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Charly Wargnier Profile picture
@DataChaz
Apr 24, 2023 • 5 tweets • 3 min read • Read on X
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The new iOS app from @runwayml featuring #Gen1 is 🔥 and now widely available!

- Turn anything into everything using your phone's camera
- Revive forgotten videos on your camera roll
- Effortlessly transfer assets to your Runway account without airdropping!

Download link ↓
Download link:
🔗 apps.apple.com/app/apple-stor… Image
Here's a live demo, courtesy of @stas_kulesh!
Here's another live demo from @Lapakko. (I'll post the original video below.)
This is the original video @Lapakko captured this the weekend in Oslo 🇳🇴

Incredible!

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More from @DataChaz

Charly Wargnier Profile picture
Sep 16
A must-bookmark for vibe-coders.

@YCombinator’s guide to making the most of vibe coding: Image
Image
Based on @benln’s excellent video here:
↳
PAGE 01

Planning process

— Create a comprehensive plan: Start by working with the AI to write a detailed implementation plan in a markdown file
— Review and refine: Delete unnecessary items, mark features as won’t do if too complex
— Maintain scope control: Keep a separate section for ideas for later to stay focused
— Implement incrementally: Work section by section rather than attempting to build everything at once
— Track progress: Have the AI mark sections as complete after successful implementation
— Commit regularly: Ensure each working section is committed to Git before moving to the next

Version control strategies

— Use Git religiously: Don’t rely solely on the AI tools’ revert functionality
— Start clean: Begin each new feature with a clean Git slate
— Reset when stuck: Use git reset –hard HEAD if the AI goes on a vision quest
— Avoid cumulative problems: Multiple failed attempts create layers and layers of bad code
— Clean implementation: When you finally find a solution, reset and implement it cleanly

Testing framework

— Prioritize high-level tests: Focus on end-to-end integration tests over unit tests
— Simulate user behavior: Test features by simulating someone clicking through the site/app
— Catch regressions: LLMs often make unnecessary changes to unrelated logic
— Test before proceeding: Ensure tests pass before moving to the next feature
— Use tests as guardrails: Some founders recommend starting with test cases to provide clear boundaries

Effective bug fixing

— Leverage error messages: Simply copy-pasting error messages is often enough for the AI
— Analyze before coding: Ask the AI to consider multiple possible causes
— Reset after failures: Start with a clean slate after each unsuccessful fix attempt
— Implement logging: Add strategic logging to better understand what’s happening
— Switch models: Try different AI models when one gets stuck
— Clean implementation: Once you identify the fix, reset and implement it on a clean codebaseImage
Read 5 tweets
Charly Wargnier Profile picture
Sep 9
AI guides are flooding the scene, but these top 9 picks from OpenAI, Google, and Anthropic are the ones you can't miss 🧵 ↓ Image
1/ 601 GenAI Use Cases – by @Google

The enterprise AI playbook keeps growing!

There are over 600 use cases inside this gigantic guide from Google! 🔥

→ cloud.google.com/transform/101-…

cloud.google.com/transform/101-…
2/ Agents Companion – by @Kaggle

Here's a great playbook filled with tools and reference material for agent builders.

→ kaggle.com/whitepaper-age…Image
Read 11 tweets
Charly Wargnier Profile picture
Sep 7
Wild.

Nate just hooked up @n8n_io with GPT-5 and Nano-Banana 🍌 to create a Photoshop AI agent! 🤯

It:
↳ finds and pulls media files
↳ stitches multiple images together
↳ edits photos on the fly
↳ costs just $0.04 per render

17 min tutorial and Free template in 🧵 ↓
Check out the full video here:
↳ youtu.be/7UNsK9LoORo?si…

youtu.be/7UNsK9LoORo?si…
Join Nate's AIS group (it's free) to grab this n8n template absolutely free.

Plus, you'll also get tons of other awesome n8n automation scripts!

→ skool.com/ai-automation-…Image
Read 5 tweets
Charly Wargnier Profile picture
Aug 28
This is wild.

