# Пов'язані статті щодо Testing

Центр новин HTX надає останні статті та поглиблений аналіз на тему "Testing", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

Claude always makes mistakes in writing code? These 12 rules reduce the error rate to 3%

Claude's Coding Errors Drop to 3% with 12 Key Rules In early 2026, Andrej Karpathy's critique of Claude's coding failures led to the creation of a CLAUDE.md file with 4 foundational rules: "Think before coding," "Prefer simplicity," "Make surgical edits," and "Execute goal-first." These effectively reduced common errors from 40% to 3% in applicable tasks. However, as Claude Code evolved into multi-step agent workflows by May 2026, new failure modes emerged. Eight additional rules were developed to address these gaps: 5. Don't make non-linguistic decisions (e.g., API retry logic). 6. Set hard token budgets to prevent runaway iterations. 7. Expose conflicts; don't average contradictory code patterns. 8. Read existing code before writing to avoid duplication. 9. Ensure tests validate real logic, not just pass. 10. Use checkpoints for long-running, multi-step tasks. 11. Follow existing conventions over introducing new patterns. 12. Fail explicitly; avoid silent failures that appear successful. Testing across 30 codebases showed the 12-rule version maintained a 76% adherence rate while reducing the overall error rate to 3%, covering new agent-specific issues. The key is to treat CLAUDE.md as a behavioral contract targeting observed failures, keeping it under 200 lines for effectiveness. Users should adapt the rules to their specific workflows.

marsbit05/14 10:07

Claude always makes mistakes in writing code? These 12 rules reduce the error rate to 3%

marsbit05/14 10:07

How to Automate Any Workflow with Claude Skills (Complete Tutorial)

This is a comprehensive guide to mastering Claude Skills, a feature for creating permanent, reusable instruction sets that automate specific workflows. Unlike simple saved prompts, Skills function like trained employees, delivering consistent, high-quality outputs by defining the entire task process, standards, error handling, and output format. The guide is structured in four phases: **Phase 1: Installation (5 minutes).** Skills are folders containing a `SKILL.md` file. The user is instructed to find a relevant Skill online, install it, test it on a real task, and compare its performance to one-off prompts. **Phase 2: Building Your First Custom Skill.** Start by rigorously defining the Skill's purpose, trigger phrases, and providing a concrete example of perfect output. The `SKILL.md` file has two parts: a YAML frontmatter with a specific name/description/triggers, and a detailed, step-by-step workflow written in natural language with examples and quality standards. **Phase 3: Testing & Optimization for Production.** Test the Skill in three scenarios: 1) a standard, common task; 2) edge cases with missing or conflicting data; and 3) a pressure test with maximum complexity. Any failure indicates a needed instruction. Implement a weekly optimization cycle to continuously refine the Skill based on real usage. **Phase 4: Building a Complete Skill Library.** The goal is to create a team of Skills for all repetitive tasks. Examples are given for industries like real estate, marketing, finance, consulting, and e-commerce. The user should list their tasks, prioritize them, and build one new Skill per week, maintaining a master document to track their library. The conclusion emphasizes the compounding time savings: ten Skills saving 30 minutes each per week reclaims over 260 hours (6.5 work weeks) per year, fundamentally transforming one's work system.

marsbit05/12 09:45

How to Automate Any Workflow with Claude Skills (Complete Tutorial)

marsbit05/12 09:45

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy China Chips, Avoid Traditional Tracks

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy Chinese Chips; Avoid Traditional Segments. The core theme is the shift in AI compute supply from NVIDIA dominance to a three-track system of GPU + ASIC + China-local chips. The key opportunity is capturing share in this expansion, while non-AI semiconductors face marginalization due to resource reallocation to AI. Key investment conclusions, in order of priority: 1. **Advanced Packaging (CoWoS/SoIC) - Highest Conviction**: TSMC is the primary beneficiary of explosive demand, driven by massive cloud capex. Its pricing power and AI revenue share are rising significantly. 2. **Test Equipment - Undervalued & High-Growth Certainty**: Chip complexity is causing test times to double generationally, structurally driving handler/socket/probe card demand. Companies like Hon Hai Precision (Foxconn), WinWay, and MPI offer compelling value. 3. **China AI Chips (GPU/ASIC) - Long-Term Irreversible Trend**: Export controls are accelerating domestic substitution. Companies like Cambricon, with firm customer orders and SMIC's 7nm capacity support, are positioned to benefit from lower TCO (30-60% vs NVIDIA) and growing local cloud demand. 4. **Avoid Non-AI Semiconductors (Consumer/Auto/Industrial)**: These segments face a weak, structurally hindered recovery due to AI's resource "crowding-out" effect on capacity and supply chains. 5. **Memory - Severe Internal Divergence**: Strongly favor HBM (Hynix primary beneficiary) and NOR Flash (Macronix). Be cautious on interpreting price rises in DDR4/NAND as true demand recovery. The report emphasizes a 2026-2027 time window, stating the AI capital expenditure cycle is far from over. Key macro variables include persistent export controls and AI's systemic "crowding-out" effect on traditional semiconductor supply chains.

