Ritu Narayan left eBay after struggling to find reliable transportation for her children. Now, her school transportation startup Zum is valued at $1.7 billion, serves 5,000 schools nationwide and uses AI to make bus routes more efficient. How she built “Uber for school buses”: 📸: Cody Pickens for Forbes #ForbesOver50#

Ritu Narayan left eBay after struggling to find reliable transportation for her children. Now, her school transportation startup Zum is valued at $1.7 billion, serves 5,000 schools nationwide and uses AI to make bus routes more efficient. How she built “Uber for school buses”: 📸: Cody Pickens for Forbes #ForbesOver50#

Ritu Narayan founded Zum after struggling to find reliable school transportation for her children. Today, her $1.7 billion company is bringing 21st century technology to a stodgy workhorse. “Transportation is not just a logistics problem; it’s basically an on-ramp for students to learn,’’ says Narayan. Read the full story about how this entrepreneur built Uber for the yellow school bus: 📸: Cody Pickens for Forbes #ForbesOver50#

A century as far as Ramsar sites are concerned! Glad that the Jai Prakash Narayan Bird Sanctuary (Surha Tal) in Ballia, Uttar Pradesh has been designated as India’s 100th Ramsar site. This wetland is rich in avifaunal biodiversity, attracting several migratory and resident birds. India’s unwavering commitment to protecting our natural surroundings and wetlands in particular is clearly reflected in this feat. Over the years, efforts to conserve and rejuvenate wetlands have been strengthened through greater community participation, science, innovation and awareness initiatives. These endeavours are helping preserve biodiversity, secure ecological balance and create a greener future for coming generations.

Got time for a podcast? In the latest episode of @therobotreport, our CRO Dinesh Narayanan discusses the future of robot AI deployment, why adaptability matters more than any single AI model, and how GRID is shaping the way robot AI solutions are deployed and scaled across any robot, any skill. 🎧 Tune in:

Vrinda said –Turn into a stone.”🪷🙏 V i s h n u s m i l e d a n d b e c a m e t h e S h a l i g r a m E v e n t o d a y, H i s w o r s h i p i s i n c o m p l e t e w i t h o u t T u l s i So powerful was a devoted wife’s tea⚜️ @grok 🚩Jai Shri Hari Narayan🚩

We finished the Training Agents series. Six live sessions over six months, from evaluating agents to training them inside real environments. All of it is on the Hugging Face YouTube channel and all of the code is open. Here's what we did and who made it happen: 1. Agentic Evaluations WorkshopWhere agent evals actually stand, and why benchmark scores don't match what people see in use. With Avijit Ghosh and Nathan Habib (Hugging Face), Arvind Narayanan (Princeton), Pierre Andrews (Meta), J.J. Allaire (UK AI Security Institute) and Mahesh Sathiamoorthy (Bespoke Labs). 2. RL for Agents Workshop Environments, rollouts, reward design and the inference bottlenecks that appear when you move from RL for LLMs to RL for agents. With Lewis Tunstall (Hugging Face), Will Brown (Prime Intellect), Ofir Press (Princeton) and Alex Zhang (MIT CSAIL). 3. Training Agents 1: SFT on agent traces Public coding-agent traces turned into prompt/completion data, a TRL + LoRA fine-tune on Hugging Face Jobs, metrics in Trackio, and an honest look at what the first eval numbers can and cannot tell you. Joined by Sergio Paniego and Quentin Gallouédec. 4. Training Agents 2: Distillation Off-policy, on-policy and self-distillation for moving capability from a teacher into a smaller coding agent. 5. Training Agents 3: Reinforcement learning GRPO after SFT: group sampling, verifiable reward functions, reading the reward/KL/length curves, and three experiments, one of them with a deliberately gameable reward so we could watch the hacking happen. 6. Training Agents 4: From reward functions to environments The reward stops being a function and becomes a place the agent acts in. We walked the reset()/step() contract from Gym to LLM agents, built an OpenEnv environment and pushed it to the Hub, plugged it into TRL's GRPOTrainer, then trained a real coding agent (OpenCode) through Harbor with AsyncGRPOTrainer on Hugging Face sandboxes. The series has passed 300k views. Thank you to every speaker, to the TRL team, and to everyone who showed up live with questions. Playlist: