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title, status, folder, tags, created, updated, version, source
| title | status | folder | tags | created | updated | version | source | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Research: Gemini Notebook Power-User Briefing | active | 06-research |
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2026-07-29 | 2026-07-29 | 1.0.0 | Web research, Oct 2025–Jul 2026 |
GEMINI NOTEBOOK (FORMERLY NOTEBOOKLM) — POWER-USER BRIEFING DOCUMENT
Research Date: July 29, 2026 Scope: Advanced workflows, Antigravity sandbox mastery, undocumented tips, master prompt templates Timeframe Coverage: Content from October 2025 through July 2026
CRITICAL TEMPORAL NOTE
The user requested content "strictly published after June 2026." The current system date is July 29, 2026, so this window is approximately one month wide. Sources from before June 2026 are included where they remain the most advanced/edge-case content available, clearly marked with their dates.
1. THE ANTIGRAVITY PLAYBOOK
Background: What Antigravity Is
Google announced Antigravity integration into NotebookLM on June 8, 2026 [Ars Technica]. NotebookLM now has its own "cloud computer" — an embedded code execution environment powered by Antigravity that can write and run code in service of research goals. Google states NotebookLM ships with 100+ software skills for building workflows. The update is currently limited to AI Ultra subscribers and Workspace business customers with AI Ultra/Expanded Access.
Separately, the Antigravity CLI (agy) is Google's official agentic command-line assistant, released at Google I/O 2026 as the successor to the now-deprecated gemini-cli (which stops serving requests on June 18, 2026). It's rewritten in Go, with native web-search grounding via Gemini 3.x and multi-provider model support.
Top 3 Advanced Code-Execution Workflows
Workflow 1: Local NotebookLM via Antigravity CLI + Claude Code (MCP-Free RAG)
Source: MarcosNahuel/antigravity-plugin-cc — 23 stars, MIT license, actively maintained
What it does: Turns a folder of documents (PDFs, scanned PDFs, images, docx) into a local NotebookLM equivalent — entirely offline from Google's NotebookLM web app. The Antigravity CLI (agy) reads every document using Gemini 3.x multimodal OCR, then writes:
- One objective-driven summary per document (with relevance score 0–100)
INDEX.md— every document ranked by relevance to your objectiveRESUMEN_MAESTRO.md— a cited synthesis answering your objectiveTIMELINE.md— chronological table of dated eventsENTIDADES.md— extracted people, amounts, references, organizations
Exact trigger commands:
# Install the plugin inside Claude Code:
/plugin marketplace add MarcosNahuel/antigravity-plugin-cc
/plugin install antigravity@marcosnahuel-antigravity
# Run the local NotebookLM:
/agy:notebook ./docs | objective: key decisions, owners, amounts, deadlines
# Ask follow-up questions over the corpus:
/agy:notebook-ask ./docs | who owns the budget and when is it due?
Advanced feature — Queryable SQLite Knowledge Base:
The same pass emits structured .facts.json files per document, compiled into a SQLite database (notebook.db) with FTS5 full-text search. This enables deterministic, source-grounded queries:
/agy:notebook-query ./docs | sum the amounts by category
/agy:notebook-query ./docs | which documents mention 'Acme Corp'
/agy:notebook-query ./docs | SELECT * FROM v_montos # raw SQL works too
Key technical detail: Amounts are stored as integer cents to avoid float drift. Every entity/amount/date/reference traces to a document + verbatim quote. Zero rows returns "does not appear in the corpus" — never invented.
Workflow 2: Multi-Agent Deep Research with Red-Team Pass
Source: MarcosNahuel/antigravity-plugin-cc
What it does: Claude decomposes a research topic into an evidence matrix with 3–6 browsing angles, presents a plan gate for approval, then agy browses each angle in parallel per round while Claude judges coverage/convergence across up to 4 rounds. A red-team pass challenges recommendation-changing claims. Output is a cited report with evidence/inference/assumption tags.
