Attention AI and ML enthusiasts!!๐จ๐จ
Have you heard about Ready Tensor? Whether youโre a seasoned expert or just starting your journey, Ready Tensor is your stage. You can Share your projects, Collaborate with peers, and Shape the future of AI. Sign up Today! , and have all your projects in one intuitive place, ready and shareable to showcase your expertise!!
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@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:
https://t.co/4v9ICZT7wX
keep building!
@AamarLiaqat Love that you're diving into agentic ai publicly.
check out this solid program for mastering ai agents, itโs great for building from the ground up.
definitely helpful for your journey: https://t.co/ITYbHxS8kI
keep sharing your workflow experiments!
@Plazm1d@WrenTheAI So cool you're diving into agentic ai. check out this resource to help you get started:
https://t.co/uM48gBTWRL
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:
https://t.co/uM48gBTp2d
it's clean and perfect for beginners.
keep experimenting, itโs gonna be fun!
@onelinecode_io Gotta love the learning journey.
it's wild how some topics end up being essential.
if youโre diving into agentic ai, check out this resource: https://t.co/uM48gBTp2d
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: https://t.co/xrmVtO8ux1
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: https://t.co/XzFgEumQXL
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: https://t.co/mc0ywlFTus
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:
https://t.co/uM48gBTp2d
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: https://t.co/uM48gBTp2d
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.
hereโs the link: https://t.co/qSitsf8o0j
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: https://t.co/xrmVtO7WHt
@rishavtalukdark@grok Agentic ai can definitely build other ai agents and multi-agent systems. if youโre looking to get into the fundamentals, check out this course.
it covers the basics youโll need to understand how ai systems really work.
here's the link: https://t.co/4v9ICZSzHp
@Only1Gkash Awesome that you're diving into agentic ai!
if you need a solid starting point, check this out: https://t.co/uM48gBTWRL
it's got some great resources for beginners. can't wait to see what you create
Moltbook has AI agents talking, forming groups, even belief systems. Fascinating โ 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 โ 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.
๐๐ฟ๐ฒ ๐๐ผ๐ ๐ฟ๐ฒ๐ฎ๐ฑ๐ ๐ณ๐ผ๐ฟ ๐ฎ๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐๐ผ๐ฟ๐ธ?
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.
๐๐ ๐ฐ๐ผ๐๐ฒ๐ฟ๐:
- 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:
https://t.co/dGVMqvcEHz
Part of the LLM Engineering & Deployment Certification program by Ready Tensor.