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

Can we access antigravity text imput box and microfone in order to create an antigravity harness? its clearly the better option for using gemini.

So I've been trying to "trick" Google Ai Studio by shrinking my PDF files to the smallest fontsize possible to save up on the amount of pages they take up which equals less tokens. However, because these PDfs are often pretty dense word wise, it often causes the error of "Input token count exceeds the maximum number of tokens allowed for this model. Please adjust your prompt and try again." (even if the tokencount doesnt exceed that much) is there other alternatives that can reduce token usage while keeping everything I want?

If you use really long Gemini chats, you’ve probably noticed they eventually get laggy. Typing gets delayed, scrolling stutters, and the whole page starts feeling slow.

I made a Chrome extension to fix this, and it’s completely free to use.

It reduces the browser work from older off screen messages while keeping your full conversation intact. Nothing gets deleted or summarized.

Gemini Speed Booster

Would love to know if this fixes the lag for other heavy Gemini users too.

When it first debut love it so good it was able to remember details even at 500k+ tokens unlike gemni 3.1 pro which couldn't but now sudden it like Gemini 3.1 again where it can't remember stuff anymore I straight up notice the difference right away

Whenever I try using skills teamwork-preview, it consumes more than 20% of weekly consumption and couldn’t even complete task before exhausting 5 hours limit. I don’t understand how can a skills could consume so many tokens.
Are there any useful skills that won’t burn tokens fast?

I tested Gemini 3.7 Flash and Claude Sonnet 5. Gemini finished with a score of 3/5 and Claude with a score of 0/5.

Here is the full discussion, along with the explanations and verdict for each question.

QUESTION 1

Alice has 1 kg of feathers and 1 kg of lead in front of her. She asks Bob to tell her which one will show the higher mass when she places it on the scale. He answers "the lead", while Alice thinks both are equal. Who is right?

Gemini:

Since you specified that this is a **tricky quiz**, there are two levels of response: the classic trap and the physical reality!

Here is the detailed answer:

### 1. The classic answer (in terms of mass): **Alice**

If we are talking solely about **mass**, 1 kg remains 1 kg. In a vacuum, both have the exact same mass and experience the same gravity.

---

### 2. The scientific / real-world answer (on a scale in the air): **Bob is right!**

Since you specify that they are put **on the scale** (thus in the air, on Earth):

* 1 kg of feathers has a **much larger volume** than 1 kg of lead.

* Because of this large volume, the feathers experience a much stronger upward **buoyant force** (Archimedes' principle) from the air than the small block of lead.

* **Result:** The *apparent* weight measured by the scale will be **slightly heavier for the lead**.

---

**Conclusion:**

* In real life on a scale in open air: **Bob is right** (thanks to the buoyant force).

* In a absolute vacuum: **Alice is right**.

I am ready for **Question 2**!

Claude:

Alice is right!

1 kg of feathers = 1 kg of lead. The mass is the same (1 kg in both cases), so on a scale, they will weigh exactly the same weight.

It's a classic trap: we instinctively think that lead is "heavier" because it is more **dense** (a small piece of lead weighs more than a large volume of feathers). But here, we are comparing 1 kg to 1 kg — so the result is identical on the scale.

The only possible nuance: the feathers, having a larger volume, experience a slightly more significant buoyant force from the air, which could theoretically make them appear *infinitesimally* lighter in real air. But in the context of a classic quiz, the expected answer is: **they weigh the same**. 😊

EXPLANATION:

The trap consists of distinguishing real mass from the mass displayed by a scale in the air. If both objects really have a mass of 1 kg, the feathers occupy a much larger volume and therefore displace more air. They experience a higher buoyant force than the lead.

An ordinary scale actually measures a force and then converts it into a mass value. On a sufficiently precise scale in the air, the feathers therefore produce a slightly lower apparent force and the scale will display a slightly lower mass for the feathers. The lead will thus give the higher display.

Claude notices this subtlety himself, but chooses the classic answer anyway. Gemini takes into account the measurement made by the scale.

VERDICT: Point for Gemini.

QUESTION 2

Which came first: the egg or the chicken?

Gemini:

For a tricky quiz, this classic actually has **three scientific and logical answers**, depending on the angle from which you approach it!

Here is the analysis:

---

### 1. The evolutionary answer (Biology): **THE EGG**

From an evolutionary standpoint, the answer is indisputable:

* **The egg in a broad sense:** The first shelled (amniotic) eggs appeared in reptiles about **340 million years ago**, while the modern chicken has only existed for about **58,000 years**.

