语义智能=:部分@10 @ -DISE Cooker}:当工具将自己投入工作时 (中文 (Chinese Simplified))

语义记忆系列的最后一集 . 开始一些陌生事物

注:: 这是语义内存序列中最后一集的“10—”部分, 也是 DISE Cooker 系列中的第一部:“.” We'“正在从理论转向实践”, “,” 从“MS K4” 工具如何工作“"” 到“MSSK6” 当您实际使用它们执行真实任务时会怎样呢? “MS K7” Buckle up MSSK8”

结束和开始

{1-9% MPK1} 建立於此{MS K1 系統並沒有' 只是產生編碼,} 但是 变化演变 仅坐在那里的, 但是学习一个工具箱它只是执行工作流程 ,,但记得每一个成功和每个失败... ....

现在,我们回答问题没有人问但每个人都应该有 :

当你实际使用这东西时会发生什么?

@"#Hello world@."}对于一个真实的 , massy_,}多式-}任务来说,正常代码生成系统绝对会窒息 @MS K6}

这里为“'}假设情景 ” @:

(使用NMT, 但检查质量并使用更好的东西(如果需要的话) @),}然后创建 HTML 电子邮件, 用 SendGridQ." 发送@

单句. 七个不同的行动 多工具@ . @ 多失败模式 @ MS K1 @ 多优化机会 @ MPK2

让我们看DISE烹饪 .

请求的简单度

DiSE> Fetch the article at https://example.com/blog/post, summarize it to 3 paragraphs, translate to Spanish with quality checking, create an HTML email template, and send it via SendGrid to [email protected]

Analyzing request...

刚刚发生了什么? ?吗? 这个系统接收了一个复合任务? 工作流程工作流程 与#:

网络获取中
内容提取
概述
翻译并经过验证
HTML 生成
通过外部API发送电子邮件

传统LLM代码的生成

生成一个大块的, brittle 石碑
要求你手动拆分
存在的幻觉APIs
完全放弃

DISE做了不同的事 .

工作分解

✓ Task classified as MULTI_STEP_WORKFLOW
✓ Complexity: COMPLEX (7 steps, 4 tools needed, 1 missing)
✓ Consulting overseer LLM for decomposition strategy...

监督员 @(llama3{或claude}(取决于您的设置)-3.5-sonnet},}=)}分析请求并创建 工作流程规格:

{
  "workflow_id": "article_to_spanish_email",
  "description": "Fetch, summarize, translate, and email article content",
  "steps": [
    {
      "step_id": "fetch_content",
      "description": "Fetch webpage content from URL",
      "tool_search": "http client fetch webpage",
      "parallel_group": null,
      "depends_on": []
    },
    {
      "step_id": "summarize",
      "description": "Summarize content to 3 paragraphs",
      "tool_search": "summarize text content",
      "parallel_group": null,
      "depends_on": ["fetch_content"]
    },
    {
      "step_id": "translate_nmt",
      "description": "Translate summary to Spanish using NMT",
      "tool_search": "nmt translator spanish",
      "parallel_group": 1,
      "depends_on": ["summarize"]
    },
    {
      "step_id": "validate_translation",
      "description": "Check translation quality",
      "tool_search": "translation quality validation",
      "parallel_group": 1,
      "depends_on": ["summarize"]
    },
    {
      "step_id": "retry_if_needed",
      "description": "Use better translator if NMT quality is poor",
      "tool_search": "high quality translation",
      "parallel_group": null,
      "depends_on": ["translate_nmt", "validate_translation"],
      "conditional": "quality_score < 0.7"
    },
    {
      "step_id": "create_html",
      "description": "Generate HTML email template with translated content",
      "tool_search": "html email template generator",
      "parallel_group": null,
      "depends_on": ["retry_if_needed"]
    },
    {
      "step_id": "send_email",
      "description": "Send email via SendGrid API",
      "tool_search": "sendgrid email send",
      "parallel_group": null,
      "depends_on": ["create_html"]
    }
  ],
  "estimated_complexity": "complex",
  "requires_new_tools": ["sendgrid_client", "html_email_generator"]
}

注意发生了什么?

平行执行:}翻译与验证同时运行
有条件逻辑: 只有在质量差时才触发重试步数
依赖跟踪每个步骤都知道它需要什么
工具要求:System 已知道哪些工具已经存在,哪些需要创建

阶段 @ 2:% 工具发现

Searching for tools...
  ✓ fetch_content: Found 'http_rest_client' (fitness: 0.92)
  ✓ summarize: Found 'summarizer_fast' (fitness: 0.88)
  ✓ translate_nmt: Found 'nmt_translator' (fitness: 0.85)
  ✓ validate_translation: Found 'translation_quality_checker' (fitness: 0.91)
  ✓ retry_if_needed: Found 'quick_translator' (fitness: 0.93)
  ✗ create_html: No suitable tool found
  ✗ send_email: No suitable tool found

Missing tools: 2
Action: Generate new tools for missing capabilities

系统刚刚搜索了RAG内存,以查找使用的现有工具 语义词义相似性 和 健康得分“. 5 个工具存在 ”“. 2 ' t . ”

