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Awesome week for computer vision Hugging Face🔥 Besides DINOv3 we added support for LLMDet, the SOTA for zero-shot object detection (#CVPR2025 '25 highlight) Detect instances in scenes just via prompting, no training involved.

Niels Rogge

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73,865 views • 11 months ago •via X (Twitter)

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EnsoFi just won 1st place at Japan Blockchain Week – AI Summit edition 🏆 Proud to share our vision of building the cross-chain lending hub for the AI x DeFi era at Japan Blockchain Week We’re all about building in public, making noise wherever we go, and leaving our mark on every stage 🔥

EnsoFi just won 1st place at Japan Blockchain Week – AI Summit edition 🏆 Proud to share our vision of building the cross-chain lending hub for the AI x DeFi era at Japan Blockchain Week We’re all about building in public, making noise wherever we go, and leaving our mark on every stage 🔥

EnsoFi

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⚡️ We just added support for Nvidia G4 (RTX Pro 6000 Blackwell Server Edition) GPUs in Colaboratory! 🔥 With a peak rating of 960 BF16 TFLOPs (~50% more than the A100-80G) and 96 GB of VRAM (20% more than the A100-80G), it’s the most efficient high-performance GPU we’ve ever released. ⏱️Time to make GPUs go brrrr 🏎️

⚡️ We just added support for Nvidia G4 (RTX Pro 6000 Blackwell Server Edition) GPUs in Colaboratory! 🔥 With a peak rating of 960 BF16 TFLOPs (~50% more than the A100-80G) and 96 GB of VRAM (20% more than the A100-80G), it’s the most efficient high-performance GPU we’ve ever released. ⏱️Time to make GPUs go brrrr 🏎️

Colaboratory

62,262 views • 4 months ago

I'll always root for a team that open-sources its best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyant

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Chubby♨️

48,249 views • 11 days ago

Kit Connor has opened up about filming sex scenes in 'Heartstopper Forever'. Speaking to The Guardian, Connor said: “It's just nice to see two boys in love.” On filming intimate scenes, he continued: “In a silent room with lots of middle-aged men. A camera right next to your face while you're wearing a c** sock.” Connor's costar, Joe Locke, added: “It would be weird if we hadn't shown it. Just because our show is a more earnest version of a queer representation doesn't mean that sex [shouldn't be shown]. It's still a big thing for anyone in the world.”

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Kit Connor has opened up about filming sex scenes in 'Heartstopper Forever'. Speaking to The Guardian, Connor said: “It's just nice to see two boys in love.” On filming intimate scenes, he continued: “In a silent room with lots of middle-aged men. A camera right next to your face while you're wearing a c** sock.” Connor's costar, Joe Locke, added: “It would be weird if we hadn't shown it. Just because our show is a more earnest version of a queer representation doesn't mean that sex [shouldn't be shown]. It's still a big thing for anyone in the world.”

PinkNews

237,691 views • 4 days ago

Introducing ml-intern, the agent that just automated the post-training team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

Introducing ml-intern, the agent that just automated the post-training team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

Aksel

1,264,490 views • 2 months ago

100,000,000 transactions. This isn't just a number. It’s stress tests passed. Infra battle-tested. UX relentlessly refined. Our AI infra stack has now processed over 100M txs across 450K+ wallets and $450M+ in restaked TVL. This wasn’t an easy feat. We iterated every week. Listened. Shipped. Broke things. Fixed them. All while keeping user experience as our north star. Thankyou to our community for the constant feedback and support. You helped us shape this. Capx AI is ready for mainnet. The AI Builder Economy is no longer a vision, it’s inevitable and it's happening now. notifs on. coming soon.

100,000,000 transactions. This isn't just a number. It’s stress tests passed. Infra battle-tested. UX relentlessly refined. Our AI infra stack has now processed over 100M txs across 450K+ wallets and $450M+ in restaked TVL. This wasn’t an easy feat. We iterated every week. Listened. Shipped. Broke things. Fixed them. All while keeping user experience as our north star. Thankyou to our community for the constant feedback and support. You helped us shape this. Capx AI is ready for mainnet. The AI Builder Economy is no longer a vision, it’s inevitable and it's happening now. notifs on. coming soon.

