BlockRhino

Co-Founder & CTO @ARAI_AI_AGENTS—On a mission to simplify AI by building tools that let you create AI Agents just like you’d craft a video game character.

[BlockRhino @TheBlockRhino]

Reflecting on creativity as the currency of the future.

People will create and share agents, movies, TV shows, and music, turning ideas into reality through AI.

People will have home servers replacing Spotify, Netflix, etc.

Sharing access for deeper connections.

[Julian Goldie SEO @JulianGoldieSEO]

N8N FULL COURSE 5 HOURS (Build & Automate Anything)

Want the full guide? DM me.

https://t.co/3lC6LBRW4D

[Tivadar Danka @TivadarDanka]

How to build a good understanding of math for machine learning?

I get this question a lot, so I decided to make a complete roadmap for you. In essence, three fields make this up: calculus, linear algebra, and probability theory.

Let's take a quick look at them!

[Aurelien @Aurelien_Gz]

this could be a masterpiece...

imagine if this ai generated ghibli style game became real.. i’d be all in

[The AI Timeline @TheAITimeline]

🚨This week's top AI/ML research papers:

- Energy-Based Transformers are Scalable Learners and Thinkers
- Dynamic Chunking for End-to-End Hierarchical Sequence Modeling
- Pre-Trained Policy Discriminators are General Reward Models
- First Return, Entropy-Eliciting Explore
- Machine Bullshit
- A Survey on Latent Reasoning
- Critiques of World Models
- SciMaster
- Skywork-R1V3 Technical Report
- Gemini 2.5
- Scaling RL to Long Videos
- MedGemma Technical Report
- RL with Action Chunking
- Small Batch Size Training for Language Models
- Modern Methods in Associative Memory
- SingLoRA
- Understanding and Improving Length Generalization in Recurrent Models
- Differential Mamba
- any4: Learned 4-bit Numeric Representation for LLMs

overview for each + authors' explanations
read this in thread mode for the best experience

[ₕₐₘₚₜₒₙ @hamptonism]

Learning Modern C++ for Finance:

[Riley Brown @rileybrown]

The TRUTH about "Vibe Marketing"

W/ @boringmarketer

1. n8n (Workflows with Agents)
2. Claude
3. MCP
4. Advanced Workflows

Time Stamps:

00:00 - Introduction
00:16 - Defining Vibe Marketing
00:53 - Real-World Use Case: AI-Assisted SEO Agency
01:52 - n8n: YouTube Video to Newsletter
04:05 - Understanding AI Agents and Workflows
10:19 - Exploring MCP in Claude
15:56 - Advanced Vibe Marketing Strategies
25:53 - Conclusion and Future of Vibe Marketing

[Damian Player @damianplayer]

WTF!! This CLIP FACTORY is WILD…

This AI Agent turns 1 longform YT video into 8 shortform clips across 4 platforms, fully automated:

→ Pulls YouTube links from a Google Sheets (provided)
→ Auto-generates 2 clips w/ captions, emojis, silence removal, reframing (1080x1920)
→ Publishes to TikTok, IG, YT Shorts & LinkedIn (+more)
→ Applies platform-specific captions
→ Tracks post links + status in Sheets

Built 100% in n8n. Runs daily. No editors or VAs. Just clips running full auto.

Follow + RT + reply “CLIP” & I’ll send you the FULL flow + setup guide FREE!

[Weaviate AI Database @weaviate_io]

Weaviate 1.31 just dropped, bringing the search precision you've been asking for with new BM25 AND/OR operators!

Here's what's new:

𝗕𝗠𝟮𝟱 𝗔𝗡𝗗/𝗢𝗥 𝗢𝗽𝗲𝗿𝗮𝘁𝗼𝗿𝘀 - Finally, granular keyword search control!

• Need ALL terms to match? Use the AND operator (perfect for product searches like "noise cancelling microphone")
• Want flexibility? Use OR with minimum match requirements (great for technical literature searches)
• Works seamlessly with hybrid search too!