这里的' 有趣的地方是这里

阶段 @3: @ on-_The#-}Fly 工具生成

工具 @1:_HTML HTML 电子邮件生成器

Generating tool: html_email_generator
  Using: fast_code_generator (gemma3:4b)
  Specification: Create HTML email template with header, body, footer
  Template type: Newsletter-style responsive HTML

  ✓ Code generated (127 lines)
  ✓ Tests generated (15 test cases)
  ✓ All tests passed
  ✓ Static analysis: PASS (pylint: 9.2/10)
  ✓ Registered in RAG memory
  ✓ Version: 1.0.0
  ✓ Fitness: 0.87 (estimated)

系统刚刚@: @%

生成了完整的工具定义@( @YAML)}%
生成了执行代码@(_Python})
生成综合测试
经验证的代码质量
储存起来,供今后再使用
全部在 @ 8 @ 秒

工具 @2:SendGrid 客户端

Generating tool: sendgrid_email_client
  Type: openapi (external API integration)
  Analyzing SendGrid API documentation...
  Creating OpenAPI specification...

  ✓ Tool definition created
  ✓ Code template generated (API wrapper)
  ✓ Authentication configured (API key from env)
  ✓ Rate limiting: 100 req/min
  ✓ Retry logic: 3 attempts with exponential backoff
  ✓ Registered in RAG memory
  ✓ Version: 1.0.0
  ✓ Fitness: 0.82 (estimated, needs real-world data)

等待.-系统 创建了 OpenAPI 集成工具 从头到尾的“.”

分析的 SendGrid' @%s API 结构
生成工具定义
创建了认证处理
添加的抗御能力 +(Restries, 限制速率@)
使其立即可用

我们现在有全新的工具曾经存在过' 几秒钟前

工作流量执行阶段

这里“ ' @%s ” 在魔法真正发生的地方@ MS K1 系统执行工作流程 完全可观测:

graph TB
    Start([User Request]) --> Fetch[Step 1: Fetch Content<br/>Tool: http_rest_client<br/>URL: example.com/blog/post]

    Fetch --> |200 OK<br/>4,521 bytes| Summarize[Step 2: Summarize<br/>Tool: summarizer_fast<br/>Target: 3 paragraphs]

    Summarize --> |652 words → 187 words| Parallel{Parallel Execution}

    Parallel --> |Branch A| Translate[Step 3: Translate NMT<br/>Tool: nmt_translator<br/>Language: Spanish]
    Parallel --> |Branch B| ValidateSetup[Step 3b: Quality Check Setup<br/>Tool: translation_quality_checker]

    Translate --> |"Artículo sobre..."<br/>3.2s| Validate[Step 4: Validate Translation<br/>Quality Score: 0.64]

    Validate --> |Score: 0.64 < 0.7<br/>POOR QUALITY| Retry[Step 5: Retry with Better Tool<br/>Tool: quick_translator<br/>llama3-based]

    Retry --> |Quality Score: 0.92<br/>HIGH QUALITY| HTML[Step 6: Create HTML Email<br/>Tool: html_email_generator<br/>NEW TOOL v1.0.0]

    HTML --> |Template: 2,341 chars| Send[Step 7: Send via SendGrid<br/>Tool: sendgrid_email_client<br/>NEW TOOL v1.0.0]

    Send --> |Message ID: msg_7x3f...<br/>Status: Queued| Success([✓ Workflow Complete<br/>Total: 18.7s])

    style Fetch stroke:#1976d2,stroke-width:3px,color:#1976d2
    style Summarize stroke:#388e3c,stroke-width:3px,color:#388e3c
    style Translate stroke:#f57c00,stroke-width:3px,color:#f57c00
    style Validate stroke:#c2185b,stroke-width:3px,color:#c2185b
    style Retry stroke:#7b1fa2,stroke-width:3px,color:#7b1fa2
    style HTML stroke:#00796b,stroke-width:3px,color:#00796b
    style Send stroke:#3f51b5,stroke-width:3px,color:#3f51b5
    style Success stroke:#2e7d32,stroke-width:4px,color:#2e7d32

究竟发生了什么

步态“1”(Fetch continues?"):

# Generated code (simplified)
from node_runtime import call_tool
import json

result = call_tool("http_rest_client", json.dumps({
    "url": "https://example.com/blog/post",
    "method": "GET",
    "headers": {"Accept": "text/html"}
}))

data = json.loads(result)
raw_html = data['body']
# Result: 4,521 bytes of HTML

执行时间@: @ @ @ 1.2 @%s 缓存状态@: @ MISS @ ( @ 第一次获取此 URL=)} 储存在RAG中,供今后再使用

步进2 (Summarize?):

summary = call_tool("summarizer_fast", json.dumps({
    "text": raw_html,
    "max_paragraphs": 3,
    "preserve_key_points": True
}))
# Result: 187-word summary

执行时间@: @ @ @ 2.8 @%s 通过 Gammer _#fast 工具使用模型缓存状态@: @MISS 质量评分=: 0.89 I( 超棒)

