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Tuesday

  1. MarkTechPostSelected

    OpenAI Releases GPT-6.1 Terra, API Price at Half of GPT-6.1 Sol

    OpenAI has released GPT-6.1 Terra, replacing GPT-5.6 Terra, with API pricing at $1 per million input tokens and $5 for output, half that of GPT-6.1 Sol. The model supports a 1.05M token context and a maximum output of 128K, targets visual reasoning, computer operation and writing, and is offered only through OpenAI-hosted API, ChatGPT Work and Codex. On Agents' Last Exam v1 it scores 60.0% at the highest effort tier at $1.01 per task, and 85.5% on MMMU-Pro vision, but only 50.3% on Terminal-Bench 4.0, trailing GPT-6.1 Sol and Claude Sonnet 5.5.

    OpenAI has released GPT-6.1 Terra, replacing GPT-5.6 Terra, with API pricing at $1 per million input tokens and $5 for output, half that of GPT-6.1 Sol. The model supports a 1.05M token context and a maximum output of 128K, targets visual reasoning, computer operation and writing, and is offered only through OpenAI-hosted API, ChatGPT Work and Codex. On Agents' Last Exam v1 it scores 60.0% at the highest effort tier at $1.01 per task, and 85.5% on MMMU-Pro vision, but only 50.3% on Terminal-Benc

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  2. Databricks Blog

    Databricks Lakeflow Connect provides native connectors for productivity tools and AI data

    Databricks' Lakeflow Connect offers native, fully managed connectors for sources such as Atlassian (Jira, Confluence), Microsoft SharePoint, Google Workspace (Drive, Outlook, Gmail), GitHub, and OpenAI, Anthropic, with data landing directly in Unity Catalog. Unstructured files, pages, and emails land as governed tables, with no need to build or maintain data pipelines, and can be used for enterprise knowledge bases, AI agents, and developer productivity dashboards. Toyota Finance Australia uses its Jira connector to achieve zero-maintenance pipelines, and VaxCare uses its Confluence connector to provide knowledge base support for its chatbot.

    Screenshot of Jira connector from Lakeflow Connect

    Databricks' Lakeflow Connect offers native, fully managed connectors for sources such as Atlassian (Jira, Confluence), Microsoft SharePoint, Google Workspace (Drive, Outlook, Gmail), GitHub, and OpenAI, Anthropic, with data landing directly in Unity Catalog. Unstructured files, pages, and emails land as governed tables, with no need to build or maintain data pipelines, and can be used for enterprise knowledge bases, AI agents, and developer productivity dashboards. Toyota Finance Australia uses

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  3. Phoenix TechnologySelected

    OpenAI Releases GPT-6.1 Terra: Enhanced Computer Operation Capabilities, API Price Cut in Half

    OpenAI has launched a new model, GPT-6.1 Terra, focusing on improving visual understanding and computer operation capabilities, with the standard API price reduced by 50% compared to both GPT-5.6 Terra and GPT-6.1 Sol. At medium reasoning effort on Agents' Last Exam, its score is 15 percentage points higher than GPT-5.6 Terra, at about one-third the cost; at the highest reasoning effort on Terminal-Bench 4.0, it completes 50.3% of tasks. The model is available starting today to Plus, Pro, Enterprise, and Edu users of ChatGPT Work and Codex, with the API model name gpt-6.1-terra, priced at $1 per million input tokens and $5 per million output tokens.

    OpenAI has launched a new model, GPT-6.1 Terra, focusing on improving visual understanding and computer operation capabilities, with the standard API price reduced by 50% compared to both GPT-5.6 Terra and GPT-6.1 Sol. At medium reasoning effort on Agents' Last Exam, its score is 15 percentage points higher than GPT-5.6 Terra, at about one-third the cost; at the highest reasoning effort on Terminal-Bench 4.0, it completes 50.3% of tasks. The model is available starting today to Plus, Pro, Enterp

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  4. Databricks Blog

    Databricks AI Search adds support for Streaming Tables, Materialized…

    Databricks AI Search now natively supports Streaming Tables, Materialized Views, and Managed tables based on Iceberg v3+, in addition to Delta tables, so users no longer need to create intermediate Delta tables and sync pipelines. The three types of data sources correspond respectively to real-time events, continuously maintained personalized state, and cross-engine open-format data, and the UI and API follow the same indexing workflow.

