DEV Community: JimmyLiao

Gemini Agentic Video Isn't Always Cheaper: A 24-Run Benchmark

JimmyLiao — Fri, 04 Sep 2026 11:23:47 +0000

A controlled Gemini 3.7 Flash benchmark shows why agentic video is excellent for long-form search—but can cost more than static processing on short clips.

If I only need one number from a long video, why should an AI model sample the entire timeline before answering?

Google launched Agentic Video Understanding on September 1. Instead of processing video at a fixed sampling rate, Gemini can decide whether to inspect the transcript, audio, or selected frame ranges based on the question.

Google reports up to 88% fewer tokens, 66% lower cost, and 7% higher quality on long-form video. Those numbers are compelling, but they do not answer the question I had while building with it:

Is agentic processing cheaper for every video and every query?

I ran the same videos and prompts through both agentic and static processing. Long-form workloads were dramatically cheaper with agentic processing. Short visual workloads were not.

Current model support

The public Gemini guide currently lists these models as supporting Agentic Video Understanding:

Gemini 3.8 Flash
Gemini 3.7 Flash
Gemini 3.6 Flash
Gemini 3.5 Flash-Lite

Gemini 3.8 Flash is the newest option and is worth evaluating for new applications. To keep this comparison controlled, however, every result in this article was produced with Gemini 3.7 Flash. Mixing model changes into an agentic-versus-static comparison would make the numbers harder to interpret.

Enabling agentic processing

I used the Interactions API. The processing mode belongs on the video input:

response = client.interactions.create(
    model="gemini-3.7-flash",  # Benchmark model; new apps can evaluate 3.8 Flash
    input=[
        {
            "type": "video",
            "uri": video.uri,
            "mime_type": video.mime_type,
            "processing": "agentic",  # Use "static" for the control
        },
        {"type": "text", "text": prompt},
    ],
)

The current public documentation says video processing defaults to static, but I set both modes explicitly. I also checked the response steps rather than assuming a successful response meant agentic processing had run.

All 12 agentic calls contained both processing_call and processing_result. None of the 12 static calls did.

Benchmark design

I used two videos that I own:

a 2-minute-21-second UI screen recording;
a 10-minute-21-second conference talk.

I tested four workloads:

Find a clear UI state change in the short video.
Find the shortest, hardest-to-notice visual change in the short video.
Summarize the long video in five timestamped points.
Find one specific detail that appears only once in the long video.

Each workload ran three times in each mode: 4 workloads × 2 modes × 3 repetitions = 24 API calls.

The table uses medians for API total_tokens and client-observed end-to-end latency. A three-run sample is still small, but it is more honest than selecting one convenient request.

Results

Workload	Agentic total tokens	Static total tokens	Token difference	Agentic / Static latency
Short video: UI detail	12,101	10,055	+20%	21.8s / 10.0s
Short video: brief motion	34,038	10,559	+222%	32.9s / 10.1s
Long video: summary	1,394	57,610	−97.6%	10.5s / 14.1s
Long video: one-off detail	4,481	57,778	−92.2%	14.5s / 12.0s

The long-form result is clear. For the 10-minute talk, agentic processing used about 2.4% of the static token count for summarization and about 7.8% for one-off detail retrieval.

The short-video result went in the opposite direction. Agentic processing used 20% more tokens for a general UI change and 222% more for a brief visual event. The brief-motion query also took more than three times as long.

There was substantial variance as well. The three agentic runs for the brief-motion query ranged from 12,300 to 56,487 total tokens. One run would have told a very different story depending on which sample I happened to receive.

Did the extra short-video cost buy better detail?

Yes—but whether it was worth paying for depends on the product requirement.

For the brief-motion query, agentic processing found a Loading agents, please wait... screen that appeared for roughly 0.2 seconds. I checked the original frames at 0.75 and 0.9 seconds: the loading message really does disappear into the full interface almost immediately.

Static processing found a different event that remained visible for about two seconds.

So the extra agentic work was not simply wasted. It found a more fleeting event. But if the product only needs one obvious UI transition, the static answer is already useful, faster, and more stable.

For the long video, both modes returned usable timestamped answers. I spot-checked a resource identifier cited around 8:20 against the original frame and confirmed it was present. Agentic processing avoided loading the full video context while still finding relevant material.

A practical selection rule

Requirement	Starting point
Long-form summaries, lectures, or meeting analysis	Agentic
Finding a statement, number, or moment in a long video	Agentic
Short clips where latency matters most	Static
Sub-second motion, tiny text, or frame-level inspection	Test both; pay the agentic cost only when the added precision matters
A global default for a production system	Route by both video length and query type

I do not treat agentic processing as a universally better replacement for static processing. I treat it as a workload-routing decision.

When a question can be answered by inspecting a transcript and a few targeted windows, agentic processing can remove most of the video tokens. When the task requires broad visual coverage or repeated high-resolution inspection, navigation itself can cost more than simply sampling the clip.

What I would ship

For an application handling mixed video workloads, I would not expose one global processing-mode switch and call it done. I would route requests using at least these signals:

video duration;
whether the query is transcript-heavy or visually precise;
latency requirements;
whether a first static answer is sufficient;
observed token variance for the workload.

I would also log the effective processing steps and token categories. If the application cannot verify that agentic processing actually ran, it cannot explain either quality or cost.

Agentic Video Understanding gives developers a useful new control, not permission to stop measuring. If you have lectures, meetings, demos, or long interviews, start with one real question, run both modes three times, and choose based on the workload rather than the feature name.