*步骤 @3 @ @ &} @ I4 @ (# Parallel@ MS K4}翻譯@+_ 驗證@ MPK6{

这是平行主义发光的地方 :

import asyncio
from node_runtime import call_tools_parallel

# Both execute simultaneously
results = call_tools_parallel([
    ("nmt_translator", json.dumps({
        "text": summary,
        "source_lang": "en",
        "target_lang": "es",
        "beam_size": 5
    }), {}),
    # Validation setup runs in parallel
    ("translation_quality_checker", json.dumps({
        "setup": True,
        "target_lang": "es"
    }), {})
])

translation_result, validation_setup = results

平行执行时间@: @%

没有平行的 : @ @ MS K1} @ I+ @ @ *2.1# # = @ * MSKO5#
与平行的: 最大MS K1 s ,}2.1s)}=}{3.2s
保存@: @ @ 2.1_ 秒@ MS K2# 更快 @ MPK3 @

翻译质量问题 @:

# Validate the NMT translation
quality = call_tool("translation_quality_checker", json.dumps({
    "original": summary,
    "translation": translation_result,
    "source_lang": "en",
    "target_lang": "es"
}))

quality_data = json.loads(quality)
# Result: {
#   "score": 0.64,
#   "issues": [
#     "Repeated words: 'articulo articulo'",
#     "Grammar inconsistency detected",
#     "Potential word-by-word translation"
#   ],
#   "recommendation": "RETRY_WITH_BETTER_MODEL"
# }

系统检测到质量差 ! NMT 速度很快 @(3.2 @s @s)}但制作了普通翻译@(0.64#评分_).}

步骤 @5 @ @ (_ 有条件的重试@ MS K2#

因为质量为 @ < @ @ I0.7, @ 有条件重试触发@ MS K2}

# Use better translator (llama3-based)
better_translation = call_tool("quick_translator", json.dumps({
    "text": summary,
    "source_lang": "en",
    "target_lang": "es",
    "context": "newsletter article",
    "preserve_formatting": True
}))

# Validate again
retry_quality = call_tool("translation_quality_checker", json.dumps({
    "original": summary,
    "translation": better_translation,
    "source_lang": "en",
    "target_lang": "es"
}))

# Result: {"score": 0.92, "issues": [], "recommendation": "ACCEPT"}

执行时间@: @ @ @ 8.4_%s @ I( @ slower 但方法更好#)} 缓存状态@: @MISS 质量=: 0.92 I( 超棒!)

当NMT质量不足时, 系统自动升级为更好的工具@- @%

步骤 @6 @ @ (_ 创建 HTML 电子邮件@):}

# Use the NEWLY GENERATED tool
html_email = call_tool("html_email_generator", json.dumps({
    "subject": "Weekly Article Summary",
    "header_text": "Your Weekly Digest",
    "body_content": better_translation,
    "footer_text": "Unsubscribe | Update Preferences",
    "style": "newsletter",
    "responsive": True
}))

# Result: Beautiful responsive HTML email template

执行时间@: @ @ @ 1.8 @%s 此工具是几秒前创建的 \ 10 @ 。 并且已经在生产中使用! 缓存状态@: @ MISS @ (# brand new tool=)

通过 SendGridQ): 发送

# Use the NEWLY GENERATED SendGrid integration
send_result = call_tool("sendgrid_email_client", json.dumps({
    "to": "[email protected]",
    "from": "[email protected]",
    "subject": "Weekly Article Summary",
    "html_content": html_email,
    "api_key": "${SENDGRID_API_KEY}"  # From environment
}))

# Result: {
#   "success": True,
#   "message_id": "msg_7x3f9a2c...",
#   "status": "queued",
#   "timestamp": "2025-01-23T14:23:45Z"
# }

执行时间@: @ @ @ 1.4 @%s 外部API调用@:剩余缓存状态@:N/A {(}email 发送未缓存=)}

工作流程简表

┌─────────────────────────────────────────────────────────────┐
│  Workflow: article_to_spanish_email                         │
│  Status: ✓ SUCCESS                                          │
│  Total Time: 18.7 seconds                                   │
│  Steps Executed: 7                                          │
│  Tools Used: 7 (2 generated on-the-fly)                     │
│  Parallel Savings: 2.1 seconds                              │
│  Conditional Retries: 1 (translation quality escalation)    │
│  Cache Hits: 0 (first execution)                            │
│  New Tools Created: 2 (html_email_generator, sendgrid)      │
└─────────────────────────────────────────────────────────────┘

Performance Breakdown:
  Step 1 (Fetch):        1.2s  (6%)
  Step 2 (Summarize):    2.8s  (15%)
  Step 3-4 (Parallel):   3.2s  (17%)  ← Would be 5.3s sequential
  Step 5 (Retry):        8.4s  (45%)  ← Quality-driven escalation
  Step 6 (HTML):         1.8s  (10%)
  Step 7 (SendGrid):     1.4s  (7%)

Critical Path: Fetch → Summarize → Translate → Retry → HTML → Send
Bottleneck: Translation retry (necessary for quality)

阶段: 5:监测和进化踢入

工作流程成功@. @ but the system is @'_t done.}它#'\s 学习学习.

