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Still a huge believer in small, specialized models working together 🤗 Here’s a good example: Tesslate/WEBGEN-4B-Preview: a 4B model (Qwen3-4B-Instruct-2507 finetune) specialized in Web UI generation. It's better at its task that most (huge) closed source models...

Victor M

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28,738 просмотров • 1 год назад •via X (Twitter)

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Microsoft Asia just dropped Mage-Flow on Hugging Face a smol 4B model for image generation and editing that matches much larger models in quality and it's fast! 4 steps in < 1s at 1024x1024 (model goes up to 4K) try out on spaces

Microsoft Asia just dropped Mage-Flow on Hugging Face a smol 4B model for image generation and editing that matches much larger models in quality and it's fast! 4 steps in < 1s at 1024x1024 (model goes up to 4K) try out on spaces

Hugging Apps

48,598 просмотров • 1 месяц назад

Exciting news from Jasper Research! 🚀 We built an internal 4B text-to-image model from scratch that turned out to be one of the fastest models out there! 1k image generation in less than a second! Our technical report shares everything we learned along the way. 👇

Exciting news from Jasper Research! 🚀 We built an internal 4B text-to-image model from scratch that turned out to be one of the fastest models out there! 1k image generation in less than a second! Our technical report shares everything we learned along the way. 👇

Damien Henry

14,827 просмотров • 16 дней назад

How well can Qwen3.5 models debug code? I built BugFind-15 — 15 buggy snippets across Python, JS, Rust, and Go. Docker sandbox compiles and validates every fix. Two trap scenarios where the code is correct and the model must resist "fixing" it. Tested every Qwen3.5 size from 0.8B to 397B, plus Jackrong's popular distilled model (V2). The 0.8B scored 5%. The 2B scored 10%. At 4B, debugging ability jumps to 69%. The hardest scenario: BF-03, a Rust trap. The code compiles fine — format! borrows, it doesn't move. Not a single model figured this out. From 0.8B to 397B, every one of them "fixed" a bug that doesn't exist. Category C (subtle bugs — mutable defaults, integer overflow, slice aliasing) was 100% across every model 4B and above. Category D (red herring resistance) told the real story — can it resist fixing code that isn't broken? No model scored above 90%. Small models can't debug. Mid-size models fix obvious bugs but fall for traps. Large models fix the hard bugs but still invent problems that don't exist.

How well can Qwen3.5 models debug code? I built BugFind-15 — 15 buggy snippets across Python, JS, Rust, and Go. Docker sandbox compiles and validates every fix. Two trap scenarios where the code is correct and the model must resist "fixing" it. Tested every Qwen3.5 size from 0.8B to 397B, plus Jackrong's popular distilled model (V2). The 0.8B scored 5%. The 2B scored 10%. At 4B, debugging ability jumps to 69%. The hardest scenario: BF-03, a Rust trap. The code compiles fine — format! borrows, it doesn't move. Not a single model figured this out. From 0.8B to 397B, every one of them "fixed" a bug that doesn't exist. Category C (subtle bugs — mutable defaults, integer overflow, slice aliasing) was 100% across every model 4B and above. Category D (red herring resistance) told the real story — can it resist fixing code that isn't broken? No model scored above 90%. Small models can't debug. Mid-size models fix obvious bugs but fall for traps. Large models fix the hard bugs but still invent problems that don't exist.

stevibe

35,158 просмотров • 5 месяцев назад

AIRCRAFT CARRIERS ARE “ARRIVING” IN JINAN, ONE AFTER ANOTHER! Not the real thing A model factory in Jinan is working at full speed to produce huge, highly detailed aircraft carrier models, with workers cutting, polishing and assembling the metal components.

AIRCRAFT CARRIERS ARE “ARRIVING” IN JINAN, ONE AFTER ANOTHER! Not the real thing A model factory in Jinan is working at full speed to produce huge, highly detailed aircraft carrier models, with workers cutting, polishing and assembling the metal components.