Official documentation: Gemini API Video Understanding

Google launch post: Introducing agentic video understanding with Gemini

Antigravity CLI Digest: v1.1.5 ~ v1.1.7 Latest Release Analysis

JimmyLiao — Sun, 26 Jul 2026 17:26:22 +0000

Antigravity CLI Digest: v1.1.5 ~ v1.1.7 Latest Release Analysis

Antigravity CLI has recently received a series of major updates! From custom Agents now supporting a brand-new Markdown format, dynamically adjustable AI reasoning depth (/effort), to Headless mode and cross-platform bug fixes, here is a breakdown of this week's most notable highlights and details.

🌟 Highlights

🤖 Custom Agents Support Markdown Format (v1.1.6): You can now define Agent roles using agent.md files (with YAML frontmatter and H1 titles), granularly controlling permissions such as subagent and commandExecutionPolicy.
⚙️ AI Reasoning Depth Control /effort (v1.1.5): Added /effort command and --effort CLI flag, allowing users to dynamically switch between fast responses and deep reasoning.
📋 Windows CJK Clipboard Fix (v1.1.7): Resolved clipboard encoding issues when copying CJK (Chinese, Japanese, Korean) characters on Windows.
🔐 Enhanced Permission Prompts for Compound Shell Commands (v1.1.7): Compound shell commands requiring authorization now display in full, ensuring total auditing safety.
🔍 Global Code Search /codesearch (v1.1.6): Search results are now streamed asynchronously in real time, supporting cancellation at any time with Esc.

📌 Changelog Breakdown

🚀 v1.1.7 (Bug Fixes & Security Improvements)

Security & Permissions: Full command view now displayed during permission prompts for compound shell commands.
Plugin Logic: Fixed an issue where disabled plugins continued executing hooks and interfering with file editing tools.
Character Compatibility: Fixed clipboard text corruption when copying CJK characters on Windows platforms.
OAuth Specs: Relaxed OAuth issuer verification for non-standard MCP providers (e.g., Salesforce, Atlassian) and completed the refresh_token authorization flow.

🚀 v1.1.6 (Custom Agents & UX Improvements)

Markdown Agent: Introduced standard agent.md format for defining custom agents with commandExecutionPolicy controls.
Command History Copying: Supports numeric parameter /copy <n> to copy the n-th previous AI response.
Search Experience: /codesearch upgraded to asynchronous streaming with real-time counters, cancelable with Esc.

🚀 v1.1.5 (Reasoning Mode & Architecture Refactoring)

Reasoning Effort: Added /effort command and status bar badge to quickly adjust deep reasoning levels.
Background Task Stability: Background tasks isolated with independent lifecycles so individual task failures do not affect the main conversation.

💡 Official CHANGELOG: https://github.com/google-antigravity/antigravity-cli/blob/main/CHANGELOG.md

Antigravity #AntigravityCLI #AI #Changelog #DevRel #OpenAB

Antigravity CLI Digest: v1.1.5 ~ v1.1.7 Latest Release Analysis

JimmyLiao — Sun, 26 Jul 2026 16:40:34 +0000

Antigravity CLI Digest: v1.1.5 ~ v1.1.7 Latest Release Analysis

🌟 Highlights

🤖 Custom Agents Support Markdown Format (v1.1.6): You can now define Agent roles using agent.md files (with YAML frontmatter and H1 titles), granularly controlling permissions such as subagent and commandExecutionPolicy.
⚙️ AI Reasoning Depth Control /effort (v1.1.5): Added /effort command and --effort CLI flag, allowing users to dynamically switch between fast responses and deep reasoning.
📋 Windows CJK Clipboard Fix (v1.1.7): Resolved clipboard encoding issues when copying CJK (Chinese, Japanese, Korean) characters on Windows.
🔐 Enhanced Permission Prompts for Compound Shell Commands (v1.1.7): Compound shell commands requiring authorization now display in full, ensuring total auditing safety.
🔍 Global Code Search /codesearch (v1.1.6): Search results are now streamed asynchronously in real time, supporting cancellation at any time with Esc.

📌 Changelog Breakdown

🚀 v1.1.7 (Bug Fixes & Security Improvements)

Security & Permissions: Full command view now displayed during permission prompts for compound shell commands.
Plugin Logic: Fixed an issue where disabled plugins continued executing hooks and interfering with file editing tools.
Character Compatibility: Fixed clipboard text corruption when copying CJK characters on Windows platforms.
OAuth Specs: Relaxed OAuth issuer verification for non-standard MCP providers (e.g., Salesforce, Atlassian) and completed the refresh_token authorization flow.

🚀 v1.1.6 (Custom Agents & UX Improvements)

Markdown Agent: Introduced standard agent.md format for defining custom agents with commandExecutionPolicy controls.
Command History Copying: Supports numeric parameter /copy <n> to copy the n-th previous AI response.
Search Experience: /codesearch upgraded to asynchronous streaming with real-time counters, cancelable with Esc.

🚀 v1.1.5 (Reasoning Mode & Architecture Refactoring)

Reasoning Effort: Added /effort command and status bar badge to quickly adjust deep reasoning levels.
Background Task Stability: Background tasks isolated with independent lifecycles so individual task failures do not affect the main conversation.

💡 Official CHANGELOG: https://github.com/google-antigravity/antigravity-cli/blob/main/CHANGELOG.md

Antigravity #AntigravityCLI #AI #Changelog #DevRel #OpenAB

Free Academic Paper Translation with TranslateGemma

JimmyLiao — Mon, 19 Jan 2026 15:06:50 +0000

The Problem: You're Wasting Hours on Research Papers

Here's a situation you might recognize:

You open an arXiv paper. It's groundbreaking work in your field. You need to understand it. But after 20 minutes staring at the abstract, you've only grasped about 60% of what's happening.

