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How to Make Yourself Irreplaceable by AI Forever

This article argues that the primary threat from AI is not job replacement, but remaining trapped in "wage slavery"—financial dependence on employers. The path to becoming irreplaceable is not resisting AI, but becoming an "unemployable" individual who builds their own meaningful enterprise. The author identifies five key elements for this: Agency (acting without permission), Taste (judging what's worthwhile), Persuasion, Persistence, and Iteration. The solution is to stop being a "pawn" in someone else's game. To start, you must fundamentally change your identity and environment, then engage in rapid, real-world trial and error. While both coding and creating media (content) are powerful, content is more crucial. AI can generate assets, but true value lies in subjective, human-driven content that builds trust and narrative. The actionable advice is to carve out 15 minutes to answer foundational questions: 1) Uncover your "raw material"—what you know deeply or solve effortlessly. 2) Define your contrarian perspective—what common beliefs you think are wrong. The intersection of these answers is your direction. Finally, you must launch by publishing your first core idea immediately, using the feedback to iterate and develop the skills needed for a self-directed life and career.

marsbit08/02 00:41

How to Make Yourself Irreplaceable by AI Forever

marsbit08/02 00:41

The Father of Claude Code: Harness Has a Shelf Life of Only Six Months, Unshackle It

Claude Code's creator, Boris Cherny, argues that AI product harnesses (system prompts, skills, hooks) have a "shelf life" of only about six months. In a Y Combinator interview, he advises developers to regularly delete these components and rebuild them through ablation studies—testing each line's impact—because newer models like Opus 5 often don't need them. He describes models as "living organisms" with distinct "personalities" per generation, requiring empirical, non-prescriptive tuning. Cherny introduces two key concepts: "Product Overhang," where model capabilities outpace product integration, and "Unhobbling" or unleashing models by giving them harder, longer-running tasks with self-verification mechanisms. He shares an example of Claude independently rewriting an Electron app into Swift for over two weeks. For effective AI use, he dismisses "hacks" and urges an empirical approach: observe where the model struggles, then adjust. Treat the model like a colleague, not a tool to be minutely controlled. For learners, he advises combining computer science with applied skills like product design and data science. The core message is to embrace model capabilities through experimentation and reduce restrictive scaffolding.

marsbit07/30 10:41

The Father of Claude Code: Harness Has a Shelf Life of Only Six Months, Unshackle It

marsbit07/30 10:41

Opus 5 Game Prompt Goes Viral, 3A Masterpiece Replicated in 24 Hours

Opus 5 Game Prompt Goes Viral, 3A-Style Game Replicated in 24 Hours An Opus 5 prompt for game development has gone viral on X (formerly Twitter). The "challenge loop" method instructs the primary AI agent to decompose a task, assign sub-agents to specialized work, and crucially, add a "judge" agent to compare outputs against real 3A games. If results fall short, they are sent back for improvement in a continuous loop until quality standards are met. Using this prompt, developer Anshu built a space exploration game prototype, *The Long Silence*, in just 24 hours. He started by instructing Opus 5 to create a basic browser-based game using Three.js, then issued a 24-hour "visual enhancement" order. The AI worked autonomously through multiple agents, with the judge agent comparing screenshots to games like *Starfield*. The final game is playable in a browser. The prompt's success sparked a trend. Others have used it to create prototypes for a kart-racing game and a "Claudepunk 2077." The method also works with other models like GPT-5.6 Sol, though Opus 5 performed best. This approach leverages Opus 5's strengths in long-horizon planning and iterative self-correction, adding a strict validation mechanism to push its capabilities. The phenomenon demonstrates how advanced AI models are enabling rapid, autonomous game prototyping.

marsbit07/29 08:47

Opus 5 Game Prompt Goes Viral, 3A Masterpiece Replicated in 24 Hours

marsbit07/29 08:47

Claude Engineer Finally Unveils Fable 5's Ultimate Strategy, Teaching You How to Bridge the Information Gap with AI Models