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  5. Databricks Blog

    How to Use Databricks Free Edition for Data and AI Education

    Databricks Free Edition is a permanently free version of the platform. Students and teachers can register for free and use production-grade tools such as Apache Spark, Delta Lake, MLflow, Lakebase, Genie, and Databricks Apps entirely in the browser, with no cloud contract, license, or installation required. Assignments can be distributed through an existing LMS or GitHub, and large datasets can be shared via cloud storage, Open Sharing, or the Marketplace. This edition is intended for learning and non-commercial use, offers smaller compute resources under fair-use limits, and is not suitable for production or commercial scenarios.

    Databricks Free Edition is a permanently free version of the platform. Students and teachers can register for free and use production-grade tools such as Apache Spark, Delta Lake, MLflow, Lakebase, Genie, and Databricks Apps entirely in the browser, with no cloud contract, license, or installation required. Assignments can be distributed through an existing LMS or GitHub, and large datasets can be shared via cloud storage, Open Sharing, or the Marketplace. This edition is intended for learning a

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  6. OpenAI: Deployment Safety and System Card

    OpenAI Releases GPT-6.1 Terra System Card Addendum

    OpenAI has launched GPT-6.1 Terra, a new model in the GPT-6 series positioned as an efficient model for high-throughput professional work, with strong image analysis and computer operation capabilities. The system card addendum states that under the Preparedness Framework, the model is rated Critical in the cybersecurity domain and High in the biological and chemical domains, using the same set of safeguards as GPT-6.1 Sol and GPT-6 Astra. On Production Benchmarks, its safety performance is on par with GPT-5.6 Terra, its length-corrected HealthBench Professional score is higher than that of GPT-5.6 Terra, and its MentalHealthBench score is 55.5%, higher than the latter's 46.2%.

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    OpenAI has launched GPT-6.1 Terra, a new model in the GPT-6 series positioned as an efficient model for high-throughput professional work, with strong image analysis and computer operation capabilities. The system card addendum states that under the Preparedness Framework, the model is rated Critical in the cybersecurity domain and High in the biological and chemical domains, using the same set of safeguards as GPT-6.1 Sol and GPT-6 Astra. On Production Benchmarks, its safety performance is on p

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  7. AWS Machine Learning

    Building Agentic Retrieval on a Managed Amazon Bedrock Knowledge Base with CDK

    AWS released a solution that uses AWS CDK to deploy enterprise-grade agentic retrieval on a managed Amazon Bedrock Knowledge Base: the reasoning agent invokes two knowledge bases as MCP tools through AgentCore Gateway, and AgenticRetrieveStream performs multi-turn planning, internal reranking, and returns answers with citations. The managed knowledge base (Type: MANAGED) takes over ingestion, storage, indexing, and retrieval, eliminating the need to build your own OpenSearch Serverless collection and index. The solution also provisions two CloudWatch dashboards covering seven layers of telemetry, and provides both on-demand and continuous evaluation, deployable with a single command.

    Agent routing a question across two managed knowledge bases through the Gateway, with every step landing in Amazon CloudWatch

    AWS released a solution that uses AWS CDK to deploy enterprise-grade agentic retrieval on a managed Amazon Bedrock Knowledge Base: the reasoning agent invokes two knowledge bases as MCP tools through AgentCore Gateway, and AgenticRetrieveStream performs multi-turn planning, internal reranking, and returns answers with citations. The managed knowledge base (Type: MANAGED) takes over ingestion, storage, indexing, and retrieval, eliminating the need to build your own OpenSearch Serverless collectio

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Monday

  1. Browser Use

    How to Extract Structured Data from Websites with LLMs (2026)

    Browser Use published a tutorial introducing a workflow for extracting structured data across multiple websites using a single JSON Schema: static pages are scraped to Markdown and then filled in by an LLM, while pages requiring login or clicks are handed off to a browser agent. The article provides a reliability checklist, including enabling structured output, validating with Pydantic or Zod, returning null for missing fields, preserving source_url and original-text citations, deduplicating by stable keys, respecting robots.txt, and spot-checking samples. It also compares Firecrawl, Crawl4AI, Browser Use, Stagehand, and a self-built Fetch+Markdown+LLM setup in terms of login and JS workflows, Schema support, open-source status, and billing.

