New York-listed NIO has become the latest Chinese carmaker to eye artificial intelligence robots, an emerging market with strong growth potential that has already attracted established players such as BYD, Chery, and GAC. Tesla is a pioneer in the field, with chief executive Elon Musk stating that its humanoid robot, Optimus, could be ready for sale by the end of 2025, Reuters reported.
In China, BYD announced a recruitment drive for its Embodied Intelligent Research Team last month, a year after investing in Shanghai-based software and robotics startup Agibot. GAC, a manufacturing partner of Toyota and Honda in China, recently showcased its humanoid robot featuring alterable wheels that can carry out tasks on stairs and grass, and in crowded places, considered complex environments. Xpeng Motors in 2021 unveiled a four-legged, low-speed rideable robot pony for kids, South China Morning Post reported.
Chinese tech giants have also begun exploring the industry, with Huawei having established a research center for embodied intelligence in Shenzhen last November, and Xiaomi’s humanoid robot prototype having debuted in 2022. Some carmakers, such as Audi and Geely-owned Zeekr, are testing robots developed with Hong Kong-listed UBTECH in their factories.
According to the Financial Times, ByteDance is planning to invest over $12 billion in artificial intelligence infrastructure in 2025. Sources familiar with the matter revealed that ByteDance has allocated a budget of RMB 40 billion ($5.5 billion) to purchase AI chips in China by 2025, doubling its spending from last year. Additionally, the company plans to invest approximately $6.8 billion overseas, using NVIDIA chips to enhance its foundational model training capabilities. However, ByteDance has denied these claims.
Why it matters: ByteDance’s big investment shows it is betting heavily on AI as a way to drive future growth. With TikTok facing restrictions in several countries, the company is turning to AI development and investment in its quest for new opportunities.
Details: ByteDance is expanding its AI development efforts both in China and internationally, aiming to boost its capabilities in a highly competitive market.
According to another report from the Financial Times, ByteDance’s full-scale commitment to generative AI is being led by its founder, Zhang Yiming.
In China, ByteDance’s AI chatbot Doubao has taken an early lead. As of December 2024, Doubao recorded 31.31 million monthly visits to rank second in AI web traffic in the country.
ByteDance has also invested in AI and data center facilities in the Southeast Asian market: BytePlus, a ByteDance subsidiary, is planning to build a data center in Thailand by 2025. In June 2024, ByteDance announced plans to invest MYR 10 billion ($2.1 billion) to establish an AI hub in Malaysia.
ByteDance also competes with major Chinese tech giants such as Baidu, Alibaba, and Tencent, all of which are heavily investing in generative AI and foundational model training.
Context: ByteDance’s ambitious AI investments come at a time when its core software business faces increasing challenges. TikTok, the company’s globally popular video-sharing app, is under regulatory scrutiny in several countries. At the same time, US-China trade tensions, particularly in the semiconductor sector, pose significant hurdles for ByteDance’s AI development. Export controls imposed by the US limit Chinese companies’ access to advanced chips like NVIDIA’s high-performance GPUs, which are essential for training large AI models. The AI sector itself is highly competitive, with major players like Baidu, Alibaba, and Tencent racing to develop advanced generative AI technologies and foundational models. These companies are not only competing on innovation but also on scale, infrastructure, and cost-effectiveness, making the AI race both a strategic and resource-intensive endeavor.
In a recent research publication: Building effective agents, Anthropic shared valuable insights about building effective Large Language Model (LLM) agents. What makes this research particularly interesting is its emphasis on simplicity and composability over complex frameworks. Let's explore how these principles translate into practical implementations using Spring AI.
While the pattern descriptions and diagrams are sourced from Anthropic's original publication, we'll focus on how to implement these patterns using Spring AI's features for model portability and structured output. We recommend reading the original paper first.
The agentic-patterns project implements the patterns discussed below.
Agentic Systems
The research publication makes an important architectural distinction between two types of agentic systems:
Workflows: Systems where LLMs and tools are orchestrated through predefined code paths (e.g., prescriptive system)
Agents: Systems where LLMs dynamically direct their own processes and tool usage
The key insight is that while fully autonomous agents might seem appealing, workflows often provide better predictability and consistency for well-defined tasks. This aligns perfectly with enterprise requirements where reliability and maintainability are crucial.