So you start the copy-paste dance:

Highlight a paragraph 📋
Open DeepL in another tab
Paste and translate
Copy translation back to your notes
Lose all formatting 😫
Repeat 47 more times...

Three hours later, you're exhausted, your notes are a mess, and you're not even sure you understood the methodology correctly.

What if I told you there's a better way?

In this post, I'll show you how to translate entire arXiv papers into beautiful bilingual HTML — original and translation side-by-side — using Google's TranslateGemma model on free Colab GPU.

Set it up once, translate forever. Let's dive in.

🎯 What Makes This Different?

Before we jump into the tutorial, let's understand why this approach beats traditional tools:

TranslateGemma is Like a Specialized Translator for Academics

Think of general translation APIs (DeepL, Google Translate) as generalist interpreters. They're great at casual conversations but sometimes stumble on domain-specific jargon.

TranslateGemma is like hiring a PhD student who speaks both languages — it understands:

Technical terminology in context
Academic writing conventions
The difference between "model" (ML model) vs "model" (fashion model)
How to preserve mathematical notation

The Bilingual HTML is Like Having Training Wheels

Instead of reading pure translation, you get:

┌─────────────────────────────────┬─────────────────────────────────┐
│ Original (English)              │ Translation (Your Language)     │
├─────────────────────────────────┼─────────────────────────────────┤
│ This work introduces Gemma...   │ 本研究介紹了 Gemma...             │
│ ...                             │ ...                             │
└─────────────────────────────────┴─────────────────────────────────┘
         ↑ Navigate with ← → keys ↑

This means:

✅ Learn English while reading in your language
✅ Check translations when something feels off
✅ Build vocabulary by seeing terms in context

⚡ Why Free Colab T4 GPU Changes Everything

The old way:

Pay $0.01-0.02 per page for API credits
Or run models locally (if you have a GPU)
Or keep copy-pasting...

The new way:

Google Colab gives you free T4 GPU access (15GB VRAM)
TranslateGemma 4B fits comfortably in that memory
Zero cost for reasonable daily usage

What You Get	The Cost
Tesla T4 GPU (15GB)	$0
TranslateGemma 4B model	$0
~3 minutes per page	$0
Unlimited papers (within daily quota)	$0

The catch? About 3 minutes per page translation time. But honestly? That's the time you'd spend copy-pasting anyway, and you get much better results.

🚀 Let's Build This: 10-Minute Setup

Instead of drowning you in theory, let's get your first paper translated. We'll explain what's happening as we go.

Prerequisites (5 minutes setup, one-time)

You'll need:

Google Account (for Colab)
HuggingFace Account (sign up free)
HF Token with read access (create here)
Accept Gemma Terms (click here)

Step 1: Open Notebook & Enable GPU (1 minute)

Click this badge:

👉 Click here to open in Google Colab

Then:

Runtime menu → Change runtime type
Select T4 GPU from dropdown
Click Save

Why T4? It's the free tier GPU that's perfect for this task — enough memory for the 4B model, but not overkill.

Step 2: Run Environment Detection (30 seconds)

Execute the first code cell (click ▶️ or press Shift+Enter):

# This auto-detects whether you're on Colab, GCP, or local Jupyter
ENV = detect_environment()

Output:

================================================================================
🔍 Environment Detection
================================================================================
🖥️  Environment: COLAB
🐍 Python: 3.10
📂 Working dir: /content
================================================================================
✅ Environment: COLAB - Ready!
================================================================================

The notebook automatically detects your runtime environment

What's happening here?
The notebook adapts to your environment automatically. Same notebook works on:

Google Colab (most users)
GCP Custom Runtime (advanced)
Local Jupyter (if you have GPU)

No need to modify code — it just works™.

Step 3: Install Dependencies (2 minutes)

Next cell installs packages based on your environment:

# Colab gets lightweight dependencies
!pip install -q huggingface_hub transformers accelerate \
             sentencepiece protobuf pymupdf pillow \
             opencc-python-reimplemented

Key package: opencc-python-reimplemented

This ensures if you're translating to Traditional Chinese (Taiwan/Hong Kong), you get 基於 not 基于. Small details matter in academic writing.

Just click ▶️ and wait for installation to complete.

Step 4: Authenticate with HuggingFace (1 minute)

The notebook will prompt for your HF token:

📝 Please enter HuggingFace Token:
   💡 Tip: Use Colab Secrets (🔑 icon) for better security
   1. Get token: https://huggingface.co/settings/tokens
   2. Accept model: https://huggingface.co/google/translategemma-4b-it

Token: █

Paste your token and press Enter. Done.

Security tip: Use Colab's built-in secrets manager (🔑 sidebar icon) instead of pasting tokens directly if you're sharing notebooks.

Step 5: Load the Model (First run: 5 min, After: 30 sec)

This is where the magic happens:

from transformers_backend import TransformersBackend

backend = TransformersBackend()
result = backend.load_model()

First run output:

🚀 Loading TranslateGemma (4B)...
   ⏳ Downloading model (~8.6GB) on first run...

Downloading: 100% |████████████████████| 8.6G/8.6G [04:32<00:00, 31.5MB/s]

✅ Model loaded!
📍 Device: cuda:0
📊 Load time: 37.8s
💾 Memory: 13.8 GB used / 15.0 GB total
🎉 Ready to translate!

What just happened?

Downloaded TranslateGemma 4B (8.6GB) to Colab's disk
Loaded model into GPU memory
Cached for future runs (next time: 30 seconds!)

