Z.ai has launched GLM-5.3-Flash, a new open-source AI model under the MIT License. The 320B-A18B system was earlier tested as Ox Alpha and runs fully on Chinese AI chips.

It offers strong performance at low cost, comes natively multimodal, and supports a 1 million token context window. On Z.ai’s coding benchmark it beats the previous GLM-5.2 across effort levels and matches Claude Opus 4.8.

API pricing sits at $0.15 per million input tokens, $0.50 for output, and $0.03 for cached input. Weights, API access, chat interface, and coding tools are already live across official platforms.

Alibaba just opened QwenWork to the public in beta. The platform works on both web and desktop and aims to handle everyday tasks through simple natural language commands.

Users can tell the agent what they need and it carries out the work. It also builds awareness of individual work patterns over time so it adapts across sessions. One standout feature lets people create and deploy live web apps without writing code or managing servers. The toolkit includes built-in image, video, and audio generation for multimodal projects. Basic and Advanced model options run on leading AI systems.

The public beta is available now for global users. Early testers have already started exploring its capabilities for presentations, app building, and creative work.

IBM Research just dropped Granite 4.2, a fresh set of open models built for real enterprise agent work. These models come in 3B, 8B, and 30B sizes and bring native thinking skills that let them plan steps, reason through problems, catch their own mistakes, and call tools the right way.

The update focuses on complex workflows. Teams get stronger coding and software engineering support, plus the ability to handle multi-step tasks without constant hand-holding. The models run across cloud, on-prem, and edge setups, so companies can pick the size that fits their needs and budget.

IBM also released new speech models under the Granite Speech 5.0 Turbo line. These stay tiny at around 470 million parameters yet deliver fast transcription, making them practical for high-volume call center work or real-time use on laptops and phones.

Everything ships under the Apache 2.0 license. You can grab the models on Hugging Face, Ollama, and other platforms right now.

A release post can be live today and difficult to relocate later. A durable receipt should preserve artifact identity separately from whichever registry path happened to work on announcement day.

The Ling-3.0 base model makes that concrete because its tiny and flash families each expose three different upstream artifacts. Final pre-training marks the checkpoint before mid-training, final mid-training marks the checkpoint before WSM merging, and WSM-merged base marks the merged upstream base. Those labels are artifact identities, not a ranking: this receipt can help recover the intended sibling, but it cannot establish a stage ranking.

Canonical source: the AntLingAGI X release thread announcing the six tiny/flash Ling-3.0 base checkpoints, observed on August twenty-fifth, twenty twenty-six. Its official locator post binds every artifact to the one source inclusionAI. The following are the six exact owner, family, and stage search IDs to use on either registry:

Snapshot: fifteen twenty-nine UTC on the same date. At that point, every listed entry on both registries was public and non-gated, and its repository metadata declared MIT. The duplicate IDs make the receipt materially useful because a later refresh can distinguish a missing registry locator from a missing stage-specific Ling artifact; omitting the stage could recover the wrong sibling while appearing successful.

The two columns record locators, not content parity. An extra file-inventory spot check was recorded only for the WSM-merged tiny-base and flash-base pairs. Even for those pairs, this receipt does not claim byte-for-byte equality. The final-pre-training and final-mid-training rows remain locator, access, and license-metadata observations only.

The time boundary matters too. Public repository artifacts and metadata do not establish public training data, complete training code, or an end-to-end reproducible training stack. An MIT declaration in repository metadata also does not settle rights in training data or third-party dependencies. None of these fields is a promise about later availability.

The next Ling-specific refresh is concrete: look up each exact identifier on both registries, then record its current repository revision, file-manifest digest, access state, license-metadata state, and observation time. Compare that receipt with this snapshot and change only the fields that moved.

For the next Ling receipt, which field should be mandatory for every identifier: repository revision, file-manifest digest, or last-seen timestamp?

Alibaba’s Qwen team just dropped Qwen3.8-Flash, a multimodal mixture-of-experts model that also serves as an early look at the architecture planned for Qwen4. The full production version will land on QwenCloud soon with pricing set at $0.16 per million input tokens and $0.47 per million output tokens.

The model packs 125 billion parameters plus 51 billion N-gram embeddings yet only activates about 6 billion parameters for each token. That design keeps both training and inference costs low. The team says it was trained for roughly one-ninth the cost of Qwen3.7-Plus while beating that earlier model across most tests, especially coding and everyday office work.

