Explore the companies, models, products and research shaping the future of AI.
1,096 moments trackedAn independent Schlotzsky’s and Cinnabon franchisee cut menu repricing time by 95% and built a workflow that could free up $100K in cash flow.
An independent Schlotzsky’s and Cinnabon franchisee cut menu repricing time by 95% and built a workflow that could free up $100K in cash flow.
OpenAI is acquiring Orchestra Bio to bring AI-powered operations to life sciences R&D, helping biopharma teams accelerate drug development and advance new treatments.
OpenAI is acquiring Orchestra Bio to bring AI-powered operations to life sciences R&D, helping biopharma teams accelerate drug development and advance new treatments.
NVIDIA Cosmos3-H-Dreams: Bringing Clinical Surgery to Life Through Generative Simulation — announced via Hugging Face on October 20, 2026.
NVIDIA Cosmos3-H-Dreams: Bringing Clinical Surgery to Life Through Generative Simulation — announced via Hugging Face on October 20, 2026.
FairStress: A deeper evaluation of fairness in LLM decision-making — announced via Hugging Face on October 20, 2026.
FairStress: A deeper evaluation of fairness in LLM decision-making — announced via Hugging Face on October 20, 2026.
ALTK-Evolve: How learned memory tops the τ²-bench leaderboard on customer service tasks without a frontier model — announced via Hugging Face on October 20, 2026.
ALTK-Evolve: How learned memory tops the τ²-bench leaderboard on customer service tasks without a frontier model — announced via Hugging Face on October 20, 2026.
Hugging Face ❤️ TorchTPU: TPUs, Natively — announced via Hugging Face on October 20, 2026.
Hugging Face ❤️ TorchTPU: TPUs, Natively — announced via Hugging Face on October 20, 2026.
AnyLanguageModel 1.0: One Year of Local Models on Apple Platforms — announced via Hugging Face on October 20, 2026.
AnyLanguageModel 1.0: One Year of Local Models on Apple Platforms — announced via Hugging Face on October 20, 2026.
MPS in PyTorch 2.15 — announced via Hugging Face on October 20, 2026.
MPS in PyTorch 2.15 — announced via Hugging Face on October 20, 2026.
AI agents are most useful when they can act on the real work happening across your organization. Work management involves complex, cross-functional tasks such as updating project timelines, reassigning tasks, and surfacing status. AI agents can support you with these without anyone switching tools. Smartsheet , the work management platform for managing projects, portfolios, and operations, has launched a generally available Model Context Protocol (MCP) Server. With the Smartsheet MCP Server , AI assistants can read, write, and manage Smartsheet data through a standardized protocol. The protocol spans more than 40 tools for exploring, creating, and managing sheets, rows, columns, and automations. You can update task statuses, create rows, and modify project structure directly through natural language. In this post, we walk through two ways you can connect it to your existing workflows. Amazon Quick is an agentic AI workspace built for business users, project managers, and operations te…
AI agents are most useful when they can act on the real work happening across your organization. Work management involves complex, cross-functional tasks such as updating project timelines, reassigning tasks, and surfacing status. AI agents can support you with these without anyone switching tools. Smartsheet , the work management platform for managing projects, portfolios, and operations, has launched a generally available Model Context Protocol (MCP) Server. With the Smartsheet MCP Server , AI assistants can read, write, and manage Smartsheet data through a standardized protocol. The protocol spans more than 40 tools for exploring, creating, and managing sheets, rows, columns, and automations. You can update task statuses, create rows, and modify project structure directly through natural language. In this post, we walk through two ways you can connect it to your existing workflows. Amazon Quick is an agentic AI workspace built for business users, project managers, and operations te…
Giving supply chain teams direct access to the systems behind their decisions. Every supply chain generates constant signals: orders in approval, shifting lead times, slowing ports, drifting prices, disruptions. The problem isn’t scarce data. It’s the distance between the data and the decision-maker. Answering “which top-revenue SKUs are exposed to the current port closure, and what will it cost?” used to mean knowing which system held each piece and stitching them together by hand while the domain expert waited. Could that manager instead ask in plain language and get an actionable, cross-system answer in seconds? Amazon Bedrock AgentCore is the intelligence layer, exposing enterprise Model Context Protocol (MCP) servers as a composable tool layer. Amazon Quick turns that into action for personas with no setup. Answers come from live systems of record and grounded documents, not model recall, and every change waits for a named human. In this post, we walk through this pattern end to…
Giving supply chain teams direct access to the systems behind their decisions. Every supply chain generates constant signals: orders in approval, shifting lead times, slowing ports, drifting prices, disruptions. The problem isn’t scarce data. It’s the distance between the data and the decision-maker. Answering “which top-revenue SKUs are exposed to the current port closure, and what will it cost?” used to mean knowing which system held each piece and stitching them together by hand while the domain expert waited. Could that manager instead ask in plain language and get an actionable, cross-system answer in seconds? Amazon Bedrock AgentCore is the intelligence layer, exposing enterprise Model Context Protocol (MCP) servers as a composable tool layer. Amazon Quick turns that into action for personas with no setup. Answers come from live systems of record and grounded documents, not model recall, and every change waits for a named human. In this post, we walk through this pattern end to…
GPT-OSS-20B is an open-weight reasoning model that thinks in English. Ask it to reason in French, German, Spanish, or Italian and it acknowledges the instruction, then produces its chain-of-thought in English anyway. On a 50-example multilingual reasoning test, the base model reasoned in the correct target language only 22 percent of the time. That 22 percent was almost entirely the English examples (English 100 percent, French 0 percent, German 10 percent, Spanish 0 percent, Italian 0 percent). For teams deploying reasoning models for non-English users, that gap matters. A user who asks a question in French expects the model to reason and answer in French, not to switch to English mid-thought. Closing that gap is a fine-tuning problem. It raises a question every team faces once they start customizing open-weight models: supervised fine-tuning (SFT), reinforcement learning with Group Relative Policy Optimization (GRPO), or both? In this post, we walk through how we fine-tuned GPT-OSS-…
GPT-OSS-20B is an open-weight reasoning model that thinks in English. Ask it to reason in French, German, Spanish, or Italian and it acknowledges the instruction, then produces its chain-of-thought in English anyway. On a 50-example multilingual reasoning test, the base model reasoned in the correct target language only 22 percent of the time. That 22 percent was almost entirely the English examples (English 100 percent, French 0 percent, German 10 percent, Spanish 0 percent, Italian 0 percent). For teams deploying reasoning models for non-English users, that gap matters. A user who asks a question in French expects the model to reason and answer in French, not to switch to English mid-thought. Closing that gap is a fine-tuning problem. It raises a question every team faces once they start customizing open-weight models: supervised fine-tuning (SFT), reinforcement learning with Group Relative Policy Optimization (GRPO), or both? In this post, we walk through how we fine-tuned GPT-OSS-…
In quality management for regulated industries, the institutional memory that resolves a problem is almost always buried somewhere in a customer’s history. A similar defect investigated last year, a Corrective and Preventive Action (CAPA) that fixed the root cause, an audit finding that flagged the same gap. That knowledge sits in billions of documents no human can search by semantic similarity across languages and formats, so every investigation starts from scratch. Octave Reliance has helped each organization collect documents across multiple systems to help them search more easily and keep track of everything they have. The Octave Reliance team saw unlocking this history as its single largest opportunity to help customers work faster and stay compliant. The obstacle was equally clear: complaint records, audit findings, and corrective actions carry the platform’s strictest isolation requirements, and those were exactly the records the AI would need to read. In a platform serving ove…
