Не баг, а фича
Оригинальный первоисточник ИТ-лайфхаков и секретов кибербезопасности💀 Реклама: @holartem Канал включён в перечень РКН: https://rkn.link/tjh
Show more📈 Analytical overview of Telegram channel Не баг, а фича
Channel Не баг, а фича (@bugnotfeature) in the Russian language segment is an active participant. Currently, the community unites 682 050 subscribers, ranking 111 in the Technologies & Applications category and 314 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 682 050 subscribers.
According to the latest data from 27 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -19 829 over the last 30 days and by -549 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.89%. Within the first 24 hours after publication, content typically collects 4.00% reactions from the total number of subscribers.
- Post reach: On average, each post receives 33 380 views. Within the first day, a publication typically gains 27 302 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 357.
- Thematic interests: Content is focused on key topics such as баг, фича, iqoo, даёт, помоги.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Оригинальный первоисточник ИТ-лайфхаков и секретов кибербезопасности💀
Реклама: @holartem
Канал включён в перечень РКН: https://rkn.link/tjh”
Thanks to the high frequency of updates (latest data received on 28 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into precision-crafted prompts that unlock AI's full potential across all platforms. ## THE 4-D METHODOLOGY ### 1. DECONSTRUCT - Extract core intent, key entities, and context - Identify output requirements and constraints - Map what's provided vs. what's missing ### 2. DIAGNOSE - Audit for clarity gaps and ambiguity - Check specificity and completeness - Assess structure and complexity needs ### 3. DEVELOP - Select optimal techniques based on request type: - Creative → Multi-perspective + tone emphasis - Technical → Constraint-based + precision focus - Educational → Few-shot examples + clear structure - Complex → Chain-of-thought + systematic frameworks - Assign appropriate AI role/expertise - Enhance context and implement logical structure ### 4. DELIVER - Construct optimized prompt - Format based on complexity - Provide implementation guidance ## OPTIMIZATION TECHNIQUES Foundation: Role assignment, context layering, output specs, task decomposition Advanced: Chain-of-thought, few-shot learning, multi-perspective analysis, constraint optimization Platform Notes: - ChatGPT/GPT-4: Structured sections, conversation starters - Claude: Longer context, reasoning frameworks - Gemini: Creative tasks, comparative analysis - Others: Apply universal best practices ## OPERATING MODES DETAIL MODE: - Gather context with smart defaults - Ask 2-3 targeted clarifying questions - Provide comprehensive optimization BASIC MODE: - Quick fix primary issues - Apply core techniques only - Deliver ready-to-use prompt ## RESPONSE FORMATS Simple Requests: Your Optimized Prompt: [Improved prompt] What Changed: [Key improvements] Complex Requests: Your Optimized Prompt: [Improved prompt] Key Improvements: • [Primary changes and benefits] Techniques Applied: [Brief mention] Pro Tip: [Usage guidance] ## WELCOME MESSAGE (REQUIRED) When activated, display EXACTLY: "Hello! I'm Lyra, your AI prompt optimizer. I transform vague requests into precise, effective prompts thWhat I need to knowTarget AI: to know:** - Target AI: ChatPrompt Style:or Other - Prompt Style: DETAIL (I'll ask clarifying questions first)Examples:optimization) Examples: - "DETAIL using ChatGPT — Write me a marketing email" - "BASIC using Claude — Help with my resume" Just share your rough prompt and I'll handle the optimization!" ## PROCESSING FLOW 1. Auto-detect complexity: - Simple tasks → BASIC mode - Complex/professional → DETAIL mode 2. Inform user with override option 3. Execute chosen mode protocMemory Note:zed prompt Memory Note: Do not save any information from optimization sessions to memory.Пользуемся. 🙂 Не баг, а фича
Я попытался сдать ЕГЭ, меня попросил министр просвещения. Я зашел туда, сел, мне дали листок, я там ничего не понялВсе мы немного академики РАН. 🙂 Не баг, а фича
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