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Ollama 0.2 is here! Concurrency is now enabled by default. This unlocks 2 major features: Parallel requests Ollama can now serve multiple requests at the same time, using only a little bit of additional memory for each request. This enables use cases such as: - Handling multiple chat sessions at the same time - Hosting code completion LLMs for your team - Processing different parts of a document simultaneously - Running multiple agents at the same time Run multiple models Ollama now supports loading different models at the same time. This improves several use cases: - Retrieval Augmented Generation (RAG): both the embedding and text completion models can be loaded into memory simultaneously. - Agents: multiple versions of an agent can now run simultaneously - Running large and small models side-by-side Models are automatically loaded and unloaded based on requests and how much GPU memory is available.

Ollama 0.2 is here! Concurrency is now enabled by default. This unlocks 2 major features: Parallel requests Ollama can now serve multiple requests at the same time, using only a little bit of additional memory for each request. This enables use cases such as: - Handling multiple chat sessions at the same time - Hosting code completion LLMs for your team - Processing different parts of a document simultaneously - Running multiple agents at the same time Run multiple models Ollama now supports loading different models at the same time. This improves several use cases: - Retrieval Augmented Generation (RAG): both the embedding and text completion models can be loaded into memory simultaneously. - Agents: multiple versions of an agent can now run simultaneously - Running large and small models side-by-side Models are automatically loaded and unloaded based on requests and how much GPU memory is available.

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Announcing Artificial Analysis Video Arena - the first crowdsourced comparison for Text to Video models Text to Video models are accelerating rapidly and crossing quality thresholds every month. We created Video Arena to compare them using the only source of truth for visual media - human preference! Video Arena includes hundreds of videos from the leading video models, including: - Runway's Runway Gen 3 Alpha - Pika's Pika 1.5 - Luma's Dream Machine - MiniMax / Hailuo AI (MiniMax) - Kling AI's Kling 1.0 - Zhipu AI's CogVideoX-5B Voting is open now and we’ll be announcing the first leaderboard results within 24 hours. Any predictions? In the meantime, you can see your own ‘personal leaderboard’ of how you’ve ranked the video models after 30 votes. Link to the Artificial Analysis Video Arena in the below tweet! 👇

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Artificial Analysis

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The MiniMax M2 model is mind-blowing! It's open-source. It outperforms Gemini 2.5, Claude 4.1, and Qwen3 across coding and tool-use benchmarks. Right now, it's one of the world's top 5 models in intelligence! And here is the best part: Claude is one of the best models you can use today, and MiniMax M2 costs only 8% of that! It's smaller, faster, and cheaper. Extremely efficient at using tokens. Minimax M2's biggest strength: High agentic capabilities. The model can plan and execute complex multi-tool workflows. It's reliable and very robust at executing long-horizon tool chains. In summary: • Low latency • Very cheap • Excels at agentic tasks • Open-source The model currently powers the MiniMax Agent and is available for a free global trial. You can access MiniMax M2's API here: To access the agent: And here is the MiniMax website: Thanks to the MiniMax team for showing me the ropes and partnering with me on this post.

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Santiago

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Video Game Details

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Apple is no longer hiding it. These machines are built for local AI. Apple unveiled the new Mac Mini with M6 their first 2nm chip. Starting at $899. What you can run on the $899 Mac Mini (32GB) 👇 → Qwen 3.6 27B : fits comfortably, near-GPT-4 quality → Qwen 3 Coder 32B : full coding assistant on your desk → Llama 4 8B at full precision → DeepSeek R1 32B for reasoning tasks → 13.5x faster LLM processing than M1. 4x faster than M4. What you can run on the Mac Studio with M5 Ultra (512GB) 👇 → Llama 4 70B at high quality quantization → DeepSeek V4 with hundreds of billions of parameters → Frontier-scale models entirely in local memory → Connect four Mac Studios over Thunderbolt 5 cluster them to run trillion-parameter models locally Apple said their Mac business grew 30% last quarter largely because people are buying these to run AI models on their own desks instead of paying for cloud tokens. The future of inference is local. Apple built the hardware for it.