Let's examine how Spring AI implements these concepts through five fundamental patterns, each serving specific use cases:
The Chain Workflow pattern exemplifies the principle of breaking down complex tasks into simpler, more manageable steps.
When to Use:
Tasks with clear sequential steps
When you want to trade latency for higher accuracy
When each step builds on the previous step's output
Here's a practical example from Spring AI's implementation:
public class ChainWorkflow {
private final ChatClient chatClient;
private final String[] systemPrompts;
// Processes input through a series of prompts, where each step's output
// becomes input for the next step in the chain.
public String chain(String userInput) {
String response = userInput;
for (String prompt : systemPrompts) {
// Combine the system prompt with previous response
String input = String.format("{%s}\n {%s}", prompt, response);
// Process through the LLM and capture output
response = chatClient.prompt(input).call().content();
}
return response;
}
}
This implementation demonstrates several key principles:
LLMs can work simultaneously on tasks and have their outputs aggregated programmatically. The parallelization workflow manifests in two key variations:
Sectioning: Breaking tasks into independent subtasks for parallel processing
Voting: Running multiple instances of the same task for consensus
When to Use:
Processing large volumes of similar but independent items
Tasks requiring multiple independent perspectives
When processing time is critical and tasks are parallelizable
The Parallelization Workflow pattern demonstrates efficient concurrent processing of multiple Large Language Model (LLM) operations. This pattern is particularly useful for scenarios requiring parallel execution of LLM calls with automated output aggregation.
Here's a basic example of using the Parallelization Workflow:
List<String> parallelResponse = new ParallelizationWorkflow(chatClient)
.parallel(
"Analyze how market changes will impact this stakeholder group.",
List.of(
"Customers: ...",
"Employees: ...",
"Investors: ...",
"Suppliers: ..."
),
4
);
This example demonstrates parallel processing of stakeholder analysis, where each stakeholder group is analyzed concurrently.
The Routing pattern implements intelligent task distribution, enabling specialized handling for different types of input.
This pattern is designed for complex tasks where different types of inputs are better handled by specialized processes. It uses an LLM to analyze input content and route it to the most appropriate specialized prompt or handler.
When to Use:
Complex tasks with distinct categories of input
When different inputs require specialized processing
When classification can be handled accurately
Here's a basic example of using the Routing Workflow:
@Autowired
private ChatClient chatClient;
// Create the workflow
RoutingWorkflow workflow = new RoutingWorkflow(chatClient);
// Define specialized prompts for different types of input
Map<String, String> routes = Map.of(
"billing", "You are a billing specialist. Help resolve billing issues...",
"technical", "You are a technical support engineer. Help solve technical problems...",
"general", "You are a customer service representative. Help with general inquiries..."
);
// Process input
String input = "My account was charged twice last week";
String response = workflow.route(input, routes);
This pattern demonstrates how to implement more complex agent-like behavior while maintaining control:
A central LLM orchestrates task decomposition
Specialized workers handle specific subtasks
Clear boundaries maintain system reliability
When to Use:
Complex tasks where subtasks can't be predicted upfront
Tasks requiring different approaches or perspectives
Situations needing adaptive problem-solving
The implementation uses Spring AI's ChatClient for LLM interactions and consists of:
public class OrchestratorWorkersWorkflow {
public WorkerResponse process(String taskDescription) {
// 1. Orchestrator analyzes task and determines subtasks
OrchestratorResponse orchestratorResponse = // ...
// 2. Workers process subtasks in parallel
List<String> workerResponses = // ...
