On Evals - getting messages on “ok so how do I actually start learning this?” there is no better way than by just doing so you can copy this to Claude Code and get started today <instructions> 1. Go look up the @harborframework and the Terminal Bench 2.0 dataset. Go look up the Harbor Skills GitHub repo for help. Pick 1 Task in the dataset and explain every single piece that’s in that task folder 2. Explain what my agent sees when it does the task, what it has to output, and how we know if it got the problem right? 3. Now let’s actually run a Task using the built in Claude Code integration, it’s just a flag 4. Once that’s done let’s read the ATIF file that was produced together and help me understand what just happened. Did we pass the task? If not can we dig into why it failed? Go check the verifier logic to see what went wrong. 5. Ok let’s try to improve our agent by adjusting the prompt. And let’s rerun on a few tasks? Is this helping? 6. Ok we’re doing evals! Using this same format, help me make my own. Let’s do this together … </instructions> Spend a few days reading a bunch of traces, actually running evals, understanding traces, internalizing agent failure modes, and being super in the loop of what the agent sees and does Have fun! Evals are super important, they don’t have to be scary. DM if I can help or just tweet out what you’re doing, someone will help I promise, we’re all learning

Was he the candidate or the interviewer?

Please pay me 100m to convert papers like openreview.net/pdf?id=3zKtaq… to blogposts! @agarwl_

Kicking off a focused deep dive into LLM inferencing - exploring optimization, quantization, and deployment pipelines. If you’ve read any great papers/blogs/videos or tried interesting frameworks lately, drop them here 👇 #LLM #AI #ML #DeepLearning #MLOps #Inference #GenAI #AIResearch #AIInfra #ModelServing

Is AI-SLOP a real phenomenon? I have never encountered machine learning slop or deep learning slop in my extensive research. Or am I, perhaps, overlooking a critical component? #AISlop #AI #ML #MLOps

Weekend vibes.... Diving into the Multilingual & Multimodal LLMs chapter, by @Tanmoy_Chak. Honestly, this book is so underrated - packed with clear explanations and solid insights.

Are you ready to build on top of a closed platform?

Is your AI image generator producing blurry results? Or are your captioning models calling beautiful photos "just okay"? The problem might be your training data! Garbage in = Garbage out. But when your dataset has millions (or even billions) of images, how do you filter out the bad ones? How do you make sure your model learns from clean, high-quality visuals? That’s where the LAION Aesthetic Predictor comes in (github.com/LAION-AI/aestheti…). This open-source tool gives each image a score from 1 to 10 based on how visually appealing it is - just like a human might rate it. It’s super useful if you're: 1) Training text-to-image models 2) Fine-tuning captioning models 3) Curating datasets where looks matter Have you used aesthetic scoring in your projects? Share your experiences in the comments below! #AI #MachineLearning #ComputerVision #GenerativeAI #ImageCaptioning

That's quite the tech stack! But real ML engineering is about solving problems, not collecting tools.

Ohh brilliant.... You designed a table with NO AI..? What a wasted opportunity for human progress.. #AI #LLM

Slightly OpenAI #OpenAI

Man, Ilya Sutskever is a total legend...! An ex OpenAI employee... Dude raised a billion dollars with no product in hand —like, how crazy is that? This guy’s new company is gonna explode.... Hitting a $5B valuation in just 3 months. Absolute 🔥.

Could AI help inspire entirely new forms of creativity? 🎨 Join our podcast host Professor @FryRSquared and our Senior Research Director, @douglas_eck as they explore the intersection between art, technology and society, our latest generative models such as Veo, and much more. Watch their conversation ↓