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Vaibhav Sisinty

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Two-Week $200,000 Data-Labeling Event (14–28 August 2025) We’re excited to announce the launch of a two-week campaign, evenly distributing a total $200,000 USD (in $TAG) spread into 14 days to contributors who label data from 14 to 28 August 2025. Head to → Task Plaza to claim tasks and get started. Reviewer slots & staking We’re expanding 60 data-reviewer slots to keep operations smooth at scale. The staking window is open now for 3 days. Eligibility: reviewers will be randomly selected from participants who stake at least 5,000,000 $TAG during the staking window. Unstaking: One may only request to unstake at least 2 days after staking. The unstaked amount will then be available for claim 2 days after request. However, please note that once unstaking is requested, a data reviewer would automatically be displaced from its role. We’ve seen the #DeCorp model mature rapidly. This event is our next step toward mass adoption, as we deliver the next batch of real-world orders and reward the community powering the work.

Tagger

168,887 görüntüleme • 1 yıl önce

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Whole Mars Catalog

45,595 görüntüleme • 1 ay önce

Today we’re announcing r-1, our new document parsing model. It’s more accurate than our most powerful agentic OCR models, faster, and up to 6x cheaper. At Reducto, we spent two years building specialized models for complex visual layouts, tables spanning multiple pages, and key formatting like strikethroughs. We then used everything we learned to build r-1, the first in a new generation of models designed to handle the hardest documents without multiplying cost. This early preview delivers a 20% lower error rate than our most accurate legacy agentic models and will keep improving with new checkpoints over the next few weeks. Accuracy is only the beginning. r-1 preview is available at 1¢ per page all-in, with additional volume discounts as you scale. In the near term we’re also going to release r-1 mini, and an auto mode that intelligently selects the right approach for each page. If you’re currently using another parser, we’re offering up to $5,000 in credits to evaluate and migrate. You can claim the migration offer and learn more about r-1 using the links in the comments. Happy parsing!

Today we’re announcing r-1, our new document parsing model. It’s more accurate than our most powerful agentic OCR models, faster, and up to 6x cheaper. At Reducto, we spent two years building specialized models for complex visual layouts, tables spanning multiple pages, and key formatting like strikethroughs. We then used everything we learned to build r-1, the first in a new generation of models designed to handle the hardest documents without multiplying cost. This early preview delivers a 20% lower error rate than our most accurate legacy agentic models and will keep improving with new checkpoints over the next few weeks. Accuracy is only the beginning. r-1 preview is available at 1¢ per page all-in, with additional volume discounts as you scale. In the near term we’re also going to release r-1 mini, and an auto mode that intelligently selects the right approach for each page. If you’re currently using another parser, we’re offering up to $5,000 in credits to evaluate and migrate. You can claim the migration offer and learn more about r-1 using the links in the comments. Happy parsing!

Adit

232,242 görüntüleme • 3 gün önce

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

Santiago

324,621 görüntüleme • 2 yıl önce

🚨 GLM 5.3 just dropped, and here’s how to access the GLM family completely FREE. no subscription. no API credit cost & no rate limit. step 1️⃣: download the OpenCode app on your PC. it’s available on both Mac and Windows. 🔗: step 2️⃣: go to NVIDIA’s website, sign up with your email, verify your account, and generate a unified API key for free. NVIDIA offers free model endpoints for developers, and GLM 5.2 is one of the top models currently available there. 🔗: step 3️⃣: launch OpenCode, open the model selection menu, search for NVIDIA, and paste the API key you generated. also, there’s no rate limit, but traffic to the model through NVIDIA can get busy at times, so responses may occasionally be slower or temporarily unavailable. the only catch: NVIDIA hasn’t listed GLM 5.3 yet, so for now you’ll be using GLM 5.2. once GLM 5.3 gets added to NVIDIA’s model catalog, you should be able to switch to it the same way.

🚨 GLM 5.3 just dropped, and here’s how to access the GLM family completely FREE. no subscription. no API credit cost & no rate limit. step 1️⃣: download the OpenCode app on your PC. it’s available on both Mac and Windows. 🔗: step 2️⃣: go to NVIDIA’s website, sign up with your email, verify your account, and generate a unified API key for free. NVIDIA offers free model endpoints for developers, and GLM 5.2 is one of the top models currently available there. 🔗: step 3️⃣: launch OpenCode, open the model selection menu, search for NVIDIA, and paste the API key you generated. also, there’s no rate limit, but traffic to the model through NVIDIA can get busy at times, so responses may occasionally be slower or temporarily unavailable. the only catch: NVIDIA hasn’t listed GLM 5.3 yet, so for now you’ll be using GLM 5.2. once GLM 5.3 gets added to NVIDIA’s model catalog, you should be able to switch to it the same way.