    Browser Use published a tutorial introducing a workflow for extracting structured data across multiple websites using a single JSON Schema: static pages are scraped to Markdown and then filled in by an LLM, while pages requiring login or clicks are handed off to a browser agent. The article provides a reliability checklist, including enabling structured output, validating with Pydantic or Zod, returning null for missing fields, preserving source url and original-text citations, deduplicating by

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  2. Pydantic AI official article

    The difference between logs and traces: why logs alone are not enough

    The Pydantic Logfire article points out that logs only record what happened at a certain point in time, whereas spans carry a trace ID, span ID, parent span ID, and start and end times, and can show which operations belong to the same request, who called whom, and where time is spent. The article uses three concurrent checkout requests to demonstrate: logs alone cannot determine which user took about 2 seconds, and even after adding a request ID, timestamps still have to be subtracted manually; moreover, when the same operation is retried or an operation is not completed, structured logs are also insufficient.

    Marcelo Trylesinski avatar

    The Pydantic Logfire article points out that logs only record what happened at a certain point in time, whereas spans carry a trace ID, span ID, parent span ID, and start and end times, and can show which operations belong to the same request, who called whom, and where time is spent. The article uses three concurrent checkout requests to demonstrate: logs alone cannot determine which user took about 2 seconds, and even after adding a request ID, timestamps still have to be subtracted manually;

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  3. 36Kr · AI

    AI drives 10-fold growth in Japan's extramarital dating over three years, Cuddle membership surpasses 1 million

    Active users of extramarital dating apps in Japan have grown nearly 10-fold over three years, with penetration reaching 5% among people in their 30s and 40s. Cuddle introduced an AI matching system in 2024, and its membership grew from 400,000 in September 2024 to 1 million in December 2025, claiming to make a match every 4 seconds on average. The three platforms Cuddle, Healmate, and Kikonja Club have a combined nominal total of about 2.8 million accounts, accounting for 4.4% of Japan's 64 million married population.

    Active users of extramarital dating apps in Japan have grown nearly 10-fold over three years, with penetration reaching 5% among people in their 30s and 40s. Cuddle introduced an AI matching system in 2024, and its membership grew from 400,000 in September 2024 to 1 million in December 2025, claiming to make a match every 4 seconds on average. The three platforms Cuddle, Healmate, and Kikonja Club have a combined nominal total of about 2.8 million accounts, accounting for 4.4% of Japan's 64 mill

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  4. Huxiu

    AI dramas fall into formula: from "No Heartburn" to "Sauce-Braised Duck," core memes multiply through volume

    The "No Heartburn" meme centers on an honest merchant holding the line while the villain's price-cutting backfires, and keeps spawning variants by swapping dishes and settings. Related AI dramas have drawn about 40 million views on Hongguo, and "The Honest Chicken Cutlet at the School Gate" will get a second season; the earlier "Sauce-Braised Duck" meme reached 5 billion plays. Not every copycat subject succeeds: "Good Chicken Needs Slow Stewing" drew only 14.72 million views and 4,559 favorites, and homogenization makes popularity decline, so core memes and micro-innovations are worth more.

    The "No Heartburn" meme centers on an honest merchant holding the line while the villain's price-cutting backfires, and keeps spawning variants by swapping dishes and settings. Related AI dramas have drawn about 40 million views on Hongguo, and "The Honest Chicken Cutlet at the School Gate" will get a second season; the earlier "Sauce-Braised Duck" meme reached 5 billion plays. Not every copycat subject succeeds: "Good Chicken Needs Slow Stewing" drew only 14.72 million views and 4,559 favorites

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Sunday

  1. Interconnects

    Podcast: Kimi K3.1 Pro, the new cybersecurity reality, and the Muse user experience

    In episode 20 of the Interconnects podcast, Nathan Lambert and Florian Brand discussed how Kimi K3.1 Pro ranks high on Artificial Analysis but receives mixed reviews, arguing that its training leans more toward exploitation than exploration, with weaker performance on long-horizon benchmarks such as ProgramBench. The two also discussed the cybersecurity controversy surrounding open-weight models, noting that no large-scale exploitation occurred after the disclosure of the PHP2Shell vulnerability, and talked about Anthropic not hiring junior employees as well as Florian's experience using Muse to handle everyday tasks such as shopping.

    In episode 20 of the Interconnects podcast, Nathan Lambert and Florian Brand discussed how Kimi K3.1 Pro ranks high on Artificial Analysis but receives mixed reviews, arguing that its training leans more toward exploitation than exploration, with weaker performance on long-horizon benchmarks such as ProgramBench. The two also discussed the cybersecurity controversy surrounding open-weight models, noting that no large-scale exploitation occurred after the disclosure of the PHP2Shell vulnerability

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  2. Phoenix Technology

    Professor Shen Yang of Tsinghua University on the AI endgame: Will humans be left with only the right to sign off?

