
Scientific SuperIntelligence @periodiclabs
Joined October 2013
- Tweets600
- Following247
- Followers918
- Likes460
Mark Huang retweeted
I asked people which neolabs under $10B have the highest talent density:
32 votes: Core Automation (@MillionInt, @_arohan_)
26 votes: Periodic Labs (@LiamFedus, @ekindogus)
20 votes: Flapping Airplanes (@spectorb, @amspector100, @aidanmantine)
18 votes: Standard Intelligence (@G413N, @devanshpandey)
8 votes: Ricursive (@annadgoldie, @Azaliamirh)
5 votes each:
- Ineffable (David Silver)
- Mirendil (@bneyshabur, @HarshMeh1a, @shayan_, @tararezaeikh)
- Recursive (@RichardSocher, @_rockt, @timshi_ai, @josh_tobin_, @CaimingXiong, @jeffclune, @tydsh, Alexey Dosovitskiy)
3 votes each:
- Applied Compute (@ypatil125, @rhythmrg, @lindensli)
- Isara (@ezhang7423, @hegasz)
- Prime Intellect (@vincentweisser, @johannes_hage)
2 votes each:
- Elorian (@AndrewDai, @yinfeiy, @SethInternet)
- Goodfire (@eric_ho, @DanJBalsam, @banburismus_)
- Inherent (@tantumscollins, @edwardfhughes, @LouisKirschAI, @kallyaleksiev)
- Trajectory (@rronak_, @QuantumArjun, @MichaelElabd)
- World Labs (@drfeifei, @jcjohnss, @BenMildenhall, @chlassner)
People couldn't pick a company they work at or founded. Disclosure: I'm a small investor in Applied Compute, Factory, Standard Intelligence, Trajectory and Wafer. I didn't vote.
Continued:
Neon’s scientific traces are long and vary widely in length. Training on them shouldn’t mean GPUs chewing through padding or waiting for each other.
Here’s how we pack sequences, balance the work, and distribute long contexts across GPUs at @periodiclabs 👇
periodic.com/news/ai-infrast…
Mark Huang retweeted
Another Neon infra tidbit: @periodiclabs accelerated checkpoint conversion ~30x down to just 1 minute, helping us deploy faster to get rapid feedback from our labs.
Our models train in Megatron, but SGLang inference consumes weights in HuggingFace format. Traditionally, conversion ran serially:
1. rebuild the entire checkpoint from individual weight tensors
2. convert the checkpoint
3. reshard it to be inference-ready
This could take 30 min for a trillion parameter model!
Noticing that conversion shouldn't require materializing the full checkpoint, @hsu_byron introduced Fast Resharding:
1. parallelize conversion across Ray actors
2. convert expert chunks directly instead of assembling the full expert tensor in memory
We upstreamed Fast Resharding to Miles in PR #1371. Now even you can deploy trained models to production in a matter of minutes.
Mark Huang retweeted
We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next.
Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon.
This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials.
Read our blog posts below.
Mark Huang retweeted
Airtable gets into the AI agent game with Superagent techcrunch.com/2026/01/27/ai…
Stop talking to a wall of predictive text and start doing real research.
Introducing @Superagent: built to turn your complex business questions into boardroom-ready answers, beautifully rendered as reports, slides, or websites.
Try it: bit.ly/466oEH3
Mark Huang retweeted
News: Airtable has hired a new CTO away from OpenAI's ChatGPT unit and made its largest startup acquisition to date: 'superagent' builder DeepSky.
CEO Howie Liu and new hire exec David Azose spoke to Upstarts exclusively about what the big moves mean for Airtable's AI strategy.
Mark Huang retweeted
Had a nice article written about @DeepSkyAI by @PoetsAndQuants!
poetsandquants.com/2025/08/2…
“A research engine already winning over hundreds of MBAs — including at Stanford GSB — for its ability to cut through noise, crush hallucinations, and deliver market-ready insights in minutes.”
—-
Try it out 3 months free using promo code “OPEN82” - deepsky.ai
Mark Huang retweeted
Looking back, when $OPEN hit $0.53, I felt powerless. I knew from first-hand experience, the company was something special but I couldn’t understand how it had become such a shadow of its former self.
Fast forward to today and Opendoor has become a story of retail empowerment and how motivated individuals can effect change at corporations.
At @DeepSkyAI, we’ve also been building to empower individuals with access to the best market data and leading-edge business AI. I use it daily to augment my own $OPEN and stock research and we’re giving away 3 months of DeepSky for free to $OPEN believers (200 reports / month).
Just use the coupon code “OPEN82” and get access to $10K+ a month of market data on top of a superagent custom-built for business analysis and market research.
Try it out with questions like:
“What is Opendoor’s Path to $82?”
