Researchers have uncovered a massive breach of Fortinet firewalls that has given Russian-speaking attackers near-unrestricted access to some of the world’s largest and most powerful organizations, including Oracle, Chevron, Lenovo, Federal Express, a NATO defense contractor, and Fortinet itself.

Nearly 74,000 Fortinet devices from more than 21,000 IP addresses in 194 countries have been compromised and their plaintext credentials exposed online, Bob Diachenko, a security researcher and head of SecurityDiscovery.com, said online and in an interview. He said he found the data after gaining access to the attackers’ command-and-control server and other infrastructure. The exposed data also included the industry, revenue, and employee count for each compromised organization.

Exceptional scale, poor opsec

Independent researcher Kevin Beaumont reported that “almost all” of the compromised devices remained online as of Wednesday morning. He went on to say that he has confirmed with multiple organizations found in the attackers’ logs that the credentials are real and current. In many cases, once the threat actors compromised the devices, they went on to access affected organizations’ centralized authentication systems, such as Radius servers and Microsoft Active Directory. The number of compromised devices comprises roughly half of all Internet-facing Fortinet firewalls, based on polling from Shodan.

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Tesco, a retail conglomerate headquartered in the United Kingdom, is moving 40,000 server workloads off of VMware amid "abusive conduct" from Broadcom, recent legal filings claim.

Tesco filed a lawsuit in the UK’s High Court against Broadcom alleging breach of contract last year. According to a September report from The Register, the lawsuit claimed that in January 2021, Tesco bought perpetual licenses for VMware’s vSphere Foundation and Cloud Foundation, a subscription to VMware Tanzu, plus support services until 2026, with the option to extend support for four additional years.

But when Broadcom took over VMware in November 2023, it would not honor the deal and instead tried to get Tesco to pay “excessive and inflated prices for virtualization software for which Tesco has already paid” and would not allow it to buy support services for its perpetually licensed software without buying “duplicative subscription-based licenses for those same Software products," the initial complaint read, The Register reported at the time.

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Introducing LifeSciBench, an expert-authored, expert-reviewed benchmark for evaluating how AI systems handle real-world life science research tasks and decisions.

This post introduces three architectural patterns designed to integrate Model Context Protocol (MCP) Apps and Agent-to-User Interface (A2UI) to solve the tradeoff between highly custom iframe environments and native, declarative rendering. By combining these approaches, developers can serve native-feeling UIs directly over MCP servers, embed complex and stateful iframe apps securely inside declarative views, or inject generative UI components into legacy systems. Ultimately, these hybrid frameworks empower engineering teams to deliver secure, performant, and brand-consistent agentic user experiences tailored to their specific project constraints.

OpenAI and Molecule.one show how a near-autonomous AI chemist using GPT-5.4 improved a key drug-making reaction, advancing medicinal chemistry research.

With context key to agents performing in production, the tech giant's latest new features aim to feed cutting-edge tools the situational awareness they require.

An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...

Today, we’re announcing Amazon Bedrock Managed Knowledge Base, a new set of capabilities that enables developers to build enterprise-grade generative AI applications with their proprietary data in minutes. Organizations building agentic AI applications need secure, reliable, and up-to-date access to enterprise-wide data to deliver accurate, fast, and trusted outcomes. Managed Knowledge Base abstracts away the complexity of building and managing retrieval-augmented generation (RAG) pipelines, allowing developers to focus on business outcomes rather than infrastructure management.

Developers building knowledge bases for their agents face three key challenges today:

• Connecting to enterprise data: Enterprise knowledge lives across disparate systems with different content types, access control lists, and document formats. Building and maintaining custom connectors for each source adds complexity that slows down development.
• Optimizing RAG accuracy: Best practices for retrieval-augmented generation keep evolving. Developers need to experiment with different parsing strategies, chunking approaches, embedding models, and agentic retrieval behaviors to get accurate answers from their data.
• Managing infrastructure at scale: Organizations need to serve large knowledge bases with millions of documents, or manage thousands of smaller knowledge bases across teams. Both patterns require reliable infrastructure, security enforcement, and cost control.

These challenges require developers to repeatedly perform undifferentiated work instead of focusing on their applications.

