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iX Workshop: How to Apply RAG Correctly—Architecture, Retrieval and Evaluation
heise online's iX is launching a three-day interactive online workshop, "KI mit eigenen Daten richtig umsetzen: RAG, Embeddings und Retrieval-Architekturen bewerten und anwenden," which explains RAG architecture and technical fundamentals and also covers cloud and on-premise deployment. The course is taught by Gregor Biswanger, a Microsoft Azure AI MVP and GitHub Copilot collaborator; the content includes prompt optimization, integrating the OpenAI and Azure OpenAI APIs, and developing and testing your own AI agents. The workshop runs online daily from 09:00 to 17:00 and is aimed at developers who want to get started with RAG systems, suitable for both beginners and advanced learners.
which explains RAG architecture and technical fundamentals and also covers cloud and on-premise deployment. The course is taught by Gregor Biswanger, a Microsoft Azure AI MVP and GitHub Copilot collaborator; the content includes prompt optimization, integrating the OpenAI and Azure OpenAI APIs, and developing and testing your own AI agents. The workshop runs online daily from 09:00 to 17:00 and is aimed at developers who want to get started with RAG systems, suitable for both beginners and adva
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How to add Tessl agent code review to a repository in 5 minutes
Tessl has published a tutorial on how to set up Tessl Code Review agent review for a GitHub repository in about 5 minutes: it reads the full pull request rather than an isolated diff, scores it against the standards the team defines in the repository, and posts a single review result. Reviews can be set to run only when a PR is opened or requested, or after every update; the handling mode can be comment-only, or approve/request changes, and it supports automatic approval once all Tessl threads are resolved. Changes only take effect for new review runs.
Tessl has published a tutorial on how to set up Tessl Code Review agent review for a GitHub repository in about 5 minutes: it reads the full pull request rather than an isolated diff, scores it against the standards the team defines in the repository, and posts a single review result. Reviews can be set to run only when a PR is opened or requested, or after every update; the handling mode can be comment-only, or approve/request changes, and it supports automatic approval once all Tessl threads a
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Ant inclusionAI Open-Sources Ming-3.0-asr-tiny: Supports Chinese-English Transcription, Dialect Recognition, and Context ASR
Ant inclusionAI has open-sourced Ming-3.0-asr-tiny on GitHub, combining the Ling-tiny-v3 language model with an audio encoder to support Chinese-English transcription, dialect recognition, and Context ASR. Dialect evaluation covers 15 test sets including Hunan, Minnan, Cantonese, and Sichuan-Chongqing; Context ASR can recognize proper nouns via hotwords in prompts or preceding dialogue. The inference stack uses Transformers 5.12.1, fla-core 0.5.2, and PyTorch 2.11.0, and provides an SGLang accelerated deployment example.
Ant inclusionAI has open-sourced Ming-3.0-asr-tiny on GitHub, combining the Ling-tiny-v3 language model with an audio encoder to support Chinese-English transcription, dialect recognition, and Context ASR. Dialect evaluation covers 15 test sets including Hunan, Minnan, Cantonese, and Sichuan-Chongqing; Context ASR can recognize proper nouns via hotwords in prompts or preceding dialogue. The inference stack uses Transformers 5.12.1, fla-core 0.5.2, and PyTorch 2.11.0, and provides an SGLang accel
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Ant Group's inclusionAI open-sources the Ming-3.0-audio-flash native audio model
Ant Group's inclusionAI has open-sourced the Ming-3.0-audio-flash native audio model on GitHub. It uses a 126B-parameter MoE architecture with 6.5B activated parameters, and there is also a Ming-3.0-audio-tiny version with 9.3B parameters and 2.7B activated parameters. The models cover Chinese and English recognition across multiple dialects and accents, long audio understanding, audio reasoning, and end-to-end speech dialogue. They support three inference methods: vLLM-Omni, SGLang, and Transformers, and are released under the MIT license.
Ant Group's inclusionAI has open-sourced the Ming-3.0-audio-flash native audio model on GitHub. It uses a 126B-parameter MoE architecture with 6.5B activated parameters, and there is also a Ming-3.0-audio-tiny version with 9.3B parameters and 2.7B activated parameters. The models cover Chinese and English recognition across multiple dialects and accents, long audio understanding, audio reasoning, and end-to-end speech dialogue. They support three inference methods: vLLM-Omni, SGLang, and Transfo
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Zihao Input Method Lands on iPhone: Built on the Rime Engine, Supports Importing Custom Schemes
The author has released Zihao, an iPhone input method built on the Rime (Zhongzhouyun) engine with Song Pinyin built in. Basic typing features are free and ad-free, and typing content is processed only on the phone. It supports importing Rime configuration folders, ZIP, YAML, or GitHub repositories with automatic updates, and dictionaries can be synced in Rime format to iCloud Drive and merged with Squirrel on Mac. The author says that in simulated tests, the first-try accuracy for sentences with 2 mistyped letters rose from 36% to 56%, and for 3 mistyped letters from 26% to 53%.
engine with Song Pinyin built in. Basic typing features are free and ad-free, and typing content is processed only on the phone. It supports importing Rime configuration folders, ZIP, YAML, or GitHub repositories with automatic updates, and dictionaries can be synced in Rime format to iCloud Drive and merged with Squirrel on Mac. The author says that in simulated tests, the first-try accuracy for sentences with 2 mistyped letters rose from 36% to 56%, and for 3 mistyped letters from 26% to 53%.
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Starling Releases Free Linux Desktop, Office Suite, Terminal, and SDK
Startup Starling has released the free Linux desktop environment Starling Desktop, the office suite Starling Office (including Writer, Slides, and Sheets), the Starling Terminal, and a self-developed SDK, and says these programs were built with AI assistance. Starling Desktop currently supports Ubuntu 26.04 LTS (x64) and WSL2 on Windows, and offers a traditional 2D interface, an experimental 3D desktop, and a separate workspace for agents; Starling Office runs in the browser or on Linux, Mac, and Windows (WSL2), and uses a ribbon interface similar to Microsoft Office. The Starling SDK uses Swift to write applications, reuses Flutter's C core engine on the desktop side, and uses Swift WebAssembly and the skwasm renderer on the web side; the related software and source code can be downloaded on GitHub.
S (x64) and WSL2 on Windows, and offers a traditional 2D interface, an experimental 3D desktop, and a separate workspace for agents; Starling Office runs in the browser or on Linux, Mac, and Windows (WSL2), and uses a ribbon interface similar to Microsoft Office. The Starling SDK uses Swift to write applications, reuses Flutter's C core engine on the desktop side, and uses Swift WebAssembly and the skwasm renderer on the web side; the related software and source code can be downloaded on GitHub.
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