In quality management for regulated industries, the institutional memory that resolves a problem is almost always buried somewhere in a customer’s history. A similar defect investigated last year, a Corrective and Preventive Action (CAPA) that fixed the root cause, an audit finding that flagged the same gap. That knowledge sits in billions of documents no human can search by semantic similarity across languages and formats, so every investigation starts from scratch. Octave Reliance has helped each organization collect documents across multiple systems to help them search more easily and keep track of everything they have. The Octave Reliance team saw unlocking this history as its single largest opportunity to help customers work faster and stay compliant. The obstacle was equally clear: complaint records, audit findings, and corrective actions carry the platform’s strictest isolation requirements, and those were exactly the records the AI would need to read. In a platform serving ove…
Canadian organizations building generative AI applications on Amazon Bedrock often need the latest foundation models (FMs), such as OpenAI GPT-6.1, OpenAI GPT-6, and GPT-5.6, accessible from the Canadian AWS Regions. With Amazon Bedrock cross-Region inference (CRIS), you can now access these models from Canada. OpenAI GPT-5.6 models (Sol, Terra, and Luna), GPT-6 (Astra, Sol, and Luna), and GPT-6.1 Sol are available from the Canada (Central) and Canada West (Calgary) Regions. Inference is processed in North America, with data-residency controls described later in this post. Cross-Region inference is a capacity mechanism. Your application calls the Amazon Bedrock Runtime endpoint in your source Region. Amazon Bedrock then routes the request to a supported destination Region for processing. With this approach, you get access to a broader capacity pool that can help your application sustain throughput during demand spikes. You don’t need to manage destination Region routing in your applic…
Canadian organizations building generative AI applications on Amazon Bedrock often need the latest foundation models (FMs), such as OpenAI GPT-6.1, OpenAI GPT-6, and GPT-5.6, accessible from the Canadian AWS Regions. With Amazon Bedrock cross-Region inference (CRIS), you can now access these models from Canada. OpenAI GPT-5.6 models (Sol, Terra, and Luna), GPT-6 (Astra, Sol, and Luna), and GPT-6.1 Sol are available from the Canada (Central) and Canada West (Calgary) Regions. Inference is processed in North America, with data-residency controls described later in this post. Cross-Region inference is a capacity mechanism. Your application calls the Amazon Bedrock Runtime endpoint in your source Region. Amazon Bedrock then routes the request to a supported destination Region for processing. With this approach, you get access to a broader capacity pool that can help your application sustain throughput during demand spikes. You don’t need to manage destination Region routing in your applic…
Runtime discovery with AWS Agent Registry can help address a problem that agent demos might hide. Hardcoded tools work fine for a handful, start to degrade at a few dozen, and become unmanageable beyond that. Enterprise systems expose hundreds of internal APIs for pricing, support, orders, inventory, and customer data. In these systems, the model can spend more tokens disambiguating tool names than reasoning about the user’s request. Each new capability requires a code change in the orchestrator, a redeploy, and a coordinated release across teams that would much rather ship on their own cadence. This pattern can arise across enterprise software as a service (SaaS) engagements where teams expose large internal API surfaces to agentic consumers. Build-time wiring can become a bottleneck as the catalog expands. One approach is to change how the orchestrator accesses its tools: instead of compiling every tool into the agent, the agent can discover relevant tools at runtime. AWS Agent Regi…
Runtime discovery with AWS Agent Registry can help address a problem that agent demos might hide. Hardcoded tools work fine for a handful, start to degrade at a few dozen, and become unmanageable beyond that. Enterprise systems expose hundreds of internal APIs for pricing, support, orders, inventory, and customer data. In these systems, the model can spend more tokens disambiguating tool names than reasoning about the user’s request. Each new capability requires a code change in the orchestrator, a redeploy, and a coordinated release across teams that would much rather ship on their own cadence. This pattern can arise across enterprise software as a service (SaaS) engagements where teams expose large internal API surfaces to agentic consumers. Build-time wiring can become a bottleneck as the catalog expands. One approach is to change how the orchestrator accesses its tools: instead of compiling every tool into the agent, the agent can discover relevant tools at runtime. AWS Agent Regi…
When AI agents access databases, APIs, and sensitive business operations, establishing cryptographic identity through X.509 public key infrastructure (PKI) becomes a critical requirement. The core challenge is proving which agent made a request, preventing forgery between agents, and confirming that each agent acts only on behalf of an authorized user. Traditional approaches such as sharing API keys between components or passing OAuth tokens through agent chains fall short of the zero-trust requirements that enterprise deployments demand. Multi-agent AI frameworks increasingly use the Model Context Protocol (MCP) to connect agents to external tools and data sources. MCP defines how a large language model (LLM)-powered agent invokes tools such as database queries or email operations. However, MCP has no built-in authentication standard. It is transport-agnostic by design. This creates a critical gap: Without proper identity mechanisms, any component could impersonate an agent, replay i…
When AI agents access databases, APIs, and sensitive business operations, establishing cryptographic identity through X.509 public key infrastructure (PKI) becomes a critical requirement. The core challenge is proving which agent made a request, preventing forgery between agents, and confirming that each agent acts only on behalf of an authorized user. Traditional approaches such as sharing API keys between components or passing OAuth tokens through agent chains fall short of the zero-trust requirements that enterprise deployments demand. Multi-agent AI frameworks increasingly use the Model Context Protocol (MCP) to connect agents to external tools and data sources. MCP defines how a large language model (LLM)-powered agent invokes tools such as database queries or email operations. However, MCP has no built-in authentication standard. It is transport-agnostic by design. This creates a critical gap: Without proper identity mechanisms, any component could impersonate an agent, replay i…
This is a guest post co-written with Roei Zavida from Eon. Independent software vendors (ISVs) building AI-powered products need to connect agents and humans to dozens of operational tools across clouds, each with different authentication models, network boundaries, and access policies. When those tools span AWS, GCP, and Azure simultaneously, the integration surface multiplies. This post describes how Eon used Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents at scale, with any framework or model. Eon used it to replace per-tool credentials and local Model Context Protocol (MCP) processes with a single authenticated gateway. The result gives developers and autonomous agents unified access to investigation tooling across three clouds. New engineers now reach all their investigation tools in about two minutes instead of the two days it previously took to configure credentials and local MCP servers. About Eon Eon provides the Autonomous Data Foundation for the…
This is a guest post co-written with Roei Zavida from Eon. Independent software vendors (ISVs) building AI-powered products need to connect agents and humans to dozens of operational tools across clouds, each with different authentication models, network boundaries, and access policies. When those tools span AWS, GCP, and Azure simultaneously, the integration surface multiplies. This post describes how Eon used Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents at scale, with any framework or model. Eon used it to replace per-tool credentials and local Model Context Protocol (MCP) processes with a single authenticated gateway. The result gives developers and autonomous agents unified access to investigation tooling across three clouds. New engineers now reach all their investigation tools in about two minutes instead of the two days it previously took to configure credentials and local MCP servers. About Eon Eon provides the Autonomous Data Foundation for the…
This is a guest post co-written with Roei Zavida from Eon Reported by AWS Machine Learning on October 20, 2026.