Amazon Bedrock Managed Knowledge Base addresses these challenges by abstracting away the multiple infrastructure components developers traditionally have to assemble and maintain themselves (storage, retrieval, embeddings, re-ranking, and foundation model selection) into a single managed primitive. By default, the service automatically selects and manages a default embeddings model, re-ranker model, and foundational model on your behalf, so you can get up to speed quickly without needing to pick or maintain one yourself. On top of this managed foundation, three core innovations further improve ease of use and accuracy:

• Native data connectors: Six pre-built ingestion connectors that natively pull enterprise data and permissions from SaaS applications, eliminating the overhead developers face in managing application-specific requirements. At launch, we support Amazon S3, SharePoint, Confluence, Web Crawler, Google Drive, and OneDrive.
• Smart Parsing: Different content types and sources require different approaches to achieve accurate retrieval. Smart Parsing handles this complexity automatically, selecting the right parsing strategy for each data type and connector to provide the highest accuracy for your agents.
• Agentic Retriever: Optimized for complex queries that require multiturn, multihop retrieval within a single knowledge base or across multiple knowledge bases. Agentic Retriever automatically infers end-user intent and draws relevant context from institutional knowledge spread across data sources and modalities.

With just a few lines of code, Amazon Bedrock Managed Knowledge Base automatically manages and scales the end-to-end RAG pipeline that powers your enterprise knowledge agents. For agent builders, it’s available as a pre-built target type in Amazon Bedrock AgentCore Gateway, reducing integration to a few lines of code, auto-generating role-based permissions, and providing observability and evaluation metrics in the AgentCore Observability dashboard.

Getting started with Amazon Bedrock Managed Knowledge Base
Creating a Managed Knowledge Base is straightforward. Navigate to the Amazon Bedrock AgentCore console or the Amazon Bedrock console, open the Knowledge Bases page, and choose Create Managed KB. The experience is the same in both consoles.

When creating a new Knowledge Bases, you can connect to your enterprise data sources by choosing from the list of supported connectors directly from a dropdown. AWS Identity and Access Management (IAM) roles are automatically created, and you can choose to edit these permissions if needed:

An optimized set of defaults will be presented, allowing you to create your knowledge base in just a few clicks. Once the data is synced, you can integrate the knowledge base with your agent or provide it as a tool for your foundation model and start querying.

Smart Parsing for accurate data ingestion
One of the key challenges in building knowledge bases is preparing diverse data types for accurate retrieval. Once you point Managed Knowledge Base at your data sources, Smart Parsing automatically determines the optimal parsing strategy for each data type and connector, no extra configuration is required.

Smart Parsing combines multiple techniques:

• Connector-specific data models: Optimized handling for each data source. For example, the Web Crawler connector preserves HTML structure including embedded images and tables, ensuring rich content is not dropped during ingestion. SharePoint connectors maintain document hierarchy and relationships between files.
• Multimodal processing: Automatic detection and processing of different content types within documents. The system identifies bounding boxes in documents, then sends them to foundation models for data extraction, captioning, and scene description in video files.
• Optimized chunking: Smart Parsing leverages foundation models to understand document structure and extract meaningful content, ensuring that complex documents with mixed formats are properly indexed. Intelligent defaults balance retrieval accuracy with performance based on document type and content structure, while advanced users can customize chunking strategies when needed.

This automated approach eliminates weeks of experimentation typically required to achieve production-quality retrieval accuracy, while still preserving the flexibility to customize when needed.

Using Agentic Retriever for complex queries
After your data is ingested, you can start querying your knowledge base. Generative AI applications often struggle with complex user queries that require reasoning, recursive multi-step retrieval, and intermediate evaluations of results. Consider a user asking two related questions: “What is the cloud infrastructure budget for the ML platform team?” and “Does our expense policy allow prepaying annual commitments?” A single retrieval step might surface documents about the ML platform team but fail to connect the budget information with the expense policy needed to fully answer the question.

Agentic Retriever solves this by creating a step-by-step query plan: 1. Which team owns the ML platform, and what is their cloud infrastructure budget? 2. What does the expense policy say about prepaying annual commitments? 3. Does the policy allow the ML platform team to prepay against this budget?