marsbit05/12 01:30

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy China Chips, Avoid Traditional Tracks

marsbit05/12 01:30

When AI Solves Security, Will DeFi Return to Its Golden Age?

The article discusses how AI is revolutionizing DeFi security, potentially ushering in a new era of innovation reminiscent of the 2020 DeFi Summer. Previously, high security costs and lengthy audit processes stifled experimentation, as developers avoided unproven ideas due to financial and time constraints. However, AI tools like Nemesis are now drastically reducing these barriers by efficiently detecting complex vulnerabilities, such as reentrancy and economic attacks, with deep contextual understanding and low false positives. These tools, combined with platforms like Battlechain, enable a streamlined workflow: code is written, AI-audited within minutes, deployed on a test chain for real-world attack simulations, and quickly refined. This process compresses development cycles from months to hours at minimal cost. Future advancements may include wallet-integrated AI audits, allowing users to scan contracts in real-time before signing transactions. AI is set to transform DeFi security across development, chain, and user layers, reopening the door to rapid experimentation and innovation. This shift could empower individual developers to create groundbreaking protocols quickly and safely, much like the early pioneers of DeFi.

marsbit04/03 10:14

When AI Solves Security, Will DeFi Return to Its Golden Age?

marsbit04/03 10:14

Performance Surpasses Opus! Anthropic Leaked Document Reveals: The New Generation Super Model Claude Mythos Is Already in Testing

According to leaked internal documents from Anthropic, the company's highly anticipated next-generation AI model, Claude Mythos, is currently in a secret testing phase. The documents reveal a new model tier named "Capybara," which represents a major technological leap, featuring a larger scale and superior intelligence that surpasses the flagship Claude Opus model. The leak also highlights significant concerns within Anthropic regarding unprecedented cybersecurity risks associated with Claude Mythos, prompting a cautious release strategy to balance advanced capabilities with safety. This development is expected to significantly raise the benchmark for large language models, intensifying competition in the AI industry and pushing the evolution of models toward deeper logical reasoning and complex task handling. The official release date for Claude Mythos remains unannounced.

marsbit03/27 03:52

Performance Surpasses Opus! Anthropic Leaked Document Reveals: The New Generation Super Model Claude Mythos Is Already in Testing

marsbit03/27 03:52

Can Shiba Inu Still Make A Comeback? Lack Of Update On Shibarium L3 Proves To Be A Problem

The Shiba Inu ecosystem is facing challenges due to a lack of updates and low sentiment, with its token trading at its lowest price since the 2022 bear market. While early testing of a Shibarium Layer-3 explorer is underway under the ShibClaw initiative, there are no clear details, timelines, or technical specifications provided. This absence of information is causing concern within the community, as the ecosystem lacks strong direction. Additionally, the Shibarium network is undergoing a backend overhaul, with explorer synchronization only 45% complete. Without clear milestones or deployment timelines for the L3, traders have little basis for optimism, making a price recovery difficult. SHIB is currently trading at $0.000006139.

bitcoinist03/24 22:02

Can Shiba Inu Still Make A Comeback? Lack Of Update On Shibarium L3 Proves To Be A Problem

bitcoinist03/24 22:02

Ethereum Roadmap Could Advance Faster With AI, Vitalik Buterin Says

Vitalik Buterin suggests that AI could significantly accelerate Ethereum's development roadmap, citing an experiment where an AI-built client (ETH2030) was created in six days for $5,750. The prototype, covering 65 roadmap items, demonstrates potential but has limitations and bugs. Buterin emphasizes that AI should enhance both speed and security through formal verification and extensive testing. While not production-ready, the experiment highlights Ethereum's ambitious goals, including 10,000+ TPS and faster finality, and underscores that AI could help complete the roadmap faster and more securely than expected.