Grab a coffee ☕ on first run. It's worth the wait.

Step 6: Configure Your Translation (30 seconds)

Now the fun part — telling it what to translate:

# Which paper?
ARXIV_ID = "2403.08295"  # Gemma paper (or any arXiv ID)

# Which pages?
SECTIONS = {
    "abstract": (1, 3),  # Pages 1-3
}

# What languages?
SOURCE_LANG = "en"
TARGET_LANG = "zh-TW"  # Traditional Chinese (Taiwan)

# Generate beautiful HTML?
SAVE_HTML = True

Customization examples:

Translate intro section only:

SECTIONS = {
    "intro": (2, 5),
}

Translate to Japanese:

TARGET_LANG = "ja"

Translate everything:

SECTIONS = {
    "full": (1, 20),  # All pages
}

Supported languages: 50+ including zh-TW, zh-CN, ja, ko, fr, de, es, pt, ru, etc.

Step 7: Hit Translate! (3 min per page)

Execute the translation cell:

# Download PDF from arXiv
pdf_path, total_pages = download_arxiv(ARXIV_ID)

# Translate page by page with progress bar
with tqdm(total=3, desc="📖 Translating") as pbar:
    for page_num in range(1, 4):
        text = extract_text(pdf_path, page_num)
        result = backend.translate(text,
                                  source_lang=SOURCE_LANG,
                                  target_lang=TARGET_LANG)
        results.append(result)
        pbar.update(1)

Real output from my test:

📥 Downloading arXiv:2403.08295
✅ Downloaded: 2403.08295.pdf (17 pages)

================================================================================
🚀 Translation Started
================================================================================
📊 Pages: 3
⏱️  Est. time: ~9 minutes

📖 Translating: 33% |████████▌         | 1/3 [02:48<05:36, 168.05s/page]
✅ Page 1: 168.05s

📖 Translating: 67% |█████████████████ | 2/3 [05:51<02:43, 163.25s/page]
✅ Page 2: 163.25s

📖 Translating: 100% |█████████████████| 3/3 [08:37<00:00, 166.29s/page]
✅ Page 3: 166.29s

================================================================================
✅ Translation Complete!
================================================================================
📊 Pages: 3
⏱️  Total: 8 min 37 sec
⚡ Avg: 2.8 min/page
================================================================================

Live translation progress with tqdm showing real-time status per page

What's happening under the hood?

For each page, the backend:

Sends text to TranslateGemma with a simple, direct prompt:

Translate the following text from en to Traditional Chinese (Taiwan, 繁體中文).
Only output the translation, do not include explanations:

[Original text here]

Translation:

Model generates translation using GPU acceleration
Extracts clean translation from output
Applies OpenCC post-processing (for zh-TW)

Pro tip: Pages with heavy math/tables take similar time — the model handles them well.

Step 8: View Results in Notebook

Immediately after translation, you'll see:

================================================================================
📄 Page 1 - ABSTRACT
================================================================================

📝 Original:
--------------------------------------------------------------------------------
This work introduces Gemma, a family of lightweight, state-of-the-art open
models built from the research and technology used to create Gemini models.
Gemma models demonstrate strong performance across academic benchmarks for
language understanding, reasoning, and safety.

🌐 Translation:
--------------------------------------------------------------------------------
論文摘要：
Gemma 是一系列基於 Gemini 的輕量級、先進的開源模型。這些模型在語言理解、
推理和安全性等方面的表現優異，並在 18 項文字任務中，在同等規模的開源模型
中表現更佳。

Notice the quality:

"lightweight" → "輕量級" ✅ (not "輕" or "光")
"state-of-the-art" → "先進" ✅ (contextually appropriate)
"benchmarks" → "基準測試" ✅ (technical term)
Traditional Chinese: 基於 ✅ (not 基于)

This is way better than copy-pasting into Google Translate.

Step 9: Download Interactive HTML (10 seconds)

The final cell generates a self-contained HTML file:

# Generate bilingual HTML
filename = f"arxiv_{ARXIV_ID}_{SOURCE_LANG}-{TARGET_LANG}.html"

# Auto-download in Colab
from google.colab import files
files.download(filename)

Output:

💾 HTML saved: arxiv_2403.08295_en-zh-TW.html
📂 Full path: /content/arxiv_2403.08295_en-zh-TW.html
📊 Size: 143.2 KB
📄 Pages: 3

📥 To view the full HTML:
   1. Download: Right-click 'arxiv_2403.08295_en-zh-TW.html' in Files panel → Download
   2. Or use auto-download (Colab native only)

Open the HTML in your browser:

Clean header with title, language pair, date, and keyboard-friendly navigation

Original English (left) and Traditional Chinese translation (right) in perfect sync

What you're seeing:

Header: arXiv:2403.08295 Bilingual Translation
Metadata: en → zh-TW | 2026-01-19 14:22
Navigation: ◄ Prev | Page 1 (1/3) | Next ▶
Hint bar: 💡 Use ← → keys (yellow background for visibility)
Section header: 📄 ABSTRACT - Page 1 ⏱️ 179.74s (shows translation time)
Dual columns: Gray background for original, white for translation

Features:

✅ Side-by-side original + translation (never lose context)
✅ Keyboard navigation (← → arrow keys for fast reading)
✅ Page counter with progress ("Page 1 (1/3)")
✅ Translation time per page (⏱️ 179.74s shown in purple header)
✅ Works offline (no internet needed after download)
✅ Mobile responsive (columns stack vertically on small screens)
✅ Clean typography (monospace for original, sans-serif for translation)

This is your forever-reference for that paper. Share it, annotate it, or keep it for later.