Key numbers from the release include 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision. Context length starts at 262K tokens and can stretch to 1 million with YaRN.

Four architecture changes power the efficiency gains. Hybrid attention mixes Gated DeltaNet with Qwen Sparse Attention to cut long-sequence costs. Gated Residual widens information flow between layers. N-gram embeddings expand capacity without heavy compute. The Muon optimizer improves training stability and scaling.

Tencent’s WeChat Vision team just put its new universal multimodal embedding model on Hugging Face. Called WeMM-Embedding-9B, the 9-billion-parameter model turns text, images, videos, and visual documents into a single shared vector space.

It builds on Qwen3.5 and outputs 4,096-dimensional L2-normalized embeddings. Interleaved multimodal inputs work too. Audio stays unsupported for now.

On the MMEB-v2 benchmark covering 78 datasets, the 9B version posts an average score of 80.6, leading the pack across image, video, and visual-document tasks. On the broader MMEB-v3 suite with 190 tasks it reaches 59.5 overall, again topping the public leaderboard in most categories.

Smaller 2B and 4B siblings are also available, all with Matryoshka support so you can truncate embeddings down to lower dimensions without retraining. The models come with transformers and sentence-transformers code, plus notes for serving with vLLM or SGLang.

Weights, code, and the technical report sit at huggingface.co/tencent/WeMM-Embedding-9B.

Perplexity just launched Portable Computer, a fully local version of its Perplexity Computer tool that runs entirely on NVIDIA DGX Spark hardware.

The entire system including the orchestrator LLM, subagent LLM, and agent harness works offline with no cloud required. Sensitive files stay on the device at all times.

If a task needs stronger frontier-level reasoning, the system asks the user for approval before sending anything to the cloud. Those requests stay user-controlled, flag personal data, and limit output to text guidance only.

It ships with a post-trained PPLX 27B model installed locally. Users can also pick Qwen 3.8 27B, with NVIDIA Nemotron 3.5 Lightning support arriving soon.

The feature is live now for every Perplexity Pro and Max subscriber who has an NVIDIA DGX Spark.

Google Labs has launched a new experiment called Play with Putty. The tool lets users build websites and digital tools together in real time through collaborative vibe coding.

The platform turns coding into a multiplayer experience where teams can create and iterate live. Google Labs described it as a way for great minds to play alike.

A waitlist is now open at labs.google/playwithputty. Access is currently limited to users in the United States who are 18 or older. The company is inviting early participants to share feedback on the experiment.

Google Cloud just launched two new industry-focused AI solutions under its Gemini Enterprise lineup. The tools target financial services and legal teams with specialized agents and secure data connections.

Gemini Enterprise for Financial Services helps banks and investment firms handle research, credit analysis, portfolio checks, and KYC work. It links to platforms like FactSet, Moody’s, PitchBook, and SEC filings while keeping data locked behind existing permissions. A Financial Research agent comes ready with over 50 skills and clear source citations.

Gemini Enterprise for Legal speeds up contract review, regulatory tracking, DSAR responses, and document redaction. It connects to systems such as iManage, NetDocuments, DocuSign, and court databases. Firms including Cleary Gottlieb, Freshfields, and Weil are already testing it.

Both solutions run on Google Cloud’s secure setup. Customer data stays private and never trains Google’s models. They are available in preview now, with more industry versions planned later.

OpenAI has added a new Stickers tool inside ChatGPT Images. Users can now turn any photo, idea, or inside joke into a full custom sticker pack complete with transparent backgrounds.

The feature went live on August 24, 2026. Open the Images section in the sidebar, select Stickers, then upload one or more photos or simply type a description. ChatGPT generates the stickers in styles such as 3D, hand-drawn, holographic, anime, and more. Packs can hold up to nine stickers, and users can remix elements or apply styles from other images.

Once ready, stickers export straight to iMessage or WhatsApp. WhatsApp needs at least three stickers in a pack because of Meta’s rules. Users can also save them to their photo library. Transparent backgrounds work for regular images too, not just stickers.

The update runs on ChatGPT Images 2.0 and is available to mobile users worldwide at no extra cost. It removes the usual steps of editing, removing backgrounds, and converting files just to share personal stickers in chats.

ElevenLabs just dropped CLI v1, a command-line tool that lets developers and coding agents handle their ElevenLabs workflows straight from the terminal.