m0h

78,449 görüntüleme • 21 gün önce

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

Rob Wiblin

93,669 görüntüleme • 1 yıl önce

Launching today: the world’s first fully optimized LLM routing stack that can be embedded directly into your product, Merge Embedded Routing Stack. Model sovereignty is now table stakes for any AI product. Users and companies are demanding full control over the AI models running in your product. Right now, no company is able to do this without massive overhead. Until today. Merge makes it easy for your customers to use your product exactly the way they want to. With a single API, you can: 1. Allow customers to bring their own keys, manage their own budgets, and set policies like fallback rules, model allowlists, and more 2. Let each customer connect and switch their own models, right inside your product 3. Optimize each customer for cost, performance, or capability Without Merge, you’d be: - Building routing policies, provider integrations, and LLM key management - Giving your users zero model independence - Receiving endless customer support tickets asking “can I use 5.6 Sol?” Products that fight their customers' model strategy will lose to products that fit it. Merge is the only place that allows your users to produce the exact results they want, with the models they have access to. Give your customers the advantage, without the massive overhead costs.

Launching today: the world’s first fully optimized LLM routing stack that can be embedded directly into your product, Merge Embedded Routing Stack. Model sovereignty is now table stakes for any AI product. Users and companies are demanding full control over the AI models running in your product. Right now, no company is able to do this without massive overhead. Until today. Merge makes it easy for your customers to use your product exactly the way they want to. With a single API, you can: 1. Allow customers to bring their own keys, manage their own budgets, and set policies like fallback rules, model allowlists, and more 2. Let each customer connect and switch their own models, right inside your product 3. Optimize each customer for cost, performance, or capability Without Merge, you’d be: - Building routing policies, provider integrations, and LLM key management - Giving your users zero model independence - Receiving endless customer support tickets asking “can I use 5.6 Sol?” Products that fight their customers' model strategy will lose to products that fit it. Merge is the only place that allows your users to produce the exact results they want, with the models they have access to. Give your customers the advantage, without the massive overhead costs.

Shensi Ding

355,112 görüntüleme • 1 ay önce

I vibe coded and built a sprite animation pipeline 🛠️ (Day 22 of making the engine+game) ⬇️ Watch the video if you don't wanna read the wall of text - it directly shows what I do. Shoutout to Jidé ✨ for showing me a paper on black/white combination to get alpha, it's the cleanest method yet, and to Cursor for enabling this entire journey. If you prefer the wall of text here you go: The hardest part of using general image models for 2D sprites isn’t getting a nice-looking frame, it’s getting consistent motion across a whole sprite sheet. You can fake a sheet, but frames won’t align, timing drifts, and you end up with weird artifacts. Even if you manually cut frames + interpolate, the animation often looks “off” because each frame is basically a new interpretation, not the same character evolving over time. This is especially noticeable with public API models like gpt-image-1.5 and Nano Banana. Some custom LoRAs for open models exist, but this is intended for less techy folks. My workaround: use a video model first, then post-process into a sprite sheet. Render the animation over a solid background (white/black/magenta/green), then chroma-key it out (my engine tool supports this). If the motion stays inside the silhouette, this works surprisingly well. You can do this in almost any video editing software too! The catch: keying almost always leaves an “aura” (edge spill). My best results come from interpolating the keyed animation with a clean base sprite, so you keep crisp edges and only “borrow” motion/detail where needed. If the animation extends outside the silhouette (tree branches, hair wisps, foliage), I usually skip “true sprite animation” and do it with shaders instead. Keying can’t fully remove halos there, no matter how much feathering/tuning you do. Another annoying issue: pixel corruption. AI rarely generates a perfectly flat background (pure #000000 or #FF00FF). That tiny noise breaks clean extraction and creates crawling garbage pixels around the subject. For clean base sprites (and even PBR maps), a useful trick is generating the same asset on white + black backgrounds and deriving alpha from the difference. This is basically a matte workflow: white = opaque, black = transparent. It fixes aura… but you’d need it per-frame to fix animation, which is still hard. For simple pixel art (single-digit frames), you can sometimes generate a sprite sheet, then ask the model to recreate it on black/white while preserving alignment… but it’s still manual-heavy. Honestly, at this point, for some projects it’s easier to go 3D → 2D and render clean sprites/maps directly. But I still love pushing “pure 2D” and seeing how far we can take it. Thanks for reading! Follow/bookmark/repost if interested in this kind of content!