🔬 Translation Quality: Let's Be Honest

I tested this on the Gemma Technical Report (arXiv:2403.08295), a genuinely complex paper with:

Model architecture details
Training methodology
Benchmark results (tables)
Mathematical notation
Lots of jargon ("multi-query attention", "RoPE embeddings", "supervised fine-tuning")

Sample: Original Text

The Gemma model architecture is based on the transformer decoder (Vaswani et al., 2017).
The core parameters of the architecture are summarized in Table 1. Models are trained on
a context length of 8192 tokens. We also utilize several improvements proposed after the
original transformer paper, and list them below:

Multi-Query Attention (Shazeer, 2019). Notably, the 7B model uses multi-head attention
while the 2B checkpoints use multi-query attention (with num_kv_heads = 1), based on
ablations that showed that multi-query attention works well at small scales.

TranslateGemma Output

Gemma 模型架構基於 Transformer 解碼器（Vaswani 等人，2017）。架構的核心參數
總結於表 1 中。模型是在 8192 個 token 的上下文長度上訓練的。我們還使用了原始
Transformer 論文之後提出的幾項改進，並在下面列出：

多查詢注意力（Shazeer，2019）。值得注意的是，7B 模型使用多頭注意力，而 2B 檢查
點使用多查詢注意力（num_kv_heads = 1），這是基於消融研究顯示多查詢注意力在小規模
下效果良好。

My Assessment

Aspect	Rating	Notes
Technical Accuracy	⭐⭐⭐⭐⭐	"multi-query attention" → "多查詢注意力" is spot-on
Terminology Consistency	⭐⭐⭐⭐⭐	Same term translated same way throughout
Grammar & Flow	⭐⭐⭐⭐⭐	Reads naturally in target language
Format Preservation	⭐⭐⭐⭐⭐	Keeps paragraphs, citations, structure intact
Context Understanding	⭐⭐⭐⭐	Gets that "ablations" means ablation studies (not medical)

Where it shines:

✅ Technical jargon (transformers, attention mechanisms, tokens)
✅ Citations format preserved: (Vaswani et al., 2017)
✅ Numbers and variables unchanged: 8192, 7B, num_kv_heads
✅ Academic tone maintained

Minor quirks:

⚠️ Sometimes literal translation where paraphrase would be smoother
⚠️ Very occasional wrong word choice (maybe 1-2 per page)

Compared to:

DeepL: Better for general text, but struggles with ML terminology
Google Translate: Faster, but often mistranslates domain terms
GPT-4/Claude API: Similar quality, but costs $0.01-0.02 per page
Human translator: Obviously better, but $$$$ and slow

For free academic translation, this is unbeatable.

⚡ Performance & Cost: The Real Numbers

Let me share actual benchmarks from my testing:

My Setup

Platform: Google Colab Free Tier
GPU: Tesla T4 (15GB VRAM)
Model: TranslateGemma 4B (~8.6GB)
Test paper: Gemma Report (arXiv:2403.08295)

Timing Breakdown

Operation	First Run	Subsequent Runs
Model download	~5 min (one-time)	-
Model loading	37.8 sec	30 sec (cached)
Translation	165-170 sec/page	Same
HTML generation	<1 sec	<1 sec

Total for 3 pages:

First ever run: ~15 minutes (including model download)
After model cached: ~9 minutes (just translation time)

GPU Usage

📊 GPU Memory:
   Total: 15.0 GB
   Model: ~8.6 GB
   Working: ~1.2 GB
   Available: ~5.2 GB

📊 Utilization:
   During translation: 95-100%
   Idle: 0%

The T4 is fully utilized during translation — that's why it's relatively fast.

Cost Comparison (10-page paper)

Method	Time	Cost	Quality
TranslateGemma + Colab	~30 min	$0.00	⭐⭐⭐⭐⭐
Claude 3.5 API	~2 min	$0.10	⭐⭐⭐⭐
DeepL Pro API	~1 min	$0.20	⭐⭐⭐⭐
Google Translate	Instant	$0.00	⭐⭐⭐
Human translator	2-3 days	$50-200	⭐⭐⭐⭐⭐

My take: If you're reading 5-10 papers per week, the time investment of TranslateGemma pays off in quality + zero cost. For one-off urgent translations, APIs are faster.

🛠️ Power User Tips

Once you've got the basics down, here are some pro moves:

Tip 1: Batch Translate Strategically

Don't translate entire papers blindly. Use this approach:

SECTIONS = {
    "abstract": (1, 1),      # Quick scan: worth deep reading?
    "intro": (2, 4),         # Context and motivation
    "conclusion": (15, 16),  # Main takeaways
}

Read these first (10 min translation). If it's relevant, come back for:

SECTIONS = {
    "method": (5, 10),
    "results": (11, 14),
}

Why? You'll save time on papers that aren't relevant to your work.

Tip 2: Translate to Multiple Languages

Learning Japanese and Chinese? Do this:

for lang in ["zh-TW", "ja"]:
    TARGET_LANG = lang
    # Run translation
    # Generate HTML

Now you have 3 versions to compare:

Original English
Traditional Chinese
Japanese

Great for building technical vocabulary across languages.

Tip 3: Fix the "Pages 2-3 Didn't Translate" Bug

If you're using an older version, pages with lots of charts/tables might fail to translate (they just return the original text).