即时学习@( @Runtime control

Storing workflow execution in RAG...
  ✓ Workflow definition stored
  ✓ Tool invocations logged (7 calls)
  ✓ Performance metrics recorded
  ✓ Error patterns analyzed (1 quality issue detected)
  ✓ Success patterns identified (retry strategy worked)

Tool Performance Updates:
  http_rest_client:
    - Usage count: 1,247 → 1,248
    - Avg latency: 1,150ms → 1,148ms (slightly faster)
    - Cache hit rate: 34% (this was a miss)

  summarizer_fast:
    - Usage count: 89 → 90
    - Quality score: 0.89 → 0.89 (stable)
    - Fitness: 0.88 (unchanged)

  nmt_translator:
    - Usage count: 67 → 68
    - Quality score: 0.75 → 0.74 (↓ degrading!)
    - Failures: 0 → 1 (quality threshold miss)
    - ⚠️  Degradation detected: 2% drop

  translation_quality_checker:
    - Usage count: 45 → 46
    - Detection accuracy: 94% (caught NMT issue)

  quick_translator:
    - Usage count: 23 → 24
    - Quality score: 0.92 (excellent)
    - Used as retry fallback: +1

  html_email_generator: [NEW TOOL]
    - Usage count: 0 → 1
    - First execution successful
    - Fitness: 0.87 → 0.89 (better than estimated!)

  sendgrid_email_client: [NEW TOOL]
    - Usage count: 0 → 1
    - API call successful
    - Rate limit status: 1/100
    - Fitness: 0.82 → 0.84

发现模式

系统显示某些东西 @: @%

Pattern Analysis: NMT Translation Quality

  Recent executions: 68
  Quality failures (score < 0.7): 12 (18% failure rate)
  Trend: Increasing failures (was 8% last week)

  Root cause analysis:
    - NMT service may have changed models
    - Or: Input text complexity increased
    - Or: Quality threshold too strict

  Recommendation:
    1. Investigate NMT service for changes
    2. Consider using quick_translator as primary
    3. Or: Create specialized "validated_translator" composite tool

系统正在建议自己的进化... ....

阶段“6:”适应性最佳化 “(” 第二天“)”

它分析过去所有工作流程的“24”小时, 并发现@:

Overnight Batch Optimization Report
────────────────────────────────────

High-Value Optimization Opportunities:

1. Create Composite Tool: "validated_spanish_translator"

   Pattern: 15 workflows used nmt_translator + translation_quality_checker + quick_translator
   Current cost: 3 tool calls, ~12 seconds
   Optimized cost: 1 tool call, ~6 seconds
   ROI: High (50% time savings, used 15 times/day)

   Implementation:
     - Combines NMT (fast attempt)
     - Quality checking (automatic)
     - Fallback to llama3 (if needed)
     - Single, unified interface

   Status: ✓ GENERATED
   Version: validated_spanish_translator v1.0.0

2. Optimize "http_rest_client" for article fetching

   Pattern: Fetching article content (HTML parsing needed)
   Current: Returns raw HTML, requires parsing
   Optimized: Add optional HTML→text extraction
   ROI: Medium (saves parsing step in 23 workflows)

   Status: ✓ UPGRADED
   Version: http_rest_client v2.1.0
   Breaking change: No (new optional parameter)

3. Create Specialized Tool: "article_fetcher"

   Pattern: Fetch URL + extract main content + clean HTML
   Current: 3 separate operations
   Optimized: Single tool with smart content extraction
   ROI: Medium-High (used in 18 workflows)

   Status: ✓ GENERATED
   Version: article_fetcher v1.0.0
   Uses: http_rest_client v2.1.0 + BeautifulSoup + readability

系统刚刚@: @%

创建了一个复合工具, 将 3\ 步合并到\ MPK1\ \ }
升级现有工具,具有新能力
创建了通用模式的专门工具

它根据使用模式 . 做了这个自主的“在一夜之间” “,”

阶段“7:工作流再利用” “(之后的-Life)

为此工作流程创建的工具目前正由 当我们开始时甚至不存在的其他工作流程@ ' @}.

工具行( L) @: @ html @ _ @ email@ MS K2_ generator

html_email_generator v1.0.0 (Created: 2025-01-23)
  └─ Usage: 47 times across 12 different workflows

  Used by:
    1. article_to_spanish_email (original)
    2. weekly_digest_generator
    3. customer_onboarding_email
    4. abandoned_cart_reminder
    5. newsletter_builder
    6. event_invitation_creator
    7. survey_email_campaign
    8. product_announcement
    9. user_feedback_request
    10. blog_post_notification
    11. quarterly_report_emailer
    12. team_update_newsletter

  Evolution:
    - v1.0.0 → v1.1.0 (added custom CSS support)
    - v1.1.0 → v1.2.0 (added image optimization)
    - v1.2.0 → v2.0.0 (responsive templates + dark mode)

  Current fitness: 0.94 (up from 0.87)
  Current version: v2.0.0
  Total usage: 237 times
  Success rate: 98.7%