bitcoinist03/02 16:41

Ethereum Roadmap Could Advance Faster With AI, Vitalik Buterin Says

bitcoinist03/02 16:41

Founders Fund, Pantera, and Franklin Templeton Join Sentient's 'Arena' to Stress-Test Enterprise-Grade AI Agents

Sentient Labs has launched Arena, a real-time, production-ready environment designed to stress-test and iteratively improve enterprise AI agents through competitive challenges. The platform addresses the growing need for reliable, explainable, and reproducible reasoning in high-stakes business workflows such as finance, compliance, and customer operations. Initial participants include Founders Fund, Pantera, and Franklin Templeton, which manages over $1.5 trillion in assets. Arena simulates complex, messy real-world scenarios—incomplete information, long contexts, ambiguous instructions, and conflicting sources—to evaluate not just correctness but full reasoning traces. This allows engineering teams to diagnose failures and track improvements over time. The first challenge focuses on document reasoning, a foundational task for areas like financial analysis and investigative research. Other participants include alphaXiv, Fireworks, OpenHands, and OpenRouter. The initiative comes as 85% of enterprises aim to become "agentic enterprises," but few have mature governance frameworks. Arena provides a vendor-agnostic benchmark to help transition AI agents from demos to production-scale reliability.

marsbit02/27 13:28

Founders Fund, Pantera, and Franklin Templeton Join Sentient's 'Arena' to Stress-Test Enterprise-Grade AI Agents

marsbit02/27 13:28

UK tests stablecoins in regulatory sandbox while proposing limits on user holdings

UK regulators are advancing a dual approach to stablecoin regulation, combining live testing within the Financial Conduct Authority’s sandbox with proposed limits on user holdings. The sandbox includes firms like Revolut and Monee Financial Technologies, trialing applications in payments, settlement, and crypto trading under strict supervision. Concurrently, the Bank of England has proposed temporary caps—£20,000 per stablecoin for individuals and £10 million for businesses—to mitigate systemic risks such as bank deposit flight. This cautious "test and contain" strategy aims to foster innovation in pound-denominated stablecoins while preventing rapid scaling until the market matures. The UK’s approach reflects the minor global role of non-dollar stablecoins and focuses on ensuring safe integration into the existing financial system.

ambcrypto02/25 21:12

UK tests stablecoins in regulatory sandbox while proposing limits on user holdings

ambcrypto02/25 21:12

AI Models Are Evolving Rapidly, How Can Workers Overcome 'AI Anxiety'?

AI models and tools are evolving rapidly, creating a sense of anxiety among professionals who feel pressured to keep up. The root of this "AI anxiety" isn't the pace of change itself, but the lack of a filter to distinguish what truly matters for one's work. Three key forces drive this anxiety: the AI content ecosystem thrives on urgency and hype, loss aversion makes people fear missing out, and too many options lead to decision paralysis. The solution is not to consume more information, but to build a personalized filtering system. "Keeping up" doesn't mean testing every new tool on day one; it means having a system to automatically answer: "Is this important for *my* work?" Three practical strategies are proposed: 1. **Build a "Weekly AI Digest" Agent:** Use automation (e.g., n8n) to gather news from trusted sources, then use an AI to filter it based on your specific job role and tasks. This delivers a concise weekly report of only the relevant updates. 2. **Test with *Your* Prompts:** When a new tool seems relevant, test it using your actual work prompts, not the vendor's perfect demos. Compare the results side-by-side with your current tools to see if it's truly better for your workflow. 3. **Distinguish "Benchmark" vs. "Business" Releases:** Most announcements are "benchmark releases" (improvements on standardized tests) that have little real-world impact. Focus only on "business releases" that offer new capabilities you can use immediately. Combining these strategies transforms AI updates from a source of stress into a manageable advantage. The real competitive edge lies not in accessing every new model, but in knowing what to ignore and what to test deeply for your specific work. The key is to stop trying to follow everything and start filtering for what truly matters.

marsbit02/09 12:19

AI Models Are Evolving Rapidly, How Can Workers Overcome 'AI Anxiety'?

marsbit02/09 12:19

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