We fixed this recently! Update to latest:

cd trans-gemma && git pull

What we changed:

Switched from complex chat template to simple direct prompt
More robust extraction logic
Better handling of mixed-content pages (text + figures)

Tip 4: Run Locally If You Have GPU

Don't want to depend on Colab quotas? Run locally:

git clone https://github.com/jimmyliao/trans-gemma.git
cd trans-gemma
pip install -e ".[examples]"

# Open notebook
jupyter notebook arxiv-reader.ipynb

The notebook auto-detects your environment (Colab vs Local) and adapts. Just works.

Requirements:

Python 3.10+
NVIDIA GPU with 10GB+ VRAM (or use CPU, but very slow)
~15GB disk space for model

Tip 5: Customize the HTML Output

The generated HTML uses vanilla JavaScript and can be easily customized. Open the notebook cell that generates HTML and modify:

Change color scheme:

.header {
  background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
}

Add dark mode:

@media (prefers-color-scheme: dark) {
  body { background: #1a1a1a; color: #e0e0e0; }
}

Adjust layout ratio:

.columns {
  grid-template-columns: 45% 55%; /* Favor translation side */
}

🤔 Common Questions

Q: "Colab says GPU unavailable?"

A: Free tier has daily quotas (typically refreshes every 12-24 hours). Try:

Wait a few hours and retry
Try off-peak times (evenings/weekends in US timezones)
Switch Google accounts if you have multiple
Upgrade to Colab Pro ($10/month) for guaranteed GPU

Q: "Model download stuck at 45%?"

A: Network hiccups happen. Try:

Restart runtime: Runtime → Restart runtime
Clear outputs: Edit → Clear all outputs
Re-run from Step 5: Model downloads resume where they left off

If still stuck after 15 minutes, it's likely a HuggingFace server issue. Wait 30 min and retry.

Q: "Translation has simplified + traditional Chinese mixed?"

A: This should be fixed in latest version. We added opencc-python-reimplemented to backend.

If still happening:

cd trans-gemma && git pull

Then restart notebook.

Q: "Can I translate non-arXiv PDFs?"

A: The current notebook is optimized for arXiv URLs. For local PDFs, modify:

# Instead of:
pdf_path, total = download_arxiv(ARXIV_ID)

# Use:
pdf_path = "/content/your_paper.pdf"
total = len(fitz.open(pdf_path))

Then run translation cells as normal.

Q: "Is this safe for commercial use?"

A: Tricky question:

Code (MIT license): Yes, use commercially
Gemma model: Read Terms of Use
Colab: Free tier meant for learning/research

My advice: Use for research/learning. If you're making money from translations, consider:

Running on your own GPU
Using Colab Pro (legitimized commercial use)
Checking Gemma's commercial terms carefully

🧠 How This Actually Works (For the Curious)

Let me pull back the curtain on the technical implementation.

Architecture Overview

┌──────────────┐
│   User       │
│  (Browser)   │
└──────┬───────┘
       │ 1. Click Colab link
       ▼
┌──────────────────────────────────┐
│   Colab Notebook                 │
│   ┌──────────────────────────┐   │
│   │ Environment Detection    │   │
│   └──────────┬───────────────┘   │
│              │ 2. Auto-config    │
│              ▼                    │
│   ┌──────────────────────────┐   │
│   │ Download arXiv PDF       │   │
│   │ (via urllib)             │   │
│   └──────────┬───────────────┘   │
│              │ 3. Extract text   │
│              ▼                    │
│   ┌──────────────────────────┐   │
│   │ PyMuPDF (page-by-page)   │   │
│   └──────────┬───────────────┘   │
│              │ 4. Send to model  │
│              ▼                    │
│   ┌──────────────────────────┐   │
│   │ TranslateGemma 4B        │◄──┼─ HuggingFace Hub
│   │ (on T4 GPU)              │   │   (model download)
│   └──────────┬───────────────┘   │
│              │ 5. Post-process   │
│              ▼                    │
│   ┌──────────────────────────┐   │
│   │ OpenCC (if zh-TW)        │   │
│   └──────────┬───────────────┘   │
│              │ 6. Generate HTML  │
│              ▼                    │
│   ┌──────────────────────────┐   │
│   │ Bilingual HTML           │   │
│   │ (side-by-side layout)    │   │
│   └──────────┬───────────────┘   │
│              │ 7. Download       │
└──────────────┼───────────────────┘
               ▼
       ┌───────────────┐
       │  User's PC    │
       │  (HTML file)  │
       └───────────────┘

Key Technical Decisions

1. Simple Prompt Over Chat Template

Initially, we used HuggingFace's apply_chat_template():

# Old approach (failed on pages with tables/math)
messages = [{
    "role": "user",
    "content": [{"type": "text", "text": text, ...}]
}]
inputs = tokenizer.apply_chat_template(messages, ...)

Problem: Pages with heavy formatting confused the template, and extraction logic failed.

Fix: Switched to dead-simple prompt:

# New approach (rock solid)
prompt = f"""Translate the following text from {source_lang} to Traditional Chinese (Taiwan, 繁體中文). Only output the translation, do not include explanations:

{text}

Translation:"""

inputs = tokenizer(prompt, return_tensors="pt")

Result: 100% success rate across all page types.

2. OpenCC Post-Processing

TranslateGemma 4B tends to output Simplified Chinese by default, even when asked for Traditional.

Solution: Always post-process for zh-TW:

if target_lang == "zh-TW":
    from opencc import OpenCC
    cc = OpenCC('s2twp')  # Simplified → Traditional (Taiwan phrases)
    translation = cc.convert(translation)

This ensures:

基于 → 基於
轻量级 → 輕量級
这些 → 這些

Taiwan readers notice these details!

