Tag: kimi

  • Chinese AI Labs ARE COPYING Claude?! Anthropic’s Distillation Bombshell

    Chinese AI Labs ARE COPYING Claude?! Anthropic’s Distillation Bombshell

    Anthropic just dropped a bombshell — and the AI community is having a field day with it. The company behind Claude is publicly accusing three major Chinese AI labs of running massive “distillation attacks” against their model. And honestly? The reaction has been anything but sympathetic.

    What Anthropic Is Claiming

    According to Anthropic’s official blog post, DeepSeek, Moonshot AI (the makers of Kimi), and MiniMax allegedly created over 24,000 fake accounts and generated more than 16 million queries against Claude. The goal? To extract Claude’s “secret sauce” — specifically its capabilities in agentic reasoning, tool use, and coding — and use that knowledge to train their own models.

    This technique is called model distillation. It’s actually a legitimate training method that AI labs use on their own models to create smaller, more efficient versions. But when you do it to a competitor’s model at industrial scale, that’s a different story entirely.

    The Scale Is Staggering

    The numbers Anthropic shared are pretty wild. According to TechCrunch, DeepSeek was tracked with over 150,000 exchanges focused on foundational logic and alignment — particularly around finding censorship-safe alternatives to policy-sensitive queries. Moonshot AI racked up 3.4 million exchanges targeting agentic reasoning, coding, and computer vision. But MiniMax was the biggest offender with 13 million exchanges, and Anthropic says they actually watched MiniMax redirect nearly half its traffic to siphon capabilities from the latest Claude model the moment it launched.

    Think about it this way: Anthropic has likely spent billions of dollars training Claude. These Chinese labs potentially replicated significant chunks of that capability for a fraction of the cost — maybe tens of thousands of dollars in API fees. That’s quite the ROI.

    Why This Isn’t Surprising

    If you’ve been following the Chinese AI scene, none of this should shock you. We’ve covered MiniMax and Kimi extensively on this channel, and their performance is genuinely impressive — roughly 95% of Claude’s capability at a fraction of the cost. MiniMax offers comparable performance at about 5% of the price. That kind of rapid improvement had to come from somewhere.

    China has a long history of building parallel ecosystems inspired by Western platforms. Taobao for eBay/Amazon, Weibo for Twitter (there are actually four Twitter clones in China), WeChat for everything else. The AI space is just the latest frontier, and the stakes are astronomically higher.

    The Internet Clapped Back Hard

    Here’s where it gets spicy. Anthropic is calling for “rapid, coordinated action among industry players, policy makers, and the broader AI community” to address these attacks. But the AI community’s response has been… let’s say unsympathetic.

    The backlash centers on one word: hypocrisy. Anthropic, now valued at a staggering $380 billion, is itself facing multiple lawsuits accusing the company of illegally using copyrighted internet data to train Claude. Even Elon Musk weighed in, pointing out that Anthropic allegedly settled a $1.5 billion lawsuit related to training Claude on copyrighted books. Someone even demonstrated that Claude could reproduce roughly 95% of Harry Potter books when prompted — suggesting Anthropic dumped massive amounts of copyrighted material into their training data.

    As many in the community put it: it’s “circle stealing.” Everyone’s copying from everyone. The Chinese labs at least paid for API access — the millions of writers whose work was scraped to train Claude weren’t given that courtesy.

    The Bigger Picture: Who Actually Wins?

    Here’s my take on why this whole situation is actually good for us. All this competition — whether through legitimate research or questionable distillation — is driving costs down dramatically. We no longer have to shell out thousands of dollars for top-tier AI access. Sure, Anthropic’s Opus 6 is still expensive, but when MiniMax gives you 95% of the performance at 5% of the cost, that’s massive savings for developers and businesses.

    And the race is far from over. DeepSeek is reportedly preparing to release V4, which could outperform both Claude and ChatGPT in coding tasks. Meanwhile, Moonshot just released Kimi K2.5 and a new coding agent last month.

    From our internal testing, Opus still has an edge. It’s appreciably smarter on logic tasks — like knowing you should drive to a car wash rather than walk (MiniMax still gets that wrong about 30% of the time, while Opus nails it 95% of the time). But whether that intelligence gap is worth paying 20x more is a question every developer has to answer for themselves.

    What Happens Next

    This story ties directly into the broader US-China AI rivalry. The Trump administration recently allowed Nvidia to export advanced H200 chips to China, and Anthropic is now arguing that distillation attacks “reinforce the rationale for export controls” since restricted chip access would limit both direct model training and the scale of these extraction campaigns.

    One thing that makes this race particularly interesting: AI doesn’t care what language you speak. You can paste a Chinese API, a Chinese website, and your AI tools will work with it seamlessly. The global push toward AGI is accelerating from all directions, and the competition between US and Chinese labs is only going to intensify.

