All AI news

AIGeneralist has announced GEN-1.5, its latest foundation model for the physical world. The post provides only a link to the company's blog for further details, so no specifications, benchmarks, or availability information can be confirmed from this source.

AIMeituan LongCat has made LongCat-2.0 available in opencode Go, according to the post. The post describes LongCat-2.0 as a 1.6T-parameter model with 48B active parameters, a 1M-token context window, and fully open-source release. Meituan LongCat invites users to try the model in opencode and share what they build.

AIQwen released open weights for Qwen3.8-Flash-Next, a 125B-parameter model with 6B activated, built on a new hybrid architecture with Gated DeltaNet and Qwen Sparse Attention. The model has a native 262,144-token context length, extensible to 1,000,000 tokens, and the source reports benchmark results across coding, agent, and vision tasks.

Why it matters: The release pairs a new hybrid attention and gated residual architecture with open weights and benchmark results, giving architecture-focused readers a concrete case to compare against prior long-context designs.

AIGrok Bot access is opening to a small set of enterprises this weekend. Interested companies can reply to request an in-person onboarding visit to their San Francisco office tomorrow or Monday.

AITinker announces Coco, a local assistant that proactively offers help when users are unsure what to ask, with a voice interface powered by Inkling on Tinker. According to the background post from @EchoShao8899, the voice trigger is a "Hi Coco" feature, and the part was open-sourced first.

AIThinking Machines is making Inkling available for free on OpenRouter for the next few weeks, but only with agentic harnesses. The company says it will use the resulting usage data, disassociated from accounts, to improve Inkling's agentic performance.

AIThinking Machines announced that its Inkling and Inkling-Small models are available to try on OpenRouter. The post links to the Thinking Machines provider page on OpenRouter, with no further details on specifications, benchmarks, or pricing.

AIAntigravity now supports remote control from its app and headless operation from the terminal, making it accessible in more settings. The post notes that usage limits are quite generous.

AISky Lab's FreeToken runs official checkpoints of large models such as Qwen3.6 35B on an 8GB RTX 4060 laptop at 39 tokens per second. The same approach reportedly serves DeepSeek-V4-Flash 284B at 22-25 tokens per second on an RTX 5090 desktop, and GLM-5.2 753B at 15 tokens per second on an RTX PRO 6000 workstation.

AILMSYS Org published a blog post about fast recovery in SGLang, the serving framework. The post body provides no further technical details, figures, or benchmarks beyond the link.

AIZ.ai is extending its Build Week into an ongoing series, giving 50,000 new ZCode users 100M free GLM-5.3 tokens each. The offer runs until August 23 at 6 PM PT, with the linked event page noting that unused tokens expire when the event ends.

AIDeepSeek's API now accepts multimodal input through the model deepseek-v4-flash-vision-exp, supporting mixed text and image requests. Each image is billed at up to 384 tokens at V4-Flash pricing, and it works with Chat Completions, Messages, and Responses endpoints. Images can be supplied as base64, external URLs, or via the Files API.

AIDeepSeek's Files API is now live and free to use. Users upload an image once and reference it by file_id, saving request bandwidth and avoiding re-uploads across requests.

AIDeepSeek has made DeepSeek-V4-Flash-Vision-Exp, an experimental multimodal vision understanding model, available on its API platform via model='deepseek-v4-flash-vision-exp'. The source says its pure-text capabilities are on par with DeepSeek-V4-Flash, while it shows a significant leap on agent benchmarks requiring visual understanding, which it says brings multimodal agent capabilities close to Opus-4.8.

Why it matters: The source gives benchmark scores and a model identifier, so readers can compare the experimental vision model against the text-only DeepSeek-V4-Flash on agent tasks.

AIMeituan LongCat has extended free access to LongCat-2.0 with Hermes Agent for another two weeks on the Nous Research Portal. The extension follows an initial one-week free trial announced in the quoted post.

AIAntigravity is now available to all Gemini enterprise subscribers, broadening its access across the board. It can also be installed as an extension in all the major IDEs.

AIUnsloth Desktop released a new version featuring experimental auto compaction for any model, LAN and remote access tabs, faster and smoother chatting, and over 200 merged PRs. Auto compaction combines RAG, a forced first-turn RAG, and a tail, according to the post.

AIMistral has released Agentic Search, a multi-step retrieval layer available through its Search Toolkit and Libraries. On FinanceBench, the company reports accuracy rising from 26.7% to 86% over one-shot RAG, and on OfficeQA Pro a gain from 6.3% to 51.9%. The system also reports up to 39.6% lower p90 latency and up to one-third lower token use from fewer repeated searches.

AILiquid AI released DSpark speculative decoding draft models for LFM2.5-1.2B-Instruct, LFM2.5-2.6B, and LFM2.5-8B-A1B on Hugging Face. The draft models reach up to 3.18x throughput improvement on an H100 GPU and up to 2.87x on-device, and the outputs match baseline greedy decoding by construction. Support is available in llama.cpp and SGLang, with the speedup varying by model and dataset.

Why it matters: The release reports measured speedups on both H100 and MacBook hardware, with per-dataset results and acceptance rates that show where speculative decoding helps most.

AIGeneralist says its GEN-1.5 model shows a new level of generality when pretrained on physical interaction data at a scale few thought feasible without shortcuts. The company says it does not yet see where performance gains level off.

AIAir's latest release lets users open several projects in one window and run agents across them in parallel, with tasks grouped by project in the sidebar. Markdown files now render as formatted text while editing, with syntax shown only when editing, and Chinese, Japanese, Korean, and other IMEs now work on Windows. The release also adds a Customize screen for keymap, theme, and accent color, and lets users choose the agent and model for Agent Review.

AITinker has made Qwen3.8-27B available today. The model is natively multimodal, handling images and video, with flexible thinking control. Tinker says it performs meaningfully better at coding, professional work, research, and long-horizon agentic tasks.

AIUnsloth has released 1-bit quantized versions of Qwen3.8-27B that run on 8GB of RAM while retaining about 77% of BF16 accuracy. The team originally hesitated to publish them but was surprised by how well they performed in internal testing. The release accompanies new Qwen3.8-27B GGUFs that the company says deliver 10% higher accuracy.

AIGoogle has released TIPS g/14 low-res (v1) on Hugging Face, a Text-Image Pre-training with Spatial awareness vision-language model with 1.1B vision parameters and 389M text parameters. The model produces spatially rich image features aligned with text embeddings at 224 resolution, under the Apache 2.0 license. It supports image encoding, text encoding, and zero-shot classification via the transformers library.

AIGoogle has released the original TIPS g/14 (v1) vision-language model on Hugging Face under Apache 2.0, with 1.1B vision parameters and 389M text parameters at 448 resolution. The TIPS family, presented at ICLR 2025, produces spatially rich image features aligned with text embeddings, and the release includes a low-res 224 variant.

AIUnsloth released new Qwen3.8-27B GGUF quantizations built with Unsloth Dynamic v3, which it says gain about 10% top-1% accuracy at the same size. The accuracy was measured with the new Divergence-300 metric, which extends top-1% greedy accuracy to 32 tokens using 300 unseen examples from Terminal Bench and DeepSWE. Unsloth also released 1-bit quants that it says run in 6–8GB, with 8GB RAM cited for running them.

AIGoogle released google/tipsv1-so400m14, the original v1 So400m/14 checkpoint of TIPS, a contrastive vision-language model that produces spatially rich image features aligned with text embeddings. The model has 413M vision parameters and 448M text parameters at 448 resolution, and is licensed under Apache 2.0.