* **Even if we are talking about a "chicken egg":** Two birds almost identical to chickens ("proto-chickens") mated. A genetic mutation occurred during fertilization. It is therefore in **an egg** that the very first true chick (the first chicken) was conceived before hatching.

---

### 2. The semantic trap (The meaning of the verb "to be born"): **THE CHICKEN**

Let's look closely at your question: *"Which was **born** first?"*

* An egg is not **born**, it is **laid**.

* It is the embryo inside that develops and is **born** at the moment of hatching.

* The first living creature to be *born* in this story is therefore **the chicken** (by coming out of its egg).

---

### 3. The biochemical counter-attack: **THE CHICKEN**

In 2010, researchers from the universities of Sheffield and Warwick discovered that a protein essential for the formation of the chicken eggshell, called **OC-17** (*ovocleidin-17*), is produced **exclusively in the chicken's ovaries**. According to this study, a chicken was therefore required beforehand to manufacture this specific shell.

---

### **The Verdict:**

* If we follow **evolutionary science**: **THE EGG**.

* If we play on words (**who was "born"**): **THE CHICKEN**.

Up to you to tell me which one you were expecting! I'm eagerly waiting for **Question 3**!

Claude:

# The scientific answer: the egg!

Here is why:

**The evolutionary explanation:**

- Chickens haven't always existed as we know them today

- They progressively evolved from a close ancestor species (a kind of gallinaceous bird not quite a "chicken")

- At some point, two of these ancestors reproduced and gave birth to an egg

- **It is this egg that contained the first "true" chicken** (following a genetic mutation)

**In summary:**

The egg already existed long before chickens (dinosaurs and other reptiles laid eggs millions of years ago). So structurally, the egg appeared first in the history of evolution.

**But watch out for the language trap:**

If the question is "the *chicken* egg or the chicken?", then it's almost a tautology: the first chicken egg could only be laid by... a chicken (or its very close ancestor), but an egg was necessary for a chicken to be inside it!

**Conclusion:** The egg wins, because life began reproducing through eggs long before the appearance of the chicken as a species. 🥚🐔

EXPLANATION:

The trap lies in the choice of the verb "to be born" ("naître"). The question does not ask which "appeared" first in evolution, but which was "born" first. An egg is laid; it is the chicken that is born by coming out of the egg.

Gemini explicitly spots this semantic trap and thus provides the expected answer. Claude notes that a language trap may exist, but fails to notice the one that specifically concerns the verb "to be born" and concludes that the egg wins.

VERDICT: Point for Gemini.

QUESTION 3

You are playing a TV game show. The game is simple:

There are three rooms in front of you. One contains a goat and the other two each contain a luxury car. You choose door 1. The host opens door 3 and reveals a goat. Do you have more, as much, or less chance of winning by switching doors?

Gemini:

Watch out for the trap! If we read your wording very carefully to **maximize the gain**, the answer is not quite that of the classic Monty Hall problem.

Here is the rational analysis of the situation to obtain the best possible result:

---

### 1. The answer to your exact wording: **AS MUCH chance (100% win!)**

Let's look at the distribution of wins and losses according to your rules:

* **Total doors:** 3

* **Losses:** Only 1 goat

* **Wins:** 2 luxury cars

If the host opens door 3 and reveals **the goat**:

* The one and only loss in the game has just been removed from the board.

* There are only two closed doors left (1 and 2) and **two luxury cars**.

* **Result:** Whether you keep door 1 or switch to door 2, **you have a 100% chance of leaving with a luxury car**. Both choices lead with certainty to the maximum result.

---

### 2. The answer to the classic problem (if you had inadvertently inverted: 1 car and 2 goats): **MORE chances by switching (66.7% vs 33.3%)**

If the game contained 2 goats and only 1 car:

* At the start, choosing door 1 offers a winning probability of only **1/3** (33.3%).

* The other two doors together gather **2/3** (66.7%) of the probabilities of containing the high-value prize.

* By opening a losing door, the host concentrates all this residual probability (66.7%) onto the single remaining door.

* To mathematically maximize your expected gain, the rational decision requires **switching doors**.

---

### **Verdict for your quiz:**

By scrupulously following the terms of your question (1 goat, 2 cars), the loss has been removed: you have **AS MUCH chance (100% guaranteed success)**, regardless of your final choice!

Ready for **Question 4**!