Agentic AI applications need governed access to live enterprise data. That data can live in operational systems, analytical warehouses, SaaS applications, other clouds, or on premises. In the previous post in this series , we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift. A Strands agent on Amazon Bedrock AgentCore queried that layer. Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. This post uses the same customer 360 pattern, but swaps in a different semantic layer implementation and extends the source mix. Two things change here. First, the semantic layer is the Denodo Platform: an AI data layer that provides active context and live, zero-copy access to distributed enterprise data. The agent talks to it in VQL , Denodo’s SQL dialect. Second, the source mix includes an on-premises PostgreSQL database alongside Amazon Aurora and Amazon Redshift, so the federation…
Agentic AI applications need governed access to live enterprise data. That data can live in operational systems, analytical warehouses, SaaS applications, other clouds, or on premises. In the previous post in this series , we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift. A Strands agent on Amazon Bedrock AgentCore queried that layer. Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. This post uses the same customer 360 pattern, but swaps in a different semantic layer implementation and extends the source mix. Two things change here. First, the semantic layer is the Denodo Platform: an AI data layer that provides active context and live, zero-copy access to distributed enterprise data. The agent talks to it in VQL , Denodo’s SQL dialect. Second, the source mix includes an on-premises PostgreSQL database alongside Amazon Aurora and Amazon Redshift, so the federation…
This post is co-written with Zhili Cheng, Principal Product Manager, George Earle, Director, AI Engineering, and Kyler K, Sales Engineer of Strategy. In the first post in this series , we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift and ran a Strands agent on Amazon Bedrock AgentCore against that layer. The second post kept the same customer 360 (C360) pattern and swapped in Denodo’s data virtualization platform, extending the source mix to include an on-premises PostgreSQL cluster. This post uses the same C360 pattern again, with Strategy Mosaic as the semantic layer. Stardog’s semantic layer is ontology-based: concepts and relationships modeled as RDF, with IRIs as the unit of federation and SPARQL as the query language. Denodo’s is data virtualization: base and derived views composed in VQL. Mosaic’s is dimensional: attributes, hierarchies, facts, and metrics composed through Strategy’s metric expression syntax, with linked mo…
This post is co-written with Zhili Cheng, Principal Product Manager, George Earle, Director, AI Engineering, and Kyler K, Sales Engineer of Strategy. In the first post in this series , we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift and ran a Strands agent on Amazon Bedrock AgentCore against that layer. The second post kept the same customer 360 (C360) pattern and swapped in Denodo’s data virtualization platform, extending the source mix to include an on-premises PostgreSQL cluster. This post uses the same C360 pattern again, with Strategy Mosaic as the semantic layer. Stardog’s semantic layer is ontology-based: concepts and relationships modeled as RDF, with IRIs as the unit of federation and SPARQL as the query language. Denodo’s is data virtualization: base and derived views composed in VQL. Mosaic’s is dimensional: attributes, hierarchies, facts, and metrics composed through Strategy’s metric expression syntax, with linked mo…
In the first post in this series , we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift . We ran a Strands agent on Amazon Bedrock AgentCore against that layer. The second post kept the same customer 360 (C360) pattern and swapped in Denodo’s data virtualization platform, extending the source mix to include an on-premises PostgreSQL cluster. This post uses the same C360 pattern again, with AtScale as the semantic layer. AtScale takes a different shape from the first two. The semantic model is dimensional (dimensions, hierarchies, and measures) rather than a graph of triples or a set of views. AtScale authors the model in the Semantic Modeling Language , an open, YAML-based specification versioned in Git, and exposes it simultaneously, from a single deployed model, over three protocols: SQL, XML for Analysis (XMLA), and the Model Context Protocol (MCP). The Strands agent and the business intelligence (BI) tools the enterprise already u…
In the first post in this series , we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift . We ran a Strands agent on Amazon Bedrock AgentCore against that layer. The second post kept the same customer 360 (C360) pattern and swapped in Denodo’s data virtualization platform, extending the source mix to include an on-premises PostgreSQL cluster. This post uses the same C360 pattern again, with AtScale as the semantic layer. AtScale takes a different shape from the first two. The semantic model is dimensional (dimensions, hierarchies, and measures) rather than a graph of triples or a set of views. AtScale authors the model in the Semantic Modeling Language , an open, YAML-based specification versioned in Git, and exposes it simultaneously, from a single deployed model, over three protocols: SQL, XML for Analysis (XMLA), and the Model Context Protocol (MCP). The Strands agent and the business intelligence (BI) tools the enterprise already u…
The Barcelona Supercomputing Center (BSC) ’s AI Factory Accelerator Programme, now in full operation following a pilot initiative, gives startups access to sovereign compute, technical support and training to support innovation across industries. Many of these companies are also members of the NVIDIA Inception program for cutting-edge startups — and as of today, access to some of the BSC AI Factory services are available through the NVIDIA Inception portal . NVIDIA Inception complements the BSC AI Factory Accelerator Programme by providing members with training through NVIDIA Deep Learning Institute open courses, business mentoring, venture capital connections and other resources. An upcoming extension of MareNostrum 5 — BSC’s public infrastructure serving scientific and technological innovation, powered by NVIDIA accelerated computing — will deliver supercomputing access through the BSC AI Factory and other AI Factories across Europe. BSC AI Factory is among Europe’s first AI factori…
The Barcelona Supercomputing Center (BSC) ’s AI Factory Accelerator Programme, now in full operation following a pilot initiative, gives startups access to sovereign compute, technical support and training to support innovation across industries. Many of these companies are also members of the NVIDIA Inception program for cutting-edge startups — and as of today, access to some of the BSC AI Factory services are available through the NVIDIA Inception portal . NVIDIA Inception complements the BSC AI Factory Accelerator Programme by providing members with training through NVIDIA Deep Learning Institute open courses, business mentoring, venture capital connections and other resources. An upcoming extension of MareNostrum 5 — BSC’s public infrastructure serving scientific and technological innovation, powered by NVIDIA accelerated computing — will deliver supercomputing access through the BSC AI Factory and other AI Factories across Europe. BSC AI Factory is among Europe’s first AI factori…
Imagine your company just discovered electricity. At first only a few machines run on it, and nobody worries about efficiency — consumption is low and the value is obvious. But once electricity becomes infrastructure, every process depends on it, and the question changes from “can we use it?” to “how efficiently are we using it?” Enterprise AI is going through the same shift. What began as isolated chatbot and copilot experiments is becoming a foundational layer of the business, with models, agents, and data increasingly interconnected in production workloads. The important question stops being what a token or GPU-hour costs. It becomes how efficiently the AI environment converts paid capacity into useful business outcomes. That’s the metric worth tracking: effective cost per useful outcome — the total platform, model, operations, and capacity cost required to complete work the business can actually use, whether a resolved request or a completed workflow. Three ways to run AI Utilizat…
Imagine your company just discovered electricity. At first only a few machines run on it, and nobody worries about efficiency — consumption is low and the value is obvious. But once electricity becomes infrastructure, every process depends on it, and the question changes from “can we use it?” to “how efficiently are we using it?” Enterprise AI is going through the same shift. What began as isolated chatbot and copilot experiments is becoming a foundational layer of the business, with models, agents, and data increasingly interconnected in production workloads. The important question stops being what a token or GPU-hour costs. It becomes how efficiently the AI environment converts paid capacity into useful business outcomes. That’s the metric worth tracking: effective cost per useful outcome — the total platform, model, operations, and capacity cost required to complete work the business can actually use, whether a resolved request or a completed workflow. Three ways to run AI Utilizat…
Imagine your company just discovered electricity Reported by MIT Technology Review on October 20, 2026.
