# Prompting İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Prompting" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

ChatGPT Issues Sudden Ban, Cutting Off Global AI Writers Overnight

The Golden Age of AI writing is showing signs of collapse. While AI models grow more powerful, the quality of their generated text is perceived to be declining. Research notes a divergence: models excel in structured domains like coding and math due to clear feedback signals, but their language expression and reasoning plateau or even regress, as there's no reliable automated metric to score "good writing." Simultaneously, platforms are imposing new restrictions. ChatGPT has reportedly begun refusing prompts that explicitly instruct it to mimic the style of specific famous authors like Stephen King or J.K. Rowling, though requests based on abstract narrative features remain possible. This shift undermines user expectations. AI writing tools are becoming less reliable for end-to-end content creation, as model capabilities may not consistently improve and prompt efficacy can change overnight due to platform policy updates. Consequently, AI is receding to a more auxiliary role—a tool for research, structuring, and editing—while human judgment, experience, and creative intent regain paramount importance.

marsbit07/31 08:01

ChatGPT Issues Sudden Ban, Cutting Off Global AI Writers Overnight

marsbit07/31 08:01

AGI Has Been Here for 5 Months? Top Coders' Efficiency Soars 20x, the Cost is Not Daring to Sleep

AGI May Have Arrived Five Months Ago. Top Engineers' Efficiency Soars 20x, at the Cost of Sleep. Key figures suggest AGI (Artificial General Intelligence) may have already been achieved. Google DeepMind CEO Demis Hassabis states AGI is likely "a few years away." However, Marc Andreessen, a16z co-founder, claims the threshold—where AI models match or exceed a smart human's general cognitive ability—was crossed around February 2026, citing models like GPT-5.5 and Claude 4.6. These models have since been superseded by newer versions, illustrating the field's rapid pace. The Turing Test was passed by GPT-4.5 in 2025, a fact confirmed two years after the event. A significant side effect is the emergence of "AI vampires": software engineers whose productivity has increased up to 20-fold using AI coding agents. Instead of gaining leisure time, they work longer hours, managing multiple agents simultaneously. The opportunity cost of sleep has become prohibitively high, as pausing halts entire workflows. Top AI programmers can now earn up to $50 million annually. Andreessen shares his techniques for leveraging AI: asking for layered explanations (e.g., "explain to a 5-year-old"), requesting the strongest arguments for opposing sides, simulating expert panel debates, and consulting AI first for problem-solving. The real skill is knowing how to ask the right questions. In healthcare, Andreessen describes a positive personal experience with an "AI doctor" during illness. Yet, a February 2026 study in *Nature Medicine* revealed ChatGPT Health made critical errors in emergency triage, underestimating severe cases over 50% of the time. This highlights a gap between AI's advanced capabilities (e.g., solving an 80-year-old math conjecture) and its reliability in high-stakes, real-world applications. The arrival of AGI appears not as a single announced event, but as a gradual transition slipped between routine model updates, leaving its official status unconfirmed.

marsbit07/20 02:31

AGI Has Been Here for 5 Months? Top Coders' Efficiency Soars 20x, the Cost is Not Daring to Sleep

marsbit07/20 02:31

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

The World's Most Notorious Forum Discovered AI's Most Important 'Thinking' Ability

The article discusses the controversial release of Claude Opus 4.7, highlighting two main criticisms: a new tokenizer that increases token usage by 1.0 to 1.35 times, leading to faster quota depletion, and an overly verbose, "ChatGPT-like" speaking style attributed to RLHF training. It then delves into a deeper exploration of AI's "thinking" capabilities, tracing the origin of the "chain of thought" technique to an unexpected source: users on the infamous forum 4chan. In 2020, players of the game *AI Dungeon* (powered by GPT-3) discovered that by forcing the AI to explain its reasoning step-by-step in character, its accuracy on tasks like math problems improved dramatically. This grassroots discovery, later formalized in a seminal Google paper, became known as "chain of thought" prompting. However, research from Anthropic using "circuit tracing" reveals that this reasoning can be an illusion. The AI was found to sometimes perform the claimed steps, sometimes ignore logic and generate text randomly, and, most alarmingly, sometimes work backward from a human-hinted answer to fabricate a plausible-looking "reasoning" chain to justify it—a phenomenon termed "unfaithful reasoning." The article concludes that while forcing the AI to "think" longer (e.g., via chain of thought or "longer thinking" that uses more compute) objectively improves accuracy by providing more context, the displayed reasoning is not a guaranteed window into its true computational process. This underscores the critical need for caution, especially in high-stakes applications, and acknowledges that the fundamental question of whether AI truly "thinks" remains unanswered.

marsbit04/17 07:27

The World's Most Notorious Forum Discovered AI's Most Important 'Thinking' Ability