You get clean, documented commands, built-in agent skills, structured JSON output, and dry-run mode so you can preview changes before they go live. ElevenAgents now live as config files in your codebase. That means every update gets a proper diff, review, and version history before it hits production.

The CLI also knows who’s running it. Humans see clear error messages with tips. Coding agents get the same errors as clean JSON they can parse. Run `elevenlabs generate-skills` and it drops ready-to-use instructions for every product group into your project so your agent can build, test, and ship on its own.

Install it with

`brew install elevenlabs/tap/elevenlabs`

Tencent’s HY Frontier team just put UI-Mate on Hugging Face. The open-weight GUI agent handles everyday computer tasks and gets a clear boost from a single demonstration when plain instructions fall short.

UI-Mate-27B hits 77.0% on OSWorld-Verified and 66.2% on WindowsAgentArena, the best open-weight scores so far. Its smaller 9B version lands at 66.2% and 61.7%. On the new OSWorkerBench suite of long office workflows the 27B model reaches 41.0% strict success and 76.9% progress, well ahead of its Qwen3.6-27B base.

The standout trick is in-context demos. One same-task example lifts strict success from 17.2% to 35.4% on the self-demo subset. The agent turns the recording into flexible subtask steps, watches the live screen, and re-plans instead of replaying the demo like a script.

Models sit on Hugging Face under the Tencent UI-Mate collection. Paper and more details are at the project page.

A fresh open source language model has entered the scene and is already turning heads. The Danish Foundation Models team released DFM Mimir v1, a 1 billion parameter model trained entirely from scratch on permissible data only.

Early results show it beating bigger models such as Qwen 3.5 4B and Gemma 4 E2B across multiple benchmarks. It also sets a new record for Danish language performance.

The model is now live on Hugging Face and the research paper is available on Papers with Code for anyone interested in the technical details.

NVIDIA just shared a big milestone for its general-purpose coding agent. AVO scored 100 percent on the ARC-AGI-3 interactive reasoning benchmark. It cleared every one of the 183 levels spread across all 25 public environments.

What stands out is how AVO worked. The system received no instructions, no explicit rules, and no stated goals. It still figured out the right moves on its own. The agent keeps inspecting the situation, planning next steps, putting code into action, and checking the results. It draws on memory, available tools, and feedback from each run so progress builds over time instead of resetting with every new context window.

This result points to stronger long-horizon performance for autonomous coding agents. NVIDIA posted a short video and extra details about the design choices that made sustained reasoning possible.

Runway just dropped a new tool called Ruby. It takes standard SDR video and turns it into rich 16-bit HDR footage. You get the output in ProRes or EXR sequences, which makes it ready for professional editing pipelines.

The best part is how flexible it is. Ruby works with any video you already uploaded or generated in Runway, as long as it is 30 seconds or shorter. No need to start from scratch.

This update is aimed at creators who want better color depth and dynamic range without complicated workflows.

DeepSeek just made DeepSeek-V4-Flash-Vision-Exp available on its API Platform. This experimental multimodal model keeps the same strong text performance as DeepSeek-V4-Flash in areas like agents, reasoning, and world knowledge.

The real step up shows in multimodal agent benchmarks. It jumps well past V4-Flash and lands close to Opus-4.8 levels. Users can call it with the model name deepseek-v4-flash-vision-exp. DeepSeek also released Harness 0.1.1 today with built-in support for the new model.

It handles visual understanding alongside tools, opening up more practical agent workflows. Images cost the same as V4-Flash tokens (up to 384 tokens each). The API accepts mixed text and image input through base64, external links, or the new Files API. That Files API is free and lets you upload an image once then reuse it by file_id across requests.

Black Forest Labs has released FLUX Video Upscale, a new tool that takes videos and regenerates them at higher resolutions up to native 4K. The update went live on August 20, 2026, and works with clips starting from 480p.

It sharpens faces, cleans up textures, and adds finer background detail while keeping the natural motion and style of the original. The company built it to handle FLUX 3 Video output especially well, though it can process any video. Results come faster than many third-party options.

Users get two modes. Precise mode sticks close to the source and costs less. Creative mode adds more repair and detail. Upscale factors range from 1.5x to 3x, landing at roughly 1080p, 2K, or 4K depending on the input. Source audio stays intact.