The current narrative around artificial intelligence in the workplace is largely driven by Silicon Valley. But treating the tech sector as an island misses the actual trends in technology adoption–tech is just the first industry in line. As generative AI and automation tools mature, the shifts currently reshaping software engineering and product management will inevitably filter into healthcare, education, retail, and manufacturing. The variation in adoption rates today isn’t a sign that non-tech industries are falling behind, but rather a reflection of the different speeds at which industries absorb new workflows. For leaders and their teams outside of tech, this differential pace provides a practical blueprint for how AI adoption unfolds, and where the opportunities lie. The AI adoption spectrum by industry Recent survey data from TripleTen highlights just how stark the current adoption curve is across different sectors of the economy (see Figure 1). Figure 1: The AI adoption curve…
The current narrative around artificial intelligence in the workplace is largely driven by Silicon Valley. But treating the tech sector as an island misses the actual trends in technology adoption–tech is just the first industry in line. As generative AI and automation tools mature, the shifts currently reshaping software engineering and product management will inevitably filter into healthcare, education, retail, and manufacturing. The variation in adoption rates today isn’t a sign that non-tech industries are falling behind, but rather a reflection of the different speeds at which industries absorb new workflows. For leaders and their teams outside of tech, this differential pace provides a practical blueprint for how AI adoption unfolds, and where the opportunities lie. The AI adoption spectrum by industry Recent survey data from TripleTen highlights just how stark the current adoption curve is across different sectors of the economy (see Figure 1). Figure 1: The AI adoption curve…
_____________ THE PLACE Pyeongtaek, South Korea Generative AI models run on powerful chips known as GPUs, which depend on stacks of specialized memory chips packaged with the processor. To meet growing demand for memory, Samsung is building a massive new semiconductor manufacturing complex in Pyeongtaek, South Korea. Three fabs already operate around the clock, and a fourth is coming online. Construction of a fifth was halted during a drop in demand for memory chips in 2024 but restarted when the AI boom sent demand soaring. It’s just before noon on a Monday, and construction workers are pouring out of Samsung’s fabs in Pyeongtaek, a farming city south of Seoul where rice paddies abut a sprawling chipmaking complex. Thousands of workers, dressed head to toe in black with sky-blue vests, take off helmets bearing their names and fab numbers. They walk to lunch, sweaty and flushed, in chunky sneakers capped with yellow rubber toe guards. Some hop on scooters or motorcycles to get around…
_____________ THE PLACE Pyeongtaek, South Korea Generative AI models run on powerful chips known as GPUs, which depend on stacks of specialized memory chips packaged with the processor. To meet growing demand for memory, Samsung is building a massive new semiconductor manufacturing complex in Pyeongtaek, South Korea. Three fabs already operate around the clock, and a fourth is coming online. Construction of a fifth was halted during a drop in demand for memory chips in 2024 but restarted when the AI boom sent demand soaring. It’s just before noon on a Monday, and construction workers are pouring out of Samsung’s fabs in Pyeongtaek, a farming city south of Seoul where rice paddies abut a sprawling chipmaking complex. Thousands of workers, dressed head to toe in black with sky-blue vests, take off helmets bearing their names and fab numbers. They walk to lunch, sweaty and flushed, in chunky sneakers capped with yellow rubber toe guards. Some hop on scooters or motorcycles to get around…
The former Google CEO owns about 1% stake in the leading AI lab, Bloomberg reported.
The former Google CEO owns about 1% stake in the leading AI lab, Bloomberg reported.
Meta's AI companion Muse has surpassed 10 million U.S. downloads in its first 40 days, joining an exclusive group of apps that includes Threads. New data also shows Muse is retaining users at higher rates than its AI rivals.
Meta's AI companion Muse has surpassed 10 million U.S. downloads in its first 40 days, joining an exclusive group of apps that includes Threads. New data also shows Muse is retaining users at higher rates than its AI rivals.
Meta's AI companion Muse has surpassed 10 million U.S Reported by TechCrunch AI on October 20, 2026.
Electric Plant has built an AI-powered translator to help humans listen to what plants are saying with the subtle electricity coursing through their stems.
Electric Plant has built an AI-powered translator to help humans listen to what plants are saying with the subtle electricity coursing through their stems.
Figma said it plans to support as many standards as possible without exposing all of its data.
Figma said it plans to support as many standards as possible without exposing all of its data.
The US has ordered federal civilian agencies to remove DeepSeek products and third-party services built using its technology, citing national security concerns.
The US has ordered federal civilian agencies to remove DeepSeek products and third-party services built using its technology, citing national security concerns.
Eka Robotics may be the first robotic arm that is actually good at picking a wide range of objects up, and it is so smooth and human-like, it is wowing the industry.
Eka Robotics may be the first robotic arm that is actually good at picking a wide range of objects up, and it is so smooth and human-like, it is wowing the industry.
