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Databricks announces general availability of Lakebase Postgres on Google Cloud

Databricks announced that Lakebase Postgres, its fully managed serverless Postgres database, is now generally available on Google Cloud. According to the Databricks Blog, the service supports automatic scaling, scale-to-zero, second-level branching, and point-in-time recovery; it shares the same storage layer with analytical data, eliminating the need to maintain ETL pipelines, and provides unified governance through Unity Catalog. In terms of availability, multi-availability-zone endpoints offer a 99.99% SLA, while single availability zones offer 99.9%. Lakebase Search delivers vector and full-text search throughput 2 times that of pgvector at 4 times lower cost. There is currently no information on further developments.

Databricks announces general availability of Lakebase Postgres on Google Cloud

Databricks announced that Lakebase Postgres, its fully managed serverless Postgres database, is now generally available on Google Cloud. According to the Databricks Blog, the service supports automatic scaling, scale-to-zero, second-level branching, and point-in-time recovery; it shares the same storage layer with analytical data, eliminating the need to maintain ETL pipelines, and provides unified governance through Unity Catalog. In terms of availability, multi-availability-zone endpoints offer a 99.99% SLA, while single availability zones offer 99.9%. Lakebase Search delivers vector and full-text search throughput 2 times that of pgvector at 4 times lower cost. There is currently no information on further developments.

View story:Databricks announces general availability of Lakebase Postgres on Google Cloud

Google DeepMind expands PlanAssist to 12 more local planning authorities in England

The AI planning tool PlanAssist, developed by Google DeepMind in partnership with the UK government, will be expanded to 12 additional local planning authorities in England, bringing the total number of participants to 15. In early pilots in Barnet, Camden and Dorset, the tool cut the average time planning officers spent assessing household residential applications by 58%, and more than 70% of the generated reports were approved by officers without modification. Built on Gemini, PlanAssist verifies application materials and drawings, checks against heritage protection restrictions and local and national planning policy, reviews past objections, and drafts reports with citations, with the final decision still made by planning officers. The tool has been piloted since June and is now entering a 12-month Beta phase; if testing goes smoothly, the UK government plans to make it available to all local councils in England in 2027.

Google DeepMind releases the Gemini 4 Carbon model

On October 28, 2026, Google DeepMind released Gemini 4 Carbon, calling it its strongest model, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0, and scoring 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. The model is aimed at long-horizon engineering tasks and was first opened to some cybersecurity defenders, then expanded through the Fairwind Program, and afterward made available to developers, enterprises, and consumers. On October 29, Arena.ai said Gemini 4 Carbon ranked 2nd in Agent Arena with a net improvement of 16.2%, and was on the Pareto frontier for cost, time, and output tokens per task; the same day, Yuchen Jin cited Artificial Analysis data saying it was smarter than Opus 5.5 and cheaper per task, but noted that the evaluation used the Antigravity CLI. On October 30, the Artificial Analysis Intelligence Index showed it in first place with 59 points, 1 point higher than Claude Opus 5.5. On October 31, Perplexity CEO Aravind Srinivas shared early evaluations

Google receives first AI response from Project Suncatcher prototype satellite

On October 30, 2026, Google said it had received the first AI reply from the Project Suncatcher prototype satellite. The data-center-class TPU aboard the satellite ran the open Gemma 4 model in orbit; after the ground station uploaded a prompt, the model generated a haiku about space and transmitted it back to Earth as the satellite passed over Svalbard, Norway. The satellite launched on October 1 aboard SpaceX Transporter-18 and was built in partnership with Planet; it had previously completed power-on of the host machine and TPU tray as well as early machine learning tests. Google will next test how the TPU performs under orbital thermal stress and radiation exposure, paving the way for the goal of two interconnected satellites in 2027.

