Justin Ji

15 posts

Justin Ji

Justin Ji

@jujipotle

CS @ Harvard

Katılım Mayıs 2020
243 Takip Edilen153 Takipçiler
emily sihan zhang
emily sihan zhang@emilyzsh·
𝓟𝓸𝓻𝓽𝓻𝓪𝓲𝓽 𝓸𝓯 𝓨𝓸𝓾 @thatsnotoptimal and i created Portrait of You, a series of generative, living works that erode with our brainrot digital consumption in real time. this public installation of self-surveillance will be running 24/7 for the next month.
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Emily Han
Emily Han@emilyhanyf·
Life update: I’ve decided to take some time off from Stanford. I've seen a lot of these "dropping out of college" posts, but I never thought I’d write one myself. To be completely honest, I never seriously considered taking a leave or dropping out. Interning at @modal this past summer changed my plans. People often talk about @modal's talent density, but working within it gave me a whole new perspective on what “exceptional” looks like in engineering, product, design, GTM, and most importantly, in company culture. I wanted to keep learning in this environment, especially now. Over just a few months interning at @modal this summer, I saw Lovable create 1M+ sandboxes on @modal, the launch of Modal SDK 1.0, GPU Performance Glossary, GPU Memory Snapshots, multi-node training clusters, @modal Notebooks, and so much more. I felt this was the moment to join and build the infrastructure for the entire AI/ML lifecycle, the way it should be. So I'm excited to share that I'm starting full-time at @modal today, alongside the announcement of @modal's Series B! It was a tough choice to step away from Stanford, even for a little while, but I’m excited to continue learning and growing with this team. If you're a student who want to work with incredible engineers, be part of a truly welcoming culture, and join in on karaoke or goat-petting events, or if you’re unsure about taking time off, I’d be happy to share my experience!
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Victoria Li
Victoria Li@victoria_r_li·
Charts and graphs help people analyze data, but can they also help AI? In a new paper, we provide initial evidence that it does! GPT 4.1 and Claude 3.5 describe three synthetic datasets more precisely and accurately when raw data is accompanied by a scatter plot. Read more in🧵!
Victoria Li tweet media
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Ayush Noori
Ayush Noori@ayushnoori·
I'm visiting exceptional startups in New York this week on the Harvard Startup Trek organized by my brilliant friends @kelschen99, @jonathanlei0, and @evatuecke. Today, we... – Visited Embedded Intelligence (ei.inc) and @Medal_TV, thank you @PimDeWitte and team for an exciting demo. – Visited @bubble, thanks @joshhaas and @emmanuel_s for insights into your founding journey, 7 years of bootstrapping, and challenges of company building (over great chocolate chip coconut cookies too 🍪)! – Visited Moment (withmoment.com) for a deep dive on fixed income trading. Fixed income is the world's most traded asset class, more than 3x size of global equities market, yet surprisingly illiquid. That may soon change, thanks to Moment (who recently partnered with @apexfintech). – Brief pit stop at the @haizelabs office and reunion with @leonardtang_ and @steveshenli. – Visited @withgraphite, thank you to @gregmfoster and @TomasReimers for sharing your origin story building a company amidst a global pandemic, pivoting based on customer feedback, and finding PMF. – Ending the day with a @generalcatalyst x @SunoMusic AI social. Thank you @jonnyjzhang, @Nim_Ravid1, @apjacob03, @KonstDaskalakis, @max_rimpel and team for hosting us at the GC NYC office and for throwing an awesome party. All this and we've only been in NYC for < 24 hours. If you're around, please reach out – would love to meet up while we're here. Everyone else on the Harvard Startup Trek: @calebcapoccia, @evatuecke, @grx_xce, @InakiArango, @jonathanlei0, @jujipotle, @kelschen99, @Nim_Ravid1, @kineticyang, Peggy Yin, Prin Pulkes
Ayush Noori tweet mediaAyush Noori tweet mediaAyush Noori tweet media
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Franklin Wang
Franklin Wang@f_x_wang·
Speed up your DPO training with zero compromises using ✨prefix sharing✨! Prefix sharing speeds up training by 1.2-1.6x 🚀across the datasets we tested and is numerically identical to standard DPO training. Paper: arxiv.org/abs/2410.20305 Code: github.com/frankxwang/dpo…
Franklin Wang tweet media
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Franklin Wang
Franklin Wang@f_x_wang·
DPO is extremely effective when scaled with on-policy data scored by synthetic judges. In this blog post, we conduct a case study on summarization, directly optimizing for objective metrics like Q&A accuracy + compression instead of typical subjective ones.
Anyscale@anyscalecompute

1/11 🥁Anyscale now offers DPO as part of its LLM training suite! This enables refinement of LLMs for domain-specific preferences, which can outperform frontier closed-models & SFT baselines. We demo the capability through a case study on summarization 🧵

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Aneesh Muppidi
Aneesh Muppidi@aneeshers·
⭐New Paper Alert ⭐ How can your #RL agent quickly adapt to new distribution shifts ? And without ANY tuning?🤔 We suggest you get on the Fast TRAC🏎️💨, our new Parameter-free Optimizer that surprisingly works. Why? Website:computationalrobotics.seas.harvard.edu/TRAC/ 1/🧵
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Justin Ji
Justin Ji@jujipotle·
@walden_yan this is incredible walden 🤯 can devin make a website that scrapes harvard mailing lists for events with free food (asking for a friend)? also would love beta access to play around with!
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Johnny Ni
Johnny Ni@JohnnyNi13·
Anyone at the new pear vc office that can let me in to visit? I'm coming to SF next weekend (also for treehacks)
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Aegis
Aegis@aegis_protocol·
Trust redesigned. Something coming soon, interact with this tweet 👀
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