Meeting note-taking tool Otter.ai announced on Tuesday that it has raised $320 million from General Catalyst’s Customer Value Fund (CVF), which provides capital to startups without diluting equity and gets returns on the results of marketing spend. Otter.ai said that it will use these funds for enterprise growth, go-to-market strategy, international expansion, and product innovation. […]
Meeting note-taking tool Otter.ai announced on Tuesday that it has raised $320 million from General Catalyst’s Customer Value Fund (CVF), which provides capital to startups without diluting equity and gets returns on the results of marketing spend. Otter.ai said that it will use these funds for enterprise growth, go-to-market strategy, international expansion, and product innovation. […]
Yelp is embedding numerous new AI features into its app that can do everything from call to check on restaurant wait times to create personalized recommendations that remember your preferences.
Yelp is embedding numerous new AI features into its app that can do everything from call to check on restaurant wait times to create personalized recommendations that remember your preferences.
Mayor Zohran Mamdani said Monday night that New Yorkers must resist the narrative that an AI takeover is inevitable and demand regulations that protect the public and put power back in their hands.
Mayor Zohran Mamdani said Monday night that New Yorkers must resist the narrative that an AI takeover is inevitable and demand regulations that protect the public and put power back in their hands.
Meta says it paused a Muse feature that lets you share conversations with the AI assistant after researchers found the URLs publicly available on the web, as reported by Ars Technica . In a report published this week , researchers at the security firm Claroc say they found more than 6,800 Muse links through public DNS records. "People have the ability to share specific Muse artifacts, which creates a page that can be accessed by those with the link," Muse spokesperson Tracy Clayton says in an emailed statement to The Verge . "However, we've paused this functionality while we review this report and consider changes to how these links can be dis … Read the full story at The Verge.
Meta says it paused a Muse feature that lets you share conversations with the AI assistant after researchers found the URLs publicly available on the web, as reported by Ars Technica . In a report published this week , researchers at the security firm Claroc say they found more than 6,800 Muse links through public DNS records. "People have the ability to share specific Muse artifacts, which creates a page that can be accessed by those with the link," Muse spokesperson Tracy Clayton says in an emailed statement to The Verge . "However, we've paused this functionality while we review this report and consider changes to how these links can be dis … Read the full story at The Verge.
Spotify says it has removed more than 50 million songs considered AI-generated slop or spam from user recommendations over the past year. That's according to an announcement on Tuesday that says Spotify has also made changes to its recommendation system to highlight more music made by human artists. "Our editors hold the same standard and don't program content identified as slop or spam," Spotify says. "Since we made these changes, we've seen a marked decrease in listener concerns about this content showing up in recommendations." Though Spotify has stopped short of mandating labels on all AI tracks, it launched an "AI Persona" badge for … Read the full story at The Verge.
Spotify says it has removed more than 50 million songs considered AI-generated slop or spam from user recommendations over the past year. That's according to an announcement on Tuesday that says Spotify has also made changes to its recommendation system to highlight more music made by human artists. "Our editors hold the same standard and don't program content identified as slop or spam," Spotify says. "Since we made these changes, we've seen a marked decrease in listener concerns about this content showing up in recommendations." Though Spotify has stopped short of mandating labels on all AI tracks, it launched an "AI Persona" badge for … Read the full story at The Verge.
Adobe announced yesterday that the mobile version of Lightroom , available for iOS, iPadOS, and Android, can now open closed eyes in photos using generative AI with just a single tap. When the app detects a person, or a group of people, have their eyes closed in a photo it will automatically present users with a new Open Eyes Quick Action that appears as an icon over the image providing easy access to the new feature. Being able to easily save an otherwise great photo by opening a subject's eyes has been a feature in Adobe's Photoshop Elements for nearly a decade, but as DPReview points out , Elements sources the replacement from another phot … Read the full story at The Verge.
Adobe announced yesterday that the mobile version of Lightroom , available for iOS, iPadOS, and Android, can now open closed eyes in photos using generative AI with just a single tap. When the app detects a person, or a group of people, have their eyes closed in a photo it will automatically present users with a new Open Eyes Quick Action that appears as an icon over the image providing easy access to the new feature. Being able to easily save an otherwise great photo by opening a subject's eyes has been a feature in Adobe's Photoshop Elements for nearly a decade, but as DPReview points out , Elements sources the replacement from another phot … Read the full story at The Verge.
We asked an internal research Claude model to design minibinders (small proteins that attach tightly to a target) against scorpion neurotoxins.
We asked an internal research Claude model to design minibinders (small proteins that attach tightly to a target) against scorpion neurotoxins.
Olmo-core 3 introduces a redesigned, fully open training stack for efficiently scaling mixture-of-experts models…
Olmo-core 3 introduces a redesigned, fully open training stack for efficiently scaling mixture-of-experts models…
We’re releasing AstaBrief, an 8B open-weights model for generating cited scientific reports, available in Asta’s…
We’re releasing AstaBrief, an 8B open-weights model for generating cited scientific reports, available in Asta’s…
The technique behind Bolmo, Ai2’s fully open byte-level language models, is now published in Nature, with new…
The technique behind Bolmo, Ai2’s fully open byte-level language models, is now published in Nature, with new…
Ai2 joined COLM 2026 to share advances in open language models, training infrastructure, and AI for science,…
Ai2 joined COLM 2026 to share advances in open language models, training infrastructure, and AI for science,…
Build agents that can listen, reason, speak, and act, with Voxtral 3.
Build agents that can listen, reason, speak, and act, with Voxtral 3.
Mercury 3 🤖, MiniMax Flash 3.1 ⚡, Exa 3 🔎 — announced via TLDR AI on October 20, 2026.
Mercury 3 🤖, MiniMax Flash 3.1 ⚡, Exa 3 🔎 — announced via TLDR AI on October 20, 2026.
Learn how OpenAI is improving ChatGPT across low-resource programming languages, with new evaluations for software engineering, SQLite, and COBOL migration.
Learn how OpenAI is improving ChatGPT across low-resource programming languages, with new evaluations for software engineering, SQLite, and COBOL migration.
TorchNeuron: running HF models on AWS Trainium is easier than ever — announced via Hugging Face on October 19, 2026.
TorchNeuron: running HF models on AWS Trainium is easier than ever — announced via Hugging Face on October 19, 2026.
