Your Computer Just Became an Autonomous AI Worker

A local, open-source AI agent delivers real automation, but not without risks

Claudebot is an open-source autonomous AI assistant gaining traction as a local alternative to cloud-based copilots like Claude Co-Work. Instead of running in the cloud, it operates directly on user-owned hardware such as a Mac Mini, high-end GPUs like the RTX 4090, or even VPS setups, giving users full control over data, privacy, and execution.

Built for real automation, Claudebot can independently handle tasks like clearing inboxes, sending emails, managing calendars, organizing files, and executing system commands. It integrates with familiar chat platforms including WhatsApp, Telegram, Discord, and Slack, allowing users to control it through everyday messaging apps.

In more advanced use cases, Claudebot has demonstrated the ability to research information on X, analyze external markets, validate signals with live data, and execute autonomous trades. The system can design its own strategies and iterate without constant human input.

Setup is handled through a guided installation wizard, with support for multiple model providers and optional hosting via AWS free tier. Its modular skill system allows users to extend capabilities such as web search, note management, file handling, and even creative workflows like video editing.

That power comes with serious security implications. Once granted system access, Claudebot can read files, install software, run commands, and interact with networks autonomously. Without proper sandboxing and strict permissions, misconfiguration could expose users to major security risks.

While the recent hype suggests novelty, the core idea is not new. Similar autonomous agent systems have existed for years, but Claudebot stands out for its accessibility, flexibility, and renewed attention.

Claudebot signals a shift from AI assistants that respond to prompts to agents that act on their own. It showcases both the productivity upside of local AI autonomy and the risks of giving powerful systems deep access to personal machines.

As autonomous agents become easier to deploy, Claudebot highlights the growing need for better safety practices, sandboxing, and user awareness.

Internal researchers accuse OpenAI of hiding data on job losses to protect its image

Several economists and researchers have resigned from OpenAI after raising concerns that the company is suppressing internal research about AI’s impact on jobs. According to internal messages and reporting, former OpenAI economist Tom Cunningham described the economic research team as drifting away from real analysis and becoming “the propaganda arm of its employer”

OpenAI’s Economists Just Resign…

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Cunningham, who was hired specifically to study AI’s economic effects, resigned in September and later joined METR, a nonprofit focused on AI safety. At least one additional researcher reportedly left shortly after. Internal sources claim OpenAI selectively highlights productivity gains while downplaying or withholding studies showing large-scale job displacement.

OpenAI leadership has defended the shift. Chief Strategy Officer Jason Kwon reportedly told staff the company must focus on “responsibility for outcomes” rather than publishing research on sensitive topics that could slow adoption or invite regulation. Critics argue this effectively buries inconvenient findings

The resignations come amid massive financial stakes. OpenAI is tied to multi-hundred-billion-dollar infrastructure investments and is reportedly targeting a trillion-dollar valuation. Former employees say this creates strong incentives to avoid publishing research that could alarm governments or the public about AI-driven job losses.

The situation contrasts sharply with Anthropic, where CEO Dario Amodei has publicly warned that up to 50% of entry-level office jobs could disappear within five years due to AI, potentially driving unemployment as high as 20%.

This isn’t just a company drama. It highlights a growing tension between AI progress, corporate incentives, and public accountability. When researchers tasked with studying societal impact resign in protest, it raises serious questions about transparency at the companies shaping the future of work.

As AI-driven layoffs accelerate and entry-level roles disappear, honest data matters more than optimism. Without transparency, governments, workers, and institutions are left unprepared for structural economic change.

A cascade of failures is closing every path forward for the company

A detailed 2026 analysis argues that Tesla’s long-term decline is no longer speculative. According to the report, Tesla is being hit simultaneously on every strategic front: falling EV sales, collapsing Full Self-Driving adoption, stalled robotaxi ambitions, a failed in-house battery strategy, and a risky pivot toward humanoid robots with no clear market value.

The analysis places responsibility squarely on Elon Musk’s repeated decisions to override engineers, cancel affordable vehicle plans, double down on unreliable AI approaches, and pursue unproven technologies. Competitors like BYD, Volkswagen, Waymo, and Hyundai are now executing successfully in the exact areas Tesla once claimed as its future advantage.