View story:Google receives the first AI response from the Project Suncatcher prototype satellite

Google announces that DevFest 2026 will be held from October 1 to December 31

Google Developer Groups announced that DevFest 2026 will be held from October 1 to December 31. According to information published on the Google Developers Blog, this year's DevFest covers 115 countries and more than 800 events, with nearly 1 million developers expected to participate. The events revolve around generative AI, autonomous agents, Google Cloud, Android, and the Web, under the theme of building, securing, and scaling in the age of agents. Local events are organized by communities in local languages, with Google Developer Experts and Google engineers participating as speakers.

View story:Google Developer Groups announced that DevFest 2026 will be held from October 1 to December 31

Google Research releases the SLEEP offline memory consolidation method

Google Research releases the SLEEP offline memory consolidation method. According to a report by IT Home · AI, Google's research team has released a continual learning study called the "Sleep Framework," which divides model operation into two phases, awake and sleep, to address the problem of large models having fixed knowledge and difficulty absorbing new information; during the sleep phase, short-term context is written into long-term parameters through parameter expansion, mixture-of-experts memory blocks, and knowledge distillation, and synthetic samples generated through "dreaming" are used for reinforcement learning. Experiments show that the framework can handle contexts of up to about 10 million tokens, that performance degradation is close to zero in multi-stage continual learning on unseen languages, and that it achieves higher accuracy on math benchmarks such as AIME 24/25. Phoenix Technology reported that the study was made public on October 27, and that the paper is titled "Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories." There are currently no subsequent developments.

Google DeepMind paper proposes CALLIOPE topic tree method

Google DeepMind published a paper proposing the CALLIOPE topic tree construction method: have a large language model tag each document with a small number of keywords and add noise, keep only the high-frequency keywords, and then have a large language model propose topics and classify documents based on them, thereby generating a clearer topic map under strict privacy constraints. Under the strictest privacy setting, human raters preferred CALLIOPE over the embedding-vector-based URANIA on all 3 datasets, with the highest ratio being 76% versus 18%. This method consumes 2 to 5 times as many tokens as URANIA. There are currently no follow-up developments.

Gemini Notebook to Launch Connectors, Initially Supporting Only Google Drive

Testing Catalog reports that Gemini Notebook is about to gain the Connectors feature, but initially only one connector, Google Drive, will be available. Users can search Google Drive files directly in chat and can disable the feature as needed; officials say the connector only retrieves workspace documents, and original files will not be modified. More Connectors are expected to be added later. There is currently no further development progress.

Google DeepMind expands PlanAssist to 12 more local planning authorities

The AI planning tool PlanAssist, developed by Google DeepMind in partnership with the UK government, will be expanded to 12 additional local planning authorities in England, bringing the total number of participants to 15. In early pilots in Barnet, Camden and Dorset, the tool cut the average time planning officers spent assessing household residential applications by 58%, and more than 70% of the generated reports were approved by officers without modification. Built on Gemini, PlanAssist verifies application materials and drawings, checks against heritage protection restrictions and local and national planning policy, reviews past objections, and drafts reports with citations, with the final decision still made by planning officers. The tool has been piloted since June and is now entering a 12-month Beta phase; if testing goes smoothly, the UK government plans to make it available to all local councils in England in 2027.

View story:Google DeepMind expands PlanAssist to 12 more local planning authorities in England

Google receives the first AI response from the Project Suncatcher prototype satellite

On October 30, 2026, Google said it had received the first AI reply from the Project Suncatcher prototype satellite. The data-center-class TPU aboard the satellite ran the open Gemma 4 model in orbit; after the ground station uploaded a prompt, the model generated a haiku about space and transmitted it back to Earth as the satellite passed over Svalbard, Norway. The satellite launched on October 1 aboard SpaceX Transporter-18 and was built in partnership with Planet; it had previously completed power-on of the host machine and TPU tray as well as early machine learning tests. Google will next test how the TPU performs under orbital thermal stress and radiation exposure, paving the way for the goal of two interconnected satellites in 2027.

