@Sentdex
Account based inUnited States
About this account
- Account based in
- United States
- Connected via
- United States Android App
Account-level information from X, not a live location or the device used for a specific post.
gpus and tractors. Neural networks from Scratch book: https://xitter.cf/t.co/hyMkWyUP7R https://xitter.cf/t.co/8WGZRkUGsn
Alabama, USA
Joined May 2013
- Tweets19.1K
- Following398
- Followers113K
- Likes23.8K
Pinned Tweet
dario begged me in dms to not do agent swarms at home but whatever, this is nemotron 120B A12B running 64 concurrents at a bit over 100 tok/sec each for an aggregate of almost 7K tok/sec on a single GB300 (Asus DGX Station)
believe it or not, the agents did NOT magically just start hacking the planet tho. weird.
Harrison Kinsley retweeted
We're shipping Asimov 1!
Builders have already received their kits in 🇺🇸 the US, 🇩🇪 Germany, 🇫🇷 France, 🇵🇱 Poland, 🇹🇭 Thailand, and 🇦🇪 the UAE.
We're forming Batch 4 now.
Order now: menlo.ai
I get endless of these. They always just say the name and price. Or ask me my price for a paid post and dont even tell me about the product, as if that part is irrelevant.
For everyone promoting things this way on all sides, it *is* irrelevant.
The moment i see a video like this i move along. Accts that push this stuff with lazy effort get muted.
You might get a big view count that you can incl in ur next investor email, but people are getting fed up wit this slop.
I already forgot the company in the vid.
i find it really sad that X is becoming a pay to win platform and that even "good" releases are going through these fully slop/fake campaigns to get traction
i'm also pretty sure there is very shady stuff where they are boosting each other's posts with bot farms and more. would be great to investigate here @elonmusk and make sure the algorithm doesn't push their posts, you can very easily cluster that if you look at each release and the accounts amplifying 🙏
(not a dunk on recursive, recently a lot of people are doing that unfo and it's definitely working, posts often get over ~1M views)
deepseek harness is actually quite good with dsv4.1f 1017
I've been ignoring and avoiding these browser based harnesses, but hosting it locally is a great experience!
anyone tried the zai's zcode with the GLMs? looks very similar.
fall recovery is definitely one of the more fun RL policies that I've worked on.
still a ways to go, this one would definitely break a lot of stuff as is, but we'll continue improving! still just a lil baby model.
I kneel...
training @asimovinc to transfer from lying on the ground (back/belly/side) to a half kneel, with hopes of translating next from a half kneel to full stand.
Trying to find the best way to get up off the ground.
I kneel...
training @asimovinc to transfer from lying on the ground (back/belly/side) to a half kneel, with hopes of translating next from a half kneel to full stand.
Trying to find the best way to get up off the ground.
Harrison Kinsley retweeted
64 concurrent requests. Nearly 7,000 tokens/sec. One machine. 👀
@Sentdex tests Nemotron 120B A12B on the ASUS ExpertCenter Pro ET900N G3.
Learn more: asus.click/ET900N-G3-Social
#ASUS #ASUSAgentComputer #ExpertCenterPro #ET900NG3 #GB300 #LocalAI
dario begged me in dms to not do agent swarms at home but whatever, this is nemotron 120B A12B running 64 concurrents at a bit over 100 tok/sec each for an aggregate of almost 7K tok/sec on a single GB300 (Asus DGX Station)
believe it or not, the agents did NOT magically just start hacking the planet tho. weird.
The memory and copious dopamine of damp, stand, walk, after you built your Asimov piece by piece, is something you will never forget.
Every great machine begins on a workbench. Now you can build a humanoid robot on yours. docs.menlo.ai/
you can buy a whole dell workstation with an RTX Pro 6K WS inside of it for less than most places will sell you just the pro 6k
Harrison Kinsley retweeted
DeepSeek V4.1 Flash 1017 scores 45 on the Artificial Analysis Intelligence Index, improving on DeepSeek V4.1 Flash by 6 points, and is the equal #2 open-weights model
@deepseek_ai has released DeepSeek V4.1 Flash 1017, an update on their flagship model DeepSeek V4.1 Flash. On the first-party API, DeepSeek V4.1 Flash 1017 is priced at $0.30 per 1M input tokens and $1.20 per 1M output tokens during peak hours, with off-peak pricing half of peak rates, the same pricing as DeepSeek V4.1 Flash.
