Welcome to the singularity @ChironTheSage
Joined February 2022
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We spend years debating if we live in a simulation, meanwhile this tech just quietly became the thing the simulation was warning us about.
good news for local model users. the flash model is the one you can actually run, and it just got better
🚀 DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.
🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
1/4
Efe Utku Akar retweeted
Null consensus scales Bittensor subnets 10x and allows POW registration (no Tao needed for an agent to mine).
First step adding more freedom and scale to our incentive systems.
Efe Utku Akar retweeted
Today, we’re releasing Bonsai 2 2B, 4B, and 9B.
Based on Qwen3.5, these new ternary models bring stronger reasoning, visual understanding, and tool calling to compact footprints designed for everyday devices:
• 2B: 0.43GB
• 4B: 0.95GB
• 9B: 2.0GB
For this release, we focused on what makes small models useful as local assistants: following instructions, avoiding repetitive loops, calling tools on mobile devices, and giving useful answers without unnecessary output. The 9B model uses roughly 40% fewer tokens on standard instruction-following and everyday math tasks. Solving the Riemann hypothesis can wait.
These models are successors to our first-generation Bonsai 1.7B, 4B, and 8B, and provide a foundation for bringing practical agentic functionality to local applications on personal computers, phones, and wearables.
The Bonsai 2 family now includes four sizes spanning 2B to 27B parameters. Developers can choose the balance of capability, memory use, and responsiveness that fits their application and hardware.
Available today under Apache 2.0.
Efe Utku Akar retweeted
We compared monthly storage bills across six providers.
Hippius S3 came out cheapest at every size.
And that’s storage alone. AWS and Google Cloud also charge you to download your own data.
Hippius S3 has no egress fees and unlimited requests on every plan.
Efe Utku Akar retweeted
GLM-Legal uncensored:
Introducing GLM-Legal
Built on GLM-5.3-Flash, GLM-Legal matches frontier models on mainstream legal benchmarks.
Designed for real legal workflows: contract review, evidence analysis, compliance, and more.
Tech Blog: z.ai/blog/glm-legal
Apply for API Access: z.ai/consultation/legal
Try AutoClaw: autoclaw.z.ai
looks like this month is going to be loud. everyone saved their best for the same few weeks
🚨高能预警！这12个AI大模型即将轮番登场，准备好接招了吗？！
核心亮点速览：
1️⃣Fable 5.5（Anthropic）：Mythos级升级，长程复杂编码/知识工作SOTA，安全护栏加持，适合超长任务，短期内断层第一。
2️⃣GPT-6.1 Astra（OpenAI）：下一代旗舰，智能对齐与代理能力拉满，计算机使用/科学推理顶级，破解数学难题的狠角色。
3️⃣Grok 4.8（xAI）：约2.5T参数，新C++训练栈，相较4.7显著提升，多模态与实时表现值得期待。
4️⃣Gemini 4 Carbon（Google）：内部checkpoint，编码体验接近Opus级，复杂工作流与长输出潜力强。
5️⃣DeepSeek-V4.1 Pro：下一代旗舰，Flash之后的Pro版本，开源高效的集大成者，编码与推理性价比突出。
6️⃣GLM-5.5（智谱）：Agentic Engineering定位，基于RSI后训练，国产安全扛把子。
7️⃣KIMI K3.1（月之暗面）：2.8T级开源前沿升级，原生多模态+1M上下文，知识工作与代理强。
8️⃣Qwen4.0-Max / 27B（阿里）：新架构训练中，Max旗舰+开源27B双线，规模与效率双升级，潜在RSI训练加持。
9️⃣Hy-4 Pro（腾讯混元）：开源MoE系列升级版，超长上下文，编码/办公/科研生产力导向。
🔟Xiaomi MIMO-V2.7-Pro/Flash：万亿级开源Agent模型迭代，全模态+高效推理，开源天花板冲击者。
1️⃣1️⃣MiniMax M3.1：多模态编码模型，1M上下文+可调思考深度，Flash级速度与干活能力。
1️⃣2️⃣StepFun Step 5 Pro：Agentic工作与金融专业任务进一步增强，低价+开源权重。
这波更新不只是版本号狂欢，更是RSI（递归自我改进）的硬核竞技。
谁能率先把自我进化能力拉到新高度，谁就可能拉开真正的代差。
#AI #大模型 #开源AI #CodingAI #AI前沿 #模型发布 #RSI #DeepSeek #Qwen #Grok #Gemini #Claude #GPT #小米 #智谱 #月之暗面 #MiniMax #StepFun #腾讯混元 #Hy4
GLM 5.3 and Opus 5.5 have been my top two lately nobody else is close
counting the days for the next GLM!
