Gamma + n8n + Claude = AI Presentation Engine that closed $47K in deck services last month...
(And it's printing money for agencies who found it)
This system generates investor-grade presentations in 11 minutes using AI automation and strategic prompting...
→ No more 20+ hours weekly trapped in PowerPoint hell
→ No more $3K-$5K agency fees for 2-week turnaround decks
→ No more designer dependencies killing your delivery speed
→ No more creative bottlenecks limiting client capacity to 4 decks monthly
Just client brief submission → autonomous presentation engine that builds Fortune 500-quality decks.
Here's how it works:
→ Form Submission Trigger (client inputs brief, n8n workflow activates instantly)
→ Strategic Narrative Builder (Claude structures investor psychology + data flow)
→ Gamma Deck Generation (renders production-ready presentations automatically)
→ Auto-Export System (delivers to Drive + email drafts without manual work)
→ Output Tracking Database (logs every deck for client delivery management)
Built with Fortune 500 presentation standards.
Runs 24/7 without supervision.
10-minute setup. Zero design skills needed.
Want the complete system?
Like + comment "GAMMA" + repost, and I'll DM it to you.
(must be following)
@azealiaslacewig You look like a piece of SH*T to me, haha 😂 have you looked at yourself before or just want to make negative perspecyof yourself? Sounds like education is not important to your life thought.
🎋🎧Imagine chatting with an AI that talks and listens at the same time—duplex LLMs are making real-time conversations feel just like talking with a friend.
Forget clunky, turn-based AI—duplex LLMs let you experience smooth, back-and-forth dialogue that adapts as you speak.
This paper introduces the Listening-while-Speaking Language Model (LSLM). By merging speech generation and real-time audio input, LSLM brings us closer to a natural, human-like dialogue system.
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》 The Essence of Full Duplex Modeling
✸ What is Full Duplex?
☆ It is the ability of a system to listen while speaking simultaneously, just like human conversation.
☆ Traditional systems work turn by turn—first listen, then speak—limiting fluidity and spontaneity.
✸ Why It Matters:
☆ Real-time interaction means smoother, more natural conversations.
☆ Enhanced responsiveness paves the way for more intuitive user interfaces in AI applications.
✸ Key Innovation:
☆ LSLM integrates two channels: an autoregressive token-based text-to-speech (TTS) for speaking and a streaming self-supervised learning (SSL) encoder for listening.
☆ The model smartly fuses these channels so that even if interrupted, it can gracefully stop and adapt, ensuring minimal disruption during conversation.
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》 Fusion Strategies: Crafting the Perfect Balance
✸ Early Fusion:
☆ Combines listening and speaking signals at the very start.
☆ Tends to mix up the channels, making it hard for the model to differentiate and predict correctly.
✸ Middle Fusion:
☆ Merges channels within each Transformer block.
☆ Achieves a harmonious balance by allowing the model to maintain clarity in both speech generation and real-time interaction—this is where the magic happens.
✸ Late Fusion:
☆ Integrates the channels just before output.
☆ While effective under clean conditions, it may struggle with noisy inputs, affecting precision in detecting interruptions.
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》 Results and Benefits
✸ TTS Performance:
☆ LSLM with middle fusion recorded a word error rate (WER) of around 4.05% in clean conditions and 4.51% in noisy settings—indicating that real-time listening doesn’t compromise speech quality.
✸ Interactive Capability:
☆ The system demonstrates high precision and recall in turn-taking, meaning it accurately detects when to pause or stop speaking during interruptions.
☆ This responsiveness is critical for building more natural and efficient AI dialogue systems.
✸ Robustness and Adaptability:
☆ Tested in both command-based and voice-based interruption scenarios, LSLM proves to be robust against environmental noise.
☆ It adapts to diverse instructions and new speakers, a significant step towards truly interactive AI.
Paper: https://t.co/ihMrhejIgY
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🔮FinRobot Multi-Agents just published: Finance Agents with 3 Chain of Thoughts + Human-like Precision
New York-based researchers built FinRobot which utilizes a multi-agent "Chain of Thought" (CoT) system, designed to mimic the cognitive steps of financial analysts, delivering structured, data-driven insights.
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🎯 Why FinRobot?
Traditional AI tools struggle with real-time adaptability and lack human-like discretion—FinRobot tackles these gaps.
⊛ Designed to rival insights from top brokerage firms, this AI delivers research comparable to human analysts, making equity research accessible and democratized.
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🌐 Three-Layer System for Precision
❈ Data-CoT Agent: Collects and integrates raw financial data (SEC filings, earnings, etc.) ensuring accuracy and foundation.
Prepares quantitative summaries to support further analysis.
❈ Concept-CoT Agent: Mimics analyst reasoning to derive actionable insights.
Handles complex analyses, such as competitive evaluations and sentiment assessments.
❈ Thesis-CoT Agent: Synthesizes findings into a coherent research report, including financial projections and valuation metrics.
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🔍 Key Features & Benefits
ꆛ Real-time Updates: A dynamic data pipeline that ensures the analysis reflects current financial metrics and trends.
ꆛ Qualitative & Quantitative Balance: Beyond numbers, FinRobot interprets competitive positioning and qualitative risks.
ꆛ User-Friendly Reports: Structures data to be intuitive and immediately actionable, ideal for investor decision-making.
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📈 Investment Analysis Capabilities
Delivers comprehensive financial summaries, including revenue, EBITDA, and margin forecasts.
Provides comparative analysis across industry peers, detailing each company's operational efficiency and market stance.
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📊 Evaluation Metrics and Quality
⊛ Uses unique metrics like
- Accuracy,
- Logicality, and
- Storytelling
to assess and refine report quality.
Expert reviews show FinRobot’s strengths in accuracy and logical coherence, though room for narrative improvement.
Paper: https://t.co/IhQ75hsCMb
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@373__manager Wow, I can’t believe he did say that. He’s unprofessional at all (given the fact that he’s not properly educated). If he was, he wouldn’t do any stupid actions. Glad to myself that I didn’t follow him.
The Queen died peacefully at Balmoral this afternoon.
The King and The Queen Consort will remain at Balmoral this evening and will return to London tomorrow.