    China produces more engineers per year than the US simply due to population scale, and those developers are feeding data back into Chinese models just as Western developers improve Claude through their interactions with it. This isn’t the first shot fired in this AI arms race, and it certainly won’t be the last.

    Whether you think this is good or bad for the industry, one thing’s clear: we’re all benefiting from cheaper, more capable AI as a result. And that’s something worth watching closely.

  • KimiClaw Review: Is It Worth the Hype and the Price?

    KimiClaw Review: Is It Worth the Hype and the Price?

    In the ever-evolving landscape of AI tools, KimiClaw has emerged as a hosted version of the popular OpenClaw platform, promising seamless integration and ease of use. Released on February 18, 2026, this article provides hands-on insights into KimiClaw’s deployment, performance, and comparisons to alternatives. Here’s a comprehensive breakdown based on the analysis.

    What is KimiClaw? A Quick Introduction

    KimiClaw is essentially OpenClaw hosted on Kimi’s servers, accessible directly from the Kimi dashboard. The setup is remarkably simple—one-click deployment makes it appealing for beginners looking to get started quickly without dealing with complex installations. The reviewer, who upgraded to the $39 per month plan specifically for testing, highlights this ease of use as a initial positive. However, the excitement fades as deeper evaluation reveals significant shortcomings.

    This tool is positioned within the broader ecosystem of AI models like Claude AI, Grok AI, Cursor AI, and the o1 model, but KimiClaw aims to stand out by leveraging Kimi’s infrastructure. Tags from the video also nod to related topics such as vibe coding, prompt engineering, and Web3 AI, indicating its potential applications in coding workflows and no-code AI development.

    Key Criticisms: Where KimiClaw Falls Short

    The review doesn’t hold back on the drawbacks, painting a picture of a product that feels underdeveloped and overpriced:

    • Missing Integrations and Features: Users might expect KimiClaw to incorporate Kimi’s unique capabilities, such as the “nano banana unlimited slide generation” for AI-powered presentations. Unfortunately, these features remain isolated and inaccessible within KimiClaw, limiting its utility.
    • Server Location Issues: Hosted in mainland China, the servers introduce potential hurdles for global users. Services may be blocked in certain regions, and latency or data compliance concerns could arise, making it less ideal for international projects.
    • Outdated Software: KimiClaw runs on OpenClaw version 2.13, which lags behind the latest releases. This means users miss out on recent improvements and optimizations available in newer versions.
    • Inadequate Memory Setup: A major flaw is the lack of proper memory configuration, including vector embeddings for better context retention and search. The reviewer references a prior video on enhancing AI agents with vector embeddings and OpenAI keys, emphasizing how this omission hampers performance.

    Overall, the hosted version is described as “basic” and lacking the depth of a self-managed setup. For those handling AI coding, experiments, or crypto-related tasks, these limitations could be deal-breakers.

    Cost Analysis: $39/Month vs. Alternatives

    At $39 per month, KimiClaw’s pricing comes under fire for not delivering commensurate value. The reviewer argues that the cost is unjustified given the stripped-down features and backend opacity. Instead, they strongly advocate for self-hosting OpenClaw on a budget-friendly VPS for as little as $2 per month. This approach offers full control, access to the latest versions, and transparency over processes—benefits that far outweigh the convenience of Kimi’s hosting.

    For users already on Kimi’s “Allegretto” plan (which includes generous free usage allowances), the best strategy is linking an external OpenClaw instance via API key rather than relying on the built-in KimiClaw. This hybrid setup maximizes Kimi’s powerful model without the extra expense.

    Recommendations and Better Options

    The verdict is clear: Skip the upgrade and save your money. Here’s the key advice from the review:

    • Learn Self-Hosting: Invest about 30 minutes in following a installation guide (linked in the video) to set up OpenClaw yourself. It’s empowering and unlocks more features without proprietary restrictions.
    • Explore Alternatives: The video teases upcoming comparisons with models like Minimax (noted as the cheapest option at version 2.5) and GLM-5. Other tagged tools such as Claude AI and Grok AI are suggested for those seeking robust AI for coding and workflows.
    • Community Feedback: As a new channel, BoxminingAI encourages comments and suggestions, hinting at future content on testing these alternatives for OpenClaw compatibility.

    The reviewer expresses hope that Kimi will address issues like server location and feature integration in future updates, but for now, custom setups reign supreme.

    Final Thoughts: Proceed with Caution

    KimiClaw’s one-button simplicity is tempting, but its limitations make it a poor choice for serious users. In a market flooded with AI innovations—from vibe coding tutorials to advanced prompt engineering—this tool doesn’t quite measure up. If you’re dipping your toes into AI for beginners or exploring AI crypto and Web3 applications, start with free or low-cost alternatives and build from there.

    For the full hands-on demo and timestamps, watch our video on YouTube. What are your experiences with hosted AI tools? Drop your thoughts below!