The system performs multi-hop retrieval and reasoning at each step, and once it has gathered sufficient relevant passages, it stops the search process and returns the top results. By abstracting away the complexity of building a separate multi-hop reasoning pipeline, this approach dramatically improves accuracy for complex queries while letting developers focus on their agentic search applications instead of orchestration logic.

You can try Agentic Retriever directly from the test panel of your knowledge base in the Amazon Bedrock AgentCore console. Select Agentic retrieval only as the retrieval type to let the system automatically plan and execute multi-step queries across your knowledge bases:

Enabling MCP with Bedrock AgentCore
Amazon Bedrock Managed Knowledge Base seamlessly integrates with AgentCore Gateway as a native target type. This integration eliminates the need for manual integration and provides built-in observability, policy enforcement, and automatic permission management.

You can navigate to the Amazon Bedrock AgentCore console or SDK and create an AgentCore Gateway or select an existing one. When adding targets to your gateway, you will find Knowledge Base as a new pre-built target type alongside other options such as MCP server, Lambda ARN, REST API, and other integrations. Simply select your knowledge base ID to expose it through the gateway:

Add targets page in AgentCore Gateway showing Knowledge Base as a new pre-built target type, with the knowledge base ID selector and runtime retrieval mode options

Gateway exposes the standard Model Context Protocol (MCP), so the knowledge base tools are automatically discovered by clients from any MCP-compatible framework, including Strands Agents, LangChain, CrewAI, LlamaIndex, and LangGraph. No custom integration code is required.

Model choice and flexibility
Amazon Bedrock Managed Knowledge Base preserves the flexibility developers expect from Amazon Bedrock. Every foundation model available on Bedrock can power the generation step, and developers can select from different embedding and re-ranking models to optimize retrieval for their specific use case, enabling teams to fine-tune accuracy and cost-performance without changing infrastructure.

Unlike managed solutions that lock you into specific model providers, Amazon Bedrock Managed Knowledge Base separates the infrastructure management (connectors, parsing, storage, retrieval orchestration) from model selection. This means you can:

• Take advantage of the latest models: Adopt the latest embedding, re-ranking, and foundation models as they become available to improve accuracy, latency, and cost for your application without rebuilding your RAG pipeline.
• Optimize for price-performance: Choose smaller, faster models for simple queries and more capable models for complex reasoning tasks, all using the same knowledge base infrastructure.
• Use Bedrock embedding models: While Smart Parsing provides optimized defaults, you can configure Bedrock embedding models when your domain requires specialized semantic understanding.
• Maintain consistency with existing applications: If you’re already using Bedrock Knowledge Bases APIs (Retrieve, StartIngest, StopIngest, IngestKnowledgeBaseDocuments), Managed Knowledge Base uses the same APIs, so migration requires no code changes, just point to the new knowledge base ID.

This approach ensures you can spend time on your generative AI application without losing the ability to change models based on evolving requirements or new model capabilities.

Get started today
Amazon Bedrock Managed Knowledge Base is available today in the US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney, Tokyo), Europe (Dublin, Frankfurt, London), and AWS GovCloud (US-West) Regions. For Regional availability and future roadmap, visit AWS Capabilities by Region.

With Bedrock Managed Knowledge Base, you pay for what you use with no upfront commitments. Pricing is based on two dimensions: the size of indexed data stored and the number of retrievals performed (on-demand). For detailed pricing information, visit the Amazon Bedrock pricing page. Bedrock is also a part of the AWS Free Tier that new AWS customers can use to get started at no cost and explore key AWS services.

These capabilities work with any open source framework such as CrewAI, LangGraph, LlamaIndex, and Strands Agents, and with any foundation model. Bedrock services can be used together or independently, and you can get started using your favorite AI-assisted development environment with the AgentCore open source MCP server.

To learn more and get started quickly, visit the Bedrock Knowledge Bases Developer Guide.

Daniel Abib

 Updated on June 19, 2026 — Fixed correct screenshots to create a new Managed KB.