Tesla’s story has shifted from “temporary setbacks” to “structural collapse.” The report suggests Tesla is no longer losing because of market conditions or politics, but because its core bets are failing at the same time. If true, this marks a turning point where Tesla’s valuation narrative no longer matches its operational reality, putting long-term investor confidence at serious risk.

The new multimodal model rivals Gemini and Claude while introducing large-scale agent swarms

Moonshot AI has released Kimi K2.5, its most advanced open-source model to date, positioning it as a serious competitor to proprietary systems like Gemini 3 and Claude Opus 4.5, particularly in coding, vision, and agent-based tasks.

Kimi K2.5 supports both text and visual input and introduces multiple operating modes, including instant generation, deep reasoning, agent workflows, and a new agent swarm system. The model was trained on roughly 15 trillion mixed text and visual tokens, delivering state-of-the-art performance across coding, vision, and real-world software engineering tasks.

The standout feature is Agent Swarm, a self-directed system where Kimi can autonomously create and coordinate up to 100 sub-agents, executing parallel workflows across as many as 1,500 tool calls. Compared to traditional single-agent systems, Moonshot claims this can reduce task execution time by up to 4.5×.

In benchmarks and demos, Kimi K2.5 has shown strong results in front-end development, application building, debugging, refactoring, and complex document generation. In one example, the agent swarm decomposed a large academic literature review into specialized sub-tasks and synthesized the results into a fully structured, citation-complete academic document.

The model also introduces video-based “vibe coding,” allowing it to observe visual interactions and translate them directly into deploy-ready code. This capability significantly lowers the barrier between visual intent and production-grade user interfaces.

Despite its performance, Kimi K2.5 remains aggressively priced, with costs reportedly around 10–20% of comparable proprietary models, while supporting a 262k token context window. As an open-source model with available weights, it can also be run locally under certain configurations.

Kimi K2.5 signals a major shift in open-source AI. It’s no longer just about matching proprietary models in benchmarks, but about surpassing them in agentic workflows, cost efficiency, and flexibility.

By combining multimodality, large context windows, and autonomous agent swarms in an open-source package, Moonshot AI is pushing advanced AI capabilities into the hands of developers, researchers, and companies without requiring closed ecosystems or massive budgets.

If these capabilities scale reliably, Kimi K2.5 could redefine expectations for what open-source AI models can deliver.

New tools, policies, and classroom experiments reveal both momentum and confusion

A late-January 2026 roundup highlights how artificial intelligence is accelerating changes across education systems, while institutions scramble to adapt policies, curricula, and teaching practices. From K–12 to higher education, schools are increasingly integrating AI tools, even as concerns grow around assessment integrity, teacher readiness, and long-term learning outcomes

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Several universities reported expanded use of AI for tutoring, lesson planning, accessibility support, and administrative tasks. At the same time, educators are experimenting with AI-assisted grading, feedback generation, and personalized learning pathways. However, many institutions still lack clear guidance on acceptable AI use, leading to inconsistent classroom policies and student confusion.

The report also notes a rise in districts formally acknowledging AI rather than attempting to ban it outright. Some schools are shifting assessments toward project-based work, oral exams, and in-class assignments to reduce overreliance on generative tools. Others are introducing AI literacy programs aimed at teaching students how to critically evaluate and responsibly use AI systems.

Despite progress, teacher preparedness remains a major challenge. Many educators report receiving little to no formal training on AI, leaving them unsure how to integrate tools effectively or detect misuse. This gap is especially pronounced in underfunded schools, raising concerns that AI adoption could widen existing educational inequalities.

Education is becoming one of the first large-scale testing grounds for human-AI collaboration. How schools respond now will shape how an entire generation learns, evaluates knowledge, and builds skills.

Without clear standards, training, and equitable access, AI risks creating fragmented learning experiences and deepening divides between institutions. But when used thoughtfully, it could also reduce teacher workload, personalize education, and better prepare students for an AI-driven workforce.

The coming months will determine whether AI becomes a stabilizing force in education or another source of disruption schools weren’t ready for.