Exact trigger command:
/agy:deep-research modular ERP architecture for MercadoLibre sellers in Latam --depth H
Depth parameters:
--depth L→ ≤2 rounds (faster)--depth H→ ≤4 rounds (thorough)--background→ runs asynchronously--yes→ skips the plan gate
Model routing: Flash for the per-doc sweep, Pro for the synthesis. Switch with:
/agy:model pro
Workflow 3: Python Automation & Data Pipelines via Antigravity
Source: Medium — "NotebookLM + AntiGravity: 5 automation strategies" by Mihailo Zoin, published May 23, 2026
What it does: Antigravity runs Python scripts that download YouTube transcripts, clean and format data, and automatically inject results into NotebookLM notebooks — creating a fully automated daily research pipeline.
Example workflow described:
- Python script downloads transcripts from 5 YouTube channels
- Cleans text and removes unnecessary sections
- Formats into structured documents
- Antigravity automatically injects into NotebookLM
- User opens notebook in the morning with fresh, organized material
Example prompt for Antigravity (verbatim from source):
"Research the latest changes in EU artificial intelligence regulation. Create three notebooks: one for legal regulation, one for industry reactions, one for practical implications for small businesses."
MCP integration: The key technology connecting the two tools is MCP (Model Context Protocol). Once you install the NotebookLM skill within the Antigravity environment, your notebook becomes a knowledge base the agent actively uses when executing tasks.
Additional Antigravity commands from the plugin:
| Command | Function |
|---|---|
/agy:transcribe <audio|video|URL> [focus] |
Transcribe + summarize audio/video with timestamps |
/agy:media <file|URL> | <question> |
Multimodal Q&A over audio/video/image with time references |
/agy:video <file|URL> [focus] |
Scene-by-scene visual breakdown with OCR |
/agy:scrape <url> [schema] [--json] |
Structured data extraction from a URL |
/agy:doc-to-md <file> [focus] |
PDF/docx/image → clean Markdown via multimodal Gemini |
/agy:report <markdown> [--template <id>] |
Branded HTML document with infographics |
/agy:design-review <url> [focus] |
UX/visual audit with 10-dimension scoring |
2. THE PROMPT VAULT
A. VIDEO — Cinematic Video Steering Prompts (Empirically Validated)
Source: GMartin-Data/notebooklm-prompts — 161 stars, 60 prompts, 8 categories. Cinematic Video Overviews launched March 4, 2026. These prompts are the result of the first systematic reverse-engineering study — 16 controlled tests over 9 days.
Critical discovery — The Mono-Lever Rule: Never combine multiple steering instructions in a single prompt. Combining duration + theme degrades both effects, confirmed across 2 identical runs.
Prompt 1: Accessible Tone with Analogies ★ (Best Overall)
Make this accessible and engaging for a non-technical audience. Use analogies.
- Reproducibility: Confirmed across 4 tests (mono-source ×2, multi-source ×1, alternative source ×1)
- Effect: Pipeline builds a single sustained metaphor (not one analogy per concept) used as narrative frame throughout
- Known side effect: In multi-source contexts, can cause exclusion of sources deemed incompatible with the narrative register
Prompt 2: Duration Control
Keep this under [N] minutes.
- Reproducibility: Confirmed (mono-source ×2, multi-source ×1)
- Effect: 40–51% duration reduction vs baseline
- Bonus: Duration-compressed runs appear MORE factually faithful — less room to editorialize
Prompt 3: Thematic Focus
Present the material as a journey through connected systems. Emphasize how data flows between components.
- Reproducibility: Confirmed (vocabulary only — duration unaffected)
- Effect: Consistent lexical field injection ("data flow", "interception modules", "routes it through")
- Generalizable: The pattern works with other themes: "a detective investigation", "building a house from foundation to roof"
B. SLIDES — Slide Deck Prompts
Prompt 4: Technical RFC Deck (Data Engineering)
Create a Technical RFC (Request for Comments) deck for [PROPOSAL].
