Grok 4 can help create McKinsey-style visuals.
No more spending hours formatting diagrams or building charts from scratch.
Here’s how to create stunning graphics in just few minutes:
Grok 4 is dangerously good.
But 99% of people are sleeping on what it can actually do.
I’ve used it to build apps, generate content, automate deep research, and more.
Here are 10 ways to use Grok 4 that feel like cheating:
AI just killed the marketing department.
You can now use Claude 4 for market research, content creation, writing viral ads, SEO optimization, and campaign planning.
Here’s the exact mega prompt we use to automate our entire marketing workflow:
Claude 4 vs ChatGPT-4o vs Gemini-2.5
Which one writes good content?
I tested all 3 of them using multiple prompts.
And you won't believe which one became my writing partner.
Here are 5 ways I tested Claude, Gemini, and ChatGPT:
(Video demos are included 👇)
📣 Big News: Bondex has successfully raised over $10M in our latest investment round! 🥳 🎉
Our funding round was led by prominent VCs such as @animocabrands, @Morningstar_vc, and @CoinList. 💰
Alongside our incredible community, their support fuels our mission to further Web3 professional networking! 🌐✨
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Google Veo3 T2V Prompting Guide.
For consistent iterations.
TIPS:
+ Start with a structure
+ Create a base prompt
+ Iterate small elements
BASE PROMPT STRUCTURE:
[PERSPECTIVE], [SHOT STYLE + DETAILS], [SUBJECT DETAILS + ENVIRONMENT], [SCENE DETAILS], [SOUND DESCRIPTION], [LIGHTING], [FILM STYLE]
(Evolution in thread)
#google #veo3 #promptshare
It's only been 48 hours since Google dropped Gemini 2.5 Pro (I/O edition)
And people can't stop building with it.
10 wild examples + links:
1. Build Interactive app from a sketch
10 AI Tools to replace your tedious work
Gamma AI —Create presentation with AI
Gemini Flash —AI image generation and editing
Perplexity AI —Web search/summarization
Replit agent —Build MVPs/mobile apps
Typefully —Social media management
Grok-3.5 —Deep research tasks
Windsurf AI —Best for AI coding
Kling AI —Create Stunning videos
ChatGPT —Brainstorming
Lovable —AI Websites
So, which is your favourite AI tool?
Crazy week in AI🤯
- NVIDIA drops open sources Parakeet ASR
- Gemini 2.5 Pro (I/O edition) drop
- OpenAI buys Windsurf for $3B
- Cursor valued at $9B
And more...
10 wild news drops + how to make sense of them: 👇
🔎 07 chỉ số Warren Buffett để tìm hidden gem & Cách áp dụng trong crypto? 💎
1. Buffett Indicator
2. P/E
3. PEG Ratio
4. ROE
5. Free Cash Flow Yield
6. Giá trị nội tại
7. Biên an toàn
8. Áp dụng vào crypto
Tác giả: @jackvi810
Mong anh em ủng hộ @gm_upside một like & retweet nếu bài viết hữu ích 😍
Vector databases are the FUTURE of data storage and retrieval in AI.
But what do you know about them?
While everyone's talking about Agents, LLMs and GenAI, vector databases are quietly powering it all behind the scenes. 𝗛𝗲𝗿𝗲'𝘀 𝘆𝗼𝘂𝗿 𝗰𝗿𝗮𝘀𝗵 𝗰𝗼𝘂𝗿𝘀𝗲 𝗼𝗻 𝘁𝗵𝗲 𝟲 𝗰𝗼𝗿𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Unstructured Data Objects →
These are your raw data: text, images, audio, or videos that don't fit neatly into traditional databases.
Vector Database →
The powerhouse that stores both your original data AND its vector representation in one place. It's like having a library where books are organized by their actual content similarity, not just alphabetically.
Vector Embeddings →
Think of these as your data's DNA code. They're arrays of numbers that represent your data in a way AI can understand.
Embedding Models →
These are the special AI models that create those vector embeddings. They've evolved from simple models like Word2Vec to sophisticated transformer models like BERT. They're like translators that convert your data into the language of vectors.
HNSW (Hierarchical Navigable Small World) →
This is the secret sauce that makes vector searches super fast. It creates multiple layers of connections between vectors, like an efficient highway system for your data. It's super scalable and allows you to balance between search speed and accuracy.
In short, vector databases:
• make searching through unstructured data efficient
• power semantic search in AI applications
• enable similarity-based recommendations
• enable RAG (Retrieval-Augmented Generation) and other modern AI applications
📌 Resources to save for later:
- A Gentle Introduction to Vector Databases: https://t.co/8s94u0CGyC
- What is a Vector Database? https://t.co/JtDnNMsn8a