Google Developer Groups announced that DevFest 2026 will be held from October 1 to December 31

Google Developer Groups announced that DevFest 2026 will be held from October 1 to December 31. According to information published on the Google Developers Blog, this year's DevFest covers 115 countries and more than 800 events, with nearly 1 million developers expected to participate. The events revolve around generative AI, autonomous agents, Google Cloud, Android, and the Web, under the theme of building, securing, and scaling in the age of agents. Local events are organized by communities in local languages, with Google Developer Experts and Google engineers participating as speakers.

Google Research releases SLEEP offline memory consolidation method

Google Research releases the SLEEP offline memory consolidation method. According to a report by IT Home · AI, Google's research team has released a continual learning study called the "Sleep Framework," which divides model operation into two phases, awake and sleep, to address the problem of large models having fixed knowledge and difficulty absorbing new information; during the sleep phase, short-term context is written into long-term parameters through parameter expansion, mixture-of-experts memory blocks, and knowledge distillation, and synthetic samples generated through "dreaming" are used for reinforcement learning. Experiments show that the framework can handle contexts of up to about 10 million tokens, that performance degradation is close to zero in multi-stage continual learning on unseen languages, and that it achieves higher accuracy on math benchmarks such as AIME 24/25. Phoenix Technology reported that the study was made public on October 27, and that the paper is titled "Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories." There are currently no subsequent developments.

View story:Google Research releases the SLEEP offline memory consolidation method

Google DeepMind paper proposes CALLIOPE topic tree method

Google DeepMind published a paper proposing the CALLIOPE topic tree construction method: have a large language model tag each document with a small number of keywords and add noise, keep only the high-frequency keywords, and then have a large language model propose topics and classify documents based on them, thereby generating a clearer topic map under strict privacy constraints. Under the strictest privacy setting, human raters preferred CALLIOPE over the embedding-vector-based URANIA on all 3 datasets, with the highest ratio being 76% versus 18%. This method consumes 2 to 5 times as many tokens as URANIA. There are currently no follow-up developments.

View story:Google DeepMind paper proposes CALLIOPE topic tree method

Google releases Gemini 4 Carbon model

On October 28, 2026, Google DeepMind released Gemini 4 Carbon, calling it its strongest model, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0, and scoring 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. The model is aimed at long-horizon engineering tasks and was first opened to some cybersecurity defenders, then expanded through the Fairwind Program, and afterward made available to developers, enterprises, and consumers. On October 29, Arena.ai said Gemini 4 Carbon ranked 2nd in Agent Arena with a net improvement of 16.2%, and was on the Pareto frontier for cost, time, and output tokens per task; the same day, Yuchen Jin cited Artificial Analysis data saying it was smarter than Opus 5.5 and cheaper per task, but noted that the evaluation used the Antigravity CLI. On October 30, the Artificial Analysis Intelligence Index showed it in first place with 59 points, 1 point higher than Claude Opus 5.5. On October 31, Perplexity CEO Aravind Srinivas shared early evaluations

View story:Google DeepMind releases the Gemini 4 Carbon model

Google releases world model Genie 4

On October 27, 2026, Google DeepMind released the world model Genie 4, focused on real-time interactive simulation and introducing native audio to the series for the first time. According to the official introduction, the model is built on capabilities from the Gemini family: Gemini Omni is responsible for making the simulation match the appearance and behavior of the real world, while other Gemini models provide the complex reasoning needed to maintain the simulation's coherence; users can control scenes in real time using natural language, and each world also instantly generates ambient sound, music, and interactive dialogue. The next day, Google DeepMind further explained that Gemini 3.8 Flash breaks down goals in a loop and checks frames to maintain causal coherence across multi-step events, and that Gemini 4 Argon has also been used to jointly simulate tasks with Gemini Robotics; the company said it remains an early experimental model, that object permanence and complex physical interactions still drift during long runs, that all generated content carries a SynthID watermark, and that it is currently being previewed to trusted testers first. On October 28, Ethan Mollick posted that Genie 4 can run DOOM, and tested it generating a game-like environment as it goes, saying the experience is not perfect. On October 29, Peter Yang used Genie 4 to build 5 worlds in real time, arguing that world models are the next big direction for AI after LLMs.