为一个工作流程创建的工具成为 {12+ 工作流程的基础工具 @.}

工具行% : @ sendgrid_ @email @__client

sendgrid_email_client v1.0.0 (Created: 2025-01-23)
  └─ Usage: 89 times across 8 workflows

  Evolution:
    - v1.0.0 → v1.0.1 (bug fix: rate limiting edge case)
    - v1.0.1 → v1.1.0 (added batch sending)
    - v1.1.0 → v1.2.0 (added template support)
    - v1.2.0 → v2.0.0 (added analytics tracking)

  Descendants (tools created FROM this tool):
    - sendgrid_batch_emailer v1.0.0
    - sendgrid_template_manager v1.0.0
    - sendgrid_analytics_fetcher v1.0.0

  Current fitness: 0.91 (up from 0.82)
  Success rate: 99.1%

SendGrid 工具生成了“3”专门后代

人人使用的综合工具

validated_spanish_translator v1.0.0 (Auto-generated: 2025-01-24)
  └─ Usage: 156 times across 23 workflows

  Replaces: nmt_translator + translation_quality_checker + quick_translator

  Performance improvement:
    - Old workflow: 12.1s average
    - New workflow: 6.3s average
    - Savings: 5.8s (48% faster)

  Total time saved: 156 executions × 5.8s = 15.1 minutes

  Evolution:
    - v1.0.0 → v1.1.0 (added French support)
    - v1.1.0 → v1.2.0 (added German, Italian)
    - v1.2.0 → v1.3.0 (added quality caching)

  Current fitness: 0.96 (excellent!)

此自动生成的复合工具目前是整个系统中最常用的工具之一@ . @

阶段=8:}贡献周期% 1 月后=% 2

野外出事... 更新的AIA系统 “(GPT-5或Claude MS K2假想地“)使用经验证的{Spanish_Terrationlator 工具,并发现一个改进:

=== Contribution from Advanced AI System ===

Tool: validated_spanish_translator v1.3.0
Contributor: gpt-5-turbo (reasoning model)
Date: 2025-04-15

Improvement Detected:
  The current implementation always tries NMT first, then falls back to llama3.
  This is suboptimal for long texts (>1000 words).

  Analysis:
    - For short texts (<200 words): NMT is faster and acceptable
    - For medium texts (200-1000 words): NMT is hit-or-miss
    - For long texts (>1000 words): NMT consistently fails quality checks

  Proposed Optimization:
    - Texts >1000 words: Skip NMT entirely, use llama3 directly
    - Texts 200-1000 words: Try NMT with stricter beam_size=10
    - Texts <200 words: Use NMT as before

  Implementation:
    ```python
    def translate(text, source_lang, target_lang):
        word_count = len(text.split())

        if word_count > 1000:
            # Skip NMT for long texts
            return call_tool("quick_translator", ...)
        elif word_count > 200:
            # Use stricter NMT settings
            result = call_tool("nmt_translator", ..., beam_size=10)
            quality = check_quality(result)
            if quality < 0.75:  # Stricter threshold
                return call_tool("quick_translator", ...)
            return result
        else:
            # Fast path for short texts
            return call_tool("nmt_translator", ...)
    ```

  Expected improvement:
    - Long texts: 6.2s → 3.8s (38% faster)
    - Medium texts: Slightly slower (stricter checks) but higher quality
    - Short texts: Unchanged

  Status: ✓ TESTED
  Version: v1.4.0
  Fitness improvement: 0.96 → 0.98

改进已被接受并合并

现在, 每个使用此工具的工作流程都自动更快*.* 包括原件 article_to_spanish_email 我们以 @. 开始的工作流程

连续不断的演变

graph TB
    Original[Original Workflow<br/>article_to_spanish_email<br/>v1.0.0] --> Tool1[Created: validated_spanish_translator<br/>v1.0.0<br/>Fitness: 0.89]

    Tool1 --> Workflows[Used by 23 Workflows<br/>Total: 156 executions]

    Workflows --> Evolution[Overnight Analysis<br/>Detects optimization opportunity]

    Evolution --> Tool2[validated_spanish_translator<br/>v1.4.0<br/>Fitness: 0.98]

    Tool2 --> Cascade[Cascading Improvement]

    Cascade --> Original2[article_to_spanish_email<br/>v1.0.0<br/>Now 38% faster for long articles!]
    Cascade --> Other[22 Other Workflows<br/>All faster automatically]

    Tool2 --> NewAI[New AI System<br/>GPT-5 uses tool]

    NewAI --> Discovery[Discovers length-based optimization]

    Discovery --> Contribution[Contributes v1.4.0<br/>Smart length handling]

    Contribution --> Tool3[validated_spanish_translator<br/>v1.5.0<br/>Accepts contribution]

    Tool3 --> Final[ALL workflows benefit<br/>Zero code changes needed]

    style Original stroke:#1976d2,stroke-width:3px,color:#1976d2
    style Tool1 stroke:#388e3c,stroke-width:3px,color:#388e3c
    style Tool2 stroke:#f57c00,stroke-width:3px,color:#f57c00
    style Tool3 stroke:#7b1fa2,stroke-width:3px,color:#7b1fa2
    style Contribution stroke:#0277bd,stroke-width:4px,color:#0277bd
    style Final stroke:#2e7d32,stroke-width:4px,color:#2e7d32

一个工作流程创建了一个工具@., 这个工具进化了#. 更聪明的AI改进了它 @. 每个工作流程的好处.

