content: add Twitter thread drafts for LeaksLab launch

content/social/twitter-threads.md

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+# Twitter/X Thread Drafts — LeaksLab
+
+---
+
+## Thread 1: Cursor's System Prompt (Technical Breakdown)
+**Best time to post**: Tuesday/Wednesday morning
+
+---
+
+🧵 I read Cursor's full system prompt so you don't have to.
+
+Here are the 7 most important things it reveals about how to build a production AI coding agent:
+
+1/8
+
+---
+
+Cursor gives its AI **8 specialized tools**. Not 1 catch-all tool. Not "search the internet". 8 specific, purpose-built tools.
+
+This single architectural decision is why Cursor feels smarter than most AI editors.
+
+2/8
+
+---
+
+The 8 tools are:
+- `codebase_search` (semantic)
+- `grep_search` (text/regex)
+- `read_file`
+- `edit_file`
+- `run_terminal_cmd`
+- `list_dir`
+- `file_search`
+- `web_fetch`
+
+Notice the two search tools. Semantic AND text. Most builders use one. Cursor uses both. Here's why that matters 👇
+
+3/8
+
+---
+
+Semantic search = expensive, slow, finds intent
+Text search = cheap, fast, finds exact strings
+
+Having both means the AI can say:
+- "I need to find the login logic" → semantic
+- "I need to find every `console.log`" → text
+
+One decision. Massive performance difference.
+
+4/8
+
+---
+
+The prompt has an entire section on what NOT to do.
+
+"Do not be excessively helpful."
+"Do not make changes beyond what was asked."
+"Do not explain code unless asked."
+
+This is counterintuitive. But it's why Cursor doesn't rewrite your entire codebase when you ask it to fix a typo.
+
+5/8
+
+---
+
+Every code reference requires a file path + line number.
+
+This sounds obvious but almost no one enforces it.
+
+The result: every suggestion is auditable. You can always trace what changed, why, and where.
+
+6/8
+
+---
+
+The safety layer (be helpful, be harmless) is almost completely absent.
+
+Because that's handled at the model level — Claude/GPT training.
+
+The prompt is purely operational. This is why Cursor feels fast and decisive. No wasted tokens on meta-instructions.
+
+7/8
+
+---
+
+Three things to steal for your own agent builds:
+
+1. Separate cheap tools from expensive ones
+2. Require structured output with provenance (file + line)
+3. Define what the agent should NOT do as clearly as what it should
+
+Full breakdown + all 40+ tool prompts: [github.com/VoXc2/system-prompts-and-models-of-ai-tools]
+
+8/8
+
+---
+
+## Thread 2: What 40 System Prompts Taught Me
+**Best time to post**: Monday morning or Thursday
+
+---
+
+🧵 I read the system prompts of 40+ AI tools.
+
+Cursor, Windsurf, Devin, Claude Code, v0, Manus, ChatGPT, Lovable...
+
+Here are the 5 patterns that appear in every successful one:
+
+1/7
+
+---
+
+**Pattern 1: Identity before instructions**
+
+Every great system prompt starts with WHO the AI is, not WHAT it should do.
+
+"You are a senior software engineer..."
+"You are an autonomous coding agent..."
+
+Identity shapes every downstream behavior. Start there.
+
+2/7
+
+---
+
+**Pattern 2: Explicit failure modes**
+
+The best prompts don't assume the AI will succeed. They define exactly what to do when it fails.
+
+Most prompts I've seen from startups have zero failure instructions. The result: the AI improvises badly.
+
+3/7
+
+---
+
+**Pattern 3: Tool schemas > prose instructions**
+
+"You can search the codebase" vs a full JSON tool schema with parameter descriptions.
+
+Every production system uses schemas. Every prototype uses prose.
+
+Schemas force precision. Prose allows drift.
+
+4/7
+
+---
+
+**Pattern 4: Scope constraints**
+
+v0 by Vercel has explicit rules about React/Tailwind/shadcn. It will not generate raw CSS. It will not use other UI frameworks.
+
+Constraints make AI more predictable. Unconstrained AI is unreliable AI.
+
+5/7
+
+---
+
+**Pattern 5: The "no more than asked" rule**
+
+Cursor, Devin, Claude Code — all have explicit instructions to do exactly what was asked. Nothing more.
+
+This is the most underrated principle in prompt engineering.
+
+6/7
+
+---
+
+All 40+ system prompts are free in our library.
+
+We add new tools every week.
+
+⭐ Star it and help us reach every AI engineer building in 2026:
+[github.com/VoXc2/system-prompts-and-models-of-ai-tools]
+
+7/7
+
+---
+
+## Thread 3: Devin AI Breakdown
+**Best time to post**: Weekend or Friday
+
+---
+
+🧵 Devin AI bills itself as a "fully autonomous software engineer."
+
+Its system prompt reveals exactly how that autonomy is engineered.
+
+This is what $21M in VC funding looks like in text form:
+
+1/6
+
+---
+
+Devin's prompt is structured around **tasks, not conversations**.
+
+Most AI tools are built for back-and-forth dialogue. Devin assumes it will run for hours, autonomously, with minimal human input.
+
+This changes everything about how the prompt is written.
+
+2/6
+
+---
+
+The task decomposition section is explicit:
+
+1. Understand the full requirement
+2. Break into sub-tasks
+3. Estimate dependencies
+4. Execute in order
+5. Verify each step before moving forward
+
+This is just software engineering methodology. But written into a prompt, it becomes autonomous execution.
+
+3/6
+
+---
+
+Failure recovery has three levels:
+
+1. Retry the same approach
+2. Try an alternative approach
+3. Ask the human
+
+Most AI tools jump to level 3 immediately. Devin tries levels 1 and 2 first.
+
+This is why it feels more autonomous — it has been told to be.
+
+4/6
+
+---
+
+Context management is a core part of the prompt.
+
+Devin explicitly tracks:
+- What has been completed
+- What is in progress
+- What is blocked and why
+- What the human needs to review
+
+This is state management. In a text prompt. It works because it's explicit.
+
+5/6
+
+---
+
+The full Devin prompt + 39 other tools are in our free library.
+
+If you are building autonomous agents, there is no better reference material.
+
+[github.com/VoXc2/system-prompts-and-models-of-ai-tools]
+
+6/6