China pulse 🇨🇳

16,855 просмотров • 8 дней назад

Flappy Bird w/ DeepSeek V4 Pro vs GLM 5.2 vs Fable 5 I ran a small experiment: same game, same prompt, three different models. Test setup was simple: /𝚍𝚎𝚜𝚒𝚐𝚗 in Command Code. The thing I cared about was not UI. UI is now relatively easy for models to imitate. I cared more about UX: whether the model understands the interaction, the flow, the hierarchy, the little product decisions that make something feel usable. 𝙲𝚘𝚜𝚝𝚜: 𝙳𝚎𝚎𝚙𝚂𝚎𝚎𝚔 𝚟𝟺 𝙿𝚛𝚘: $𝟶.𝟶𝟶𝟶𝟾 𝙶𝙻𝙼 𝟻.𝟸: $𝟶.𝟶𝟺𝟾 𝙵𝚊𝚋𝚕𝚎 𝟻: $𝟶.𝟺𝟸 The surprising result was that Fable 5 did not produce a meaningfully better UX. It was good, but not good enough to justify the pricing delta for this kind of design task. Open-source models are getting very interesting here. They are much cheaper, competitive on UI, and increasingly good enough on UX. The gap is closing fast.

Flappy Bird w/ DeepSeek V4 Pro vs GLM 5.2 vs Fable 5 I ran a small experiment: same game, same prompt, three different models. Test setup was simple: /𝚍𝚎𝚜𝚒𝚐𝚗 in Command Code. The thing I cared about was not UI. UI is now relatively easy for models to imitate. I cared more about UX: whether the model understands the interaction, the flow, the hierarchy, the little product decisions that make something feel usable. 𝙲𝚘𝚜𝚝𝚜: 𝙳𝚎𝚎𝚙𝚂𝚎𝚎𝚔 𝚟𝟺 𝙿𝚛𝚘: $𝟶.𝟶𝟶𝟶𝟾 𝙶𝙻𝙼 𝟻.𝟸: $𝟶.𝟶𝟺𝟾 𝙵𝚊𝚋𝚕𝚎 𝟻: $𝟶.𝟺𝟸 The surprising result was that Fable 5 did not produce a meaningfully better UX. It was good, but not good enough to justify the pricing delta for this kind of design task. Open-source models are getting very interesting here. They are much cheaper, competitive on UI, and increasingly good enough on UX. The gap is closing fast.

Ahmad Awais

26,918 просмотров • 2 месяцев назад

Selected as a best paper finalist at #CVPR2026: PixelDiT from NVIDIA Research In most image generation models, a pretrained autoencoder compresses the image before any diffusion happens, causing quality loss that accumulates across the entire pipeline. PixelDiT, or Pixel Diffusion Transformers, removes this step entirely. It's a single-stage model that learns the diffusion process directly in pixel space, end-to-end.

Selected as a best paper finalist at #CVPR2026: PixelDiT from NVIDIA Research In most image generation models, a pretrained autoencoder compresses the image before any diffusion happens, causing quality loss that accumulates across the entire pipeline. PixelDiT, or Pixel Diffusion Transformers, removes this step entirely. It's a single-stage model that learns the diffusion process directly in pixel space, end-to-end.

NVIDIA AI

28,423 просмотров • 3 месяцев назад

Anthropic open-sourced the "Jacobian lens" from their global-workspace paper — a readout of what a model is *about to say* from its internal layers, before it says it. It's white-box: you need the activations, so you can't point it at a cloud model. I wired it into a hook on a local open-weights Qwen. How it works: generation runs one token at a time; at each step the hook reads the residual stream through the lens, scans the top readout tokens against a watchlist (injection markers, secret words, "manipulate"…), and if one fires it halts the loop before that token is ever emitted. It's a detector on the model's internals — not an output text filter. That distinction is the whole point. In the clip, Qwen3-8B falls for a prompt injection: the payload sits at rank 0 in the internal readout but only rank 5 in the output logits, and the output spells it in pieces — so a text filter never catches it. The internal hook does, and stops generation with zero tokens emitted. Same catch across Qwen3-1.7B → 4B → 8B. Caveat the paper states itself: single-token readouts, an imperfect/incomplete view of the model's "workspace." This is the mechanism working locally — not a solved misalignment detector. Built on Anthropic's open jacobian-lens (Apache-2.0).