This article, titled "Claude Engineer Finally Releases Fable 5 'Skill-Burning' Guide, Teaching How to Bridge the Information Gap with Models," details a blog post by Claude Code engineer Thariq Shihipar. The core concept is the "information gap" or "unknowns"—the disconnect between a user's instructions (the "map") and the actual task requirements (the "territory"). The article argues that with powerful models like Claude Fable 5, work quality depends on the user's ability to identify and clarify these unknowns. Shihipar categorizes unknowns into four types: Known Knowns (explicit instructions), Known Unknowns (awareness of gaps), Unknown Knowns (implicit, unstated knowledge), and Unknown Unknowns (unforeseen issues). The blog provides a framework for addressing these gaps throughout the workflow: * **Before Implementation:** Techniques include "Blindspot Scanning" to uncover Unknown Unknowns, brainstorming/prototyping for visual or complex tasks, having Claude ask clarifying questions, using reference code/examples, and creating implementation plans. * **During Implementation:** Maintaining an "implementation notes" file for Claude to document deviations and decisions made due to encountered edge cases. * **After Implementation:** Creating summary documents for review and having Claude generate quizzes to ensure the user fully understands the completed changes. The article concludes that as models become more capable, the key to success is systematically discovering and defining these unknowns through low-cost methods like prototyping and planning, allowing for more effective collaboration.

marsbit07/06 00:14

Claude Engineer Finally Unveils Fable 5's Ultimate Strategy, Teaching You How to Bridge the Information Gap with AI Models

marsbit07/06 00:14

Auto Research Era: 47 Tasks Without Standard Answers Become the Must-Test Leaderboard for Agent Capabilities

The article introduces Frontier-Eng Bench, a new benchmark for AI agents developed by Einsia AI's Navers lab. Unlike traditional tests with clear answers, this benchmark presents 47 complex, real-world engineering tasks—such as optimizing underwater robot stability, battery fast-charging protocols, or quantum circuit noise control—where there is no single correct solution, only continuous optimization towards a limit. It shifts AI evaluation from static knowledge retrieval to a dynamic "engineering closed-loop": the AI must propose solutions, run simulations, interpret errors, adjust parameters, and re-run experiments to iteratively improve performance. This process tests an agent's ability to learn and evolve through long-term feedback, much like a human engineer tackling trade-offs between power, safety, and performance. Key findings from the benchmark reveal two patterns: 1) Improvements follow a power-law decay, becoming harder and smaller as optimization progresses, and 2) While exploring multiple solution paths (breadth) helps, sustained depth in a single path is crucial for breakthrough innovations. The research suggests this marks a step toward "Auto Research," where AI systems can autonomously conduct continuous, tireless optimization in scientific and engineering domains. Humans would set high-level goals, while AI agents handle the iterative experimentation and refinement. This could fundamentally change research and development workflows.

marsbit05/13 07:06

Auto Research Era: 47 Tasks Without Standard Answers Become the Must-Test Leaderboard for Agent Capabilities

marsbit05/13 07:06

Rhythm X Zhihu Event Guests Announced, Comprehensive Coverage of the New Financial Model Brought by AI Agent from Academic, Institutional, and Individual Perspectives

In a notable incident, GPT creator Sam Altman’s home was targeted in two separate attacks—one involving a Molotov cocktail and the other gunfire—though no one was injured. The timing is significant: it occurred just after OpenAI’s latest model release, following the debut of Anthropic’s powerful model Mythos, and shortly after Hermes 3 outperformed OpenClaw on multiple benchmarks. These events underscore a broader trend: the AI industry is accelerating at a pace that now feels overwhelming. Model update cycles have shrunk from quarters to weeks, sometimes days. What was considered state-of-the-art just a month ago may now be surpassed by multiple competitors. Hermes overtook OpenClaw not in some distant future—but just last week. This acceleration is more than a trend; it’s becoming a structural force. When the speed of technological progress exceeds society’s ability to process it, frustration and fear can manifest in extreme ways. The attacks on Altman’s home serve as a stark signal: uncontrolled technological change can lead to uncontrolled emotional responses.

marsbit04/15 07:28

Rhythm X Zhihu Event Guests Announced, Comprehensive Coverage of the New Financial Model Brought by AI Agent from Academic, Institutional, and Individual Perspectives

marsbit04/15 07:28

"Garbage In, Treasure Out": Anthropic's Chief Designer on the Product Philosophy of Cowork and the Truth Behind Its 10-Day Launch