3. Dynamic Environment Detection

Same notebook runs on Colab, GCP, or local Jupyter:

def detect_environment():
    try:
        import google.colab
        return 'colab'
    except ImportError:
        pass

    if os.path.exists('/opt/conda/envs/py310'):
        return 'gcp'

    return 'local'

Then:

if ENV == 'colab':
    # Lightweight installs
elif ENV == 'gcp':
    # Custom runtime configs
else:
    # Local includes PyTorch

Why? Colab has PyTorch pre-installed; local doesn't. One notebook, zero friction.

4. Progressive HTML Generation

Instead of loading entire paper at once, the HTML uses:

// Page navigation with keyboard shortcuts
let currentPage = 0;
function showPage(n) {
  document.querySelectorAll('.page').forEach(p => p.style.display = 'none');
  document.getElementById(`page-${n}`).style.display = 'block';
  currentPage = n;
}

document.addEventListener('keydown', e => {
  if (e.key === 'ArrowLeft') showPage(currentPage - 1);
  if (e.key === 'ArrowRight') showPage(currentPage + 1);
});

Benefit: Even 50-page papers load instantly in browser.

🎯 Who This Is (and Isn't) For

✅ Perfect For:

Graduate Students

Reading 5-10 papers per week
Budget: $0
Time: Can wait 3 min/page for quality translations
Bonus: Learn English terminology via side-by-side reading

Non-native English Researchers

Deep-reading important papers
Want to understand, not just skim
Appreciate bilingual layout for learning

AI/ML Engineers Keeping Current

Track latest arXiv preprints
Translate abstract + intro first, decide if worth full read
Free tier is plenty for 2-3 papers daily

❌ Not Ideal For:

Urgent Deadlines

If you need a paper translated in 5 minutes, use Claude/GPT-4 API
They're faster (~10 sec/page), just costs $0.01-0.02 per page

Large-Scale Translation

Translating 100 papers → you'll hit Colab quotas
Consider running on your own GPU or cloud instance

Commercial Translation Services

Check Gemma Terms of Use carefully
May need different licensing

🔮 What's Next for This Project

I'm actively developing this, and here's what's coming:

Short-term (Next Month)

🔜 Auto-language detection from paper metadata
🔜 DOCX/Markdown output formats (not just HTML)
🔜 Batch mode: Translate multiple papers in one go
🔜 Dark mode for HTML output

Medium-term (Next Quarter)

🔜 Gemma 2/3 support when released
🔜 Terminology glossary extraction (build your own vocab list)
🔜 Figure/table captions translation
🔜 API mode for programmatic access

Long-term (This Year)

🔜 Web UI (no notebook required)
🔜 Mobile app for reading on-the-go
🔜 Community translations (share & reuse)

Want to contribute? Pull requests welcome! → GitHub

🚀 Your Turn: Translate Your First Paper

Alright, you've read 3000+ words about this. Time to actually try it.

5-Minute Challenge:

Pick a paper: Go to arXiv.org, find something interesting
Copy the ID: It looks like 2403.08295 (from URL)
Click this badge:
Enable T4 GPU: Runtime → Change runtime type → T4 GPU
Run all cells: Runtime → Run all (or Ctrl+F9)
Wait ~10 minutes (first run includes model download)
Download the HTML and open in browser

Boom. You just translated an academic paper for free.

💬 Let's Make This Better Together

I built this because I was frustrated with copy-pasting papers into translators. It solved my problem. Now I'm sharing it with you.

If this helped you:

⭐ Star the repo: github.com/jimmyliao/trans-gemma
🐛 Report bugs: Open an issue
💡 Share ideas: Start a discussion
🤝 Contribute code: Pull requests welcome!

Follow for updates:

🌐 Blog: jimmyliao.dev
🐙 GitHub: @jimmyliao
🐦 Twitter/X: @jimmyliao

📚 Resources & References

Official Links

TranslateGemma Model: https://huggingface.co/google/translategemma-4b-it
Gemma Family: https://ai.google.dev/gemma
This Project: https://github.com/jimmyliao/trans-gemma

Open Translate: Offline Translation Web Service by another developer
Gemma Technical Report — the paper I used for testing

Technical References

🙏 Acknowledgments

This project builds on incredible work by:

Google DeepMind for TranslateGemma and Gemma family
HuggingFace for making model distribution seamless
Google Colab for free GPU access
PyMuPDF team for reliable PDF parsing
OpenCC project for Chinese conversion

Open source makes projects like this possible. Thank you! 🙌

P.S. If you made it this far, you're either genuinely interested or an excellent skimmer. Either way, I appreciate you reading. Now go translate something! 📚✨

Questions? Comments? Horror stories about academic translation? Drop them below! 👇

Antigravity Rules & Workflows Guide: AI-Powered Development Standards

JimmyLiao — Sun, 14 Dec 2025 14:46:52 +0000

https://memo.jimmyliao.net/p/antigravity-rules-and-workflows-guide

Enterprise level Agentic dev workflow with Gemini CLI, ADK, and Antigravity

JimmyLiao — Tue, 09 Dec 2025 22:14:01 +0000

https://memo.jimmyliao.net/p/enterprise-level-agentic-dev-workflow

This is the the the speaker experience from DevFest Taichung, DevFest Taipei. You can check the online deck:

https://devfest2025taipei-jimmyliao.web.app/

Use Gemini-CLI with Gemini-3 to add test coverage

JimmyLiao — Tue, 18 Nov 2025 17:27:36 +0000

https://www.youtube.com/watch?v=SQHV2Kek5fA

Using ADK + the latest Gemini 3 Pro + Google Map to create an AI Travel Agent!

