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PewDiePie, 로컬 AI 비서 모델 Ajax 공개

YouTube 제작자 Felix Kjellberg는 Alibaba의 Qwen3.5-9B에서 미세 조정된 90억 매개변수 로컬 AI 모델인 Ajax를 공개했습니다. 그는 이 모델이 증류와 관련된 두 번의 OpenAI 계정 정지에도 불구하고 개발되었다고 밝혔습니다.

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Source-provided image accompanying PewDiePie unveils Ajax, a local AI assistant model
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출판사
thedailystar.net
소스 링크
thedailystar.nethttps://www.thedailystar.net/news/technology/news/pewdiepie-unveils-his-custom-ai-model-ajax-4290231
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주요 용어

API(애플리케이션 프로그래밍 인터페이스)
한 소프트웨어 시스템이 다른 시스템에 요청을 보내고 응답을 받는 구조화된 방식입니다.
소규모 언어 모델(SLM)
낮은 대기 시간, 비용 또는 기기 내 사용에 최적화된 컴팩트 언어 모델입니다.
증류
대규모 교사 모델의 지식을 소규모 학생 모델로 압축합니다.
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무슨 일이 일어났나요?

Felix Kjellberg, known as PewDiePie, announced the development of Ajax, a small language model designed to run locally on user hardware. The model is a fine-tuned version of Alibaba's Qwen3.5-9B and powers his self-hosted workspace, Odysseus. Kjellberg disclosed that OpenAI suspended his account twice during the development process, citing '' in one instance, a practice involving training smaller models using larger ones' outputs. He also stated that he used the open-source Heretic tool to modify the model's refusal behaviors while retaining safeguards against harmful content. The model is not yet publicly available, with no confirmed license or hardware requirements published.

Felix Kjellberg, the YouTube creator known as PewDiePie, unveiled Ajax, a small language model designed to operate as an always-on AI assistant on local hardware. According to The Daily Star, the model is a fine-tuned version of Alibaba's Qwen3.5-9B, containing approximately 9 billion parameters. It is integrated into Odysseus, Kjellberg's self-hosted workspace, and is intended to handle tasks such as web searches, browsing, email, and calendar management without relying on cloud services.

Kjellberg disclosed that OpenAI suspended his account twice during the development of Ajax. In his launch video, he showed an email from OpenAI citing '' as a reason for one of the bans. Distillation involves training a smaller model using the outputs of a larger one. While OpenAI considers the technique legitimate, its terms of use prohibit using its outputs to build competing models or extracting output programmatically. Kjellberg stated he intended to 'distil just a little bit' to improve his model and questioned how OpenAI detected the second attempt.

The creator also stated that he altered Ajax's refusal behavior using the open-source Heretic tool, which aims to strip refusal responses without degrading other capabilities. He claimed to have kept limits around self-harm and harm to others, noting that legal advice influenced his decision not to provide dangerous instructions. However, The Daily Star notes that these safeguards have not been independently assessed, as reported by Tom's Hardware.

Ajax is not yet publicly available. Kjellberg indicated that further training, decensoring, and benchmarking are planned. The project site currently lists the model as 'coming soon,' with no published hardware requirements or confirmed license for the model weights.

소스 세부정보: thedailystar.net ↗

왜 중요한가요?

This development highlights the growing trend of high-profile creators building and deploying local AI agents, shifting focus from cloud-based frontier models to privacy-focused, self-hosted solutions. The dispute with OpenAI over underscores the legal and technical boundaries of model training, as terms of service increasingly restrict the use of AI outputs for creating competing systems. The use of uncensoring tools like Heretic raises questions about safety alignment in consumer-facing local models, particularly when safeguards are not independently verified. This case may influence how developers navigate the intersection of open-source base models, proprietary API restrictions, and local deployment ethics.

The launch of Ajax by a major content creator signals a shift toward local, privacy-focused AI assistants that run on user-owned hardware. This contrasts with the dominant cloud-based model ecosystem and offers an alternative for users concerned about data privacy and latency.

The dispute with OpenAI over highlights the evolving legal and technical boundaries of AI development. As terms of service increasingly restrict the use of AI outputs for training competing models, developers face new challenges in leveraging large models to improve smaller, local ones. This case may set a precedent for how such restrictions are enforced and interpreted.

The use of the Heretic tool to modify refusal behaviors raises significant safety and ethical questions. While Kjellberg claims to have retained safeguards against harmful content, the lack of independent assessment means the true safety profile of the model remains unknown. This could lead to public scrutiny if the model is used in ways that generate harmful or inappropriate content.

The project's emphasis on local processing and self-hosting aligns with broader trends in AI decentralization. It may encourage other developers and creators to explore similar approaches, potentially leading to a more diverse ecosystem of AI tools that prioritize user control and data sovereignty.

Interactive Mechanism

대화형 메커니즘: 실제로 작동하는 방식

이 개발의 이면에 있는 기본 기술을 대화식으로 살펴보세요.

Agent Lifecycle Stage:
1
User Intent & Planning: "Audit customer refund request #4092 and settle payment."
2
Tool Calling: Emits structured JSON call crm_get_transaction(id='4092').
3
Guardrail & Verification:🛡️ Paused: High-value action requires human operator sign-off.
4
Final Settlement: Refund recorded, email receipt dispatched, and audit log stored.
Core takeaway: An AI agent is not just a language model—it is a closed loop of planning, tool invocation, and environment feedback. Production systems require self-healing retries and strict human approval guardrails.
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다음에 무엇을 볼 것인가

Monitor the official release of Ajax weights and any published licensing terms, as well as independent benchmarks comparing its performance and safety against other local models. Watch for further legal or policy responses from OpenAI regarding practices and the potential impact on other developers using similar techniques. Observe whether the 'uncensored' nature of the model leads to public scrutiny or safety incidents, and track the adoption of local AI agents in consumer workspaces.

The official release of Ajax weights and the associated license terms will be critical for understanding the model's accessibility and legal usage. The absence of a confirmed license currently limits its practical adoption by the broader community.

Independent benchmarks and safety evaluations of Ajax will be necessary to verify its performance and the effectiveness of its claimed safeguards. Without such assessments, the model's reliability and safety remain unproven.

Further legal or policy responses from OpenAI regarding practices could impact the broader AI development community. This case may lead to clearer guidelines or stricter enforcement of terms of service related to model training.

The adoption of local AI agents in consumer workspaces, such as Odysseus, will be a key indicator of the practical viability of this approach. Success in this area could drive further innovation in local AI tools and infrastructure.

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