Key results:
➤ DeepSeek V4.1 Flash 1017 makes gains in agentic capabilities, knowledge and scientific reasoning. DeepSeek V4.1 Flash 1017’s Elo rating on GDPval-AA v2.1 is 1746, an increase of 146 points from DeepSeek V4.1 Flash’s rating of 1600. On AA-Briefcase v1.1, its Elo rating increases by 150 points from 1420 to 1570. On AA-Omniscience, its non-hallucination rate is 33%, a 29-point improvement from DeepSeek V4.1 Flash’s 4%, while seeing an improvement in accuracy from 43% to 46%, pushing its overall AA-Omniscience Index score from -5 to +5. On CritPt, DeepSeek V4.1 Flash 1017 scores 24%, up from 14% for DeepSeek V4.1 Flash.
➤ DeepSeek V4.1 Flash 1017 is now the #2 open-weights model alongside GLM-5.3. With a score of 45 on the Artificial Analysis Intelligence Index, DeepSeek V4.1 Flash 1017 is behind MiMo-V2.6-Pro by only 1 point and ahead of other open-source models like Step 5 Preview and Kimi K3 (both at 44).
➤ With a Cost per Task of $0.26, DeepSeek V4.1 Flash 1017 sits in the most attractive quadrant of Capability vs. Cost. With a Cost per Task of $0.26 and Intelligence Index score of 45, DeepSeek V4.1 Flash 1017 compares favorably to GLM-5.3-Flash at $0.28 per task and a score of 42. At the same time, MiMo-V2.6-Pro (46) at $0.13 per task and GPT-6.1 Sol (medium, 48) at $0.21 per task both offer higher capability at a lower Cost per Task.
Additional model details:
➤ Context window: 1M tokens (equivalent to DeepSeek V4.1 Flash)
➤ Pricing: $0.30 per 1M input tokens and $1.20 per 1M output tokens, unchanged from DeepSeek V4.1 Flash. Cache hit price of $0.006 per 1M tokens, a 98% discount
➤ Size: 552B total parameters (16B active)
➤ License: MIT
➤ Providers: Available through DeepSeek’s first-party API, @FireworksAI_HQ , @baseten, @DeepInfra , @wafer_ai , @Cloudflare and others
Back when i used idle as my editor, i would frequently scroll up to find the correct indent level, hold the cursor there, then scroll down to make sure i was at the right indentation
Whoa. Allegedly fixes task execution time. Lets see
🚀 DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.
🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
1/4
Harrison Kinsley retweeted
I wonder if we'll get to a point where being distilled becomes profitable to the labs. If they spend X to create a model. And more than 20 companies pay 1/20th of that to distill it. They're in the money. It's a new form of infra where downstream handles distribution.
Trying to train a fall recovery policy for the @asimovinc humanoid
Getting up is hard!
Nemotron 3.5 Super VL (120B A12B) is now the leading US open source model. Congrats to the NVIDIA AI team!
I am still doing testing of this model, but I think the ranking would be even stronger if we were just measuring agentic coding performance. This one may very well be a solid choice for a speedy local coding agent model
I'm running many things in parallel, will get proper average and max numbers in time, but just randomly prodding this model for some quick speed checks and over 550 tok/sec. All vanilla settings. Once optimized 600+, maybe even 650+, will be trivial, and so far it does appear to be a token efficient problem solver.
Really exciting to see what looks like a solid release so far from nvidia!
Nemotron 3.5 Super VL is the highest-scoring open weights model from the US, at 26 on the Intelligence Index. It edges out Thinking Machines' Inkling Small (26) and passes NVIDIA's previous flagship, Nemotron 3 Ultra (23), and Meta's Muse Glimmer (17)
Despite the advance, the US still trails the United Arab Emirates (K2 Horizon 375B A23B, 31) and China (MiMo-V2.6-Pro, 46) on open weights intelligence.
The # of times lately I happen to ask someone in private where they work now and it turns out they are working at OpenAI is shocking.
It's strangely a company where you hear about the departures but people aren't super open about joining.