Today, we’re announcing the general availability of Web Search on Amazon Bedrock AgentCore, a fully managed tool that enables agents to ground responses in current, cited web knowledge with zero data egress from customer’s secured AWS environment.

Web Search uses a built-in connector target on Bedrock AgentCore Gateway using the Model Context Protocol (MCP). Your agent sends a natural-language query, and Web Search returns most relevant snippets, source URLs, titles, and publication dates that the model can reason over to produce a grounded response.

It is built on Amazon’s search infrastructure, informed by years of experience powering agentic search experiences across Alexa+, Amazon Quick, and Kiro. It uses a multi-source grounding approach that combines Amazon’s web index with structured knowledge graph data. Beyond standard web results, this gives agents access to Amazon Knowledge Graph with verified facts, helping them retrieve more relevant and accurate responses than traditional web search alone.

With this launch, you can focus on building agents instead of manually adding web search to agents on Bedrock AgentCore and managing its infrastructure. Your AI agent looks at user question, retrieves the latest facts, and then takes any necessary action grounded in current developments beyond a model’s training data. You can also meet enterprise governance policies without sending user prompts and retrieval queries to external search API providers outside of AWS.

Web Search on Bedrock AgentCore in action
To get started, create the Bedrock AgentCore Gateway with Web Search tool target in the Bedrock AgentCore console. When the Gateway URL is created, you can interact with API call, Command Line Interface (CLI), or MCP Inspector.

To add Web Search tool target when creating the Gateway, choose MCP target as a target protocol and Connectors as a target type. You can select the Web Search tool as a preconfigured target to retrieve most relevant web search results including links, snippets, and metadata.

After creating your gateway, you can find the Web Search tool target on the detail page of your gateway. You can also add a new Web Search tool target to an existing gateway.

To interact with Web Search tool, use the sample invocation code in the View invocation code section. You can use code snippets through Python codes with API requests, MCP Python SDK, Strands MCP Client, and MCP Inspector.

For example, you can interact with the MCP Inspector, an interactive developer tool for testing and debugging MCP servers. When you connect to the MCP server through the Gateway resource URL, you will find a Web Search tool for each connector target on the Gateway. Enter input the web search query and choose Run Tool to get the results.

To learn more about how to use Web Search on Bedrock AgentCore, visit the Bedrock AgentCore Gateway documentation.

Customer voices
Some of our customers had early access to this new feature. This is what they shared with us:

Benchling helps scientists accelerate R&D, making it easy to centralize scientific data, collaborate across teams, and access insights. Nicholas Larus-Stone, Head of AI Agents at Benchling shared “Scientists using Benchling AI can now ask about a target they’re actively working on and get answers grounded in both their institutional data in Benchling and published literature. The result is more complete science, and hypothesis generation done right. Because we’re using the Web Search tool on Amazon Bedrock AgentCore, customers have a secure, governed environment to bring that high quality published data into their workflows without compromising how they manage their data.”

Gen Digital leads consumer and small business cyber safety, offering antivirus, antimalware, identity and privacy protection, virtual private networks, and cloud backup. Iskander Sanchez-Rola, Senior Director of AI & Innovation, Gen Digital shared “With the Web Search tool on Amazon Bedrock AgentCore, Norton Revamp helps professionals build their online reputation with current, grounded content ideas shaped by what’s actually happening in the world today. What we value most is that AWS uses its own search index and keep queries within our trusted AWS environment.”

To read more customer stories, visit the Amazon Bedrock Customers.

Now available
Web Search on Amazon Bedrock AgentCore is generally available today in the US East (N. Virginia) Region. For Regional availability and a future roadmap, visit the AWS Capabilities by Region.

You can get started with Web Search on Bedrock AgentCore with no upfront commitments. Pricing is simple and usage-based. You are charged based on the number of search queries your agents submit to the web search. Web Search is priced at $7 per 1,000 queries. New AWS customers also receive up to $200 in Free Tier credits. To learn more, visit the Amazon Bedrock AgentCore pricing page.

Try it in the Amazon Bedrock AgentCore console and send feedback to AWS re:Post for Amazon Bedrock AgentCore or through your usual AWS Support contacts.

— Channy

Updated on June 18, 2026 — Added a clear pricing statement for Web Search in Bedrock AgentCore.