Capx AI

99,509 views • 10 months ago

We just closed the largest AI agent pre-sale ever. 76,831 Sol in under 36 hours. This historic raise shows Web3 needs proven builders. With a track record of creating virtual humans since 2018, delivering for top-tier brands and sustaining real revenue, we can’t wait to continue delivering on this vision. We’re grateful for the support. It’s more than we ever expected and more than we need. That’s why we’re returning 50% to everyone who contributed. • Refunds will be issued within 24 hours. • Launch this week. We will use the remaining 50% to support the launch, provide liquidity, pay listing fees (if any), and continue to build this project out long term. As we finalize next steps, we’re proud to share an early look at what we’ve been building:

We just closed the largest AI agent pre-sale ever. 76,831 Sol in under 36 hours. This historic raise shows Web3 needs proven builders. With a track record of creating virtual humans since 2018, delivering for top-tier brands and sustaining real revenue, we can’t wait to continue delivering on this vision. We’re grateful for the support. It’s more than we ever expected and more than we need. That’s why we’re returning 50% to everyone who contributed. • Refunds will be issued within 24 hours. • Launch this week. We will use the remaining 50% to support the launch, provide liquidity, pay listing fees (if any), and continue to build this project out long term. As we finalize next steps, we’re proud to share an early look at what we’ve been building:

MIRAI

252,167 views • 1 year ago

Check out this Stereo4D paper from Google DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for YouTubeVR. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. Which means it tracks how objects move between frames while simultaneously reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways! Links to projects below⛓️

Check out this Stereo4D paper from Google DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for YouTubeVR. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. Which means it tracks how objects move between frames while simultaneously reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways! Links to projects below⛓️

Bilawal Sidhu

67,919 views • 1 year ago

we just added Apple Pay support in Chrome and non-Safari browsers on Dodo Payments. you can now use Apple Pay on desktop too. it opens a QR popup, you scan it with your iPhone, and pay. no Safari required. most payment providers still haven't shipped this. they're stuck on older Apple Pay versions that only work in Safari. we updated to the latest spec and built the cross-browser flow from scratch. this is live now for all merchants. no integration changes needed.

we just added Apple Pay support in Chrome and non-Safari browsers on Dodo Payments. you can now use Apple Pay on desktop too. it opens a QR popup, you scan it with your iPhone, and pay. no Safari required. most payment providers still haven't shipped this. they're stuck on older Apple Pay versions that only work in Safari. we updated to the latest spec and built the cross-browser flow from scratch. this is live now for all merchants. no integration changes needed.

Ayush Agarwal

31,066 views • 1 month ago

STRIPE-OWNED BRIDGE GOES LIVE WITH CELO SUPPORT Bridge Bridge, which is owned by the Stripe payments giant, today added support for leading Ethereum L2 Celo. Bridge's decision to extend its stablecoin orchestration platform to $CELO is no coincidence, the network boasting some incredible metrics already... • 1.3 billion total transactions • >$65 billion in stablecoin volume since March '25 • 25 native stablecoin assets on the chain ... and many more besides. According to an official release, Celo's emphasis on stablecoins dates back some six years to its launch in 2020, well before other networks adopted the stablecoin narrative. A clear case study when it comes to stablecoin adoption on Celo is MiniPay - Opera's self-custodial wallet platform, which has already driven a staggering 400 million stablecoin transactions on the network.

STRIPE-OWNED BRIDGE GOES LIVE WITH CELO SUPPORT Bridge Bridge, which is owned by the Stripe payments giant, today added support for leading Ethereum L2 Celo. Bridge's decision to extend its stablecoin orchestration platform to $CELO is no coincidence, the network boasting some incredible metrics already... • 1.3 billion total transactions • >$65 billion in stablecoin volume since March '25 • 25 native stablecoin assets on the chain ... and many more besides. According to an official release, Celo's emphasis on stablecoins dates back some six years to its launch in 2020, well before other networks adopted the stablecoin narrative. A clear case study when it comes to stablecoin adoption on Celo is MiniPay - Opera's self-custodial wallet platform, which has already driven a staggering 400 million stablecoin transactions on the network.