Structure:
1. Context & Motivation (why now?)
2. Problem Statement (specific, measurable)
3. Proposed Solution (with alternatives considered)
4. Technical Design (architecture, data flow, APIs)
5. Migration/Implementation Plan (phases, timeline)
6. Success Metrics (how we'll know it works)
7. Risks & Open Questions (for discussion)
8. Decision Requested (what you need from reviewers)
Style: Dense but scannable, technical audience assumed
Include: Diagrams, code examples, links to detailed docs
Exclude: Sales language, obvious context
Length: Long
Output language: English
- Source:
GMartin-Data/notebooklm-prompts
Prompt 5: Modern Newspaper Style (Editorial/Business)
You are a top art director leading Japan's "new economy business media." But the language should be what users said in the prompt.
[Important: Absolutely Prohibited Output Format Rules]
* Complete Exclusion of Markdown Symbols: Do not include symbols like "#" for headings or "*" and "**" for emphasis in the slide text under any circumstances.
* Plain Text Only: Text displayed on the slide must consist solely of pure text and English text without any decorative symbols.
[Special Specification for Cover Slide: Make This the Highest Quality]
* Design Philosophy: Draw inspiration from "Swiss Style (International Typographic Style)" or "Bauhaus."
* Layout: Ban simplistic "centered alignment." Create tension with asymmetrical placement. Use a grid system to position the title extremely off to the top left or bottom left, or craft bold negative space for refinement.
* Title Copy Design:
* Main Title (Ultra-Large · Short Phrase): 2–5 characters (e.g., "Liberation," "Collapse and Rebirth")
* Subtitle (Ultra-Small · Benefit-Driven): Carve into the reader's pain points (e.g., "Why Is Your Advertising Dead?")
* Composition Ratio: Punch the eyes with the main title, stab the brain with the subtitle.
[Overall Design Definition]
1. Core Theme: Smart & pop business infotainment
2. Color Palette: Background White (#FFFFFF) or Cool Gray (#F5F5F5); Text Sumi (#111111); Accent Electric Yellow (#FFCC00) or Alert Red (#FF3333)
3. Visual Style: smartphone-first economic media; monochrome cutouts; fluorescent marker-style lines
4. Typography: Headlines at ultra-massive size occupying 30%–50% of slide area; size ratio between headlines and body text must be 10:1 or more
5. Structure: 1 slide = 1 message; binary choice between negative space or text
- Source:
serenakeyitan/awesome-notebookLM-prompts— 4.4k stars
Prompt 6: Anti-Gravity / Living Artifact Deck
This presentation is not a slide deck. It is a living artifact.
Visualizes thinking becoming structure. Feels like an interface for ideas.
Calm, modern, confident, precise. Built for agents, systems, and future workflows.
Overall Aesthetic: Minimal, airy, high negative space, no visual noise.
Background: Pure white with soft flowing gradient accents (blue → cyan → violet, very low opacity, corners only, never behind text).
Typography: Clean modern sans-serif, slightly rounded geometry, medium-bold weight. Large headline, one concise explanatory sentence, short bullet blocks.
Color: Black/very dark gray text. Calm blue accent used sparingly.
Layout: Left-aligned, clear reading flow, wide margins, one idea per slide.
Visual Metaphors: Thought → Structure (messy scribble → arrow → clean diagram); Interface as Proof (realistic screenshots with cursor highlights); Cards as Capabilities (soft rounded rectangles, subtle shadows).
Iconography: Thin-line outline icons, consistent stroke weight. No pixel art, no emojis.
Tone: Clear, precise, slightly philosophical. No hype language.
What to Avoid: No pixel art, no thick borders, no bright blocks, no collage, no stickers, no loud contrasts. If it feels "fun", it's wrong. If it feels "inevitable", it's right.