As I navigate my career change after Ai2, I wanted to share my views of how this blog relates to my missions and broader work. In my farewell post, I summarized my three goals right now as:

1. Provide clarity in the evolution of frontier models.

2. Create a vibrant and diverse open (model) ecosystem.

3. To build institutions that make these goals possible.

Within this, Interconnects is at its core a bit different than many of the highly-polished, professional newsletters on this platform – and this is becoming intentional.

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How Interconnects fits into my career goals

Interconnects is the tip of the spear of all of my missions in AI. It is meant to start a conversation and to let the reader into the mind of someone at the frontier. This insight makes the writing sometimes a bit raw, sometimes a bit too technical, but it is the map of how I progress my thinking in the ever changing world.

This style of writing has helped me create very strong relationships with the core group of readers, many of who listen to the voiceovers I do for these posts. The plan is to keep operating and refining the Interconnects experience around those loyal fans. These are to a large part people building the frontier AI ecosystem — researchers at labs, top investors, policymakers obsessed with the frontier, and students aspiring to have one of those roles.

I’m very happy with this sort of raw, high-voice outcome for the blog. It is not something I sought out, but rather accepted as I saw it coming and realized it would be disproportionately successful in a near-future of vast AI slop media. With years of trying to squeeze writing into a busy schedule, the only sort of writing I had time for was that which had a style very closely matching how I think.

I’m also very happy to be an independent voice. As a person I don’t do well with some power structures like having a boss, and I think there are very few people without extreme financial conflicts of interest that are willing and allowed to write. Through a wide job search, few companies were genuinely excited about me continuing writing.

Over the past few months, I considered taking Interconnects in more of a direction like SemiAnalysis or Stratechery, where it is my full-time gig and number one priority, but it didn’t seem like the right fit for what I am trying to achieve. I’m trying to build an open ecosystem and a movement for true open-science at the frontier of AI. These areas are very narrowly populated and trying to influence them with only commentary, analysis, and related research products wouldn’t work for me.

These sorts of full-time outcomes are definitely still one of my dreams, and I will do it at some point. The dream of this is also one of the reasons I take conflicts of interest seriously. Though, in this era of AI I can’t be fully on the outside.

In this vein, I wanted to disclose two advising agreements I recently signed. I don’t view them as a compromise of the above independence, as I’ll happily quit if I feel like I can’t speak my mind, but as a form of support in accomplishing my missions.

If I want to make a true open-science ecosystem I have some catching up to do with how the frontier labs approach post-training. The two companies I’m advising, whose leadership I’ve become friends with, are Arcee AI and Mercor. Arcee should be fairly obvious as the no-nonsense player building open-weight models. Mercor will make more sense over time, but they’re a close ally to a lot of my goals in transparent evaluations, open post-training, and neutrality with respect to the leading labs. These advising agreements are based on me wanting to learn more, and I don’t suspect I will ever engage in the very cursory advising roles that are more of name-stamping.

I keep an up-to-date disclosures statement at the end of the Interconnects about page: https://www.interconnects.ai/about.

Otherwise, my full-time job should still be in the non-profit sector as long as I get the next few months of logistics right.

Some operations & audience notes

Interconnects has cultivated an excellent, niche, and largely technical audience with representatives of all the top companies and labs (recently crossed 70K subscribers). I intend to protect this niche audience rather than trying to expand to bigger pastures. I think this success in audience alignment is reflected in my ~900 paid subscribers supporting it with infrequent paywalled content. I appreciate the support greatly, as the money has let me expand Interconnects operations and quality over the last 18 months.

I created Interconnects AI, LLC last January along with business bank accounts. Since then I’ve made some money, but I’ve reinvested it (and more) back into the business and the various AI services I need to try to write these articles. So, at this moment going full-time on Interconnects is a pretty risky financial proposition for me. In fact the Interconnects bank account has hovered around $0 for months (I’m personally fine having another job). This made me hesitate in going all-in on it, but in reflections I concluded that I would have more impact in AI by building these systems than focusing on commentary.