// 3. Results are combined into final response
return new WorkerResponse(/*...*/);
}
}
Usage Example:
ChatClient chatClient = // ... initialize chat client
OrchestratorWorkersWorkflow workflow = new OrchestratorWorkersWorkflow(chatClient);
// Process a task
WorkerResponse response = workflow.process(
"Generate both technical and user-friendly documentation for a REST API endpoint"
);
// Access results
System.out.println("Analysis: " + response.analysis());
System.out.println("Worker Outputs: " + response.workerResponses());
The Evaluator-Optimizer pattern implements a dual-LLM process where one model generates responses while another provides evaluation and feedback in an iterative loop, similar to a human writer's refinement process. The pattern consists of two main components:
Generator LLM: Produces initial responses and refines them based on feedback
Evaluator LLM: Analyzes responses and provides detailed feedback for improvement
When to Use:
Clear evaluation criteria exist
Iterative refinement provides measurable value
Tasks benefit from multiple rounds of critique
The implementation uses Spring AI's ChatClient for LLM interactions and consists of:
public class EvaluatorOptimizerWorkflow {
public RefinedResponse loop(String task) {
// 1. Generate initial solution
Generation generation = generate(task, context);
// 2. Evaluate the solution
EvaluationResponse evaluation = evaluate(generation.response(), task);
// 3. If PASS, return solution
// 4. If NEEDS_IMPROVEMENT, incorporate feedback and generate new solution
// 5. Repeat until satisfactory
return new RefinedResponse(finalSolution, chainOfThought);
}
}
Usage Example:
ChatClient chatClient = // ... initialize chat client
EvaluatorOptimizerWorkflow workflow = new EvaluatorOptimizerWorkflow(chatClient);
// Process a task
RefinedResponse response = workflow.loop(
"Create a Java class implementing a thread-safe counter"
);
// Access results
System.out.println("Final Solution: " + response.solution());
System.out.println("Evolution: " + response.chainOfThought());
Spring AI's Implementation Advantages
Spring AI's implementation of these patterns offers several benefits that align with Anthropic's recommendations:
The combination of Anthropic's research insights and Spring AI's practical implementations provides a powerful framework for building effective LLM-based systems. By following these patterns and principles, developers can create robust, maintainable, and effective AI applications that deliver real value while avoiding unnecessary complexity.
The key is to remember that sometimes the simplest solution is the most effective. Start with basic patterns, understand your use case thoroughly, and only add complexity when it demonstrably improves your system's performance or capabilities.
Spring AI MCP 0.5.0 is now available: Spring AI makes it trivial to build MCP clients and services. MCP, of course, is a protocol that allows you to integrate arbitrary external data sources into your LLMs, like Claude.
In last week's installment of A Bootiful Podcast, I talked to the good, the great, the legendary Dr. Dave Syer. We explored the awesome (and experimental!) new Spring gRPC project.
Wow! This post on using Spring AI and Vaadin to build an application that accepts images as inputs and then converts them into editable grids of data in a Vaadin UI is something to behold!
I love SivaLabs' amazing post looking at the JdbcClient, which has been in Spring Framework for more than a year. I have replaced all my JDBC work with the JdbcClient, vastly preferring it to the JdbcTemplate for my work.
Dhaval Shah has a great post on diving into virtual threads. It’s an insightful read comparing parallel processing with virtual threads, Project Reactor, and the JDK!
Enjoy the updates, and I’ll catch you next week—probably still sandy, sunburned, and thinking about Spring!
We just published our annual list of 10 Breakthrough Technologies. Every year, our reporters and editors look for promising technologies poised to have a real impact on the world. We consider dozens of advances across the fields of AI, biotech, computing, and climate. We can’t see the future, but we expect these ten breakthroughs to affect our world in a big way, for decades to come.
Here are the ten items on this year’s list:
The Vera C. Rubin Observatory: A powerful new telescope will help astronomers study dark matter, explore the Milky Way, and untangle other cosmic unknowns.
Generative AI search: Generative search promises to make finding what you’re looking for simple and quick. It may signal the end of traditional search engines and the rise of personal AI assistants.
Small language models: Cheaper and less power-hungry AI models can now stand with the heavyweights across a range of specific tasks.
Cattle burping remedies: A food supplement that significantly reduces the amount of methane that cattle belch is now available in dozens of countries.
Robotaxis: Driverless cars have completed years of beta testing, and they are now finally becoming available to the public.
Cleaner jet fuel: New fuels made from used cooking oil, industrial waste, or even gasses in the air could help power planes without fossil fuels.
Fast-learning robots: We’re getting closer to general-purpose robots that could be dropped into new environments and tackle a variety of tasks on our behalf, almost instantly.
Long-acting HIV prevention meds: A new drug could help us end AIDS once and for all—if we can ensure access for those who need it.
Green steel: Making steel is one of the largest industrial sources of carbon dioxide. The first industrial green-steel plant, which uses hydrogen made with renewable power is scheduled to begin operations next year in northern Sweden.
Stem-cell therapies that work: Experimental transplants of lab-made cells seem to be helping treat two very different conditions—epilepsy and type 1 diabetes.