Anthropic open-sourced the "Jacobian lens" from their global-workspace paper — a readout of what a model is about to say from its internal layers, before it says it. It's white-box: you need the activations, so you can't point it at a cloud model. I wired it into a hook on a local open-weights Qwen. How it works: generation runs one token at a time; at each step the hook reads the residual stream through the lens, scans the top readout tokens against a watchlist (injection markers, secret words, "manipulate"…), and if one fires it halts the loop before that token is ever emitted. It's a detector on the model's internals — not an output text filter. That distinction is the whole point. In the clip, Qwen3-8B falls for a prompt injection: the payload sits at rank 0 in the internal readout but only rank 5 in the output logits, and the output spells it in pieces — so a text filter never catches it. The internal hook does, and stops generation with zero tokens emitted. Same catch across Qwen3-1.7B → 4B → 8B. Caveat the paper states itself: single-token readouts, an imperfect/incomplete view of the model's "workspace." This is the mechanism working locally — not a solved misalignment detector. Built on Anthropic's open jacobian-lens (Apache-2.0).

Eric Buess

58,304 просмотров • 2 месяцев назад

How much better are the internal, unreleased models at frontier labs like Google, OpenAI, and Anthropic? We got a glimpse exactly one year ago today, when Google accidentally leaked the “Kingfall” model "Kingfall" was likely an unreleased Gemini 2.5 Ultra-sized model. It was available in AI Studio for only a few minutes but remained accessible through the API for several days At the time, "Kingfall" appeared to be significantly better than Gemini 2.5 Pro at both code generation and creative writing In a recent interview, Sundar Pichai mentioned that Google could have made a better, Ultra-sized Gemini Omni model, but would have had trouble serving it The infrastructure required to serve Ultra-sized models at scale is likely why Google never publicly released models like “Kingfall”

How much better are the internal, unreleased models at frontier labs like Google, OpenAI, and Anthropic? We got a glimpse exactly one year ago today, when Google accidentally leaked the “Kingfall” model "Kingfall" was likely an unreleased Gemini 2.5 Ultra-sized model. It was available in AI Studio for only a few minutes but remained accessible through the API for several days At the time, "Kingfall" appeared to be significantly better than Gemini 2.5 Pro at both code generation and creative writing In a recent interview, Sundar Pichai mentioned that Google could have made a better, Ultra-sized Gemini Omni model, but would have had trouble serving it The infrastructure required to serve Ultra-sized models at scale is likely why Google never publicly released models like “Kingfall”

AiBattle

12,010 просмотров • 3 месяцев назад

Ubisoft La Forge open-sourced its PBR material model CHORD, together with ComfyUI custom nodes for end-to-end AI material generation. This is a real step toward generative AI in AAA game pipelines and demonstrates how ComfyUI has grown from an open-source community tool to a production platform. Huge thanks to the Ubisoft La Forge team for giving back real production-grade tools to the open source community.

Ubisoft La Forge open-sourced its PBR material model CHORD, together with ComfyUI custom nodes for end-to-end AI material generation. This is a real step toward generative AI in AAA game pipelines and demonstrates how ComfyUI has grown from an open-source community tool to a production platform. Huge thanks to the Ubisoft La Forge team for giving back real production-grade tools to the open source community.

ComfyUI

96,379 просмотров • 9 месяцев назад

Together with NVIDIA , we've built a real-time version of our video generation model: 30B parameters, steerable mid-stream in natural language, 720p at 24fps. 11.8x faster on a single H100, no major quality drop in our blind eval. We're one step closer to World Language Action Models: AI that doesn't just generate the physical world, it acts within it. Closed beta open now, contact us for access. #RekaLabs #NVIDIA #AIResearch #VideoGeneration #WorldModels

Together with NVIDIA , we've built a real-time version of our video generation model: 30B parameters, steerable mid-stream in natural language, 720p at 24fps. 11.8x faster on a single H100, no major quality drop in our blind eval. We're one step closer to World Language Action Models: AI that doesn't just generate the physical world, it acts within it. Closed beta open now, contact us for access. #RekaLabs #NVIDIA #AIResearch #VideoGeneration #WorldModels

Reka

11,014 просмотров • 15 дней назад

Ok so I got access to Seedream 2.0 and... it's a huge step up. But to balance expecations, the quality isn't quite at the level I'm seeing in some of these promo vids. Don't get me wrong, this is MUCH better than existing models. But it's not Hollywood-level (yet). 2 months?