But wait, there's more in this release:

𝗠𝗨𝗩𝗘𝗥𝗔 𝗳𝗼𝗿 𝗠𝘂𝗹𝘁𝗶-𝗩𝗲𝗰𝘁𝗼𝗿 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 Compress those massive ColBERT/ColPali embeddings without sacrificing search quality. Faster imports, smaller storage footprint.

𝗔𝗱𝗱 𝗩𝗲𝗰𝘁𝗼𝗿𝘀 𝘁𝗼 𝗘𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀 Changed your mind about embedding models? No problem. Add new vectors without recreating your entire collection.

𝗛𝗡𝗦𝗪 𝗦𝗻𝗮𝗽𝘀𝗵𝗼𝘁𝘁𝗶𝗻𝗴 Watch your startup times get faster by 10- 15x! A 70s startup would be done in 5s instead! 🚀

𝗡𝗲𝘄 𝗠𝗼𝗱𝗲𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀

• Cohere v3.5 reranker & v4 multimodal embeddings
• VoyageAI v3.5 models
• model2vec for super-fast static embeddings

Plus a massive list of performance improvements that make everything faster and more efficient.

Ready to upgrade? Weaviate 1.31 is available now on GitHub and coming to Weaviate Cloud shortly.

Learn more in the release blog post by @_jphwang: https://t.co/K3OOYWC4oW

What feature are you most excited about?

[Poonam Soni @CodeByPoonam]

Anthropic just dropped Free AI courses on:

- Prompt Engineering
- Building agents
- Best practices for Agentic Coding
- Collaborate with AI systems

.... and so much more!

9 best guides you don’t want to miss:

[Aurelien @Aurelien_Gz]

this is next level..

the way the fish follows the cursor on this website is just incredible..

[Sahil Bloom @SahilBloom]

You need to hear this…

[Kirk Borne @KirkDBorne]

2nd Edition, 746 pages, massive!
⬇️
Modern #ComputerVision with #PyTorch #DeepLearning — from practical fundamentals to advanced applications and #GenerativeAI: https://t.co/x6pP05ZTa9 via @PacktDataML #ad
——
#DataScience #MachineLearning #AI #ML #GenAI #DataScientist
——
𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
🟣Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models

🔵Build solutions for real-world computer vision problems using PyTorch

🟠All code files are available on GitHub and can be run on Google Colab

[Javarevisited @javarevisited]

10 Best System Software Concepts
➡️Load balancers
➡️APIs
➡️Caches
➡️Databases
➡️Network Protocols
➡️Message queues
➡️CDNs
➡️High-level details about ML and Big data
➡️CAP Theorem
➡️Monitoring and analytics
read - https://t.co/GudhicBm57

[Ashish Pratap Singh @ashishps_1]

LeetCode was HARD until I Learned these 15 Patterns:

1. Prefix Sum
2. Two Pointers
3. Sliding Window
4. Fast & Slow Pointers
5. LinkedList In-place Reversal
6. Monotonic Stack
7. Top ‘K’ Elements
8. Overlapping Intervals
9. Modified Binary Search
10. Binary Tree Traversal
11. Depth-First Search (DFS)
12. Breadth-First Search (BFS)
13. Matrix Traversal
14. Backtracking
15. Dynamic Programming Patterns

I've created a free resource to help you learn, practice and master these patterns plus many more.

👉 Check it out at https://t.co/FqIaiFlTvH

♻️ Repost to help others in your network.

[Quant Science @quantscience_]

This guy literally just shared the code for his hedge fund's algorithmic trading strategy live:

[Quant Science @quantscience_]

Stock Prediction AI: Using Machine Learning and Deep Learning to predict stock price movements in Python.

The Python code is 100% free on GitHub.

Let's dive in (bookmark this):

[Quant Science @quantscience_]

99% of algorithmic traders lack a system that uses:

1. Feature stores
2. Orchestration
3. Model training & evaluation
4. Performance monitoring

I have good news. We just built it:

[Mustafa @oprydai]

Every programmer must know about memory.

[Kirk Borne @KirkDBorne]

[700-page PDF e-Book] #Algorithms for Decision-Making — brilliant and comprehensive eBook from MIT: https://t.co/cZde89XPxi