I vibe coded and built a sprite animation pipeline 🛠️ (Day 22 of making the engine+game) ⬇️ Watch the video if you don't wanna read the wall of text - it directly shows what I do. Shoutout to Jidé ✨ for showing me a paper on black/white combination to get alpha, it's the cleanest method yet, and to Cursor for enabling this entire journey. If you prefer the wall of text here you go: The hardest part of using general image models for 2D sprites isn’t getting a nice-looking frame, it’s getting consistent motion across a whole sprite sheet. You can fake a sheet, but frames won’t align, timing drifts, and you end up with weird artifacts. Even if you manually cut frames + interpolate, the animation often looks “off” because each frame is basically a new interpretation, not the same character evolving over time. This is especially noticeable with public API models like gpt-image-1.5 and Nano Banana. Some custom LoRAs for open models exist, but this is intended for less techy folks. My workaround: use a video model first, then post-process into a sprite sheet. Render the animation over a solid background (white/black/magenta/green), then chroma-key it out (my engine tool supports this). If the motion stays inside the silhouette, this works surprisingly well. You can do this in almost any video editing software too! The catch: keying almost always leaves an “aura” (edge spill). My best results come from interpolating the keyed animation with a clean base sprite, so you keep crisp edges and only “borrow” motion/detail where needed. If the animation extends outside the silhouette (tree branches, hair wisps, foliage), I usually skip “true sprite animation” and do it with shaders instead. Keying can’t fully remove halos there, no matter how much feathering/tuning you do. Another annoying issue: pixel corruption. AI rarely generates a perfectly flat background (pure #000000 or #FF00FF). That tiny noise breaks clean extraction and creates crawling garbage pixels around the subject. For clean base sprites (and even PBR maps), a useful trick is generating the same asset on white + black backgrounds and deriving alpha from the difference. This is basically a matte workflow: white = opaque, black = transparent. It fixes aura… but you’d need it per-frame to fix animation, which is still hard. For simple pixel art (single-digit frames), you can sometimes generate a sprite sheet, then ask the model to recreate it on black/white while preserving alignment… but it’s still manual-heavy. Honestly, at this point, for some projects it’s easier to go 3D → 2D and render clean sprites/maps directly. But I still love pushing “pure 2D” and seeing how far we can take it. Thanks for reading! Follow/bookmark/repost if interested in this kind of content!

Startracker 🔺

20,181 görüntüleme • 7 ay önce

OpenAI just launched GPT-Live, and this is what it really means: They have made ChatGPT Voice sound like a human being instead of slow robot. “Listen while it speaks”. Do you remember how you used to wait until the AI finished speaking? Now you can interrupt it with “Wait, no, I mean…” and it will accept your request and keep going. "Asynchronous reasoning" It solves the problem of the "awkward pause". The developers divided the brain of the AI into two parts: one for communication and the other for doing calculations and looking for solutions in the background. Finally, it can do everything at once. Connects to GPT-5.5 If more advanced reasoning or the use of tools is required, GPT-Live can also can also contact our cutting-edge models, like GPT-5.5, without interrupting the flow of the conversation. “6 round trips to 1”. Think of round trips as the digital handshake. Previously, your phone had to go through six handshake round trips before saying hello. Now, it only needs to do it once and it can greet you right away.

OpenAI just launched GPT-Live, and this is what it really means: They have made ChatGPT Voice sound like a human being instead of slow robot. “Listen while it speaks”. Do you remember how you used to wait until the AI finished speaking? Now you can interrupt it with “Wait, no, I mean…” and it will accept your request and keep going. "Asynchronous reasoning" It solves the problem of the "awkward pause". The developers divided the brain of the AI into two parts: one for communication and the other for doing calculations and looking for solutions in the background. Finally, it can do everything at once. Connects to GPT-5.5 If more advanced reasoning or the use of tools is required, GPT-Live can also can also contact our cutting-edge models, like GPT-5.5, without interrupting the flow of the conversation. “6 round trips to 1”. Think of round trips as the digital handshake. Previously, your phone had to go through six handshake round trips before saying hello. Now, it only needs to do it once and it can greet you right away.

