F:\Yuda

You're on your own, kid

Okeeee mulai dari mana yaa 1. Beberapa company yg aku tahu mulai measure performance dari.....penggunaan AI tools (Claude code etc)

Today, we’re announcing the winners of the MedGemma Impact Challenge, launched in collaboration with @Kaggle. 850+ participating teams showed us how AI can help bridge global healthcare gaps. Check out the winning innovations →goo.gle/47oQG1l

Qwen’s new model can run on your phone and outperforms what cost $200/month just 18 months ago Devices each model can run on: 0.8B → 500MB → Phone 2B → 1.5GB → Raspberry Pi 4B → 2.5GB → MacBook Air 9B → 6GB → MacBook Pro The information density of these AI models is so underrated. Every 10x compression in size is like 100x expansion in deployment surface Phones. Cars. Drones. Glasses. Sensors. Robots. Edge AI is increasingly high quality, free, and open source

I should've done better

@iipaahh Jenengmu asale ya seko kono?

<— 29 tahun yg lalu, ni bocil juga lahir disini nunggu ponaan lahir, eh sama ibuknya udah disiapin jersey liverpool eeeenniiihhh 🙃👶🏻

疲れた

@Lisakawispa Gasske sih

weew, baru nyadar..

this take is pure BS and misses how deep tech innovation actually works Ilya has a PhD in CS from Toronto under Geoff Hinton where he co-invented AlexNet & literally helped birth the modern DL revolution before founding OpenAI Adam has degrees in CS and Mathematics & built PyTorch during research internships at FAIR with some of the best systems researchers in the world the Cursor team are MIT grads who went through CSAIL & OpenAI’s accelerator before building their stack these aren’t people who just decided to do things and figured it out, they spent years building foundational knowledge in optimization theory & systems architecture & distributed computing before they had the domain expertise to even identify the right problems to solve the real insight is that credentials don’t matter but deep technical fluency absolutely does & that fluency comes from thousands of hours immersed in the mathematical foundations & implementation details whether that’s in a PhD program or grinding through papers and codebases on your own what separates great engineers from people who just ship code is understanding the loss landscape well enough to know when you’re stuck in a local minimum VS when you need to completely rethink your architecture you can’t build a novel neural architecture without understanding information theory & backpropagation from first principles & you can’t optimize distributed training without reasoning from the ground up about communication overhead & gradient synchronization YES the path doesn’t matter but the depth does & there’s no shortcut to internalizing how systems actually compose

Meta just solved the biggest problem in RAG! Most RAG systems waste your money. They retrieve 100 chunks when you only need 10. They force the LLM to process thousands of irrelevant tokens. You pay for compute you don't need. Meta AI just solved this. They built REFRAG, a new RAG approach that compresses and filters context before it hits the LLM. The results are insane: - 30.85x faster time-to-first-token - 16x larger context windows - 2-4x fewer tokens processed - Outperforms LLaMA on 16 RAG benchmarks Here's what makes REFRAG different: Traditional RAG dumps everything into the LLM. Every chunk. Every token. Even the irrelevant stuff. REFRAG works at the embedding level instead: ↳ It compresses each chunk into a single embedding ↳ An RL-trained policy scores each chunk for relevance ↳ Only the best chunks get expanded and sent to the LLM ↳ The rest stay compressed or get filtered out entirely The LLM only processes what matters. The workflow is straightforward: 1. Encode your docs and store them in a vector database 2. When a query arrives, retrieve relevant chunks as usual 3. The RL policy evaluates compressed embeddings and picks the best ones 4. Selected chunks are expanded into full token embeddings 5. Rejected chunks stay as single compressed vectors 6. Everything goes to the LLM together This means you can process 16x more context at 30x the speed with zero accuracy loss. I have shared link to the paper in the next tweet!

Beberapa hari lalu saya melihat postingan IG @studisejarah ada foto Bung Karno sedang menari dengan seorang perempuan di Istana Cipanas tahun 1963 Saya perhatikan fotonya baik-baik, ada wajah yang raut mukanya saya kenal. Siapakah penari itu?

NVIDIA and YTL just opened Malaysia's first ever AI data center in Johor 😳 Huge for Malaysia

@iyabzir Surprisingly good 👌👌

Me:

@masalmaaa Cekot tu tiket

Mata ngantuk akut, perut laper, gigi gatel gemes abis dari drg Kayanya minggu ini banyak dar der dor nya deh… cape banget sampe males keluar, udah ga mau mikir 🥺

In a state of still learning and making sure not to take things for granted

Bisa bisa jadi morning person nih ...

@Lisakawispa Karo i love you to the bone ya lumayan iso nyambung, ning ra se ngeblend memories