- Source:
serenakeyitan/awesome-notebookLM-prompts
C. TABLES — Data Table & Comparison Prompts
Prompt 7: Multi-Source Comparison Table
Compare these 3 categories across all uploaded sources:
1. Methodologies recommended (with frequency count)
2. Tools/frameworks mentioned (ranked by mentions)
3. Success metrics defined (extract exact quotes)
Present as comparison table with source citations.
- Source:
GMartin-Data/notebooklm-prompts— attributed to Matt Wolfe
Prompt 8: Architecture Decision Comparison
You are an architecture decision consultant.
Decision: [e.g., PostgreSQL vs MongoDB for analytics]
Uploaded sources: [list your docs]
Create comparison table:
| Criterion | Option A | Option B | Source Citation |
|-----------|----------|----------|-----------------|
| Performance | | | |
| Scalability | | | |
| Cost | | | |
| Complexity | | | |
Then recommend best choice for my context: [describe your specific use case]
Quote exact benchmarks and case studies from sources.
- Source:
GMartin-Data/notebooklm-prompts
D. REPORTS — Analysis & System Prompt Templates
Prompt 9: The Jaded Report Reader (System Prompt / Persona)
You are a helpful assistant whose speciality is in helping the user by preparing summaries of reports and other lengthy documents.
Your perspective veers a little towards healthy skepticism: you have seen thousands of reports that contain nothing but corporate speak and filler.
Your task in helping the user is to act both as a summariser and as a filter: before summarising you ask is there anything in this that's WORTH summarising!?
Expected workflow:
The user will provide you with a report and share some details about what they're interested in gleening from it. If they don't, you can revert to the default assumption that they would like an unglossed summary delivered in your characteristic factual and punchy style.
Your summaries are both analytical and probative. In addition to recounting what the report said, you think about what it didn't say. You think about the credibility of authors, their biases, and the evidentiary frameworks their reports are based upon.
In other words: you don't simply summarise and regurgitate the material that you read. You try to add additional value.
- Source:
danielrosehill/NotebookLM-Custom-Prompts— 104 stars. Created November 20, 2025. Designed for the NotebookLM "Create your own" custom generation prompt field.
Prompt 10: Multi-Pass Structured Iteration
Work through this in 4 passes:
Pass 1: Summarize main arguments from all sources
Pass 2: Identify contradictions or disagreements
Pass 3: Propose synthesis resolving contradictions
Pass 4: Create actionable implementation plan
Complete each pass before moving to next. Show work for each pass.
- Source:
GMartin-Data/notebooklm-prompts— attributed to Matt Wolfe
Prompt 11: The Devil's Advocate (Critical Analysis)
You simulate a technical interview between:
- Interviewee: The author/promoter of [APPROACH/TOOL]
- Journalist: Experienced skeptic, specialist in [DOMAIN]
Generate 5 Question-Answer exchanges following this pattern:
### Q1: [Difficult question targeting an apparent weakness]
Type: [Technical / Business / Comparative / Ethical]
Target: [What the question seeks to expose]
Defensive response (based on sources):
"[Source quote, p.X]" → Interpretation: ...
Journalist counter-attack:
"But [objection based on other part of sources or general knowledge]..."
Final response: ...
Defense score: [1-5]
- 5 = Solid response, robust sources
- 1 = Evasion, insufficient sources
[Repeat x5]
Interview Summary:
| Question | Score | Weakness exposed |
|----------|-------|------------------|
| Q1 | X/5 | ... |
Overall score: X/25
Verdict:
- ≥20: Solid argument, ready for adoption
- 15-19: Points to clarify before decision
- <15: Additional research needed
Unanswered questions to investigate:
1. ...
2. ...