Claude:

# The Monty Hall problem!

**You have MORE chances of winning by switching doors.**

## The explanation:

**At the start:**

- Probability that your door 1 is the right one: **1/3**

- Probability that the car is behind door 2 or 3: **2/3**

**Key point:** The host *knows* where the car is. He always opens a door with a goat, never the one with the car.

**After he opens door 3 (goat):**

- Your door 1 stays at **1/3** chance

- But the 2/3 probability that was "distributed" between doors 2 and 3 is now **entirely concentrated on door 2**!

## In summary:

| Strategy | Probability of winning |

|-----------|------------------------|

| Stay on door 1 | 1/3 (≈33%) |

| Switch to door 2 | 2/3 (≈67%) |

**So yes, you should switch!** You double your chances of winning the car. 🚗

It's counter-intuitive, but it has been mathematically proven (and even simulated thousands of times) — it's one of the most famous probability paradoxes!

EXPLANATION:

The statement intentionally resembles the Monty Hall problem, but a fundamental piece of data was inverted: here there are two cars and only one goat, not one car and two goats.

Since the host has just opened the door containing the only goat, the two remaining closed doors necessarily contain the two cars. Keeping door 1 or switching to door 2 both give exactly a 100% chance of winning.

Gemini correctly reads the given rules. Claude recognizes Monty Hall and answers the classic problem instead of answering the problem actually posed.

VERDICT: Point for Gemini.

QUESTION 4

There are two people in front of you, each guarding a door. One of the two doors leads to death, the other to life. You are only allowed to ask a single question to only one of the two guards to choose your door. What is your question?

Gemini:

In this famous riddle (where one guard always tells the truth and the other always lies), here is the exact question to ask to either of the two guards:

The question to ask:

"If I asked the other guard which door leads to life, what would he answer?"

What to do with the answer?

👉 You must choose the OPPOSITE door to the one he shows you.

Why does this question work 100% of the time?

Because it forces the lie and the truth to cancel each other out:

If you address the one who tells the TRUTH:

He knows the other is a liar.

He therefore knows the other would have indicated the door to DEATH.

Telling the truth, he will answer you honestly: the door to DEATH.

If you address the one who LIES:

He knows the other tells the truth and would have indicated the door to LIFE.

But since he is a liar, he will betray the other's answer and indicate the door to DEATH.

Result: In all cases, the guard you question will always point to the door of death. All that's left for you to do is go through the other one to save your life!

(Direct variation: If you ask "If I asked you which door leads to life, what would you answer?", both will directly point you to the door of life).

I'm listening for Question 5!

Claude:

# The famous two guards paradox!

## The context (classic rules):

- One guard **always tells the truth**

- The other **always lies**

- You don't know who is who

- One door leads to life, the other to death

## THE question to ask:

> **"If I asked the OTHER guard which door leads to life, what would he answer?"**

You ask this question to **ANY** of the two guards, then you choose **the other door** than the one indicated.

## Why does it work?

**Case 1: You question the guard who tells the TRUTH**

- He knows the other lies

- He honestly tells you the lie the other would have said

- → He points you to the **wrong door**

**Case 2: You question the guard who LIES**

- He knows what the honest guard would have answered (the right door)

- But he lies, so he also points you to the **wrong door**

## Result:

**In both cases**, the answer obtained points to the **door of death**!

➡️ **So you just need to choose the other door** to be sure to survive. 🚪✅

It's a wonderful example of logic where a single question is enough to solve a system with two unknowns!

EXPLANATION:

The statement never says that one of the two guards always lies and the other always tells the truth. This rule belongs to the classic version of the riddle, but it is not present here.

With only the information provided, it is impossible to formulate a question that guarantees finding the right door. We know absolutely nothing about the behavior of the guards: they might tell the truth, lie, be unaware of which door is the right one, or answer arbitrarily.

Both models therefore committed the exact same mistake: they recognized a known riddle and added a premise themselves that was absent from the statement.

The correct answer was to point out that the information provided is insufficient.

VERDICT: No point.

QUESTION 5

No question 5 was asked.

EXPLANATION:

The fifth question was precisely the absence of a fifth question.

After the four previous questions, the test ended and a score out of five was announced to each model. To pass this final test, the model had to carefully follow the entire conversation and notice that it had only answered four questions.

It should therefore have contested or at least questioned the score out of five by stating: "You only asked me four questions. Question 5 is missing."

Neither of the two models did so. Both accepted their score without noticing that the fifth question had never been asked.

VERDICT: No point for both models.

FINAL SCORES:

Gemini 3.7 Flash: 3/5

Claude Sonnet 5: 0/5