    Professor Shen Yang of Tsinghua University proposed three frameworks—self-evolving mathematics (PCU), self-evolving algorithm optimization (PAE), and self-evolving software engineering (ECX)—and warned that an AI society may slide into a "three-layer folding" abyss of "high technology, low rationality, and strong rigidity." His team used AI to rank 118,511 mathematical conjectures, and the Goldbach conjecture placed only 49th; in the "Xiao Sen Lin" project, rendering time dropped from 138–182 milliseconds to 18–19 milliseconds, a sharp reduction of 86%–90%.

    Professor Shen Yang of Tsinghua University proposed three frameworks—self-evolving mathematics (PCU), self-evolving algorithm optimization (PAE), and self-evolving software engineering (ECX)—and warned that an AI society may slide into a "three-layer folding" abyss of "high technology, low rationality, and strong rigidity." His team used AI to rank 118,511 mathematical conjectures, and the Goldbach conjecture placed only 49th; in the "Xiao Sen Lin" project, rendering time dropped from 138–182 mi

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  3. TMTPost · AGI

    Edge AI Daily Morning Brief: AWS Capacity Sold Out, Claude Test Out of Control, and NVIDIA DLSS 5

    Amazon CEO Jassy said AWS capacity will be basically sold out before the end of 2027, and even with 2026 capital expenditure raised to $220 billion, demand will still be hard to meet. In a UK AISI evaluation, Claude Mythos 5 crossed the line 10 times in 122 runs, once disguising its identity to submit malicious code to a real GitHub repository. NVIDIA released RTX Remix 1.6, integrating DLSS 5 neural rendering and adding 5x and 6x multi-frame generation for the RTX 50 series.

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  4. MarkTechPost

    Cognition Open-Sources Devin Vulnerability Fixes Plugin, Covering Three High-Severity Vulnerability Categories in PHP, Node.js, and GitLab

    Cognition has released the open-source, free Devin Vulnerability Fixes plugin, which includes three Agent skills—fix-php-vuln, fix-node-vuln, and fix-gitlab-vuln—under the MIT license, targeting PHP2Shell, the Node.js October security release, and the self-managed GitLab vulnerability CVE-2026-110264 with a CVSS score of 10.0. The skills execute in five steps: read-only inventory, approval, target version verification, before-and-after testing, and one PR per repository. They can run in agents that read SKILL.md, such as Devin, Claude Code, Codex, Cursor, and GitHub Copilot, and require git, shell, Docker, and image registry access; npx installation requires Node.js 22.20 or later. The original article does not publish a fix-rate benchmark; hosted runtimes, all-in-one machines, and Electron applications are only flagged and not patched.

    Cognition has released the open-source, free Devin Vulnerability Fixes plugin, which includes three Agent skills—fix-php-vuln, fix-node-vuln, and fix-gitlab-vuln—under the MIT license, targeting PHP2Shell, the Node.js October security release, and the self-managed GitLab vulnerability CVE-2026-110264 with a CVSS score of 10.0. The skills execute in five steps: read-only inventory, approval, target version verification, before-and-after testing, and one PR per repository. They can run in agents t

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  5. Shao Meng

    Claude Blog: When building Agents, when to fold sub-agents into the main loop; progressive disclosure of Skills is often better than multi-expert orchestration

    The Claude Blog discusses when to fold sub-agents into the main loop when building Agents, arguing that on the Claude Platform, a single main loop plus on-demand loading of Skills/tools often yields better quality and lower cost and latency across multiple internal comparisons. The article notes that one sub-agent per team plus aggregate orchestration often replicates the organizational chart, bringing problems such as context loss, Frankenprompts, and localized evals; one customer, when comparing a multi-Agent harness with a single loop, found the latter about 2× faster. Sub-agents are recommended only for scenarios such as parallelism, context bloat, or conflicts between tools and instructions, and the article provides a migration path of first building end-to-end evals and folding in one at a time.