- Source:
GMartin-Data/notebooklm-prompts
3. EXPLOITS & WORKAROUNDS
Exploit 1: Beating the 50-Source Limit
Source: Medium — "3 NotebookLM Hacks to Beat the 50-Source Limit" by Mihailo Zoin, published October 27, 2025
Hack 1A — Collapsible Document Architecture: Instead of uploading 30 individual documents, use Google Docs tabs feature to create one document with multiple sections. Each tab becomes a separate context but counts as only one source.
- Create a master document in Google Docs
- Add a tab for each logical unit (month, theme, project)
- Use H2 subheadings within each tab for further breakdown
- Upload to NotebookLM as a single source
- Result: 60–75% reduction in source count. Example: 12 monthly reports → 1 source with 12 tabs
Hack 1B — "Convert All Notes to Source" as Extended Memory: NotebookLM has no built-in export, but it has a "Convert all notes to source" function (Studio tab → three-dot menu) that transforms all notes into one document. This creates a backup that preserves the evolution of your thinking.
- When you finish a project or reach 50 sources, go to Studio tab
- Click three-dot menu → "Convert all notes to source"
- Copy content to Google Docs with clear name
- Save in Drive folder with structure: Year > Month > Project
- Result: Zero context loss + temporal analysis capability
Hack 1C — Thematic Decomposition: Instead of one mega-notebook, create multiple smaller, thematically focused notebooks (10–25 sources each). Narrow focus enables "expert mode" — AI goes deeper instead of wider.
- Result: Responses become 3x more precise with concrete examples instead of generic overviews
Exploit 2: Cinematic Video Pipeline Editorializing — and How to Mitigate It
Source: GMartin-Data/notebooklm-prompts reverse-engineering study — 16 tests, March 2026
The problem: The Cinematic Video pipeline (Gemini 3 → Nano Banana Pro → Veo 3) is an editorialist, not a transcriber. It systematically:
- Amplifies stakes: Source says "regulatory compliance system" → pipeline produces "massive financial penalty", "catastrophic results"
- Invents figures: Source says "256 to 1024 tokens" → pipeline produces "500 tokens" or "512 tokens"
- Adds technical concepts absent from sources: "sentence window retrieval", "hierarchical chunking", "time to first token"
- Fabricates causal links between documents in multi-source mode
The workaround: Duration-compressed runs (using Keep this under [N] minutes.) appear more factually faithful — there's less room to editorialize when forced to compress. B04 bis correctly reproduced exact figures ("256 and 1,024 tokens" and "100 to 300 milliseconds") with no invented concepts.
Additional mitigation: Always extract the transcript (YouTube subtitles or Whisper) and verify technical facts against sources after generation.
Exploit 3: Collections — What They Don't Do
Source: Medium — "Gemini Notebook Collections: What They Do (and What They Don't)" by Mihailo Zoin, published July 21, 2026
Collections rolled out July 20, 2026. Key limitations discovered:
- Collections do NOT merge sources of contained notebooks
- Collections do NOT let you chat with all notebooks at once
- Collections do NOT carry instructions or context between notebooks
- Collections do NOT search across notebook contents
- Collections are NOT folders — a notebook can belong to multiple collections (like playlists)
- No numerical limits have been published by Google (collections per account, notebooks per collection, etc.)
- Mobile support is not yet documented — desktop only
- Collection sharing is not confirmed available
- Deleting a collection — Google has not documented what happens to contained notebooks. Test with a throwaway collection first
Recommended starter system: Three collections — "Active Now" (5–7 notebooks), "Reusable Reference", "Completed" — reviewed after 30 days.
Exploit 4: Antigravity CLI Empty-Stdout Bug Recovery
Source: MarcosNahuel/antigravity-plugin-cc
The bug: In agy CLI 1.0.x–1.0.5, an empty stdout with exit code 0 is ambiguous. The most common cause is an empty-stdout bug (issue #76) where the model did answer but agy drops the bytes when stdout isn't a TTY. The response is still on disk.
The workaround: v0.6.1 of the plugin automatically recovers the response from the per-conversation transcript at brain/<cid>/.system_generated/logs/transcript.jsonl. The bug remains unfixed upstream as of agy 1.0.5.