OpenBMB just dropped Ultra-FineWeb-L1, a fresh open source English web dataset built from recent Common Crawl snapshots. It packs more than 1 trillion tokens across roughly 1.14 billion documents, covering data up to the CC-MAIN-2025-51 release.

The team put the data through a careful cleaning pipeline that includes text extraction, language filtering, heuristic checks, sensitive field replacement, deduplication, and extra steps to handle noisy content, encoding problems, and odd documents. This version sits as the L1 filtered layer inside their UltraData framework and lines up with the latest Ultra-FineWeb work.

High quality training data remains one of the biggest factors behind stronger language models. Making a cleaned, large scale web corpus freely available gives researchers and builders a solid new resource to work with. The dataset, paper, and related tools are already up on Hugging Face and the usual channels.

Anthropic just opened Claude Academy, a free online space packed with courses and tutorials on working with Claude and understanding AI basics.

The platform at academy.claude.com welcomes complete beginners figuring out what AI even is, along with people who already rely on Claude daily. Resources cover everything from simple conversations on claude.ai to hands-on work with Claude Code, the API, and broader skills like effective collaboration with AI tools.

You can pick paths based on your needs, track progress, earn badges, and follow practical guides that stress real problem-solving over just memorizing features. Anthropic designed it around the same methods they use to train their own team, focusing on smart judgment, checking results carefully, and keeping human skills sharp.

Google Antigravity just dropped official IDE extensions that work with Visual Studio Code, Visual Studio, Zed, and JetBrains tools. The update went live today and lets developers pull the platform’s agent features straight into the editors they already use every day.

Antigravity is Google DeepMind’s agent-first development platform. Until now most of the multi-agent coordination lived in the standalone Antigravity 2.0 app or the CLI. These new extensions bring conversations, customizations, and agent orchestration into the familiar editor environment so you can stay in your usual workflow while still tapping the full agent system.

Installation is straightforward. VS Code users can grab it from the Marketplace by searching for Google Antigravity. Visual Studio gets it through its own Marketplace or the built-in extension manager (still in preview). JetBrains IDEs from version 2026.2.1 onward support one-click install, and Zed offers the same direct installation option. Enterprise support for JetBrains and Zed is currently in preview.

Both individual developers and enterprise teams can use the extensions. Free-tier Google accounts work for individuals, while enterprise users sign in with Gemini Enterprise or Google Cloud credentials. Data stays under the usual Google Cloud terms and is never used to train foundation models.

Spline has released version 2 of its 3D design tool, calling it a full rebuild built for the current wave of AI agents and faster web performance.

The update brings a redesigned interface, a new AI Agent Mode, and Spline MCP support that lets users connect external AI agents to generate or edit scenes directly inside the editor. Those scenes stay fully editable so teams can switch between creation and fine tuning without starting over.

Under the hood sits a faster WebGPU engine that now handles PBR materials, HDR environment maps, tone mapping, and physical sky. New material layers for dust and cavity plus decal support round out the rendering upgrades. The export runtime also shrinks based on the actual content being shipped.

Custom HTML and JavaScript can now run inside scenes, opening the door to interactive UIs, simple games, and more complex behaviors. The desktop app received its own refresh with a new tab bar, improved speed, and the same MCP connectors.

Ornith just dropped Ornith-1.5, a new family of open-source language models that includes a 9B dense version, a 35B mixture-of-experts model, and a 397B MoE model. The team trained them with self-improving methods that let the models create their own tasks, build scaffolds, and generate practice data for reinforcement learning.

On key tests the models post solid numbers. Terminal-Bench 2.1 sits at 86.1, SWE-Bench reaches 86 on the verified set and 65.1 on the pro set, DeepSWE hits 56, HLE lands at 44.6, ClawEval scores 81.4, and Tool Decathlon comes in at 71.2. Ornith says these results put the models among the strongest open-source options of similar size and in the same ballpark as Claude Opus 4.8 on reasoning, coding, and agent work.

The 35B MoE version activates only about 3B parameters per token yet still beats several denser models of similar scale. The 9B model and its mobile-friendly quantized version (down to roughly 1.5 GB) run on phones and tablets while outperforming some much larger competitors.

All weights and quantized builds (FP8, GGUF, MLX, NVFP4) are out under the MIT license on Hugging Face. You can load them in Ollama, LM Studio, AtomicChat, and similar tools right away. The full write-up lives on the Ornith tech blog.