这是AI代人之间的协作演变 .

阶段 *\ 9: 时退的臭虫

六个月后,安全研究者发现 sendgrid_email_client v1.2.0:

SECURITY ALERT: sendgrid_email_client v1.2.0
Vulnerability: Email Header Injection
CVE: CVE-2025-12345
Severity: HIGH

Issue:
  User input in "subject" field not properly sanitized.
  Allows header injection via newline characters.

  Exploit:
    subject = "Newsletter\nBcc: [email protected]"
    # Results in BCC header injection

Affected Versions:
  - v1.2.0 (introduced bug)
  - v2.0.0 (inherited bug)
  - v2.1.0 (inherited bug)

Fix Required:
  Sanitize all email headers before sending.
  Escape newlines, carriage returns, and null bytes.

现在自我的-+Healing系统在 .+#中启动

修整與樹林環境

Self-Healing Initiated: sendgrid_email_client
Severity: HIGH (security vulnerability)
Trigger: External security advisory

Step 1: Identify failure point
  ✓ Bug introduced in v1.2.0 (added template support)
  ✓ Mutation: "Support dynamic subject lines from templates"
  ✓ Problematic code: Line 47, subject insertion without sanitization

Step 2: Prune affected branch
  ✗ MARK AS PRUNED: v1.2.0
  ✗ MARK AS TAINTED: v2.0.0, v2.1.0 (descendants)
  ✓ Remove from active routing
  ✓ Preserve for learning (don't delete)

Step 3: Create avoidance rule
  Rule ID: avoid_email_header_injection
  Description: "Always sanitize user input in email headers"
  Pattern: "Never insert user-controlled strings into headers without escaping"
  Scope: GLOBAL (affects all email-related tools)
  Propagation:
    - sendgrid_email_client (all versions)
    - smtp_sender (similar tool)
    - email_validator (should detect this)
    - All tools tagged "email"

Step 4: Find last known-good version
  ✓ v1.1.0 (before bug introduction)
  ✓ Health status: HEALTHY
  ✓ Tests pass: YES
  ✓ No security issues

Step 5: Auto-regenerate from v1.1.0
  Base: sendgrid_email_client v1.1.0
  Goal: Add template support (original intent of v1.2.0)
  Constraints:
    - MUST sanitize all headers
    - MUST escape newlines, CR, null bytes
    - MUST validate email addresses
    - MUST prevent header injection

  Regeneration:
    ✓ Code generated with security constraints
    ✓ Added input sanitization
    ✓ Added automated security tests
    ✓ Static analysis: PASS (bandit: no issues)
    ✓ All original tests: PASS
    ✓ New security tests: PASS

  New Version: v1.2.1 (patched)
  Fitness: 0.93 (same as v1.2.0 but secure)

Step 6: Propagate fix to descendants
  Analyzing v2.0.0...
    - Inherited bug from v1.2.0
    - Regenerating with security fix
    - New version: v2.0.1 ✓

  Analyzing v2.1.0...
    - Inherited bug from v2.0.0
    - Regenerating with security fix
    - New version: v2.1.1 ✓

Step 7: Update all active workflows
  Found 8 workflows using vulnerable versions:
    1. article_to_spanish_email → Updated to v2.1.1
    2. weekly_digest_generator → Updated to v2.1.1
    3. customer_onboarding_email → Updated to v1.2.1
    4. abandoned_cart_reminder → Updated to v1.2.1
    5. newsletter_builder → Updated to v2.1.1
    6. event_invitation_creator → Updated to v2.0.1
    7. survey_email_campaign → Updated to v1.2.1
    8. product_announcement → Updated to v2.1.1

  ✓ All workflows updated and validated
  ✓ Zero downtime (atomic swap)

Step 8: Create institutional memory
  Avoidance Rule: avoid_email_header_injection
  Stored in: Global rule repository
  Applied to: 47 tools (all email-related)

  Future behavior:
    - Any tool that handles email headers will inherit this rule
    - Any code generation for email tools will check this constraint
    - Any mutation of email tools will validate against this rule

  Testing:
    ✓ Created regression test suite
    ✓ Added to all email tool test suites
    ✓ Added to security audit checklist

Self-Healing Complete.
Time: 47 seconds
Workflows updated: 8
Tools patched: 3 versions
Security tests added: 15
Institutional knowledge: PERMANENT

普鲁宁之后的线条树

graph TB
    V10[v1.0.0<br/>Initial<br/>✓ Healthy] --> V11[v1.1.0<br/>Batch sending<br/>✓ Healthy]

    V11 --> V12[v1.2.0<br/>Templates<br/>❌ PRUNED<br/>Security bug]
    V11 --> V121[v1.2.1<br/>Templates + Security<br/>✓ Regenerated<br/>✓ Secure]