Ask us anything! (We’ll be here responding to your questions this Friday, January 10 at 12 p.m. EST, but feel free to get 'em in early.) Proof pics here.
I have tried several monitor setups—two landscape; one landscape and one portrait; various 27” and 32” combos, a ‘mini’ ultrawide, and finally (for now!) a proper ultrawide, curved screen.
The 13 June “Ramstein” meeting is part of the NATO Defence Ministers’ meeting. According to the published agenda, a meeting of the Ukraine-NATO Council on the defense ministers’ level will also be held today.
Ukrainian Defense Minister Rustem Umerov will participate in both events. It is the last ministerial meeting before the NATO summit in Washington in July. Over these two days, ministers are expected to come to agreements on issues that will be formally approved by the leaders of the Alliance countries in a month.
The priority is the sustainable long-term support of Ukraine. This includes strengthening NATO’s role in coordinating military assistance to Ukraine and conducting training for its Defense Forces.
Earlier, NATO Secretary General Jens Stoltenberg visited Hungary and held talks with Prime Minister Viktor Orban.
The visit’s purpose was to persuade Budapest not to block new initiatives to help Ukraine, as unanimity among NATO members is required for their approval. Hungary said it would not block the decisions but would not send funding or its troops to Ukraine.
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According to Nazar Voloshyn, a spokesman for the Khortytsia Operational-Strategic Grouping of Forces, Russian actions became “extremely active” starting on 10 June in the Chasiv Yar area. The enemy has almost continuously stormed the town with the aid of tanks and other armored vehicles.
Voloshyn stated that the occupiers are also trying to conduct assault operations supported by aircraft and drones. Russians are actively using unguided air-launched rockets and deploying unmanned aerial vehicles in the outskirts of Chasiv Yar, according to him.
Situation in Chasiv Yar as of 13 June 2023. Map: Deepstatemap.
Despite the onslaught, Ukrainian defense forces have successfully held their positions and even reinforced them in certain areas, the spokesman said. As of the early hours of 13 June, five combat engagements had already taken place – three Russian attacks were repelled near Andriivka, while two clashes were still ongoing near Ivanivske.
While the situation remains “somewhat tense”, the invading forces have suffered significant losses, according to Voloshyn. Over the past day alone, 90 Russian troops were killed and 138 wounded in this particular sector of the front.
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The UK Government has announced a new package of 50 sanctions designations and specifications aimed at degrading Russia’s ability to fund and sustain its illegal war in Ukraine. The measures, announced while Prime Minister Rishi Sunak attends the G7 Leaders Summit in Italy, are part of coordinated action with the UK’s G7 partners to support Ukraine.
These new sanctions target various entities and individuals involved in Russia’s war effort. Notably, the UK has imposed its first sanctions on vessels in Putin’s “shadow fleet,” used by Russia to circumvent existing sanctions and continue trade in Russian oil. The UK is also targeting suppliers of munitions, machine tools, microelectronics, and logistics to Russia’s military, including entities based in China, Israel, Kyrgyzstan, and Türkiye, as well as ships transporting military goods from North Korea to Russia.
Furthermore, the sanctions crack down on institutions at the heart of Russia’s financial system, such as the Moscow Stock Exchange, in coordination with the United States, which designated the exchange a day earlier, on 12 June.
UK Prime Minister Sunak emphasized that the UK will “always stand shoulder to shoulder with Ukraine in its fight for freedom,” and that “cutting off [Putin’s] ability to fund a prolonged conflict is absolutely vital.” British Foreign Secretary David Cameron stated that the sanctions are “starving Putin of the revenue he desperately needs to fund his war chest and making it harder to supply his war machine.”
According to the press release, sanctions have deprived Russia of over $400 billion worth of assets and revenues since February 2022, equivalent to four more years of funding for the invasion. The UK has sanctioned over 2,000 individuals and entities under its Russia sanctions regime, including 29 banks accounting for over 90% of the Russian banking sector and over 130 oligarchs and family members with a combined net worth of around £147 billion or $187.6 at the time of the invasion.
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The Ukrainian border guards received evidence pointing to an illegal art dealing in progress. The relevant information was forwarded to their Polish counterparts who confirmed the initial tip. The two border agencies, together with Dutch officials, then launched a joint operation to recover the artwork.
