You don't need privilege to succeed in tech | Golang • System Design • MLOPS • Kubernetes • AI • Tech
Joined October 2024
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See i told ya. Apple takes time but comes out with the most eye catching shit. Apple UI designers prolly worked over time for ts.
GPT-6 Astra, trained on ~100K+ NVIDIA Grace Blackwell NVLink72. From ChatGPT to o1 to Astra in 4 years.
AGI has arrived. Congratulations @OpenAI team.
400K GPUs coming online next.
Shubh retweeted
This is life-changing tech.
We need less hype, and more stuff like this.
Imagine how many people this can help 🤯
Today, we’re launching Bland Speech v3 - The world's first Human Speech Engine.
In @designarena’s Audio Realism benchmark, Speech v3 is the top model, outranking Elevenlabs, Grok, Cartesia, and OpenAI.
Trained on over 100 million real, human conversations: businesses, developers, and creators can now create any audio they can imagine.
To showcase this, we used 5 seconds of old footage to help James, a 49 year old father who recently suffered a stroke, get his voice back.
Try it for free at bland.ai/speech
Full length documentary below🧵
Shubh retweeted
Here are S-tier learning advises if you're studying AI:
1. Learn by building, not just watching courses.
2. Don't chase every new framework.
3. Choose projects that genuinely interest you.
4. Read papers.
5. Implement papers yourself.
6. Study mathematics deeply.
7. Learn probability and linear algebra.
8. Build strong theoretical foundations first.
9. Develop critical thinking.
10. Understand why algorithms work.
Here are S-tier learning advises if you're studying AI:
1. Learn by building, not just watching courses.
2. Don't chase every new framework.
3. Choose projects that genuinely interest you.
4. Read papers.
5. Implement papers yourself.
6. Study mathematics deeply.
7. Learn probability and linear algebra.
8. Build strong theoretical foundations first.
9. Develop critical thinking.
10. Understand why algorithms work.
Bro got 80k followers in one hour.
For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.
AI will transform every industry, power every company, and be built by every country.
Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.
The world needs both frontier closed models and frontier open models.
images.nvidia.com/pdf/Open-W…
Will be spending time reading this paper by @ilyasut and @geoffreyhinton . They were dropping these bangers back in 2014 btw.
The titanic dataset is the hello world program for ML engineers. But, it isn't enough.
Here are Top 6 Free Datasets + Projects for Practicing Computer Vision / NLP / Tabular ML
1. CIFAR-10: Image classification dataset with 10 object classes (Find here: archive.ics.uci.edu/dataset/…)
2. Oxford-IIIT Pet Dataset: Pet breed classification images (Find here: robots.ox.ac.uk/~vgg/data/pe…)
3. IMDb Movie Reviews: Movie review sentiment analysis dataset (Find here: ai.stanford.edu/~amaas/data/…)
4. AG News: News topic classification dataset (Find here: huggingface.co/datasets/fanc…)
5. California Housing: House price regression dataset (Find here: scikit-learn.org/stable/modu…)
6. Credit Card Fraud Detection: Imbalanced fraud detection dataset (Find here: kaggle.com/datasets/mlg-ulb/…)
IMPORTANT ML INTERVIEW PROBLEM:
How do you decide when to go for deep learning for a project?
Use deep learning when:
1. You're making high performance model
2. For highly complex data (eg: cat or dog in a photo)
Use machine learning when:
1. If data is less then use ML.
2. Cost. Deep learning is cost hungry.
3. Interoperability. Features are self generated in DL so we don’t know what the deep learning model learns as features.