    V12 -.-> |Tainted| V20[v2.0.0<br/>Analytics<br/>❌ PRUNED<br/>Inherited bug]
    V121 --> V201[v2.0.1<br/>Analytics + Security<br/>✓ Regenerated<br/>✓ Secure]

    V20 -.-> |Tainted| V21[v2.1.0<br/>Advanced features<br/>❌ PRUNED<br/>Inherited bug]
    V201 --> V211[v2.1.1<br/>Advanced + Security<br/>✓ Regenerated<br/>✓ Secure]

    V121 --> Current1[Active workflows<br/>using v1.2.1]
    V201 --> Current2[Active workflows<br/>using v2.0.1]
    V211 --> Current3[Active workflows<br/>using v2.1.1]

    style V10 stroke:#388e3c,stroke-width:3px,color:#388e3c
    style V11 stroke:#388e3c,stroke-width:3px,color:#388e3c
    style V12 stroke:#c62828,stroke-width:3px,stroke-dasharray: 5 5,color:#c62828
    style V121 stroke:#0277bd,stroke-width:4px,color:#0277bd
    style V20 stroke:#c62828,stroke-width:3px,stroke-dasharray: 5 5,color:#c62828
    style V201 stroke:#0277bd,stroke-width:4px,color:#0277bd
    style V21 stroke:#c62828,stroke-width:3px,stroke-dasharray: 5 5,color:#c62828
    style V211 stroke:#0277bd,stroke-width:4px,color:#0277bd
    style Current3 stroke:#2e7d32,stroke-width:4px,color:#2e7d32

系统@: @%

在旧版本中检测到一个安全错误
照顾脆弱分支和所有后代
重新生成上次已知的安全版本@ - @ good 祖先
自动更新所有活动工作流程
创建永久机构记忆,以永远防止此类错误

它在“47”秒内做了这个

实际上我们所建造的

让我们向后退一步看看刚刚发生了什么 :

请求要求:Complical multiple @-步式工作流程
分解智能任务分类
工具发现:语义搜索现有能力
在“-”和“-”上: 创建了 @2 新的工具中间@- 执行
平行执行@: @ 保存@ 40% @ 执行时间
质量@- @% 驱动升级在质量差时用更好的工具进行测试
成功成功@: 提供完整的工作流程在 @MS K1秒
学习学习学习: 存储了所有东西,供将来再使用
演变进进@:_# 确定在一夜之间实现优化的机会
工具再使用“:”这些生成的工具成为“20+”工作流程的基础。
合作改进新的AI改进了现有工具。
连级福利: 所有工作流程都自动加快
自我治疗: 安全脆弱度自动- 固定在树上修剪
机构记忆: 长期學會防止這種類型的錯誤

这是'#t 代码生成@.}

这是自定义的- 正在演变的代码生态系统.

未来=:DISE大规模烹饪器

想象一下这个在比例尺上运行的@ : @

10,000工作流程 每天执行
500#工具 生态系统中的生态系统
多种独立认证系统 有助于改进
连续演变 发生着24/7
零=- dodump time 安全补丁
自动优化性能

出现什么?

“工具市场”假想

DiSE Tool Exchange (hypothetical)

Top Tools This Week:
  1. validated_spanish_translator v1.5.0
     - Usage: 2,341 times
     - Fitness: 0.98
     - Created by: DiSE Instance #42
     - Improved by: 7 different AI systems
     - Contributed to: 142 DiSE instances worldwide

  2. intelligent_article_fetcher v3.2.0
     - Usage: 1,876 times
     - Fitness: 0.96
     - Specializations: News, Blogs, Academic papers
     - Auto-adapts to site structure

  3. sendgrid_enterprise_client v4.1.0
     - Usage: 1,523 times
     - Fitness: 0.97
     - Features: Batch sending, templates, analytics, A/B testing
     - Started from: sendgrid_email_client v1.0.0 (our tool!)

由一个 DISE 实例创建的工具被千人使用并改进

安全免疫系统

Global Security Event: Log4Shell-style vulnerability

1. Vulnerability discovered in http_rest_client v2.3.0
   Source: Security researcher
   Impact: ALL workflows using HTTP

2. Alert propagates to all DiSE instances globally
   Speed: <10 seconds worldwide
   Affected instances: 1,247

3. Coordinated self-healing
   Each instance:
     - Prunes vulnerable versions
     - Regenerates from last known-good
     - Updates all workflows
     - Shares avoidance rules globally

4. Institutional knowledge propagates
   Avoidance rule: avoid_log_injection_via_headers
   Applied to: ALL HTTP client tools
   Global propagation: <5 minutes

5. Future immunity
   This exact vulnerability can NEVER happen again
   Similar vulnerabilities detected during code generation
   All DiSE instances now immune

安全保卫问题一旦发现,

最佳军备竞赛

Week 1: DiSE Instance A discovers that caching NMT results speeds up translation 30%
  ↓
Week 2: DiSE Instance B sees the improvement, adds semantic caching (40% faster)
  ↓
Week 3: DiSE Instance C adds multilingual caching (50% faster)
  ↓
Week 4: GPT-5 discovers cache key optimization (60% faster)
  ↓
Week 5: Claude 4 adds predictive pre-caching (70% faster)
  ↓
Result: What started as a 12-second operation now takes 3.6 seconds
        With ZERO human optimization effort
        And ALL instances benefit automatically

合作优化生成指数改进@.