On Ukraine’s territory, the Dutch Embassy’s liaison officer was involved in the effort. As a result, the stolen 17th century painting by the Dutch master Jan Linsen (1603-1635) has been successfully located in an apartment in Poland and seized.
Law enforcement detained the perpetrator and pressed charges. The individual will be remanded in custody for three months as the investigation is led by the District Prosecutor’s Office of Kraków, Poland.
A Ukrainian man has been charged with dealing in stolen property and could face up to 10 years in prison. A local border guard spokesperson noted that the recovered painting is now safely stored at the Royal Castle in Kraków.
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In a letter to EU’s top diplomat Josep Borrell seen by Reuters, dated 11 June, the ministers stated that the “free movement of holders of Russian diplomatic and service passports, accredited in one host state, across the whole Schengen area is easing malign activities.”
The ministers, from the Czech Republic, Denmark, Estonia, Latvia, Lithuania, the Netherlands, Poland, and Romania, said that intelligence, propaganda “or even preparation of sabotage acts are the main workload for a large number of Russian ‘diplomats’ in the EU.“
While expulsions were acknowledged as important, the letter stated that the threat remained.
“We believe the EU should strictly follow the reciprocity principle and restrict the movement of members of Russian diplomatic missions and their family members to territory of a state of their accreditation only,” the ministers said, adding that “this measure will significantly narrow operational space for Russian agents.”
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A Chinese AI content creator has challenged cybersecurity firm 360 Security Technology over its use of an image of a woman wearing an ancient costume, which he claims he created via an AI model to showcase a “partially redrawn feature” on the company’s search engine.
The argument between creator DynamicWang and 360 Security escalated as the two parties failed to settle, with the company’s vice president Liang Zhihui saying it is willing to resort to legal action.
Repaint photo at 360 Security’s AI products launch event(Left); Original image by creator “DynamicWang”(Right). Credit: 21jingji Credit:21jingji
Why it matters: As the emerging technology of artificial intelligence is increasingly being used to create content, authorship attribution lacks a clearly defined legal framework.
Details: Even before DynamicWang publicly asked 360 Security to apologize over the alleged copyright infringement on June 8, the firm was in the midst of a public opinion storm, this time over its perceived disrespect toward women. When founder Zhou Hongyi introduced the repainting function at the company’s AI product launch on June 6, he used “sexy” as a prompt to ask the AI-powered search engine to redraw a woman’s breasts of the female in the controversial picture, which DynamicWang since claimed was based on his AI-generated work.
The creator said 360 Security should acknowledge the infringement and compensate him with a symbolic RMB 1 ($0.14). He said the firm “stole” the image generated by his self-trained AWPortrait model.
According to Liang, the author made a request to the company that he wants 360 Security paid him ten times the model price, together with the cost of compensation.
Local media outlet NBD quoted DynamicWang as interpreting the price to use his model as RMB 4,499 is for small- and medium-sized AI startups. This was not the price set for public-listed 360 Security, the creator said.
Each side has its own version of the story. While the creator mentioned compensation and apology were “prerequisites,” and buying the model was optional, Liang said purchasing the author’s AWPortrait model for ten times the price was “beyond our knowledge,” according to a WeChat screenshot of his conversation with DynamicWang. The current argument between the two sides is whether DynamicWang owns the copyright to the images generated by the model he trained.
In his latest Weibo post late Wednesday, the creator introduced himself as a photographer whose images are posted on Unsplash, Getty Images’ free gallery site, and that the training data he used to feed AWPortait model included his photography and Unsplash images, which he says means there are “no copyright issues” on his side.
Context: In January, the Beijing Internet Court granted copyright protection for an artificial intelligence-generated image, ruling that the image involved had an element of “originality” due to the plaintiff inputting multiple prompts and adjusting the parameters before generating a picture of a young woman through the text-to-image AI model Stable Diffusion. The ruling was seen as the first such AI-generated image copyright infringement case in China.
Why it matters: The rapid evolution of AI technology across multiple sectors has driven a surge in demand for advanced process chips. Increasing orders from major clients is expected to further push TSMC to innovate and develop advanced manufacturing processes.
Details: Currently, TSMC’s 3nm lineup includes N3, N3E, N3P, N3X, and N3A, according to the Economic Daily News report.