Second, as AI services get more expensive (e.g. Fable becoming API only), I’m going to need to spend more out of pocket to make this happen. I’m happy to do this in the near term, but I’m starting to optimize the blog to have more consistent financial growth, so when I want to go all in on writing in a few years I have a safety net.

I don’t do special offers, free trials, etc. for Interconnects paid subscribers (mostly to mitigate noise in the Discord community), but if you have the means to support this project it would mean a lot to me as I center my career around it. Joining a lab or a well-paying startup would be a much simpler path for me and my family but it’s never felt like the right thing to do.

I have a very arbitrary goal of reaching the 1000 paid subscribers orange checkmark on Substack this summer. So you can help and/or just watch my attempts to make it happen.

In this vein, I wanted to be direct in sharing how I view a few core operational components of Interconnects, and what you can expect going forward.

1. All comments will be paywalled. Whenever I have a popular post without paywalled comments I get a flood of low-quality posts — many of which are obviously AI generated. This is a detriment of the highly selective audience we’ve built. If Substack supports a feature like “only users with a paid subscription somewhere on the platform can engage,” I’d implement it. The blog comments, Substack chat, and Discord will be spaces where I perform active curation to maintain a 0% AI slop rate.

2. Slightly more articles will be paywalled. I want to keep experimenting with what is the right way to do this, but the only metric I can rely on for increasing influence of the blog is revenue. Views, likes, etc. are all vanity metrics which don’t reliably measure this type of content. Cultivating a highly engaged audience is existential to me in attempting to maintain an AGI-proof expertise.

3. Slightly more in-person events. With a small community that I respect, I have to opportunity to translate that to excellent real-world experiences. I expect to keep these small, but I want to be more proactive at organizing them so loyal readers know what to expect. The few coming soonest will be for my book launch, which should be in the next month or two. Plus, I know people always want to meet likeminded folks in AI!

Together these should make it easier and more enjoyable to be a loyal fan for Interconnects. I’m looking forward to continuing convincing my fans that the support is worthwhile.

Thanks for reading! My career wouldn’t be possible without all of the support.

On 19 April 2026, the Honor Lightning humanoid robot ran a half-marathon in 50 minutes and 26 seconds, beating the human world record by 7 minutes and the best robot time from 2025 by almost 2 hours.

How did Honor do it? Is there some magical technology or technique that unlocked this performance? How did the company beat the significantly better-known Unitree (which reportedly had to supply its robot with an ice backpack to try and complete the race without overheating)? My doctoral thesis involved building and controlling hopping and running robots, and since then I’ve tried to design and build efficient commercial legged robots, giving me a decent idea of the constraints involved. In this article, we take a look at the fundamental underlying constraints to try and answer these questions.

The Physics of Running

Running consists of alternating phases of a leg pushing against the ground (“stance phase”) and the body flying through the air (“aerial phase”). In the aerial phase, the body falls due to gravity, losing vertical momentum. The leg in stance phase pushes against the ground to redirect the vertical momentum upward, while the other leg swings forward to reposition for the next foothold.

Electric motors use energy to produce torque—the higher the torque, the more energy is lost as heat. Adding a gear train after the motor amplifies its torque and reduces its speed. A large reduction helps with torque production, but since the rotor of the motor itself has to spin faster, it becomes very sluggish at accelerating its output. This is obviously bad for the swing phase described above. These competing effects mean that for a particular motor, there is usually a sweet spot for the gear ratio:

The power consumed by a robot leg is minimized at an optimal gear ratio (30:1 in this example).Avik De/Datawrapper

How Honor Did It

While the Lightning’s motor specifications are not published, the hip and knee motors roughly have a 110-to-150-millimeter outer diameter. For an approximate set of motor parameters, I looked to the ILM115x25 motor due to its relevant size and detailed specifications.

We can use a simple physics model to estimate the power consumption for running at 7 meters per second (the Lightning’s average half-marathon speed) as gear ratio varies:

The light blue curve shows how to pick the optimal gearing (45:1). The dark blue curve shows how much heat will be produced in the knee motor, ~150W for the optimal gearing.Avik De/Datawrapper

We see that the drivetrain is not magical: with a gear ratio chosen for this task (we’ll return to this below), the approximate robot power consumption would be a very reasonable 400 watts.