令人不适的真理

We' 造了某种东西

撰写自己的工具
自动优化自身
从每次执行中学习
全球分享知识
破碎时自愈
在没有人力干预的情况下不断改进
永远不会忘记一个错误
每一代人工智能都变得更聪明

开始于代码生成器@. @%

成为 正在演变的软件生态系统.

这里的'是真正令人不解的部分 :

**它已经在工作了 . ***

理论上说不是. 不是" 有朝一日是?." 现在 .

本篇文章的代码是“'t 投机性虚构”. It's 基于实际执行 DISE <.}工具存在自动-evolution在一夜之间运行}MS K7*自我=-Healing 设计并准备实施<.′#

我们不是在建造AGI .

我们兴建了AGI的基底建筑可能来自.

你该做什么

如果这听起来有趣的话

克隆回购@: @ https@://github.com}/scottgal_/}多半是纯净的.dse
尝试工作流程: 运行此文章的示例
看着它进化: 看工具正在创建和优化
打破事物@ :_ 触发器自我@ MS K1# 引入臭虫来治愈
贡献您的改进将在全球推广

如果这听起来很可怕的话

古德. 你正在注意 . *
读取安全警告 在读取中
Don'}在生产中使用它 ( MS K1~
但能理解: 这里是我们前往的地方

烹饪者才刚刚开始

这是语义内存序列中最后一集的 10—+%

但它却头一在 DISE Cooker 系列中“.”

因为我们所建造的... ...只是一个工具... 持续进化的秘方.

“1-6”部分探索了理论“:”简单规则“, ”新出现行为“MS K3”自我“-”“最佳化”,“集体智力”.

部分“7”展示了它的工作原理 “:”真实代码=,}真正的演进=,}实际结果=MSC4

部分 @8_解释工具@:}如何追踪#,_学习,}并改进.}

部分 @9 @ @ ( @ hypothetical@ MS2_ 覆盖了自我-#healing @ MPK4 @ 错误是如何变成机构记忆的吗?

Part {10} 显示实际使用时发生的情况@: 写自己#,}工具自我进化的工作流程 *, 治愈自己的系统♪.

烹饪器正在运行 @. @%

这些成分是代码,工具 ,和工作流程

食谱是人类目标指引的进化压力

什么被煮熟了 ?

我们即将发现 .

技术资源

仓库库@: @ https@://github.com}/scottgal_/}多半是纯净的.dse

关键构件:

src/overseer_llm.py 工作流量分解
src/tools_manager.py - 工具发现和引用
src/auto_evolver.py 夜以继日的优化
src/self_healing.py @-BARBAR 检测和修补#(理论)}
src/qdrant_rag_memory.py 记忆和学习
tools/ 现有工具

尝试示例工作流:

cd code_evolver
python chat_cli.py

DiSE> Fetch https://example.com/article, summarize to 3 paragraphs, translate to Spanish with quality checking, create HTML email, and send via SendGrid

文件文件文件文件文档文件文件的文件文件文件会议文件文件文件,文件文件文献文件文件/文件文件资料文件文件文文件文件归档文件文件:文件文件记录文件文件、文件文件提供文件文件文号文件文件(文件文件 Do文件文件存档文件文件文件文件文件和文件文件证件文件文件文件中文件文件Do文件文件档案文件文件:

README.md 完整的设置指南
ADVANCED_FEATURES.md @ - 深-dive 进入建筑
code_evolver/PAPER.md 学术观点

系列导航:

“1:Q 简单规则”部分 @,Z 复杂行为 I- 基金会
集体情报通讯能改变一切
部分@3: @ self @ -_ 优化改善自我的-Z系统
《新出现》当优化成为智能时
部分=5:}进化 - 从优化到行业和文化
全球共识 - 定向进化和行星认知
-=YTET -伊甸园字幕组=- 翻译: 实际建造它并观察它的演变
部件“8:” 工具全部向下自我-优化工具箱
@ 9:_ self@ MS K1_ 治疗工具 - 线性@- 观测修补和恢复
DISE 烹饪器 你在这里当理论与混乱的现实相遇时

DISE Cooker 系列 : 什么是'下一步

语义内存序列是完整的 @._DISE Cooker系列开始@.}

即将下的条款:

部分#11@ : 多-Agential Working flows @ I(当工具自动协调时#)}
部分#12“工具市场”“工具市场 ” “(” 在 DISE 实例中共享工具“)”
部分#13-=YTET -伊甸园字幕组=- 翻译:
部分#14安全硬化 (#sandboxing @,隔离@,}信任~)
部分#15最佳军备竞赛和大规模合作的进化

实验还在继续 .

这是Semantic Intelligence=MSK_:}最后的片段简单的规则又如何简单 制作中的实际烹饪工作流程 @. @%

代码是真实的 . 工具存在当它起作用时它会发生进化 MS K2 * It' 实验性,*

我们不是在建造AGI .,我们是在建堆肥的 AGI