语义记忆系列的最后一集 陌生事物的开始
注: 这是第10部分——语义记忆系列中的最后一部分和DISE Cooker系列中的第一个。我们正在从理论转向实践,从“如何运用工具”到“实际使用工具执行真正任务时会发生什么”。
这个系统不仅产生代码,而且产生代码。 进进变化 不只是坐在那里的工具,但是 学习学习 不仅执行工作流程的工具包,而且 记得 每一个成功和每一个失败。
现在,我们回答一个问题,没有人问,但每个人都应该有:
当你实际使用这东西时会怎么样?
不是玩具的例子,不是"你好世界"。 是一个真正的,乱七八糟的,多步骤的任务, 正常的代码生成系统 绝对会窒息。
情况是这样的:
“去这个网页,取回内容,总结内容,翻译成西班牙文(使用NMT,但检查质量,必要时使用更好的东西),然后创建HTML电子邮件,用SendGrid发送。”
单句。 7个不同的行动。 多重工具、多重故障模式、多重优化机会。
让我们看DISE做饭。
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...
系统收到一个复合任务,不是"写一个函数",不是"翻译这个文本" 工作流程工作流程 与:
传统LLM代码的生成将要么:
DISE做了一些不同的事情。
✓ Task classified as MULTI_STEP_WORKFLOW
✓ Complexity: COMPLEX (7 steps, 4 tools needed, 1 missing)
✓ Consulting overseer LLM for decomposition strategy...
监督员(Lama3 或claude-3.5-sonnet,视您的设置而定)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:
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个没有
有意思的地方就在这里
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)
系统公正:
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)
等等,系统只是 创建了 OpenAPI 集成工具 从头开始,它:
我们现在有2个新工具 15秒前就不存在了
这里就是魔法真正发生的地方。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
步骤1(扩展内容):
# 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秒 快取状态: MISS( 第一次获取此 URL) 储存在RAG中,供今后再使用
步骤2(合并):
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秒 使用的模型:通过Gammer_fast工具拍摄的马拉马3 缓存状态: MSSS 质量评分:0.89(优秀)
第3和4步( Parallel: 翻译+验证) :
这就是平行主义发扬光大的地方:
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:
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很快(3.2s),但制作了普通翻译(0.64分)。
步骤5(有条件重试):
由于质量 < 0. 7, 有条件重试触发 :
# 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秒(较慢,但更好) 缓存状态: MSSS 质量:0.92(优异!)
当NMT质量不足时,系统自动升级为更好的工具。
第6步(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秒 这个工具是10秒前发明的 并投入生产! 缓存状态: MISS (新字型工具)
步骤7(通过SendGrid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秒 对外宣传倡议对外电话:成功 缓存状态: N/ A (电子邮件发送未缓存)
┌─────────────────────────────────────────────────────────────┐
│ 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)
工作流程成功,但系统没有完成,是 学习学习学习.
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
系统注意到一些东西:
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
该系统正在提出其自身的演变。
批量优化器在一夜之间运行。 它分析了过去24小时的所有工作流程, 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
系统公正:
它根据使用模式,在一夜之间自主地完成了这个任务。
快速前进 1 周。 为此工作流程创建的工具正在被 当我们开始时甚至不存在的其他工作流程.
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。
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个特殊后代。
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!)
这一自动生成的复合工具现已成为整个系统最常用的工具之一。
发生了一些野外的事情 更新的 AI 系统 (GPT-5或Claude 4,假设)使用经验证的_spanish_translorator 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
进步被接受和合并!
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 我们开始的工作流程 。
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
一个工作流程创造了一个工具。这个工具演变了。一个更聪明的人工智能改进了它。每个工作流程都有好处。
这是不同代人之间合作的演变。
6个月后 灾难袭击 一名安全研究者发现 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.
现在自我愈合系统开始启动
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
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:
它在47秒内做到了这一点。
让我们退后看看刚刚发生了什么:
这不是代号生成。
这是一个自我演化的代码生态系统。
想象一下这个在规模上运行的 :
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!)
千人正在使用并改进由 DISE 实例创建的工具 。
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
曾经发现的一个安全问题, 到处固定, 永远无法解决。
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,产生指数式改进。
我们建造了一件东西:
这是以代码生成器开始的 。
成为 自动演变的软件生态系统.
下面是真正令人不安的部分:
已经起作用了
不是理论上的 不是"某天" 现在就去
这篇文章中的代码不是推测性的虚构。 它基于 DISE 的实际实施。 工具存在。 RAG 记忆有效 。 自动革命在一夜之间运行。 自我愈合是设计并准备执行的 。
我们不是在建造AGI
我们正在建造AGI可能诞生的基底
如果这听起来很有趣的话:
如果这听起来很可怕:
这是第10部分 语义记忆系列的最后一部分
但它是 头一 在DISE烹饪系列。
因为我们建造的不仅仅是一个工具 持续进化的秘方.
第1至6部分探讨了理论:简单规则、突发行为、自我优化、集体智慧。
第七部分显示了它的工作效果:实际代码、实际演变、实际结果。
第8部分解释了工具:如何跟踪、学习和改进。
第9部分(假设)涵盖自我治疗:错误如何成为机构记忆。
第10部分显示实际使用时发生的情况:写作的工作流程, 自我进化的工具, 自我愈合的系统。
厨师正在运行。
这些要素是代码、工具和工作流程。
配方是以人类目标为指导的进化压力。
什么被煮熟了?
我们马上就会知道的
仓库仓库https://github.com/scottgal/ mostlylucid.dse https://github.com/scottgal/ mostlylucid.dse https://github.com/scottgal/ mostlylucid.dse https://github. com/scottgal/ mostlylulucid.dse https://github.dse https://getgal/scottgal/ mostlylucid.dse https: https://github.com/scottgal/ mostlylucid.dse https: https://github.com/scottgal/scatgal/ mostlylulucid.dse https https https://gitub.com/gitub.com/s/sctgatgal/ mostlylucidcid.dse /dse http.dse http: http http http: http: http: http: http: http: http: http:// http:// http:// http:// http://gitubbb. https://gitubbb.com/ https.com/ https. https. https. https. https.dse https. https. https. https. http. http.dse https. https. https. https. https. https. https.dse. https. https.dse. https. https. https. https. https. https. https. https. https. https. https. https. http. http. http. http. http. http. http. http. http. http. http. http://g.d. http://g. http. http://g.
关键构件:
src/overseer_llm.py - 工作流程分解src/tools_manager.py - 工具发现和调用src/auto_evolver.py - 夜间优化src/self_healing.py - 虫子检测和修复(理论)src/qdrant_rag_memory.py - 记忆和学习tools/ - 50+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
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README.md - 完整设置指南ADVANCED_FEATURES.md - 深入建筑结构code_evolver/PAPER.md - 学术观点系列导航:
语义内存序列已经完成。 DISE Cooker 序列开始 。
即将到来的条款:
实验还在继续
这是《语义情报》的最后一部分第十部分:简单规则 复杂行为 自我优化 出现 进化 全球共识 引导合成进化 自我优化工具 自我愈合系统 自我愈合系统 生产中的实际烹饪工作流程。
代码是真实的,工具是存在的,进化会发生。 它是实验性的,偶尔不稳定的, 并且绝对是“虚拟编码”的。 但是它有效。 有点。 有时。 当它起作用的时候, 它就真的神奇了。
我们不是在建造AGI,我们正在建造AGI的堆肥。
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