However, the dissipated knee power ( typically the main thermal limiting factor) is approximately 150 W. This is almost an unavoidable consequence—running at human speeds with a humanoid-size robot will inevitably generate this amount of heat! Over a prolonged period, keeping the motor from overheating would be a challenge, but the Lightning has a trick up its sleeve:

According to Honor, the liquid-cooling pipes penetrate deep into the motors like capillaries. The high-power liquid pump has a heat-exchange flow rate of more than 4 liters per minute. Each of the four drive motors in the lower limbs is equipped with an independent liquid-cooling circuit.

Liquid cooling is not new, but it’s definitely not a commodity. It has shown up in research periodically, and on the commercial side Apptronik tried it for a few of its prototypes but (to my knowledge) does not use it on its main Apollo platform. Basic air-convection-based cooling would not continuously be able to extract 150 W out of the knee motor, and so the cooling technology is a key enabler of this type of performance.

Why Others Couldn’t Compete

Why did Honor’s competitors, including more established and widely shipped humanoids such as from Unitree or Agibot, not compete as well?

We can use the same model to generate an equivalent energetics plot for walking at 1.5 m/s, a much more modest but potentially more common activity for a commercial humanoid robot:

The solid and dashed light blue lines show a running-optimized design, while green lines show a walking-optimized design. The optimal ratio for walking is much lower (30:1 vs. 45:1). However, the power dissipated in the knee motor while running [dark blue] is much higher at 30:1 vs. 45:1—the price to pay for running with a walking-optimized design.Avik De/Datawrapper

The plot adds a new green curve for the walking power, and the optimal gearing is significantly different!

Let’s say you design your robot to excel at the normal walking task and choose the green design with 30:1 gearing. The knee motor power to run a half marathon is over 300 W (red arrow), more than two times what we had with the running-optimized design. It wouldn’t be so surprising to need ice packs!

Conversely, visually following the green curve shows that the running-optimized robot wastes more power for walking. Using larger motors sized for running increases the weight of the robot and wastes power when it is standing or walking. The larger motors also pose practical issues like bumping into objects while operating in homes or factories.

Closing Thoughts

Honor’s half-marathon performance was an impressive engineering effort and result. It didn’t need any magical leaps in technology, but the deployment of the capillary motor cooling solution is a notable advance without which this running pace would have been unsustainable. The cooling, weight optimization, and robustness advances may well be useful for more practical purposes like carrying heavy payloads down the line.

The Honor Lighting robot [right] has much larger motors driving its legs than the Unitree H1 robot, making it a more efficient runner but a less efficient walker.Left: Wei Zhiyang/Zhejiang Daily Press Group/VCG/Getty Images; Right: VCG/Getty Images

However, the Lightning is not as well-suited to other tasks as a robot designed for greater versatility. Engineering is always characterized by trade-offs, and making the correct ones separates good products from great ones. With consistently improving AI language models, this very human skill is becoming the most valuable one an engineer can have.

The news coverage seemed to overly focus on the fact that the human half-marathon record had been broken by a robot. Machines and humans have very different capabilities and constraints, so why should we ever have expected the half-marathon time for a robot and human to be related? As in Deep Blue’s 1997 defeat of Garry Kasparov in chess, where it couldn’t physically move the pieces, the Honor robot’s capabilities are much narrower than a human running elbow to elbow with other runners while visually navigating the course without GPS. Comparing the robot runner to a human runner is just an apples-to-oranges comparison, which only risks diminishing Honor’s engineering achievement on one hand and human athletic achievement on the other.

The Department of the Treasury, as part of its continuing effort to reduce paperwork and respondent burden, invites the general public and other federal agencies to comment on the proposed information collection listed below, in accordance with the Paperwork Reduction Act of 1995.

The General Services Administration (GSA) is seeking public comment on the draft of a new General Services Administration Acquisition Regulation (GSAR) clause regarding basic safeguarding of data within Large Language Model Artificial Intelligence Systems (LLMs). Due to the complexity of the issue, GSA is publishing this notification and draft clause to gather feedback from stakeholders before taking future action (e.g., deviation and/or formal rulemaking).

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