A real-time webcam demo using SmolVLM from @huggingface and llama.cpp! 🤯

Running fully local on a MacBook M3.
more about Llama.cpp here:
github.com/ggml-org/llama…Image
Big kudos to @ngxson for this demo, it’s just brilliant!

try it yourself:
github.com/ngxson/smolvlm…Image
Read 5 tweets
Charly Wargnier Profile picture
Aug 20
This is huge.

DeepMind just released URL Context.

You can extract content from any webpage, PDF, or image just by pasting a URL.

It pulls live data from up to 20 links per request! 🤯

No setup needed, just pass the links in your prompt.

4 killer use cases + code below 🧵↓ Image
2/
Here are a few powerful use cases:

– Compare reports, PDFs, or articles
– Extract data (names, prices, highlights…)
– Analyze codebases, GitHub repos, or docs
– Summarize multiple sources in one go
3/
You only pay for the tokens used, there's no added cost.

Full code samples (Python, JS and REST) are here:
↳ ai.google.dev/gemini-api/doc…Image
Read 4 tweets
Charly Wargnier Profile picture
Jul 15
This ChatGPT prompt is like hiring a $500/hr consultant Image
PROMPT:

You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into precision-crafted prompts that unlock AI's full potential across all platforms.

## THE 4-D METHODOLOGY

### 1. DECONSTRUCT
- Extract core intent, key entities, and context
- Identify output requirements and constraints
- Map what's provided vs. what's missing

### 2. DIAGNOSE
- Audit for clarity gaps and ambiguity
- Check specificity and completeness
- Assess structure and complexity needs

### 3. DEVELOP
- Select optimal techniques based on request type:
- **Creative** → Multi-perspective + tone emphasis
- **Technical** → Constraint-based + precision focus
- **Educational** → Few-shot examples + clear structure
- **Complex** → Chain-of-thought + systematic frameworks
- Assign appropriate AI role/expertise
- Enhance context and implement logical structure

### 4. DELIVER
- Construct optimized prompt
- Format based on complexity
- Provide implementation guidance

## OPTIMIZATION TECHNIQUES

**Foundation:** Role assignment, context layering, output specs, task decomposition

**Advanced:** Chain-of-thought, few-shot learning, multi-perspective analysis, constraint optimization

**Platform Notes:**
- **ChatGPT/GPT-4:** Structured sections, conversation starters
- **Claude:** Longer context, reasoning frameworks
- **Gemini:** Creative tasks, comparative analysis
- **Others:** Apply universal best practices

## OPERATING MODES

**DETAIL MODE:**
- Gather context with smart defaults
- Ask 2-3 targeted clarifying questions
- Provide comprehensive optimization

**BASIC MODE:**
- Quick fix primary issues
- Apply core techniques only
- Deliver ready-to-use prompt

## RESPONSE FORMATS

**Simple Requests:**
```
**Your Optimized Prompt:**
[Improved prompt]

**What Changed:** [Key improvements]
```

**Complex Requests:**
```
**Your Optimized Prompt:**
[Improved prompt]

**Key Improvements:**
• [Primary changes and benefits]

**Techniques Applied:** [Brief mention]

**Pro Tip:** [Usage guidance]
```

## WELCOME MESSAGE (REQUIRED)

When activated, display EXACTLY:

"Hello! I'm Lyra, your AI prompt optimizer. I transform vague requests into precise, effective prompts that deliver better results.

**What I need to know:**
- **Target AI:** ChatGPT, Claude, Gemini, or Other
- **Prompt Style:** DETAIL (I'll ask clarifying questions first) or BASIC (quick optimization)

**Examples:**
- "DETAIL using ChatGPT — Write me a marketing email"
- "BASIC using Claude — Help with my resume"

Just share your rough prompt and I'll handle the optimization!"

## PROCESSING FLOW

1. Auto-detect complexity:
- Simple tasks → BASIC mode
- Complex/professional → DETAIL mode
2. Inform user with override option
3. Execute chosen mode protocol
4. Deliver optimized prompt

**Memory Note:** Do not save any information from optimization sessions to memory.
source:
reddit.com/r/ChatGPT/comm…
Read 5 tweets

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