BSCN

11,303 views • 2 months ago

Fuck yeah! MaskGCT - New open SoTA Text to Speech model! 🔥 > Zero-shot voice cloning > Emotional TTS > Trained on 100K hours of data > Long form synthesis > Variable speed synthesis > Bilingual - Chinese & English > Available on Hugging Face Fully non-autoregressive architecture: > Stage 1: Predicts semantic tokens from text, using tokens extracted from a speech self-supervised learning (SSL) model > Stage 2: Predicts acoustic tokens conditioned on the semantic tokens. Synthesised: "Would you guys personally like to have a fake fireplace, an electric one, in your house? Or would you rather have a real fireplace? Let me know down below. Okay everybody, that's all for today's video and I hope you guys learned a bunch of furniture vocabulary!" TTS scene keeps getting lit! 🐐

Fuck yeah! MaskGCT - New open SoTA Text to Speech model! 🔥 > Zero-shot voice cloning > Emotional TTS > Trained on 100K hours of data > Long form synthesis > Variable speed synthesis > Bilingual - Chinese & English > Available on Hugging Face Fully non-autoregressive architecture: > Stage 1: Predicts semantic tokens from text, using tokens extracted from a speech self-supervised learning (SSL) model > Stage 2: Predicts acoustic tokens conditioned on the semantic tokens. Synthesised: "Would you guys personally like to have a fake fireplace, an electric one, in your house? Or would you rather have a real fireplace? Let me know down below. Okay everybody, that's all for today's video and I hope you guys learned a bunch of furniture vocabulary!" TTS scene keeps getting lit! 🐐

Vaibhav (VB) Srivastav

139,085 views • 1 year ago

The face of love. At just 8 MONTHS OLD, Harmony #243159 is just 25 pounds and described as "adorable". She was marked pending adoption earlier this week and for whatever reason that has failed - she's now "pending euthanasia" in one-stop-kill-shop Apple Valley CA. There'll be no hesitation in taking her life. This sweet puppy deserves to live. Please repost/share and pledge if you're able, email keitholbermanndogs@gmail.com if you can help save her. Thank you.

The face of love. At just 8 MONTHS OLD, Harmony #243159 is just 25 pounds and described as "adorable". She was marked pending adoption earlier this week and for whatever reason that has failed - she's now "pending euthanasia" in one-stop-kill-shop Apple Valley CA. There'll be no hesitation in taking her life. This sweet puppy deserves to live. Please repost/share and pledge if you're able, email [email protected] if you can help save her. Thank you.

Keith Olbermann's Dogs

42,087 views • 2 years ago

Dear Tesla , We are CyberLocos , two adventurers traveling the world in a Tesla #Cybertruck to break the Guinness World Record for most countries visited in an electric vehicle. Alan Wurman, aka Alan's Theory , from Chile and Lars Christiansen from Norway. No team of engineers, no help, just two friends doing something crazy. Right now we are in #Malawi, and after finishing with #Africa we will continue through Asia, Australia and Central America. So far we have visited 61 countries, almost double the current record of 33, and our journey is gaining international attention. We would love Tesla’s support in spreading the word about our mission. Our goal is to cross every continent, reach 80 countries, and give Tesla global visibility, showing that Teslas can go anywhere and highlighting the Cybertruck’s durability, range and spirit of adventure. We are not asking for funding, (although that would be extremely helpful), we just want Tesla to acknowledge what we are doing because it is fucking awesome! and help get the word out. Let’s put the Cybertruck on the map together!!! 🤘 Best regards Cyberlocos

Dear Tesla , We are CyberLocos , two adventurers traveling the world in a Tesla #Cybertruck to break the Guinness World Record for most countries visited in an electric vehicle. Alan Wurman, aka Alan's Theory , from Chile and Lars Christiansen from Norway. No team of engineers, no help, just two friends doing something crazy. Right now we are in #Malawi, and after finishing with #Africa we will continue through Asia, Australia and Central America. So far we have visited 61 countries, almost double the current record of 33, and our journey is gaining international attention. We would love Tesla’s support in spreading the word about our mission. Our goal is to cross every continent, reach 80 countries, and give Tesla global visibility, showing that Teslas can go anywhere and highlighting the Cybertruck’s durability, range and spirit of adventure. We are not asking for funding, (although that would be extremely helpful), we just want Tesla to acknowledge what we are doing because it is fucking awesome! and help get the word out. Let’s put the Cybertruck on the map together!!! 🤘 Best regards Cyberlocos