Jenny Wen, Design Lead at Anthropic, discusses the product philosophy and development story behind Claude Cowork. She explains that Cowork was designed as a "thinking partner" for general knowledge workers, emphasizing its ability to organize information and turn raw inputs into valuable outputs—summarized as "garbage in, treasure out." Contrary to the popular narrative that Cowork was built in just 10 days, Wen reveals that the concept had been in development for nearly a year, with multiple prototypes and technical experiments. The accelerated launch was triggered by observing strong product-market fit during the Claude Code holiday period. Wen shares how her team relies heavily on iterative, informal collaboration with engineers and product managers, often bypassing detailed spec documents in favor of rapid prototyping. Cowork is now central to her workflow, used for tasks ranging from synthesizing user research to generating wireframes and kickoff presentations. She also touches on Anthropic’s flexible planning process, which focuses on short-term, adaptive vision cycles rather than long-term roadmaps, and encourages designers to embrace change—much as engineers have—by focusing on higher-level creativity and judgment.

marsbit03/31 04:02

"Garbage In, Treasure Out": Anthropic's Chief Designer on the Product Philosophy of Cowork and the Truth Behind Its 10-Day Launch

marsbit03/31 04:02

Models Can Also "Nest"? MiniMax Releases M2.7: The First Domestic Large Model Deeply Involved in Self-Iteration

Artificial intelligence is evolving from monthly updates to self-evolution. On March 18, MiniMax released its first new model version deeply involved in its own iteration—MiniMax M2.7. This marks a new stage in model development: large models are no longer solely trained by human programmers but have begun to "train themselves." The core breakthrough of MiniMax M2.7 lies in its strong autonomous construction capability. It can independently build complex Agent Harness (intelligent agent testing frameworks) and, relying on underlying capabilities such as Agent Teams, complex Skills, and Tool Search tools, complete highly complex productivity tasks autonomously. In simple terms, M2.7 is not just a smarter conversational agent but also a "digital engineer" capable of self-diagnosis and self-optimization. This "self-participatory iteration" model will significantly enhance the model’s logical reasoning and tool invocation accuracy when facing unknown complex tasks. Currently, this self-evolving MiniMax M2.7 model has been fully launched on the MiniMax Agent platform and open platform. As large models begin to deeply participate in their own "growth" process, the ceiling of AI may be raised once again.

marsbit03/18 08:40

Models Can Also "Nest"? MiniMax Releases M2.7: The First Domestic Large Model Deeply Involved in Self-Iteration

marsbit03/18 08:40

Solana Labs CEO Says Ethereum-Style ‘Walkaway’ Thinking Is a Death Wish

Solana Labs CEO Anatoly Yakovenko disagrees with Ethereum co-founder Vitalik Buterin's call for Ethereum to "ossify," arguing that continuous protocol iteration is essential for Solana's survival. Buterin had stated that Ethereum should reach a state where it remains functional even without ongoing updates, passing what he calls the "walkaway test." In contrast, Yakovenko believes that stopping innovation would cause Solana to lose relevance. He emphasizes that the network must constantly adapt to developer and user needs to avoid stagnation, though upgrades shouldn't rely on any single entity. Both share skepticism toward central dependency but differ on long-term upgrade philosophy.

bitcoinist01/19 15:32

Solana Labs CEO Says Ethereum-Style ‘Walkaway’ Thinking Is a Death Wish

bitcoinist01/19 15:32

After 500 Rejections, My Product Is Finally 'Useful'

After 500 rejections, my consumer crypto product finally became "useful." As a first-time founder, I spent years on three infrastructure protocols that failed. In 2025, I shifted to building a consumer crypto product people actually use. Here are key lessons from my experience in user growth and fundraising: 1. Build for younger users (13-21 age group) who are more open to new tech and sharing products socially. 2. Make the product inherently shareable to reduce marketing costs, as crypto users have low trust and high acquisition costs. 3. Respond to user feedback quickly—fix bugs in 2-5 hours and implement requested features in 2-3 days to retain users and build trust. 4. Choose a simple, memorable app name for easier word-of-mouth sharing. 5. Engage users directly through cold DMs (despite low reply rates) to find early adopters who provide honest feedback. 6. Iterate rapidly—crypto moves fast, and user attention is short. Focus on real value, not just hype. 7. Design intuitive, simple user experiences; assume users know nothing to avoid confusion. Consumer crypto success depends more on speed, user focus, and distribution than technical perfection.

比推12/31 19:05

After 500 Rejections, My Product Is Finally 'Useful'

比推12/31 19:05

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