N3E, which entered mass production in the fourth quarter of last year, primarily serves AI accelerators, high-end smartphones, and data centers. N3P is expected to enter mass production in the latter half of this year and will become the preferred chip process for mainstream applications including mobile devices, consumer electronic products, base stations, and networking by 2026. N3X and N3A are designed for customized demands such as high-performance computing and automotive clients.
The latest high-end chips this year are all adopting the 3nm process, according to Chinese media outlet Icsmart. Apple will introduce the M4 series processors this year and is set to release the next-gen A18 series processors for its new iPhone16 series this September. Later this year, Qualcomm will launch its flagship mobile platform Snapdragon 8 Gen 4, while Nvidia is set to unveil the RTX 50 series graphics cards. AMD will release the fifth-gen EPYC Turin processors in the second half of the year, with plans to launch the MI350 series next year.
TSMC, as the exclusive supplier of Apple’s A-series processors for the new iPhones, will see a significant increase in demand for related 3nm orders. Apple’s iPhone 16 series this year is expected to increase sales volume by 5% year-over-year to 92-95 million units, the Economic Daily News report forecasted.
To ensure stable supply over the next two years, TSMC’s strategy is to convert some of its 5nm equipment to support 3nm production. Industry sources indicate that TSMC is likely to up its production of 3nm wafers to between 120,000 and 180,000 a month.
Context:The total production value of the top ten semiconductor foundries in the first quarter reached $29.2 billion, a decrease of 4.3% compared to the previous quarter, according to market research firm TrendForce.
In the first quarter, TSMC led with a revenue of $18.85 billion and a market share of 61.7%, followed by Samsung with $3.36 billion and an 11% market share. Shanghai-based chip foundry SMIC took third place with $1.75 billion in revenue and a 5.7% market share.
Russia carries out repeated indiscriminate aerial attacks on settlements near the frontline, possibly to demoralize the residents and depopulate the areas.
According to the Donetsk Oblast Prosecutor’s Office, the bomb impacted residential buildings, injuring five local civilians – a 65-year-old man and four women aged 63 to 88 years old. They suffered head injuries and multiple lacerations and were hospitalized to receive medical treatment.
Aftermath of Russia’s aerial bomb attack on Kostiantynivka, Donetsk Oblast, on 10 June 2024. Photo: Donetsk Oblast Prosecutor’s Office.
At least 13 apartment buildings sustained damage from the powerful blast wave, with shattered windows and broken glass throughout the residential units, according to the report.
The Donetsk Regional Prosecutor’s Office has opened an investigation into the attack for violating the laws and customs of war, which carries a maximum penalty of 12 years imprisonment.
The KAB-500 guided air bomb weighing 370-560 kg depending on its variant, can carry a 250-380 kg warhead.
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The Azov Brigade, recognized for its determined yet ultimately unsuccessful defense of the Azovstal steel mill in Mariupol during the early stages of Russia’s full-scale invasion of Ukraine in 2022, is considered a highly effective combat unit. At the end of the siege of Mariupol, Ukraine ordered the remaining troops in the steel factory to surrender to Russian forces for survival, leaving over 900 in captivity as of early May this year.
Back, in 2015, the US House passed an amendment barring Azov from using the US arms, based solely on media reports labelling Azov as a “neo-Nazi” and “fascist” unit. The image of a “Nazi unit” was amplified internationally by Russian propaganda outlets and was even used by Russia to justify its full-scale invasion of Ukraine by the claims that Ukraine allegedly needs to be “denazified.”
Now absorbed into the Ukrainian National Guard as a formal unit since 2015, the brigade, originally a volunteer militia, will have access to the same US military assistance as any other unit.
“After thorough review, Ukraine’s 12th Special Forces Azov Brigade passed Leahy vetting as carried out by the US Department of State,” the agency said in a statement, referring to the “Leahy Law” that prevents US military assistance from going to foreign units credibly found to have committed major human rights violations. The State Department found “no evidence” of such violations, its statement says.
Lifting the ban had been a top priority for Ukrainian officials, who argue that the brigade could have defended Azovstal more effectively in 2022 with access to US equipment. Additionally, members of the brigade were also prohibited from participating in training sessions organized by the US military.
Russian President Vladimir Putin has frequently cited the racist and ultranationalist allegations against the Azov Battalion to support his claim that Ukrainian fighters and their leaders in Kyiv are neo-Nazis. The shift in US policy is likely to reignite those Russian critiques, the Washington Post notes.
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