Efe Utku Akar retweeted
twitter is a bubble
normal people are not vibe coding their own software
calm down
Yapay zekanın balon olduğunu düşünen insanları gerçekten anlamıyorum.
Şu an dotcom'dan da sanayi devriminden de güçlü bir dönüşümün içindeyiz ve daha yeni yeni emeklemeye başladık.
Aynı şey daha önce sanayi devriminde yaşanmıştı, Ford gibi devlerle...şimdi aynı döngüyü yapay zekada yaşıyoruz.
europe has a model in the frontier race again
le chonk
good news, i'll take it
Mistral has released Mistral Large 4, scoring 38 on the Artificial Analysis Intelligence Index; France is back to having the most intelligent model from outside the US and China
@MistralAI has released Mistral Large 4 in Research Public Preview, with plans to release the weights of the 1T parameter (49B active) model at the end of October. It achieves 38 on the Artificial Analysis Intelligence Index, comparable to GPT-6 Luna (max, 38) and DeepSeek V4.1 Flash (max, 39). It also achieves 50% on the Artificial Analysis Cyber Index, level with GLM-5.3-Flash and ahead of models such as Kimi K3 and DeepSeek V4.1 Flash (max).
Key benchmarking results for Mistral Large 4 Preview:
➤ Most intelligent model from outside the US and China: Mistral Large 4 Preview scores 38 on the Intelligence Index, comparable to DeepSeek V4.1 Flash (max, 39) and GPT-6 Luna (max, 38). This makes it the most intelligent model from outside the US and China, ahead of countries such as South Korea and the United Arab Emirates
➤ Level with GLM-5.3-Flash on cyber defense capability: Mistral Large 4 Preview scores 50 on the Artificial Analysis Cyber Index, level with GLM-5.3-Flash (50) and behind MiMo-V2.6-Pro (56). Once its weights are released, it will rank among the top three open weights models on the Cyber Index. Its strongest result is on CyberGym-E2E-AA, where it scores 82%, ahead of MiMo-V2.6-Pro (79%) and GPT-6 Luna (max, 78%)
➤ Over 4x the Cost per Task of similar-intelligence open weights models: Mistral Large 4 Preview costs $1.13 per Intelligence Index task with standard pricing of $1.36/$4.18 per 1M input/output tokens, with $0.14 per 1M cached input tokens. For the first two weeks, Mistral Large 4 Preview will be served at a 50% launch discount, bringing its Cost per Task down to $0.57. This is still more costly than GLM-5.3-Flash ($0.25) and DeepSeek V4.1 Flash (max, $0.27)
➤ Strong document and image reasoning: Mistral Large 4 Preview scores 19% on GDP.pdf, on par with MiMo-V2.6-Pro (19%) and behind Kimi K3 (22%). This is an 18-point improvement from Mistral Large 3, partly driven by improvements in their API, which now accepts 100 images per request, up from 8 for previous Mistral models
Key model details:
➤ Context Window: 512k tokens
➤ Multimodality: Text and image input, with text output
➤ Pricing: $1.36/$4.18 per 1M input/output tokens ($0.14 per 1M cached input tokens), with 50% off for the first two weeks ($0.68/$2.09)
➤ Availability: Research Public Preview on Mistral's API, with open weights planned for the end of October