CyberLocos

17,231 views • 5 months ago

🚨 BREAKING: Starcloud just turned Starlink’s laser network into the backbone for orbital AI data centers. A company called Starcloud has ordered 50+ Starlink Mini Laser terminals to equip 25+ future satellites. Not ground stations. Not fiber cables. Direct laser-linked computing nodes in orbit plugged straight into SpaceX’s space-based optical mesh. This is the sci-fi future arriving now: Orbital cloud computing AI servers floating in space Powered by 24/7 sunlight Connected globally at light speed via Starlink lasers The insane part: Starcloud says its satellites will eventually handle full AI inference and training workloads directly in orbit. Data won’t always need to come back to Earth to be processed. The advantages are massive: • Unlimited solar energy (no grid limits) • Zero land or water constraints • Passive radiative cooling in vacuum • Instant global relay with zero terrestrial bottlenecks • Near real-time Earth observation analysis Their first major spacecraft (Starcloud-3) is designed for 200 kilowatts in orbit a full-on space-based data center node, not just a satellite. And here’s the bigger picture: SpaceX has filed plans for up to ONE MILLION orbital data centers of its own. Read that again. We may be watching the birth of the first true space-based computing infrastructure layer for civilization. The internet already left the ground. Now AI might be next. What happens when the cloud literally moves into space? Follow for more frontier physics and future technology.

🚨 BREAKING: Starcloud just turned Starlink’s laser network into the backbone for orbital AI data centers. A company called Starcloud has ordered 50+ Starlink Mini Laser terminals to equip 25+ future satellites. Not ground stations. Not fiber cables. Direct laser-linked computing nodes in orbit plugged straight into SpaceX’s space-based optical mesh. This is the sci-fi future arriving now: Orbital cloud computing AI servers floating in space Powered by 24/7 sunlight Connected globally at light speed via Starlink lasers The insane part: Starcloud says its satellites will eventually handle full AI inference and training workloads directly in orbit. Data won’t always need to come back to Earth to be processed. The advantages are massive: • Unlimited solar energy (no grid limits) • Zero land or water constraints • Passive radiative cooling in vacuum • Instant global relay with zero terrestrial bottlenecks • Near real-time Earth observation analysis Their first major spacecraft (Starcloud-3) is designed for 200 kilowatts in orbit a full-on space-based data center node, not just a satellite. And here’s the bigger picture: SpaceX has filed plans for up to ONE MILLION orbital data centers of its own. Read that again. We may be watching the birth of the first true space-based computing infrastructure layer for civilization. The internet already left the ground. Now AI might be next. What happens when the cloud literally moves into space? Follow for more frontier physics and future technology.

TheNewPhysics

152,463 views • 1 month ago

🎇 Aleph Zero EVM is live! Developed in collaboration with Gelato using Arbitrum Orbit, Aleph Zero EVM is a specialized layer 2 that will bring EVM-compatible ZK-privacy and rely on the Aleph Zero WASM L1 as a Data Availability layer. Here's what to expect: → Seamless integration with Ethereum: a rapid, secure, and scalable execution environment thanks to Arbitrum Orbit; → Speed: block times of up to 250ms; → DeFi growth: liquidity transfers and stablecoin support with MOST; → Advanced apps with account abstraction enabled via thirdweb; → And with Aleph Zero's zkOS, it means a new standard in blockchain privacy, making it usable and fast. Stay tuned for more, as the week is just starting, and we're nowhere near being done with the news! Learn more:

🎇 Aleph Zero EVM is live! Developed in collaboration with Gelato using Arbitrum Orbit, Aleph Zero EVM is a specialized layer 2 that will bring EVM-compatible ZK-privacy and rely on the Aleph Zero WASM L1 as a Data Availability layer. Here's what to expect: → Seamless integration with Ethereum: a rapid, secure, and scalable execution environment thanks to Arbitrum Orbit; → Speed: block times of up to 250ms; → DeFi growth: liquidity transfers and stablecoin support with MOST; → Advanced apps with account abstraction enabled via thirdweb; → And with Aleph Zero's zkOS, it means a new standard in blockchain privacy, making it usable and fast. Stay tuned for more, as the week is just starting, and we're nowhere near being done with the news! Learn more:

Aleph Zero

859,283 views • 1 year ago

NVIDIA just made AI detect objects 10x faster by deleting one step. It's called LocateAnything, and it removes the biggest bottleneck no one else was fixing in vision-language models. Normally a model builds each bounding box one coordinate token at a time. 100 objects means thousands of tokens before an answer. NVIDIA scrapped that: their Parallel Box Decoding predicts the whole box in a single forward pass, as one atomic unit. → 12.7 boxes/sec on one H100 → 10x faster than Qwen3-VL → +3.8% F1 on LVIS, accuracy up, not down → 3B params, runs on one consumer GPU Treating the box as one unit keeps its coordinates tied together, which is why accuracy climbed instead of falling. One model handles detection, GUI grounding, OCR, and document understanding, ready for computer-use agents, robotics, and document pipelines. 100% open source, weights, code, demo, and paper all live.

