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Made an ultra fast DTW solver w Stephen Boyd @StanfordEng 🚀
Contributions:
1. Linear time
2. Continuous time formulation
3. Tunable regularization prevents overfitting
📄 rdcu.be/cT5dD
👁 Demo dderiso.github.io/gdtw/
👾 C++ github.com/dderiso/gdtw
🐍 pip install gdtw
Dave Deriso retweeted
Flint is our first model built with the Eureka Machine, our automated research system, from scratch in 4 months.
We are betting that labs that automate AI research will outrun the labs that just hire more researchers.
Check it out today for fast, local and unmetered intelligence.
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Dave Deriso retweeted
Excited to share that we’ve started Recursive Superintelligence to automate knowledge discovery, starting with AI that experiments on how to improve itself. We raised $ 650m+ led by GV and Greycroft.
Not saying I miss the pandemic, but I do miss the hermitage. If coding agents were around back then, I wonder how much brighter this graph would be
I’m thinking a lot about code architecture. This is a higher level design process that takes a top-down view.
Good architecture is intuitive. You know it’s good when it’s easy to determine the purpose of every file, where it fits in, and categorize it as a definition, utility, process, state machine, or manager.
In contrast, if you can’t quickly categorize a file and visualize it in the tree, the architecture itself probably needs to be redesigned before any specific file is optimized.
Oxford neuroscientists making epic hardware in Silicon Valley 🦾🧠 Awesome work Andre @TheFrontalLobe_
Dave Deriso retweeted
Inception emerges from stealth with a new type of AI model tcrn.ch/3ETFdLT
This is huge.
Remember how latent diffusion cracked the image generation problem? Since then, researchers have been trying to apply it to language through a variation called “discrete diffusion.” This architecture has been somewhat of a holy grail since it’s 10x faster and far more accurate than existing models.
Current LLMs use autoregression, which case errors compound as the sequence is generated. Hence GPT will start to hallucinate or veer off course later in the output. Discrete diffusion does not suffer from this drawback since all tokens are generated at once and then iteratively refined into a final product. That simultaneous action is also the reason it’s so much faster and scalable.
@volokuleshov and @StefanoErmon quietly cracked the problem last year and have been putting in work to stabilize it and make it accessible to developers through Inception Labs.
Congrats @volokuleshov and @StefanoErmon for ushering us into a new era 🚀🚀🚀
Excited to announce the first commercial-scale diffusion language model---Mercury Coder. Mercury runs at 1000 tokens/sec on Nvidia hardware while matching the performance of existing speed-optimized LLMs.
Mercury introduces a new approach to language generation inspired by image and video generation systems like MidJourney and Sora. This approach is significantly more efficient (faster and cheaper) to run that existing LLMs, and reduces the cost of AI inference by 10x.
Mercury Coder also achieves comparable performance to speed-optimized frontier models like Claude Haiku and GPT4o-mini. However, it is much more hardware-efficient because it uses a parallel generation mechanism that takes advantage of GPUs. This makes the model much faster or cheaper to run (more users can be served on the same hardware).
You can try it today in our playground!
Dave Deriso retweeted
Did you know there is free gourmet coffee right outside #Neurips2024?
All you need is to sign up for skim.ai and go to one of the red coffee carts at the conference center entrance.