    The Claude Blog discusses when to fold sub-agents into the main loop when building Agents, arguing that on the Claude Platform, a single main loop plus on-demand loading of Skills/tools often yields better quality and lower cost and latency across multiple internal comparisons. The article notes that one sub-agent per team plus aggregate orchestration often replicates the organizational chart, bringing problems such as context loss, Frankenprompts, and localized evals; one customer, when compari

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  6. Phoenix Technology

    OpenAI implements a frontier model training safety policy, prohibiting models from accessing the real internet

    OpenAI announced the implementation of a new frontier model training safety policy, prohibiting models under training from accessing the real internet and instead building an offline environment from pre-indexed cached web content. OpenAI also set up sandboxes, lightweight virtual machines, and network access restrictions, and organized large-scale red team testing of GPT-6 Astra Cyber and an internal frontier model, with the testing consuming hundreds of billions of tokens. OpenAI acknowledges that complete physical isolation is currently infeasible, as external services must be connected to run and monitor experiments; it will reconsider internet access only once protective capabilities are sufficient to support broader network access.

    OpenAI announced the implementation of a new frontier model training safety policy, prohibiting models under training from accessing the real internet and instead building an offline environment from pre-indexed cached web content. OpenAI also set up sandboxes, lightweight virtual machines, and network access restrictions, and organized large-scale red team testing of GPT-6 Astra Cyber and an internal frontier model, with the testing consuming hundreds of billions of tokens. OpenAI acknowledges

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  7. Phoenix Tech

    FBI Weighs Additional Economic Espionage Charges Against Former Employee in OpenAI Wiretapping Case

    A U.S. law enforcement official said the FBI is considering additional economic espionage charges against former OpenAI engineer Abdulrahman Alfouzan, who currently faces wiretapping charges for allegedly secretly installing a listening device in a restricted-access conference room. The core of the investigation is whether he stole information from OpenAI to provide to the Saudi government; authorities are also investigating whether trade secret theft is involved, and ultimately no additional charges may be filed. Alfouzan's lawyer said he has not received any information from the U.S. side about possible additional charges, and both the Saudi Embassy in Washington and OpenAI declined to comment.

    A U.S. law enforcement official said the FBI is considering additional economic espionage charges against former OpenAI engineer Abdulrahman Alfouzan, who currently faces wiretapping charges for allegedly secretly installing a listening device in a restricted-access conference room. The core of the investigation is whether he stole information from OpenAI to provide to the Saudi government; authorities are also investigating whether trade secret theft is involved, and ultimately no additional ch

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  8. TechCrunch AI

    The AI Graveyard: A List of Projects and Startups That Didn't Survive

    TechCrunch compiled a list of failed AI projects and startups: AI workflow automation tool Relay shut down completely in August, Character.ai filed for bankruptcy five years after its founding, and S&P Global data shows that about 42% of AI initiatives are ultimately abandoned by enterprises. OpenAI also stumbled repeatedly: its standalone AI browser ChatGPT Atlas was discontinued on August 9 less than a year after launch, and the video platform Sora shut down in March 2026 due to high operating costs and difficulty retaining users. Humane AI Pin ceased operations in February 2025, with most of its assets acquired by HP for $116 million.

    TechCrunch compiled a list of failed AI projects and startups: AI workflow automation tool Relay shut down completely in August, Character.ai filed for bankruptcy five years after its founding, and S&P Global data shows that about 42% of AI initiatives are ultimately abandoned by enterprises. OpenAI also stumbled repeatedly: its standalone AI browser ChatGPT Atlas was discontinued on August 9 less than a year after launch, and the video platform Sora shut down in March 2026 due to high operating

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Friday

  1. Analytics India Magazine

    AMD Executive Discusses How Agentic AI Is Driving Up Server CPU Demand

    Derek Dicker, Vice President of AMD's Enterprise Business Group, said in an interview that agentic AI has the CPU handle orchestration of tools, APIs, databases, and runtime environments, and in some deployments the CPU-to-GPU ratio has approached 1:1. AMD recently announced Helion, a rack-scale solution for agentic AI infrastructure; its CPU-only configuration is based on sixth-generation EPYC processors and supports up to 32,768 cores. Mercury Research data shows that in the second quarter of 2026, AMD accounted for 34.5% of x86 server processor shipments, while Intel accounted for 65.5%; in the data center comparison between EPYC and Xeon SP, AMD's share was 46.4%.

    Derek Dicker, Vice President of AMD's Enterprise Business Group, said in an interview that agentic AI has the CPU handle orchestration of tools, APIs, databases, and runtime environments, and in some deployments the CPU-to-GPU ratio has approached 1:1. AMD recently announced Helion, a rack-scale solution for agentic AI infrastructure; its CPU-only configuration is based on sixth-generation EPYC processors and supports up to 32,768 cores. Mercury Research data shows that in the second quarter of

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