JimmyLiao — Tue, 18 Nov 2025 17:26:18 +0000

[Building an AI Travel Advisor with Gemini 3 Pro + ADK + Google Maps]

https://memo.jimmyliao.net/p/gemini-3-pro-gemini-cli

Using ADK + the latest Gemini 3 Pro + Google Map to create an AI Travel Agent!

Also, try on AI Studio: https://goo.gle/try-gemini3

Gemini3

GDE

ADK

Hands-on Test of Gemini 3 Pro - Exploration using Gemini-CLI

JimmyLiao — Tue, 18 Nov 2025 17:24:18 +0000

The Gemini 3 has been launched!

And check it out using the Gemini-CLI!

[Hands-on Test of Gemini 3 Pro - Exploration using Gemini-CLI]

https://memo.jimmyliao.net/p/gemini-3-pro-adk-google-maps-a

[Building an AI Travel Advisor with Gemini 3 Pro + ADK + Google Maps] Using ADK + the latest Gemini 3 Pro + Google Map to create an AI Travel Agent! https://memo.jimmyliao.net/p/gemini-3-pro-adk-google-maps-ai #Gemini3 #GDE #ADK

JimmyLiao — Tue, 18 Nov 2025 16:33:52 +0000

用 Gemini 3 Pro + ADK + Google Maps 打造 AI 旅遊顧問

Repo 幫忙按個 Star, Fork, 跟貢獻吧！透過這個連結直接在 AI Studio 試試吧: https://goo.gle/try-gemini3

memo.jimmyliao.net

The Gemini 3 has been launched! And check it out using the Gemini-CLI! [Hands-on Test of Gemini 3 Pro - Exploration using Gemini-CLI] https://memo.jimmyliao.net/p/gemini-3-pro-gemini-cli

JimmyLiao — Tue, 18 Nov 2025 16:32:27 +0000

實戰測試 Gemini 3 Pro - 使用 Gemini-CLI 探索 - by JimmyLiao(NET)

透過這個連結直接在 AI Studio 試試吧: https://goo.gle/try-gemini3

memo.jimmyliao.net

DEV Community: JimmyLiao

Gemini Agentic Video Isn't Always Cheaper: A 24-Run Benchmark

Current model support

Enabling agentic processing

Benchmark design

Results

Did the extra short-video cost buy better detail?

A practical selection rule

What I would ship

Antigravity CLI Digest: v1.1.5 ~ v1.1.7 Latest Release Analysis

Antigravity CLI Digest: v1.1.5 ~ v1.1.7 Latest Release Analysis

🌟 Highlights

📌 Changelog Breakdown

🚀 v1.1.7 (Bug Fixes & Security Improvements)

🚀 v1.1.6 (Custom Agents & UX Improvements)

🚀 v1.1.5 (Reasoning Mode & Architecture Refactoring)

Antigravity #AntigravityCLI #AI #Changelog #DevRel #OpenAB

Antigravity CLI Digest: v1.1.5 ~ v1.1.7 Latest Release Analysis

Antigravity CLI Digest: v1.1.5 ~ v1.1.7 Latest Release Analysis

🌟 Highlights

📌 Changelog Breakdown

🚀 v1.1.7 (Bug Fixes & Security Improvements)

🚀 v1.1.6 (Custom Agents & UX Improvements)

🚀 v1.1.5 (Reasoning Mode & Architecture Refactoring)

Antigravity #AntigravityCLI #AI #Changelog #DevRel #OpenAB

Free Academic Paper Translation with TranslateGemma

The Problem: You're Wasting Hours on Research Papers

🎯 What Makes This Different?

TranslateGemma is Like a Specialized Translator for Academics

The Bilingual HTML is Like Having Training Wheels

⚡ Why Free Colab T4 GPU Changes Everything

🚀 Let's Build This: 10-Minute Setup

Prerequisites (5 minutes setup, one-time)

Step 1: Open Notebook & Enable GPU (1 minute)

Step 2: Run Environment Detection (30 seconds)

Step 3: Install Dependencies (2 minutes)

Step 4: Authenticate with HuggingFace (1 minute)

Step 5: Load the Model (First run: 5 min, After: 30 sec)

Step 6: Configure Your Translation (30 seconds)

Step 7: Hit Translate! (3 min per page)

Step 8: View Results in Notebook

Step 9: Download Interactive HTML (10 seconds)

🔬 Translation Quality: Let's Be Honest

Sample: Original Text

TranslateGemma Output

My Assessment

⚡ Performance & Cost: The Real Numbers

My Setup

Timing Breakdown

GPU Usage

Cost Comparison (10-page paper)

🛠️ Power User Tips

Tip 1: Batch Translate Strategically

Tip 2: Translate to Multiple Languages

Tip 3: Fix the "Pages 2-3 Didn't Translate" Bug

Tip 4: Run Locally If You Have GPU

Tip 5: Customize the HTML Output

🤔 Common Questions

Q: "Colab says GPU unavailable?"

Q: "Model download stuck at 45%?"

Q: "Translation has simplified + traditional Chinese mixed?"

Q: "Can I translate non-arXiv PDFs?"

Q: "Is this safe for commercial use?"

🧠 How This Actually Works (For the Curious)

Architecture Overview

Key Technical Decisions

🎯 Who This Is (and Isn't) For

✅ Perfect For:

❌ Not Ideal For:

🔮 What's Next for This Project

Short-term (Next Month)

Medium-term (Next Quarter)

Long-term (This Year)

🚀 Your Turn: Translate Your First Paper

5-Minute Challenge:

💬 Let's Make This Better Together

📚 Resources & References

Official Links

Related Articles

Technical References