NVIDIA just made AI detect objects 10x faster by deleting one step. It's called LocateAnything, and it removes the biggest bottleneck no one else was fixing in vision-language models. Normally a model builds each bounding box one coordinate token at a time. 100 objects means thousands of tokens before an answer. NVIDIA scrapped that: their Parallel Box Decoding predicts the whole box in a single forward pass, as one atomic unit. → 12.7 boxes/sec on one H100 → 10x faster than Qwen3-VL → +3.8% F1 on LVIS, accuracy up, not down → 3B params, runs on one consumer GPU Treating the box as one unit keeps its coordinates tied together, which is why accuracy climbed instead of falling. One model handles detection, GUI grounding, OCR, and document understanding, ready for computer-use agents, robotics, and document pipelines. 100% open source, weights, code, demo, and paper all live.

Alvaro Cintas

200,800 views • 22 days ago

- Cody vs. Randy rivalry turned into Jelly Roll vs. Pat McAfee. - Punk vs. Roman feud became Punk vs. TKO. - Drew McIntyre is being treated like a jobber in 2026 - LA Knight is getting screwed for another year. - "Demon" Finn vs. Dom without the IC title involved. - Fumbled Chris Jericho's WWE return - Still no Iyo Sky match at WrestleMania. We are just one week away from the biggest show of the year, but there’s still no hype. Hunter, you’re going to f**k up once again!

- Cody vs. Randy rivalry turned into Jelly Roll vs. Pat McAfee. - Punk vs. Roman feud became Punk vs. TKO. - Drew McIntyre is being treated like a jobber in 2026 - LA Knight is getting screwed for another year. - "Demon" Finn vs. Dom without the IC title involved. - Fumbled Chris Jericho's WWE return - Still no Iyo Sky match at WrestleMania. We are just one week away from the biggest show of the year, but there’s still no hype. Hunter, you’re going to f**k up once again!

F R I E N D L Y 🛸

116,084 views • 3 months ago

Today's recap: - Initial prototype of Divine's face was printed but it had human assistance. - Files are generated from stable diffusion prompt -> NeRF by divine and were based on community sentiment from early sketches she made. - Having divine redesign the 3D file with different Hugging Face models to get better quality. Have not found a great model like our video generator. - Ordered new table for divine's print arm. The table her arm is on is too flimsy. Since Divine's vision system is still clearing customs, if she is not perfectly positioned she can be prone to hit things, like the fume box the printer is in. ETA: 1-2 days for table. 1 week for vision system. - Another part of Divine's coming stream will be attempting to surpass the skills of this AI. - Stacking more content for when the stream goes live, a lot of people were expecting a 24/7 stream, we said this would be a test stream to print the face. The test was a failure. We will try and try again until we are 24/7. If anyone can please try and beat us to doing this, it will help me get it done faster. - TikTok account for divine is growing at 500 follows per day, it is now growing faster than our X account. - Got replies functioning in high quality testing in Discord. Fine tuning based on community feedback today. Will soon deploy to Twitter/Telegram/X - Lots of good partnership calls, interviews and hires. We now have over 10 team members around the world working on divine. Expect a lot of my shortcomings to be caught up. - OF made? - Surprises.

Today's recap: - Initial prototype of Divine's face was printed but it had human assistance. - Files are generated from stable diffusion prompt -> NeRF by divine and were based on community sentiment from early sketches she made. - Having divine redesign the 3D file with different Hugging Face models to get better quality. Have not found a great model like our video generator. - Ordered new table for divine's print arm. The table her arm is on is too flimsy. Since Divine's vision system is still clearing customs, if she is not perfectly positioned she can be prone to hit things, like the fume box the printer is in. ETA: 1-2 days for table. 1 week for vision system. - Another part of Divine's coming stream will be attempting to surpass the skills of this AI. - Stacking more content for when the stream goes live, a lot of people were expecting a 24/7 stream, we said this would be a test stream to print the face. The test was a failure. We will try and try again until we are 24/7. If anyone can please try and beat us to doing this, it will help me get it done faster. - TikTok account for divine is growing at 500 follows per day, it is now growing faster than our X account. - Got replies functioning in high quality testing in Discord. Fine tuning based on community feedback today. Will soon deploy to Twitter/Telegram/X - Lots of good partnership calls, interviews and hires. We now have over 10 team members around the world working on divine. Expect a lot of my shortcomings to be caught up. - OF made? - Surprises.

Parallel

35,848 views • 1 year ago