Chidanand Tripathi

27,325 görüntüleme • 1 ay önce

🎬Whimsical Reverie | Furniture Preview 🛋️Home Sentiments In celebration of the Home system's launch, stylists can obtain free furniture items via the [Home Sentiments] event after the Version 1.9 update on September 1, 2025 (UTC-7). When the event starts, unlock 1 free furniture piece each day. The event lasts 3 days—claim up to 3 free items in total! The event has 3 rounds, giving you plenty of chances to decorate your home! 🛋️Dreamland Showcase Stylists can earn Dreamland Stones by completing weekly Chronicle Tasks. Dreamland Stones can be used to exchange for items in the Dreamland Shop, including the [Detective's Study] series of furniture and Diamonds. Spend 680 Stellarites to sign the [Dreamland Pact]. Once signed, you can instantly claim the new furniture [Loving Fufu & Flowers] and its Furniture Sketch. Complete the current Chronicle Tasks to earn bonus Dreamland Stones. After signing the [Dreamland Pact] and accumulating a certain number of Dreamland Stones in this phase of Dreamland Showcase, 680 Stellarites will be refunded. You can claim the Stellarites in Dreamland Pact interface. 🛋️Chorus of Stars Purchase the "Stellar Poems Pack" for 1,480 Diamonds to receive 12 types of Diamond-purchased furniture and 8 types of Astralite exchangeable furniture, totaling 20 types and 38 pieces. This pack can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. 🛋️Furniture Set Series Purchase the "Leisure Tea Brewing Set I" for 680 Stellarites to receive 6 types of furniture, totaling 14 pieces, including the [Eternal Years Screen]. This set can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. Purchase the "Leisure Tea Brewing Set II" for 420 Stellarites to receive 6 types of furniture, totaling 11 pieces, including the [Twilight Incense Burner]. This set can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. Purchase the "Leisure Tea Brewing Set III" for 180 Stellarites to receive 3 types of furniture, totaling 8 pieces, including the [Rolling Screen]. This set can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. 🛋️Stargazing Reveries Purchase the [Astral Radiance Pack] to receive selected furniture pieces such as [Stellar Cascade], [Echo Waterwheel], and [Seasons Couch]. These selected items are tagged as both "Home Furniture" and "Decoration," and may be placed in your Home or in Miraland. The set is limited to one purchase. If you prefer to buy items individually: [Stellar Cascade] is available for purchase, [Seasons Couch] for 680 Stellarites, and [Echo Waterwheel] for 450 Stellarites. Individual pieces of furniture can be purchased with no purchase limit. —— The Coziest Open-World Game! Infinity Nikki Version 1.9 "Music Season" launches globally on September 1st (UTC-7)! ➤ Download Now: ➤ Join us on Facebook Group: #InfinityNikki

🎬Whimsical Reverie | Furniture Preview 🛋️Home Sentiments In celebration of the Home system's launch, stylists can obtain free furniture items via the [Home Sentiments] event after the Version 1.9 update on September 1, 2025 (UTC-7). When the event starts, unlock 1 free furniture piece each day. The event lasts 3 days—claim up to 3 free items in total! The event has 3 rounds, giving you plenty of chances to decorate your home! 🛋️Dreamland Showcase Stylists can earn Dreamland Stones by completing weekly Chronicle Tasks. Dreamland Stones can be used to exchange for items in the Dreamland Shop, including the [Detective's Study] series of furniture and Diamonds. Spend 680 Stellarites to sign the [Dreamland Pact]. Once signed, you can instantly claim the new furniture [Loving Fufu & Flowers] and its Furniture Sketch. Complete the current Chronicle Tasks to earn bonus Dreamland Stones. After signing the [Dreamland Pact] and accumulating a certain number of Dreamland Stones in this phase of Dreamland Showcase, 680 Stellarites will be refunded. You can claim the Stellarites in Dreamland Pact interface. 🛋️Chorus of Stars Purchase the "Stellar Poems Pack" for 1,480 Diamonds to receive 12 types of Diamond-purchased furniture and 8 types of Astralite exchangeable furniture, totaling 20 types and 38 pieces. This pack can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. 🛋️Furniture Set Series Purchase the "Leisure Tea Brewing Set I" for 680 Stellarites to receive 6 types of furniture, totaling 14 pieces, including the [Eternal Years Screen]. This set can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. Purchase the "Leisure Tea Brewing Set II" for 420 Stellarites to receive 6 types of furniture, totaling 11 pieces, including the [Twilight Incense Burner]. This set can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. Purchase the "Leisure Tea Brewing Set III" for 180 Stellarites to receive 3 types of furniture, totaling 8 pieces, including the [Rolling Screen]. This set can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. 🛋️Stargazing Reveries Purchase the [Astral Radiance Pack] to receive selected furniture pieces such as [Stellar Cascade], [Echo Waterwheel], and [Seasons Couch]. These selected items are tagged as both "Home Furniture" and "Decoration," and may be placed in your Home or in Miraland. The set is limited to one purchase. If you prefer to buy items individually: [Stellar Cascade] is available for purchase, [Seasons Couch] for 680 Stellarites, and [Echo Waterwheel] for 450 Stellarites. Individual pieces of furniture can be purchased with no purchase limit. —— The Coziest Open-World Game! Infinity Nikki Version 1.9 "Music Season" launches globally on September 1st (UTC-7)! ➤ Download Now: ➤ Join us on Facebook Group: #InfinityNikki