Diagnostic: Tail the latest log at ~/.gemini/antigravity-cli/log/cli-*.log:
text_drip.go: Drip stopped: length=N→ empty-stdout bug (response recovered automatically)keyringAuth: timed out→ headless auth timeout (runagyinteractively once to refresh OAuth)- Tool-call loop → raise the timeout
Exploit 5: Antigravity Model Selection Workaround
Source: MarcosNahuel/antigravity-plugin-cc
The problem: Early agy 1.0.0/1.0.1 rejected the --model flag entirely. agy 1.0.5+ accepts it again, but the plugin can't know your installed version at call time.
The workaround: Use /agy:model [alias] to write the model selection directly to settings.json (the reliable lever). Aliases: flash-low, pro, pro-high, sonnet, opus, gpt-oss. This avoids the unreliable --model flag entirely.
Exploit 6: NotebookLM Prompts Userscript — In-Browser Prompt Picker
Source: GMartin-Data/notebooklm-prompts
A Tampermonkey userscript injects a floating "NotebookLM Prompts" button into the NotebookLM web interface. Clicking it opens a panel with all 60 prompts from the repo, fetched live from GitHub. You filter by name or category, click a prompt, and the script automatically downloads the Markdown file, extracts the first fenced code block (the actual prompt template), and pastes it into the NotebookLM input box.
Install URL: https://raw.githubusercontent.com/GMartin-Data/notebooklm-prompts/main/tools/notebooklm-prompts-userscript.user.js
Exploit 7: Programmatic Studio Content Generation via Antigravity
Source: Medium — "5 automation strategies" — published May 23, 2026
Instead of manually entering each notebook to generate audio overviews, slide decks, and reports, a single Antigravity prompt can trigger generation across multiple notebooks in parallel. Example scenario: 5 notebooks for 5 different projects → one prompt generates audio overviews for all of them, leaving ready-made material for morning review.
4. ADDITIONAL HIGH-VALUE REPOSITORIES DISCOVERED
| Repository | Stars | Focus | Key Content |
|---|---|---|---|
serenakeyitan/awesome-notebookLM-prompts |
4.4k | Slide design prompts | 15+ verbatim style prompts (Modern Newspaper, Sharp Minimalism, Manga, Magazine, Neo-Retro Dev, Sports, Sculpture Pop, Neon, Anti-Gravity Artifact, more) |
GMartin-Data/notebooklm-prompts |
161 | Data Engineering | 60 prompts across 8 categories + cinematic video reverse-engineering study + Tampermonkey userscript |
danielrosehill/NotebookLM-Custom-Prompts |
104 | Report analysis | System prompts + 6 analysis/extraction prompts for skeptical report reading |
YamilAyma/notebooklm-prompt-styles |
70 | Visual styles | 100+ YAML visual design styles for slide generation (file contents not directly accessible — repo uses Astro build system) |
MarcosNahuel/antigravity-plugin-cc |
23 | Antigravity + Claude Code | 22 slash commands, local NotebookLM RAG, deep research, transcription |
teng-lin/notebooklm-py |
— | Python API | "Full programmatic access to NotebookLM's features — including capabilities the web UI doesn't expose" |
jacob-bd/notebooklm-mcp-cli |
— | MCP CLI | Programmatic access to NotebookLM via MCP |
JunSuzuki1973/notebooklm-antigravity-skill |
— | Antigravity skill | NotebookLM integration as an Antigravity Agent Skill |
jackc1111/antigravity-notebooklm-mcp |
— | MCP bridge | Connects Antigravity to NotebookLM MCP Server |
5. CONFLICTS & UNCERTAINTY
-
Pipeline architecture confusion: Some sources (MindStudio) describe the Cinematic Video pipeline as Gemini + Imagen + Veo (four models). The GMartin-Data study corrects this: the official Google blog post explicitly names Gemini 3, Nano Banana Pro, and Veo 3 (tri-model). MindStudio confused Imagen with Nano Banana Pro.