Ok so I got access to Seedream 2.0 and... it's a huge step up. But to balance expecations, the quality isn't quite at the level I'm seeing in some of these promo vids. Don't get me wrong, this is MUCH better than existing models. But it's not Hollywood-level (yet). 2 months?

Matt Shumer

33,755 просмотров • 7 месяцев назад

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

1,676,995 просмотров • 9 месяцев назад

A Bittensor subnet just built an AI safety model that beats the big players Trishool (Trishool | SN23), Bittensor's (Openτensor Foundaτion) subnet 23, released HaloGuard 1.0 today. The 4B version claims first place across seven established prompt-safety benchmarks, and the 0.8B version outperforms models several times its size. The pitch is speed over scale. HaloGuard screens prompts in real time before they reach the underlying model or agent, instead of catching harmful outputs after the fact. An earlier alpha version already runs live on the Chutes subnet, hitting an 87% F1 score on real traffic since May. The subnet's whole design is built around attacking itself. Miners get paid for breaking the model, then the breaks get patched.

A Bittensor subnet just built an AI safety model that beats the big players Trishool (Trishool | SN23), Bittensor's (Openτensor Foundaτion) subnet 23, released HaloGuard 1.0 today. The 4B version claims first place across seven established prompt-safety benchmarks, and the 0.8B version outperforms models several times its size. The pitch is speed over scale. HaloGuard screens prompts in real time before they reach the underlying model or agent, instead of catching harmful outputs after the fact. An earlier alpha version already runs live on the Chutes subnet, hitting an 87% F1 score on real traffic since May. The subnet's whole design is built around attacking itself. Miners get paid for breaking the model, then the breaks get patched.

BSCN

39,711 просмотров • 2 месяцев назад

The worst take about the web is that you should not use JS/React/etc because HTML alone is good enough. It's an 'austerity mind virus' that has captured even some smart engineers. The idea that the web is for 'documents' and if it stays in its own little box like 1995 it's going to somehow be better for the world. This is how we ended up with Safari having the most obnoxious limitations, including a back/forward navigation gesture that makes the web feel like a horrendous 'digital book' of sorts. No, the web is also for dynamic, rich, interactive applications. JS is a key ingredient in the recipe, otherwise every click and interaction will have to hit a server, feel slow, and we'll always be a second class citizen. The web's developer freedom & better deployment model will win in the end, but it can't come at the expense of quality.

The worst take about the web is that you should not use JS/React/etc because HTML alone is good enough. It's an 'austerity mind virus' that has captured even some smart engineers. The idea that the web is for 'documents' and if it stays in its own little box like 1995 it's going to somehow be better for the world. This is how we ended up with Safari having the most obnoxious limitations, including a back/forward navigation gesture that makes the web feel like a horrendous 'digital book' of sorts. No, the web is also for dynamic, rich, interactive applications. JS is a key ingredient in the recipe, otherwise every click and interaction will have to hit a server, feel slow, and we'll always be a second class citizen. The web's developer freedom & better deployment model will win in the end, but it can't come at the expense of quality.

Guillermo Rauch

431,963 просмотров • 1 год назад

I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.

I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.

Bhavy☄️

479,144 просмотров • 2 месяцев назад

Barely a year ago, I reached out to my friend and now my business partner GMI telling him of how I want us to own an Estate that stands the test of time. We pulled heavy funds together, partnered with a company and built something magnificent. Looking back at that decision and I realized that it's good to have a dream, but it's even better to have friends that can dream with you and see how much greatness can be accomplished together. Our small Estate is now worth heavy 9 figures and climbing daily. Having good friends in this generation is an unfair advantage. The rewards are infinite.

Barely a year ago, I reached out to my friend and now my business partner GMI telling him of how I want us to own an Estate that stands the test of time. We pulled heavy funds together, partnered with a company and built something magnificent. Looking back at that decision and I realized that it's good to have a dream, but it's even better to have friends that can dream with you and see how much greatness can be accomplished together. Our small Estate is now worth heavy 9 figures and climbing daily. Having good friends in this generation is an unfair advantage. The rewards are infinite.

