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Sunday

  1. Story1 reports · 1 facts

    Zihao launches a Rime engine-based iPhone input method on the App Store

    On October 20, 2026, the author released Zihao, an iPhone input method. The input method is now available on the App Store, is based on the Rime (Zhongzhou Yun) engine, and comes with Wusong Pinyin built in. According to a report by Shaoshupai the same day, its basic typing features are free and ad-free, and typed content is processed only on the phone; it supports importing Rime configuration folders, ZIP files, YAML files, or GitHub repositories and updating them automatically, and the lexicon can be synced to iCloud Drive in Rime format and merged with Mac Squirrel. The author says that in simulated tests, the one-pass accuracy rate for sentences with 2 wrong letters rose from 36% to 56%, and for 3 wrong letters from 26% to 53%. There are currently no subsequent developments.

    day, its basic typing features are free and ad-free, and typed content is processed only on the phone; it supports importing Rime configuration folders, ZIP files, YAML files, or GitHub repositories and updating them automatically, and the lexicon can be synced to iCloud Drive in Rime format and merged with Mac Squirrel. The author says that in simulated tests, the one-pass accuracy rate for sentences with 2 wrong letters rose from 36% to 56%, and for 3 wrong letters from 26% to 53%. There are

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  2. Story1 reports · 1 facts

    heise online launches three-day hands-on RAG workshop

    heise online's iX is launching a three-day online interactive workshop, "Implementing AI Correctly with Your Own Data: Evaluating and Applying RAG, Embeddings and Retrieval Architectures," which explains RAG architectures and technical foundations and covers both cloud and on-premise deployment. The course is taught by Gregor Biswanger, a Microsoft Azure AI MVP and GitHub Copilot collaborator, and covers prompt optimization, integrating the OpenAI and Azure OpenAI APIs, and developing and testing self-built 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; it is suitable for both beginners and advanced participants. There are currently no subsequent developments.

    which explains RAG architectures and technical foundations and covers both cloud and on-premise deployment. The course is taught by Gregor Biswanger, a Microsoft Azure AI MVP and GitHub Copilot collaborator, and covers prompt optimization, integrating the OpenAI and Azure OpenAI APIs, and developing and testing self-built 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; it is suitable for both beginners and advanc

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  3. Story1 reports · 1 facts

    Tessl Code Review supports reading the full PR and publishing reviews

    Tessl Code Review now supports reading the full pull request rather than only viewing an isolated diff, and publishes a review result based on it. According to a tutorial published on the Tessl product and engineering blog on October 19, 2026, users can set up the agentic code review for a GitHub repository in about 5 minutes: it scores against the standards the team defines in the repository and publishes the review result. Reviews can be set to run only when a PR is opened or requested, or after every update; the handling can be comment-only, or approve/request changes, and supports automatic approval once all Tessl discussion threads are resolved. The tutorial also notes that the relevant changes only take effect for new review runs. There are currently no subsequent developments.

    lishes a review result based on it. According to a tutorial published on the Tessl product and engineering blog on October 19, 2026, users can set up the agentic code review for a GitHub repository in about 5 minutes: it scores against the standards the team defines in the repository and publishes the review result. Reviews can be set to run only when a PR is opened or requested, or after every update; the handling can be comment-only, or approve/request changes, and supports automatic approval

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  4. Story1 reports · 1 facts

    Author uses Codex and DeepSeek-V4.1-Flash-1017 to generate a four-quadrant sticky note app

    On October 25, 2026, an author used the Chinese-localized version of Codex, DeepSeek-V4.1-Flash-1017, and the Hermes agent to generate a four-quadrant sticky note app from a single prompt, and automatically uploaded and published it to GitHub. According to the same report, the finished product supports Chinese and English, a time capsule, free resizing of sticky notes, and local saving, and is packaged as a single-file portable app; the first version generated a static index.html in about six to seven minutes, and subsequent iterations changed it to a sticky note format, with the whole process costing a little over four yuan. There are currently no subsequent independent development facts.

    d version of Codex, DeepSeek-V4.1-Flash-1017, and the Hermes agent to generate a four-quadrant sticky note app from a single prompt, and automatically uploaded and published it to GitHub. According to the same report, the finished product supports Chinese and English, a time capsule, free resizing of sticky notes, and local saving, and is packaged as a single-file portable app; the first version generated a static index.html in about six to seven minutes, and subsequent iterations changed it to

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  5. Story1 reports · 1 facts

    Replit CEO proposes a vision for the future of work at "autonomous companies"

    At TechCrunch Disrupt, Replit CEO Amjad Masad laid out a vision for the future of work at "autonomous companies": AI agents take on most of the foundational work, while humans retain judgment and taste. He used Replit's customer service workflow as an example of this idea: tickets are first reviewed by an agent that examines GitHub issues, the codebase, and Linear tickets to complete initial triage; once a human decides on a fix, another agent is deployed with a single click to carry it out. At present, this vision remains at the level of public remarks, and there is no qualified factual evidence of subsequent implementation progress.

    e foundational work, while humans retain judgment and taste. He used Replit's customer service workflow as an example of this idea: tickets are first reviewed by an agent that examines GitHub issues, the codebase, and Linear tickets to complete initial triage; once a human decides on a fix, another agent is deployed with a single click to carry it out. At present, this vision remains at the level of public remarks, and there is no qualified factual evidence of subsequent implementation progress.

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Saturday

  1. Story1 reports · 1 facts

    Birla AI Labs releases time series foundation model Yug-1

    Birla AI Labs has released the time series foundation model Yug-1. The model is used to predict outcomes, identify patterns, and detect anomalies in interconnected datasets. According to Analytics India Magazine, Yug-1 outperforms other first-generation TSMFs on the GiftEval benchmark and is also competitive with newer-generation models, taking less than six months from training to deployment. Yug-1 has been made available to developers and researchers for testing on GitHub and Hugging Face. There are currently no subsequent development facts.

    tion model Yug-1. The model is used to predict outcomes, identify patterns, and detect anomalies in interconnected datasets. According to Analytics India Magazine, Yug-1 outperforms other first-generation TSMFs on the GiftEval benchmark and is also competitive with newer-generation models, taking less than six months from training to deployment. Yug-1 has been made available to developers and researchers for testing on GitHub and Hugging Face. There are currently no subsequent development facts.

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