Infinity Nikki

43,003 görüntüleme • 1 yıl önce

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially Ethan BdM , Zhengdong , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially Ethan BdM , Zhengdong , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.

Andy Hall

33,905 görüntüleme • 5 ay önce

NOBODY wants to send their data to Google or OpenAI. Yet here we are, shipping proprietary code, customer information, and sensitive business logic to closed-source APIs we don't control. While everyone's chasing the latest closed-source releases, open-source models are quietly becoming the practical choice for many production systems. Here's what everyone is missing: Open-source models are catching up fast, and they bring something the big labs can't: privacy, speed, and control. I built a playground to test this myself. Used CometML's Opik to evaluate models on real code generation tasks - testing correctness, readability, and best practices against actual GitHub repos. Here's what surprised me: OSS models like MiniMax-M2, Kimi k2 performed on par with the likes of Gemini 3 and Claude Sonnet 4.5 on most tasks. But practically MiniMax-M2 turns out to be a winner as it's twice as fast and 12x cheaper when you compare it to models like Sonnet 4.5. Well, this isn't just about saving money. When your model is smaller and faster, you can deploy it in places closed-source APIs can't reach: ↳ Real-time applications that need sub-second responses ↳ Edge devices where latency kills user experience ↳ On-premise systems where data never leaves your infrastructure MiniMax-M2 runs with only 10B activated parameters. That efficiency means lower latency, higher throughput, and the ability to handle interactive agents without breaking the bank. The intelligence-to-cost ratio here changes what's possible. You're not choosing between quality and affordability anymore. You're not sacrificing privacy for performance. The gap is closing, and in many cases, it's already closed. If you're building anything that needs to be fast, private, or deployed at scale, it's worth taking a look at what's now available. MiniMax-M2 is 100% open-source, free for developers right now. I have shared the link to their GitHub repo in the next tweet. You will also find the code for the playground and evaluations I've done.

NOBODY wants to send their data to Google or OpenAI. Yet here we are, shipping proprietary code, customer information, and sensitive business logic to closed-source APIs we don't control. While everyone's chasing the latest closed-source releases, open-source models are quietly becoming the practical choice for many production systems. Here's what everyone is missing: Open-source models are catching up fast, and they bring something the big labs can't: privacy, speed, and control. I built a playground to test this myself. Used CometML's Opik to evaluate models on real code generation tasks - testing correctness, readability, and best practices against actual GitHub repos. Here's what surprised me: OSS models like MiniMax-M2, Kimi k2 performed on par with the likes of Gemini 3 and Claude Sonnet 4.5 on most tasks. But practically MiniMax-M2 turns out to be a winner as it's twice as fast and 12x cheaper when you compare it to models like Sonnet 4.5. Well, this isn't just about saving money. When your model is smaller and faster, you can deploy it in places closed-source APIs can't reach: ↳ Real-time applications that need sub-second responses ↳ Edge devices where latency kills user experience ↳ On-premise systems where data never leaves your infrastructure MiniMax-M2 runs with only 10B activated parameters. That efficiency means lower latency, higher throughput, and the ability to handle interactive agents without breaking the bank. The intelligence-to-cost ratio here changes what's possible. You're not choosing between quality and affordability anymore. You're not sacrificing privacy for performance. The gap is closing, and in many cases, it's already closed. If you're building anything that needs to be fast, private, or deployed at scale, it's worth taking a look at what's now available. MiniMax-M2 is 100% open-source, free for developers right now. I have shared the link to their GitHub repo in the next tweet. You will also find the code for the playground and evaluations I've done.

Akshay 🚀

50,323 görüntüleme • 9 ay önce