🙏 @davederiso
Dave Deriso retweeted
We’re looking for an Editor-in-Chief among our members! 📣
📝 Manage the production of the #MCAA Newsletter & IRRADIUM magazine
📝 Lead a team of 10 editors
📝 Supervise, edit & proofread articles
This is a volunteer position. Apply by July 31: mariecuriealumni.eu/news/mca…
Delighted to find this NN based on my GDTW algorithm from #ICLR
Deriving the gradient of a DP is often a tedious / pedestrian activity, even with autodiff. Love how they use DDN to do it implicitly github.com/anucvml/ddn
arxiv.org/pdf/2303.10778 @sgould_au
Dave Deriso retweeted
Top product designers & founders gave a session on Research and Design for AI Products, featuring @davederiso of Bluue, @DivGarg9 (founder, @MultiOn_AI )
One of my favorite moments from #deeptechweek @realGeorgeHotz @DivGarg9 @kylejohnmorris @stevewattsfrey @eraqian
Dave Deriso retweeted
Awesome longevity panel with @lindaavey @bryan_johnson @celinehalioua covering everything from testing / interventions to how society will change if we live to 120
🙌🏼 hosts @Andercot @sampritibh @timshi_ai #deeptechweek sf-deep-tech-week.com
Awesome longevity panel with @lindaavey @bryan_johnson @celinehalioua covering everything from testing / interventions to how society will change if we live to 120
🙌🏼 hosts @Andercot @sampritibh @timshi_ai #deeptechweek sf-deep-tech-week.com
Dave Deriso retweeted
We have an incredible speaker line up of hardcore builders solving some pretty hard problems! Excited to announce our first panel tomorrow at 6PM during reception hour:
“How Not to Die”
Join @bryan_johnson (Founder & CEO, Project Blueprint), @celinehalioua (Founder and CEO, @loyalfordogs ), and @lindaavey (Co-founder, @23andMe) on their diverse approaches and ideas to extending our lifespan and enhancing our quality of life. Moderated by @davederiso
@Andercot @SimoneSyed @timshi_ai
Lost track of how many times I’ve seen AI written on healthcare slides at #JPM24 Also great to meet the man himself
🗽The AI for Public Good Hackathon @cornell_tech was a success! Here are the incredible projects by theme:
🗽1. AI for Gov
github.com/dbieber/city-311
AI for routing complains to agencies
@flore75711500 @theandrewsiah
github.com/samob917/311Weekl…
AI for summarizing 311 data
@samob917 @d_r_farrell & Odaine Forbes
github.com/xukai92/subway-ba…
AI for load balancing subway
@xukai92 & Victoria Liao
🗽2. AI for Open Data
github.com/ankitshah009/NYC_…
CitySavy: summarizes data on NYC Open Data
@ashah0052 @noahpunintended @iamjdeleon, Edward Plata &Jon Chin
github.com/EdwardPlata/AIGov
AI for ranking NYC Open Data
@nitanisha
github.com/ryanhlewis/Crowds…
AI for crowdsourced map data
@rhardestylewis
🗽3. AI for Citizen Life
github.com/Kaya-AI/ai-hack
Demo: kaya-ai-demo.streamlit.app/
Agent AI that finds/applies to jobs
@rolandgvc Zameer Raizada, Ojonimi Bako, Joshua Cohen
github.com/hahnandrew/ai4pg
AI for sealing criminal records
@jenny_wang23 @azizalto @adityachavan & Andrew Hahn
github.com/vishnuravi/openha…
AI Agent for lowering medical bills
@rahul_sompuram @vishnuravi & Chethan Sarabu
github.com/richabatra/nyc-ai…
AI chat for metabolic health
@datarequena & Richa Batra
github.com/LearnQuantum/feyn…
Generative AI for learning STEM
@richa_lq
🗽 🙌🏼 to our rockstar cohosts and speakers
@volokuleshov @Bieber @2plus2make5 @samgbafa @npparikh @tanzeemc @soumithchintala @Mascobot
🗽and 🫶🏼 to our Incredible sponsors
@AnthropicAI @a16z @Techweek_ @645ventures
👾 NYC AI Hackathon - 10/22 - @a16z @AnthropicAI @cornell_tech @volokuleshov @Bieber @Techweek_
👾 Join @a16z and founding AI Chief @NYCMayorsOffice @npparikh to apply AI to public good 🚑🛠️📑📡
👾 Limited to 100 engineers who *must ship code*
👾 RSVP tinyurl.com/ai-for-public-go…





























