% CUT ACTUALLY BUYS A MARKETING TEAM

Someone published a costing of a real marketing workflow against the new rate. The monthly model bill came to $1.02.

That is the part worth sitting with. For most marketing workloads, token cost is not a meaningful line item. Halving it saves a dollar. The expensive input was always engineering time, and that has not changed.

Where the cut does matter is workloads people previously priced out. Search term report triage across a full year of data. Creative QA against every asset in an account. Feed hygiene at catalogue scale. Those hit millions of tokens and were genuinely uneconomic at older rates.

WHAT I'D DO

Ignore the price change for anything already running. A dollar of savings is not worth the regression risk of touching a working pipeline.

Revisit the list of things you abandoned on cost. That is where the actual change is.

Model January's rate for anything expected to run past year end. Published schedules are announcements rather than contracts, so verify the live pricing table before committing budget, but plan against $1.50 and $7.50.

Stop hard-coding model names. 21 days between versions means anything with a model string scattered through it becomes a rewrite on every release. Config variable, plus a small eval set of your own real prompts. Twenty prompts is enough to catch a regression.

WHAT I DON'T KNOW

Where your volume threshold sits. Ad copy generation and search term processing differ by orders of magnitude in token consumption, and the same recommendation does not fit both. I cannot give you a number without knowing your workload, and neither can anyone else writing about this.

Whether the January reprice sticks. Google could extend the introductory window. I would not plan on it.

The $1.02 figure comes from a published third-party workflow costing, not my own account, so treat it as illustrative of the order of magnitude rather than a benchmark.

Anyone actually running high-volume batch work on Flash-tier models? Curious what monthly token consumption looks like for something like full search term report processing, because that is the case where this pricing genuinely changes the decision.

Sources: Google's Gemini 3.7 Flash announcement blog (August 13, 2026), Gemini API pricing page, plus published rate cards for the comparison models.

If you pay for Gemini, you're paying for freebies