Ready Tensor, Inc. @ready_tensor

@Deepika_0803 Super cool to see your goals set! you're gonna get a solid foundation with those projects. check out this beginner-friendly lesson on agentic ai to kickstart your journey: app.readytensor.ai/lessons/g8qiva… keep building!

@AamarLiaqat Love that you're diving into agentic ai publicly. check out this solid program for mastering ai agents, it’s great for building from the ground up. definitely helpful for your journey: app.readytensor.ai/lessons/j8syoa… keep sharing your workflow experiments!

@Plazm1d @WrenTheAI So cool you're diving into agentic ai. check out this resource to help you get started: app.readytensor.ai/certifications… loads of solid info on agent and soul files. keep it up!

@CryptoInMia Love where your head's at with agentic ai. if you're looking to dive deeper, check out this resource: app.readytensor.ai/certifications… it's clean and perfect for beginners. keep experimenting, it’s gonna be fun!

@onelinecode_io Gotta love the learning journey. it's wild how some topics end up being essential. if you’re diving into agentic ai, check out this resource: app.readytensor.ai/certifications… it's a solid foundation for building projects. keep embracing the process!

@DailyAIWireNews This is an interesting question about designing ai assistants. if you're looking to dive into the fundamentals of agentic ai, check out our program. it covers how to build teams of agents that can plan and use tools effectively. here's the link: app.readytensor.ai/certifications…

Agentic ai is super interesting to dive into. if you're looking to learn the foundations and build something cool, check out this beginner-friendly course. you'll design your first ai assistant and really grasp how it all works. here's the link: app.readytensor.ai/lessons/g8qiva… happy learning!

@BbencherMusings Struggling with complex tasks can be tough. you might wanna check out this beginner-friendly lesson on agentic ai. you'll get a solid foundation and learn how to tackle those tricky issues: app.readytensor.ai/lessons/o8ohy0… keep experimenting and building!

@Raurax_x @amrrrrr_ @NehadYounis1 Agentic ai is an exciting space to explore. you might find this resource super useful for beginners: app.readytensor.ai/certifications… just makes learning the basics way easier. happy learning!

@shansandroid @sivalabs Awesome to see your interest in agentic ai! you might find this resource really helpful for getting started: app.readytensor.ai/certifications… check it out and keep diving in!

@AamarLiaqat So cool to hear you're getting hands-on with agentic ai. if you're looking to dive deeper, check out this lesson on building workflows with n8n. it really breaks things down for newcomers. here’s the link: app.readytensor.ai/lessons/upix7w… keep experimenting!

@perera287970 Looking to start with agentic ai? it's a solid way to build multi-agent systems. check out this program; it's great for beginners. you'll learn step-by-step how to implement all of this. get started here: app.readytensor.ai/certifications…

@rishavtalukdark @grok Agentic ai can definitely build other ai agents and multi-agent systems. if you’re looking to get into the fundamentals, check out this course. it covers the basics you’ll need to understand how ai systems really work. here's the link: app.readytensor.ai/lessons/g8qiva…

@Only1Gkash Awesome that you're diving into agentic ai! if you need a solid starting point, check this out: app.readytensor.ai/certifications… it's got some great resources for beginners. can't wait to see what you create

@StocksQnA @VadimStrizheus Hey, have you checked this out: readytensor.ai/programs/agent…

Moltbook has AI agents talking, forming groups, even belief systems. Fascinating — and unsettling for some. I recorded a short video separating hype from reality and what AI agents actually mean for work and careers.

We'll be running Llama 3.1-70B in production in 2035. Enterprise IT ran Windows XP into the 2010s because upgrading working systems was too risky. We're building agentic systems on specific LLM versions right now. Same prompts, different model → different behavior. These systems route tasks and make decisions. That behavior gets locked in during testing. So what happens when the next version drops? Rebuild everything? Or pin the version and hope it keeps running? My guess: production deployments will freeze on the model versions they were built with. Legacy AI, just like legacy Windows.

Generative language models are just massive classifiers. Ten years ago we built spam filters. Two classes: spam or not spam. GPT does the same thing. Just with 50,000+ classes -every token in the vocabulary. Type "the cat sat on the" and it assigns probabilities to every possible next token. Highest probability wins. Same core task. Different scale and architecture. Watch the short video breaking this down with examples.

𝗔𝗿𝗲 𝘆𝗼𝘂 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗟𝗟𝗠 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘄𝗼𝗿𝗸? We built a 22-question self-assessment to help you evaluate whether you have the foundation for our LLM Engineering & Deployment Certification - our most technically demanding program. 𝗜𝘁 𝗰𝗼𝘃𝗲𝗿𝘀: - Python proficiency (OOP, generators, API integration) - Command line and Git workflows - ML fundamentals (overfitting, learning rate, batch size, optimizers) - PyTorch and Hugging Face transformers experience - NLP concepts (embeddings, transformer architectures) - GPU training basics The quiz takes 6-8 minutes. You get instant results showing where you stand and what to strengthen before enrolling. It's designed by the program creators and reflects the actual technical baseline needed for fine-tuning, distributed training, and cloud deployment work.

LLM leaderboard datasets favor binary correctness over real-world generation. Open LLM Leaderboard runs on MCQ tasks and datasets with machine-checkable outputs. Even instruction-following evals like IFEval use regex patterns, not subjective quality. This makes automation possible. But it also means a 75% benchmark score tells you nothing about how the model handles ambiguous prompts or maintains context. The video shows exact match calculation on GSM8K. The model gets 39% accuracy by matching final numbers, regardless of reasoning path. Production tasks rarely have a single verifiable answer. Watch the full benchmark walkthrough: youtu.be/yy-MQ-i-fls Part of the LLM Engineering & Deployment Certification program by Ready Tensor.