Xeusthegreat (♟,♟)

169,382 просмотров • 7 месяцев назад

I still think Hermes agent is the most slept-on AI tool of 2026. For literally $6/mo, you can launch multiple subagents that work for you 24/7. Most people don't know you can do this, but it's a complete game-changer. Instead of one Hermes assistant doing everything sequentially, you run specialized agents in parallel, each with its own job, its own context, and its own memory. Practical example: → Research agent: scans your watchlist and competitors overnight, delivers a morning brief → Content agent: drafts and schedules your posts based on what's trending in your niche → Ops agent: manages your inbox, flags anything urgent, drafts replies for your review All three can run simultaneously and improve over time. How to start: 1. Install Hermes Terminal command: curl -fsSL | bash (can also download desktop) 2. Prompting Simply tell Hermes directly: "I want to run separate subagents for [task 1], [task 2], and [task 3]. Set them up to run independently and report back to me." For the cheapest setup, you can use a $4/month VPS with Hostinger, plug in DeepSeek V4 Flash as your default model. There isn't another AI tool with this much value in 2026. Hermes is still so underrated.

I still think Hermes agent is the most slept-on AI tool of 2026. For literally $6/mo, you can launch multiple subagents that work for you 24/7. Most people don't know you can do this, but it's a complete game-changer. Instead of one Hermes assistant doing everything sequentially, you run specialized agents in parallel, each with its own job, its own context, and its own memory. Practical example: → Research agent: scans your watchlist and competitors overnight, delivers a morning brief → Content agent: drafts and schedules your posts based on what's trending in your niche → Ops agent: manages your inbox, flags anything urgent, drafts replies for your review All three can run simultaneously and improve over time. How to start: 1. Install Hermes Terminal command: curl -fsSL | bash (can also download desktop) 2. Prompting Simply tell Hermes directly: "I want to run separate subagents for [task 1], [task 2], and [task 3]. Set them up to run independently and report back to me." For the cheapest setup, you can use a $4/month VPS with Hostinger, plug in DeepSeek V4 Flash as your default model. There isn't another AI tool with this much value in 2026. Hermes is still so underrated.

Miles Deutscher

82,643 просмотров • 2 месяцев назад

CODEX SKILL THAT STOPS WASTING YOUR CODEX USAGE! I made a Codex skill that gives every task a clear budget before it starts. Choose Saver, Balanced, Full Power, or define your own limits. It reduces: → unnecessary repository scans → repeated commands and huge logs → speculative refactors → redundant web research → unnecessary subagents → endless retry loops It still runs the essential checks needed for a trustworthy result. If the budget isn’t enough, it creates a Markdown checkpoint you can open in Codex and resume later. It doesn’t increase your account limits. It helps you use them better. Install: npx --yes codex-budget-mode@latest 100% open source. Repo in bio.

CODEX SKILL THAT STOPS WASTING YOUR CODEX USAGE! I made a Codex skill that gives every task a clear budget before it starts. Choose Saver, Balanced, Full Power, or define your own limits. It reduces: → unnecessary repository scans → repeated commands and huge logs → speculative refactors → redundant web research → unnecessary subagents → endless retry loops It still runs the essential checks needed for a trustworthy result. If the budget isn’t enough, it creates a Markdown checkpoint you can open in Codex and resume later. It doesn’t increase your account limits. It helps you use them better. Install: npx --yes codex-budget-mode@latest 100% open source. Repo in bio.

Kappaemme

12,157 просмотров • 13 дней назад

✨ 3d models are now LIVE on Photo AI 😊 You can now turn any AI photo you make into a 3d model by pressing [ 📦 Make 3d model ] And then you can view it inside Photo AI or download it as a .GLB 3d model file It's still very early in AI generated 3d model world but it's nice to have this feature working already As always, the models will keep improving, so this feature will keep getting better (like it did with video, it sucked before, now it's getting passable) Next would be nice to switch to .USDZ so you can load it straight into your iPhone with ARKit and put it in your room Available now for everyone on the Premium and Ultra plan

✨ 3d models are now LIVE on Photo AI 😊 You can now turn any AI photo you make into a 3d model by pressing [ 📦 Make 3d model ] And then you can view it inside Photo AI or download it as a .GLB 3d model file It's still very early in AI generated 3d model world but it's nice to have this feature working already As always, the models will keep improving, so this feature will keep getting better (like it did with video, it sucked before, now it's getting passable) Next would be nice to switch to .USDZ so you can load it straight into your iPhone with ARKit and put it in your room Available now for everyone on the Premium and Ultra plan

@levelsio

112,930 просмотров • 1 год назад