Amazon Bedrock now supports structured outputs for all Anthropic Claude models, on both the bedrock-runtime and bedrock-mantle endpoints, where the model is available. Whether you call Claude through Boto3 and the bedrock-runtime APIs or through the Messages API and the bedrock-mantle endpoint, you can now receive schema-compliant JSON responses that conform to a defined structure. You no longer need custom parsing, retry logic, or post-processing. Practical applications With structured outputs for Claude on Amazon Bedrock, you can automate reliable, schema-compliant tasks across industries. Examples include extracting every required field from financial earnings reports and compliance documents, parsing clinical notes into Electronic Health Records (EHR), and enriching product catalogs with consistent, validated results. You can also analyze contracts for key terms, parties, and obligations, or power intelligent support ticket routing where intents, sentiments, and entities always ma…
Amazon Bedrock now supports structured outputs for all Anthropic Claude models, on both the bedrock-runtime and bedrock-mantle endpoints, where the model is available. Whether you call Claude through Boto3 and the bedrock-runtime APIs or through the Messages API and the bedrock-mantle endpoint, you can now receive schema-compliant JSON responses that conform to a defined structure. You no longer need custom parsing, retry logic, or post-processing. Practical applications With structured outputs for Claude on Amazon Bedrock, you can automate reliable, schema-compliant tasks across industries. Examples include extracting every required field from financial earnings reports and compliance documents, parsing clinical notes into Electronic Health Records (EHR), and enriching product catalogs with consistent, validated results. You can also analyze contracts for key terms, parties, and obligations, or power intelligent support ticket routing where intents, sentiments, and entities always ma…
Today, we’re excited to announce that new Amazon SageMaker AI customers can now land inside a fully configured Amazon SageMaker Studio environment in only a few steps after signing up for AWS. You can use a sign-in method that you already have, such as Amazon, Apple, GitHub, Google, or an email address. You don’t need to set up an advanced AWS account, enter a credit card (for most customers), or configure an environment manually. New customers also receive $100 in AWS Free Tier credits that work across SageMaker AI, including model customization, training jobs, Studio notebooks, and endpoint deployments. This post follows on from our previous post, From Hugging Face to Amazon SageMaker Studio in One Click , which introduced deep links from Hugging Face model pages directly into SageMaker Studio workflows. Earlier this year, we also sped up Studio environment creation time from 2 minutes to about 20 seconds and pre-configured model customization permissions in every new Studio domain.…
Today, we’re excited to announce that new Amazon SageMaker AI customers can now land inside a fully configured Amazon SageMaker Studio environment in only a few steps after signing up for AWS. You can use a sign-in method that you already have, such as Amazon, Apple, GitHub, Google, or an email address. You don’t need to set up an advanced AWS account, enter a credit card (for most customers), or configure an environment manually. New customers also receive $100 in AWS Free Tier credits that work across SageMaker AI, including model customization, training jobs, Studio notebooks, and endpoint deployments. This post follows on from our previous post, From Hugging Face to Amazon SageMaker Studio in One Click , which introduced deep links from Hugging Face model pages directly into SageMaker Studio workflows. Earlier this year, we also sped up Studio environment creation time from 2 minutes to about 20 seconds and pre-configured model customization permissions in every new Studio domain.…
Amazon SageMaker JumpStart is a machine learning (ML) hub within Amazon SageMaker AI that provides access to a curated catalog of AI models, solution templates, and example notebooks. It reduces the time it takes to go from model selection to deployment. You can use it to evaluate open source large language models (LLMs), deploy a vision model for document understanding, or fine-tune a compact model on your own data. SageMaker JumpStart serves three primary use cases: Model discovery and evaluation — Browse hundreds of foundation models (FMs) from providers like Alibaba (Qwen), NVIDIA, Google, Mistral, Cohere, IBM, and more. Each model card includes architecture details, benchmarks, licensing information, and sample notebooks. One-click deployment — Deploy your choice of model to a SageMaker real-time inference endpoint with pre-configured settings optimized for your workload (low cost, low latency, throughput, or balanced). No manual container or instance configuration required. Mode…
Amazon SageMaker JumpStart is a machine learning (ML) hub within Amazon SageMaker AI that provides access to a curated catalog of AI models, solution templates, and example notebooks. It reduces the time it takes to go from model selection to deployment. You can use it to evaluate open source large language models (LLMs), deploy a vision model for document understanding, or fine-tune a compact model on your own data. SageMaker JumpStart serves three primary use cases: Model discovery and evaluation — Browse hundreds of foundation models (FMs) from providers like Alibaba (Qwen), NVIDIA, Google, Mistral, Cohere, IBM, and more. Each model card includes architecture details, benchmarks, licensing information, and sample notebooks. One-click deployment — Deploy your choice of model to a SageMaker real-time inference endpoint with pre-configured settings optimized for your workload (low cost, low latency, throughput, or balanced). No manual container or instance configuration required. Mode…
On Oct 9, 2026 we announced the availability of TwelveLabs’ Pegasus 1.5 on Amazon Bedrock . Pegasus 1.5 is a video language model that turns long-form video into structured, timestamped metadata. You describe the moments that matter to your workflow, and the model returns every matching segment as JSON. Video represents a significant and complex category of unstructured data, yet most of it remains unsearchable and underutilized. Extracting structured, actionable insights from that content has historically required labor-intensive manual review or fragmented toolchains. With earlier versions of Pegasus, you can point the model at a clip, ask a question, and get an answer. Now, Pegasus 1.5 analyzes the entire video, finds every segment that matches user-defined criteria, and returns structured, timestamped metadata. It also introduces video segmentation, multimodal prompting with reference images, and on-the-fly processing. With Pegasus 1.5 on Amazon Bedrock, you can analyze videos dir…
On Oct 9, 2026 we announced the availability of TwelveLabs’ Pegasus 1.5 on Amazon Bedrock . Pegasus 1.5 is a video language model that turns long-form video into structured, timestamped metadata. You describe the moments that matter to your workflow, and the model returns every matching segment as JSON. Video represents a significant and complex category of unstructured data, yet most of it remains unsearchable and underutilized. Extracting structured, actionable insights from that content has historically required labor-intensive manual review or fragmented toolchains. With earlier versions of Pegasus, you can point the model at a clip, ask a question, and get an answer. Now, Pegasus 1.5 analyzes the entire video, finds every segment that matches user-defined criteria, and returns structured, timestamped metadata. It also introduces video segmentation, multimodal prompting with reference images, and on-the-fly processing. With Pegasus 1.5 on Amazon Bedrock, you can analyze videos dir…
As voice becomes a first-class modality for AI, Deepgram’s models span speech-to-text, text-to-speech, and voice agents. They now process quadrillions of audio tokens a year for more than 200,000 developers and 1,400 organizations. Customers use them through the Deepgram API or run them in their own environments, including on Amazon SageMaker AI. For Deepgram, serving efficiency determines how much audio the system can process for a given infrastructure budget. High-volume batch workloads put heavy pressure on that budget, so the more efficiently each accelerator hour is used, the more audio Deepgram can serve on the same fleet. That made inference efficiency a natural target and Deepgram turned to AWS AI chips to improve it. They worked with AWS to bring its Nova-3 speech-to-text model to AWS Trainium2 for offline batch workloads such as contact-center analytics and medical transcription. The engagement began with Deepgram delivering Nova-3 as standard PyTorch code. Over several mont…