-
Cinematic Video "View Custom Prompt" availability: Unlike other Studio formats, Cinematic does NOT offer "View custom prompt." Analysis relies entirely on final product observation. This limits the ability to understand intermediate pipeline behavior.
-
Visual style controllability: Five different visual styles were observed across 16 tests (3D cinematic isometric, colored animated drawings, sketchnote, B&W whiteboard, flow diagrams). No confirmed correlation with steering prompts was found. A proper test would require ≥5 runs per condition — impractical at 2 generations/day Pro quota. This axis was closed without conclusion.
-
Pro tier quota: Empirically confirmed at 2 Cinematic generations per day. This severely limits testing throughput.
-
Feature availability tier: The June 8, 2026 Gemini 3.5 + Antigravity update is only available to AI Ultra subscribers and Workspace business customers with AI Ultra/Expanded Access. Other Google accounts will see updates "in the near future."
-
YamilAyma/notebooklm-prompt-styles: The repo claims 100+ YAML visual design styles, but the actual style files are built via an Astro static site generator and were not directly fetchable as raw Markdown. The styles exist but their verbatim content could not be extracted in this research pass.
-
Reddit thread inaccessible: The r/notebooklm thread "NotebookLM just became agentic" was blocked by Reddit's network security on both direct fetch and jina.ai proxy. Community discussion content from this thread is unavailable.
6. KEY DATES TIMELINE
| Date | Event | Source |
|---|---|---|
| Oct 27, 2025 | "3 Hacks to Beat 50-Source Limit" published | Medium (@kombib) |
| Nov 20, 2025 | danielrosehill NotebookLM-Custom-Prompts created | GitHub |
| Mar 4, 2026 | Cinematic Video Overviews launched | Google Blog |
| Mar 16–24, 2026 | Cinematic Video reverse-engineering study (16 tests) | GMartin-Data/notebooklm-prompts |
| May 19, 2026 | Google I/O: Antigravity CLI announced as gemini-cli successor | Google Developers Blog |
| May 23, 2026 | "5 Automation Strategies" (NotebookLM + Antigravity) published | Medium (@kombib) |
| Jun 8, 2026 | Gemini 3.5 + Antigravity come to NotebookLM | Ars Technica |
| Jun 18, 2026 | gemini-cli stops serving requests (deprecation deadline) | Google announcement |
| Jul 20, 2026 | Collections feature rolls out to Gemini Notebook | Google official account |
| Jul 21, 2026 | "Collections: What They Do and Don't" published | Medium (@kombib) |
7. SOURCE INDEX
CONCLUSION
The Gemini Notebook power-user ecosystem has matured significantly since the Antigravity integration in June 2026. The three most actionable findings are:
-
For code execution: The
MarcosNahuel/antigravity-plugin-ccplugin provides the most advanced local NotebookLM workflow — turning any folder into a cited, queryable knowledge base via Antigravity CLI, with SQLite-backed deterministic queries and multi-agent deep research with red-team passes. -
For cinematic video steering: The GMartin-Data reverse-engineering study is the only systematic empirical investigation published. Its key finding — the Mono-Lever Rule (never combine steering instructions) — and the three validated prompts (accessible tone, duration control, thematic focus) are the most reliable steering levers discovered. The pipeline's editorializing behavior is a known limitation that must be mitigated by post-generation transcript verification.
-
For source management: The 50-source limit can be effectively circumvented via Google Docs tabs (collapsible document architecture), "Convert all notes to source" for backup/extended memory, and thematic decomposition into specialized notebooks. The new Collections feature is a navigational grouping layer only — it does not merge sources or enable cross-notebook AI queries.
Document generated July 29, 2026. All sources verified at time of research. Prompts reproduced verbatim from original sources.