2026-06-06 Saturday

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Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

"Codex Goal Mode: How to Make AI Work Continuously Toward a Specific Goal" OpenAI's Codex "goal mode" (/goal) transforms the AI from a reactive code assistant into a proactive execution agent capable of working autonomously for hours or even days to achieve a defined objective. To maximize its effectiveness, follow these key principles: 1. **Define Clear, Verifiable Exit Criteria:** The goal prompt should be a concise, measurable success condition, not a lengthy specification. Use quantifiable metrics like "reduce build time by 30%" or "achieve 100% test parity." 2. **Provide Initial Guidance and Tools:** Direct Codex toward likely problem areas and specify available tools (e.g., browsers, testing environments) to prevent it from exploring unproductive paths. 3. **Enable Progress Measurement:** Equip Codex with ways to track advancement, such as creating comparison tools for visual tasks or evaluation sets, ensuring it can gauge its own progress. 4. **Use a Realistic Execution Environment:** For tasks like performance optimization, provide access to environments that closely mimic production (e.g., similar configs, databases) to yield valid results. 5. **Be Cautious with Visual Goals:** Avoid vague "pixel-perfect" instructions. Instead, supplement visual references with functional checklists or design system specifications to prevent Codex from obsessing over minor details. 6. **Implement Progress Tracking:** For long-running tasks, have Codex commit code to draft PRs, update progress documents, or send Slack updates to maintain visibility into its work. 7. **Review and Consolidate Results:** Once the goal is met, instruct Codex to review its work, clean up ineffective experimental code, and reflect on what strategies succeeded or failed. Ultimately, using goal mode shifts the developer's role from writing prompts to managing a persistent engineering agent—defining objectives, establishing metrics, configuring environments, and conducting final reviews.

marsbit3m ago

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

marsbit3m ago

From Ethereum to AI's 'CROPS': What Exactly Is This 'Slow Variable' That Vitalik Has Repeatedly Emphasized?

Recently, Vitalik Buterin has frequently emphasized the concept of "CROPS," first outlined in the Ethereum Foundation's March mandate as core principles guiding its focus: Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. CROPS represents Ethereum's commitment to providing foundational capabilities for user sovereignty—enabling asset ownership, identity expression, and coordination without reliance on centralized platforms or surrendering ultimate control. This framework is gaining new urgency with the rise of AI, particularly AI agents managing digital assets and automating transactions. While AI offers convenience, it risks centralizing user data, intent, and control if dependent on opaque, centralized services. Vitalik argues for "CROPS AI"—AI that is open, privacy-preserving, secure, and capable of local execution to maintain user agency. He highlights convergence between "CROPS Ethereum access layers" and "CROPS AI," such as using zero-knowledge proofs for private remote LLM calls and Ethereum RPC reads, ensuring users can access services without exposing sensitive information. Ultimately, CROPS is not just an abstract ideal but a practical guide for Ethereum's development and AI integration. It addresses the critical long-term question: as digital systems grow more powerful, how can users retain control over their privacy, assets, and autonomy? In an AI-driven era, these principles may define Ethereum's enduring value—prioritizing verifiable, secure, and user-centric design over short-term optimizations like speed and cost alone.

marsbit7m ago

From Ethereum to AI's 'CROPS': What Exactly Is This 'Slow Variable' That Vitalik Has Repeatedly Emphasized?

marsbit7m ago

Huawei Cloud Rejects Token Price War, Zhou Yuefeng Seeks a New Winning Formula for AI Cloud

At the 2026 Huawei Cloud INSPIRE Creator Conference, CEO Zhou Yuefeng outlined Huawei Cloud's distinct strategy in the competitive AI cloud market. Instead of engaging in price wars based on token volume or Maas revenue—a common focus for rivals like Alibaba Cloud and ByteDance's Volcano Engine—Huawei Cloud is shifting the competition towards real-world productivity gains. Zhou highlighted three core differentiators: a fully domestic computing stack (Ascend, Kunpeng), a focus on government and enterprise clients rather than consumer internet, and a deep commitment to open-source ecosystems. To this end, Huawei Cloud launched a suite of new products under the "Agentic Infra" paradigm, including the AICS Lingqu computing cluster, AMS memory storage, and the ModelArts Next platform. These aim to solve enterprise challenges in deploying AI agents, such as latency, memory, scheduling, and security. The strategy further involves creating specialized industry zones ("AI Dream Factories") for sectors like healthcare and embodied intelligence. For example, a smart medical zone developed with Shanghai Ruijin Hospital aims to democratize expert-level diagnostic capabilities. In essence, Huawei Cloud is positioning itself not as a commodity token provider, but as the foundational infrastructure for industrial AI, leveraging its domestic supply chain and hybrid cloud solutions to serve sectors where productivity, not just scale, is the ultimate measure of value.

marsbit2h ago

Huawei Cloud Rejects Token Price War, Zhou Yuefeng Seeks a New Winning Formula for AI Cloud

marsbit2h ago

70% of the Public Opposes AI, Americans Hope the U.S. Loses the AI War

70% of Americans believe AI development is moving too fast, with growing public resistance evolving from online criticism to real-world protests and violence. This widespread anti-AI sentiment stems from fears of job losses, rising utility costs, environmental damage, threats to democracy, and financial instability. Key incidents illustrate the backlash: Google's former CEO Eric Schmidt was loudly booed at a graduation for promoting AI; AI company ads are vandalized; protests and even violent attacks target AI firms and data centers. Polls show deep public pessimism and strong local opposition to data center construction, often surpassing resistance to nuclear power plants. The core grievances are economic and practical: AI is seen as automating jobs, concentrating wealth, and increasing household electricity and water bills due to massive data center resource demands. Environmentalists also oppose AI's high energy use and carbon emissions. This opposition has turned AI into a major political issue in the US. While the Trump administration prioritizes AI innovation for global competition, bipartisan pushback is growing. Democrats and factions within the MAGA movement are forming temporary alliances to support stricter regulations and local bans on new data centers, pressuring the administration to choose between its tech industry backers and its voter base. The situation highlights a profound national divide over AI's future.

marsbit3h ago

70% of the Public Opposes AI, Americans Hope the U.S. Loses the AI War

marsbit3h ago

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