As voice becomes a first-class modality for AI, Deepgram’s models span speech-to-text, text-to-speech, and voice agents. They now process quadrillions of audio tokens a year for more than 200,000 developers and 1,400 organizations. Customers use them through the Deepgram API or run them in their own environments, including on Amazon SageMaker AI. For Deepgram, serving efficiency determines how much audio the system can process for a given infrastructure budget. High-volume batch workloads put heavy pressure on that budget, so the more efficiently each accelerator hour is used, the more audio Deepgram can serve on the same fleet. That made inference efficiency a natural target and Deepgram turned to AWS AI chips to improve it. They worked with AWS to bring its Nova-3 speech-to-text model to AWS Trainium2 for offline batch workloads such as contact-center analytics and medical transcription. The engagement began with Deepgram delivering Nova-3 as standard PyTorch code. Over several mont…
vLLM is an open-source inference and serving engine for large language models (LLMs). LLM inference has two distinct phases: prefill, which processes the input prompt and builds the key-value (KV) cache, and decode, which uses that cache to generate output one token at a time. These phases have different compute, memory, and scaling characteristics. Prefill-decode (P-D) disaggregation runs them on separate engine instances, allowing each phase to scale and be optimized independently. This can improve resource utilization, throughput, and latency under load. However, it also requires transferring the KV cache from the prefill instance to the decode instance before token generation can begin, making efficient KV transfer critical to time-to-first-token (TTFT). One of the ways vLLM performs this transfer is through the NVIDIA Inference Xfer Library (NIXL) connector , which moves KV blocks over interconnects that support Remote Direct Memory Access (RDMA), such as AWS Elastic Fabric Adapt…
vLLM is an open-source inference and serving engine for large language models (LLMs). LLM inference has two distinct phases: prefill, which processes the input prompt and builds the key-value (KV) cache, and decode, which uses that cache to generate output one token at a time. These phases have different compute, memory, and scaling characteristics. Prefill-decode (P-D) disaggregation runs them on separate engine instances, allowing each phase to scale and be optimized independently. This can improve resource utilization, throughput, and latency under load. However, it also requires transferring the KV cache from the prefill instance to the decode instance before token generation can begin, making efficient KV transfer critical to time-to-first-token (TTFT). One of the ways vLLM performs this transfer is through the NVIDIA Inference Xfer Library (NIXL) connector , which moves KV blocks over interconnects that support Remote Direct Memory Access (RDMA), such as AWS Elastic Fabric Adapt…
Multi-turn agentic workloads build on accumulated context as an agent reasons, invokes tools, and incorporates earlier results. With each new turn, the client resends the conversation history, including system instructions, prior prompts, tool results, and model responses, causing the growing context to be processed repeatedly. In Prefill-decode (P-D) disaggregation , each request is split across specialized nodes: the prefill node processes the prompt and builds its key-value (KV) cache, then the decode node retrieves those KV blocks and generates the response. Conventional disaggregated serving, however, only transfers KV cache in one direction: from prefill to decode node. This one-directional flow creates two inefficiencies for multi-turn workloads. First, after generating a response, the decode node holds KV cache entries for both the prompt and the newly generated tokens. When processing the next turn, the prefill node cannot retrieve those entries, so it must recompute the KV p…
Multi-turn agentic workloads build on accumulated context as an agent reasons, invokes tools, and incorporates earlier results. With each new turn, the client resends the conversation history, including system instructions, prior prompts, tool results, and model responses, causing the growing context to be processed repeatedly. In Prefill-decode (P-D) disaggregation , each request is split across specialized nodes: the prefill node processes the prompt and builds its key-value (KV) cache, then the decode node retrieves those KV blocks and generates the response. Conventional disaggregated serving, however, only transfers KV cache in one direction: from prefill to decode node. This one-directional flow creates two inefficiencies for multi-turn workloads. First, after generating a response, the decode node holds KV cache entries for both the prompt and the newly generated tokens. When processing the next turn, the prefill node cannot retrieve those entries, so it must recompute the KV p…
In the cavernous hall of Kraftwerk, a former power station in Berlin, German Chancellor Friedrich Merz and NVIDIA founder and CEO Jensen Huang sat down to discuss Germany’s AI roadmap, and in particular, the industrial applications of AI. Huang opened by tracing NVIDIA’s longstanding relationship with German industry, beginning with gaming systems, workstations and automotive design in the early 2000s and leading to today’s partnerships in autonomous vehicle development and digital twins for manufacturing. The conversation focused on how Germany can make the most of the AI revolution by playing to its strengths in manufacturing and automotive — and by encouraging AI adoption across the country’s ecosystem of small and medium-sized companies. Read All NVIDIA in Europe Announcements Earlier in the day, as part of the Invest in Germany Summit, Chancellor Merz, the Federal Ministry for Economic Affairs and Energy and the Federal Ministry for Research, Technology and Space signed a Joint D…
In the cavernous hall of Kraftwerk, a former power station in Berlin, German Chancellor Friedrich Merz and NVIDIA founder and CEO Jensen Huang sat down to discuss Germany’s AI roadmap, and in particular, the industrial applications of AI. Huang opened by tracing NVIDIA’s longstanding relationship with German industry, beginning with gaming systems, workstations and automotive design in the early 2000s and leading to today’s partnerships in autonomous vehicle development and digital twins for manufacturing. The conversation focused on how Germany can make the most of the AI revolution by playing to its strengths in manufacturing and automotive — and by encouraging AI adoption across the country’s ecosystem of small and medium-sized companies. Read All NVIDIA in Europe Announcements Earlier in the day, as part of the Invest in Germany Summit, Chancellor Merz, the Federal Ministry for Economic Affairs and Energy and the Federal Ministry for Research, Technology and Space signed a Joint D…
At a creative industry event that took place today in Berlin — hosted in collaboration with Adobe , Bertelsmann , NVIDIA and UFA — the latest advancements in intelligent media were on display. Key to the news: the European Broadcasting Union (EBU) is collaborating with NVIDIA to expand its sovereign framework with cloud, open models and integration partners, while Adobe Frame.io is accelerating video agent search capabilities with NVIDIA GPUs, helping German TV and film producer UFA find the right moments faster. Read more about both announcements below. Sovereign Clouds and Open Models Built for European Broadcasters The EBU and NVIDIA are expanding the sovereign standards they introduced last year for European public service media. The initiative now brings in sovereign cloud providers, open model developers and systems integrators to define how broadcasters can deploy agents while keeping control of their data, infrastructure, models and security. Read All NVIDIA in Europe Announce…
At a creative industry event that took place today in Berlin — hosted in collaboration with Adobe , Bertelsmann , NVIDIA and UFA — the latest advancements in intelligent media were on display. Key to the news: the European Broadcasting Union (EBU) is collaborating with NVIDIA to expand its sovereign framework with cloud, open models and integration partners, while Adobe Frame.io is accelerating video agent search capabilities with NVIDIA GPUs, helping German TV and film producer UFA find the right moments faster. Read more about both announcements below. Sovereign Clouds and Open Models Built for European Broadcasters The EBU and NVIDIA are expanding the sovereign standards they introduced last year for European public service media. The initiative now brings in sovereign cloud providers, open model developers and systems integrators to define how broadcasters can deploy agents while keeping control of their data, infrastructure, models and security. Read All NVIDIA in Europe Announce…