“Who would be good candidates for next Opendoor CEO”
“How can Opendoor leverage its data for AI?”
Sign-up here!
Deepsky.ai
Mark Huang retweeted
Replying to @markatgradient
@markatgradient recently sat down with @amanda_robs and @tnachen from @OssStartup to catch up on everything we've been working on 🔥
🎥 podcasters.spotify.com/pod/s…
✅ Enterprise AI Automation
✅ Long Context Models
✅ Enterprise Industry Use Cases
Thank you both for having us! Looking forward to the next one!
Mark Huang retweeted
I recently talked to a team from @CrusoeAI and @Gradient_AI_ on their effort to create an open LLM with a one-million-token context window.
Thanks to @LeoPekelis @_ethan and Peter McGregor for sharing key lessons learned from the project.
venturebeat.com/ai/how-gradi…
Mark Huang retweeted
Join our Fireside chat 🔥 on Wednesday with @markatgradient , co-founder of @Gradient_AI_ and @sagecodes as they discuss how longer LLM context can improve agentic workflows!
eventbrite.com/e/improve-age…
Mark Huang retweeted
What an episode! Join our Co-Founder & Chief Architect, @markatgradient, as he breaks down the intricacies of scaling Llama3 beyond a 1M context window on the latest episode on @latentspacepod!
🔗 latent.space/p/gradient
✅ History of Long Context
✅ Intro to RoPE, ALiBi, and Ring Attention
✅ Synthetic Data for Long Context
✅ Beyond NIAH
Shout out to @swyx and @FanaHOVA for the conversation and the opportunity to share some of our work.
Mark Huang retweeted
We're excited about how our 1M Context Length @AIatMeta Llama-3 70B performed against RULER. Dive into the results or checkout how we made this possible (e.g. scaling rotational embeddings).
✅ RULER Results: gradient.ai/blog/ruler-vs-gr…
✅ Made Possible By: gradient.ai/blog/scaling-rot…
Mark Huang retweeted
Gradient AI Introduces Llama-3 8B Gradient Instruct 1048k: Setting New Standards in Long-Context AI
Researchers at Gradient introduced the Llama-3 8B Gradient Instruct 1048k model, a groundbreaking advancement in language models. This model extends the context length from 8,000 to over 1,048,000 tokens, showcasing the ability to manage long contexts with minimal additional training. Utilizing techniques like NTK-aware interpolation and Ring Attention, the researchers significantly improved training efficiency and speed, enabling the model to handle extensive data without the typical performance drop associated with longer contexts.
The researchers employed techniques such as NTK-aware interpolation and Ring Attention to efficiently scale the training of long-context models. They achieved a significant speedup in model training by progressively increasing the context length during training and using advanced computational strategies. This approach allowed them to create a model capable of handling extensive data without the typical performance drop associated with longer contexts.
Quick read: marktechpost.com/2024/05/21/…
Model Card: huggingface.co/gradientai/Ll…
@Gradient_AI_ #artificiallntelligence
Mark Huang retweeted
Curious about Long Context Windows? Our Chief Scientist, @LeoPekelis, will be live on @AIMakerspace on Wed. May 22nd @ 10am PST to chat about:
🔗 lu.ma/longllama
✅ Extending Llama 3’s Context Length > 1M
✅ Long Context Evals (NIAH, RULER, etc.)
✅ RAG vs. Long-Context
Mark Huang retweeted
Lately we've been exploring several open-source frameworks, in addition to building our own in-house abstractions to create more robust AI systems. Take a look at our learnings and deep dive into DSPy.
🔗 gradient.ai/blog/achieving-g…
✅ GPT-4 Performance at 10x Lower Cost
✅ Structured Prompt Formatting → Better Response Formatting
✅ Increases Accuracy & Reduced Response Size
A huge thank you to @lateinteraction and the DSPy team for their contributions in developing this framework.
Mark Huang retweeted
Going live with @markatgradient to talk about @Gradient_AI_ 's multimillion context window Llama 3 fine tune in less than 10 minutes 👀
check it out --> lu.ma/9q8o7s7e
#ai #llama3 #llm #genai
Mark Huang retweeted
If you want long context APIs or to chat agents, fill out this form or DM me forms.gle/xWJhPBGks39MjjnY6
Mark Huang retweeted
Very exciting week concluded, here's some light catch up video for you (and as always, if you want to dive deeper, check out the newsletter and/or podcast in the comments below)
This week we were mostly focused on LLama-3 (it was a week-aversary after all) and Phi3
📅 ThursdAI - April 25th - TL'DR version
Here's a quick summary of the whole @thurdsAI_pod show for April 25th.





