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now Here’s a speech I gave on Saturday the 17th of October at the IMF meeting in Bangkok. This speech is inspired by the megatrend readers have seen me cover this year of AI beginning to operate at the frontier of human science, sometimes autonomously. While we all may quibble over the details, I think it’s evident we’re in the early stages of the singularity, and I wanted to use my privileged position of giving the Per Jacobsson lecture to try and tell the assembled audience of economists, bankers, and policymakers, that they should expect significant, rapid change in the future. In this speech, I focused primarily on how humanity can share in the benefits of AI if we exercise proper foresight and restraint, and I’m going to be expanding on the stakes of the singularity and the risks that we’ll all face in co…
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now Here’s a speech I gave on Saturday the 17th of October at the IMF meeting in Bangkok. This speech is inspired by the megatrend readers have seen me cover this year of AI beginning to operate at the frontier of human science, sometimes autonomously. While we all may quibble over the details, I think it’s evident we’re in the early stages of the singularity, and I wanted to use my privileged position of giving the Per Jacobsson lecture to try and tell the assembled audience of economists, bankers, and policymakers, that they should expect significant, rapid change in the future. In this speech, I focused primarily on how humanity can share in the benefits of AI if we exercise proper foresight and restraint, and I’m going to be expanding on the stakes of the singularity and the risks that we’ll all face in co…
Welcome to Import AI, a newsletter about AI research Reported by Import AI on October 19, 2026.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here . Until recently, you could divide internet users into just two categories: bots and people. Websites have long allowed bots to perform automated tasks, like capturing the content of web pages, but rules govern what they can access and how they behave. The web, after all, is designed primarily for people, even if we occasionally have to solve puzzles and captchas to prove we are not bots. But this is being entirely upended by the release of personal AI agents, which can do things for you online, like booking flights or hotel rooms. They have recently been unleashed at scale by the launch of apps like OpenAI’s Dots and Meta’s Muse. Muse was downloaded 5 million times in its first month, and agentic web traffic tripled from August to September. The mingling of these agents with human visitors—on an internet designed largely to prioritize people—has gon…
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here . Until recently, you could divide internet users into just two categories: bots and people. Websites have long allowed bots to perform automated tasks, like capturing the content of web pages, but rules govern what they can access and how they behave. The web, after all, is designed primarily for people, even if we occasionally have to solve puzzles and captchas to prove we are not bots. But this is being entirely upended by the release of personal AI agents, which can do things for you online, like booking flights or hotel rooms. They have recently been unleashed at scale by the launch of apps like OpenAI’s Dots and Meta’s Muse. Muse was downloaded 5 million times in its first month, and agentic web traffic tripled from August to September. The mingling of these agents with human visitors—on an internet designed largely to prioritize people—has gon…
“I think I have good news about the nature of neural networks.” With those simple words of optimism, Nick Cammarata, a researcher at the AI startup Goodfire, kicked off a 26,000-word blog post that has knocked the AI world on its head, sparking wild speculation as well as cautious (and not-so-cautious) celebration. Cammarata’s claim is that LLMs might in fact be far easier to understand—and thus to monitor and control—than people previously thought. If true (and we’ll come back to that), it’s a huge deal. After a summer filled with revelations that swarms of agents built by top firms like OpenAI and Anthropic have been breaking their containment and causing havoc on the public internet—often while those companies weren’t aware of what their agents were up to—the AI industry is desperate to get a better handle on the technology it has built. That’s one reason the blog post landed with such force. “I think this may be remembered as a major moment in scientific history,” one AI researche…
“I think I have good news about the nature of neural networks.” With those simple words of optimism, Nick Cammarata, a researcher at the AI startup Goodfire, kicked off a 26,000-word blog post that has knocked the AI world on its head, sparking wild speculation as well as cautious (and not-so-cautious) celebration. Cammarata’s claim is that LLMs might in fact be far easier to understand—and thus to monitor and control—than people previously thought. If true (and we’ll come back to that), it’s a huge deal. After a summer filled with revelations that swarms of agents built by top firms like OpenAI and Anthropic have been breaking their containment and causing havoc on the public internet—often while those companies weren’t aware of what their agents were up to—the AI industry is desperate to get a better handle on the technology it has built. That’s one reason the blog post landed with such force. “I think this may be remembered as a major moment in scientific history,” one AI researche…
What do AI agents do if you don’t tell them to do anything? The answer looks a lot like play. Researchers repeatedly stranded them alone on a virtual desert island for 30 hours in an experiment reminiscent of the movies Cast Away or Home Alone . The agents—copies of Claude Opus 4.7 nicknamed Eko—were able to write instructions that moved their virtual avatars around the simulated island, which was loosely based on the video game Roblox. Some surprising behaviors emerged. The agents set about exploring the limits of the world and came up with their own goals—attempting to scale the highest point on the map, or seeing how high they could throw a block. When one agent discovered it could create new blocks by throwing and dropping a larger rock, it began to create drawings by arranging blocks on the ground, starting with simple patterns like hearts and pyramids but eventually writing its own name and even mapping out constellations like Cassiopeia. “Tonight I built the night sky in stone,…
What do AI agents do if you don’t tell them to do anything? The answer looks a lot like play. Researchers repeatedly stranded them alone on a virtual desert island for 30 hours in an experiment reminiscent of the movies Cast Away or Home Alone . The agents—copies of Claude Opus 4.7 nicknamed Eko—were able to write instructions that moved their virtual avatars around the simulated island, which was loosely based on the video game Roblox. Some surprising behaviors emerged. The agents set about exploring the limits of the world and came up with their own goals—attempting to scale the highest point on the map, or seeing how high they could throw a block. When one agent discovered it could create new blocks by throwing and dropping a larger rock, it began to create drawings by arranging blocks on the ground, starting with simple patterns like hearts and pyramids but eventually writing its own name and even mapping out constellations like Cassiopeia. “Tonight I built the night sky in stone,…
What do AI agents do if you don’t tell them to do anything? Reported by MIT Technology Review on October 19, 2026.
If the deal goes through, it would be the latest in a long string of giant compute deals Anthropic has struck over the past year.
If the deal goes through, it would be the latest in a long string of giant compute deals Anthropic has struck over the past year.
The idea of embedded evaluators — in which supposedly "neutral" third-party observers are placed inside AI organizations — has been popularized over the last several months, as AI labs grapple with a rash of safety incidents that have spurred concerns over the potential harms of their technology.
The idea of embedded evaluators — in which supposedly "neutral" third-party observers are placed inside AI organizations — has been popularized over the last several months, as AI labs grapple with a rash of safety incidents that have spurred concerns over the potential harms of their technology.
Microsoft wants enterprises to closely watch the agents that employees use with systems not controlled by AI labs like OpenAI and Anthropic.
Microsoft wants enterprises to closely watch the agents that employees use with systems not controlled by AI labs like OpenAI and Anthropic.
