{
  "count": 8,
  "topics": [
    {
      "name": "Agent",
      "slug": "agent",
      "url": "https://hydblog.xyz/en/topics/agent/",
      "articleCount": 1,
      "articles": [
        {
          "id": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code/",
          "title": "Three Claude Code implementations: an agent engineering notebook",
          "description": "Reading CoreCoder, Claude Code, and learn-claude-code side by side pulls Agent Loop, tools, context, concurrency, multi-agent, and security boundaries into a single line.",
          "llmSummary": "Agent engineering notes drawn from three implementations. CoreCoder is a minimal implementation written by hand in Python, Claude Code is the production implementation, and learn-claude-code is the teaching implementation. Coverage spans the Agent Loop's transition condition and interrupt repair, tool definitions and file-editing strategy, context injection and three tiers of compression, the long-lived state of QueryEngine, parallel tools and sub-agents, the Hook and Task extension points, multi-agent collaboration, memory / MCP / background tasks / scheduled tasks, and the security defenses around paths and commands.",
          "url": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code/",
          "pubDate": "2026-08-23T13:00:00.000Z",
          "tags": [
            "Agent",
            "Claude Code",
            "source reading",
            "engineering practice"
          ],
          "markdownUrl": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code.md"
        }
      ]
    },
    {
      "name": "Attention mechanism",
      "slug": "attention-mechanism",
      "url": "https://hydblog.xyz/en/topics/attention-mechanism/",
      "articleCount": 1,
      "articles": [
        {
          "id": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes/",
          "title": "Transformer learning notes: from the overall architecture to multi-head attention",
          "description": "Transformer study notes organized by mechanism: first how the data flows through the encoder and the decoder, then, one by one, Token, word embeddings, positional encoding, Q/K/V attention, the difference between training and inference, the causal mask, the feed-forward network, multi-head attention, and layer normalization.",
          "llmSummary": "Transformer study notes ordered whole-first and then part-by-part: the data flow through the encoder and the decoder; the three questions to answer before learning large models (neural networks, attention, what PyTorch is made of); what Token, the word embedding matrix, and positional encoding each solve; the meaning of Q, K, and V, why the Q·K dot product stands for relevance, and why the dot product is divided by the square root of the dimension before softmax; the difference between self-attention and cross-attention, and why cross-attention needs no mask; training predicts several tokens in parallel while inference generates one token at a time, which is why a causal mask is needed to prevent peeking, implemented by setting later positions to negative infinity so that softmax turns them into 0; the definitions of the feed-forward network, multi-head attention, and layer normalization, including how layer norm and batch norm differ in which dimension they normalize over.",
          "url": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes/",
          "pubDate": "2026-04-04T13:00:00.000Z",
          "tags": [
            "Transformer",
            "Deep learning",
            "Attention mechanism",
            "Study notes"
          ],
          "markdownUrl": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes.md"
        }
      ]
    },
    {
      "name": "Claude Code",
      "slug": "claude-code",
      "url": "https://hydblog.xyz/en/topics/claude-code/",
      "articleCount": 1,
      "articles": [
        {
          "id": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code/",
          "title": "Three Claude Code implementations: an agent engineering notebook",
          "description": "Reading CoreCoder, Claude Code, and learn-claude-code side by side pulls Agent Loop, tools, context, concurrency, multi-agent, and security boundaries into a single line.",
          "llmSummary": "Agent engineering notes drawn from three implementations. CoreCoder is a minimal implementation written by hand in Python, Claude Code is the production implementation, and learn-claude-code is the teaching implementation. Coverage spans the Agent Loop's transition condition and interrupt repair, tool definitions and file-editing strategy, context injection and three tiers of compression, the long-lived state of QueryEngine, parallel tools and sub-agents, the Hook and Task extension points, multi-agent collaboration, memory / MCP / background tasks / scheduled tasks, and the security defenses around paths and commands.",
          "url": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code/",
          "pubDate": "2026-08-23T13:00:00.000Z",
          "tags": [
            "Agent",
            "Claude Code",
            "source reading",
            "engineering practice"
          ],
          "markdownUrl": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code.md"
        }
      ]
    },
    {
      "name": "Deep learning",
      "slug": "deep-learning",
      "url": "https://hydblog.xyz/en/topics/deep-learning/",
      "articleCount": 1,
      "articles": [
        {
          "id": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes/",
          "title": "Transformer learning notes: from the overall architecture to multi-head attention",
          "description": "Transformer study notes organized by mechanism: first how the data flows through the encoder and the decoder, then, one by one, Token, word embeddings, positional encoding, Q/K/V attention, the difference between training and inference, the causal mask, the feed-forward network, multi-head attention, and layer normalization.",
          "llmSummary": "Transformer study notes ordered whole-first and then part-by-part: the data flow through the encoder and the decoder; the three questions to answer before learning large models (neural networks, attention, what PyTorch is made of); what Token, the word embedding matrix, and positional encoding each solve; the meaning of Q, K, and V, why the Q·K dot product stands for relevance, and why the dot product is divided by the square root of the dimension before softmax; the difference between self-attention and cross-attention, and why cross-attention needs no mask; training predicts several tokens in parallel while inference generates one token at a time, which is why a causal mask is needed to prevent peeking, implemented by setting later positions to negative infinity so that softmax turns them into 0; the definitions of the feed-forward network, multi-head attention, and layer normalization, including how layer norm and batch norm differ in which dimension they normalize over.",
          "url": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes/",
          "pubDate": "2026-04-04T13:00:00.000Z",
          "tags": [
            "Transformer",
            "Deep learning",
            "Attention mechanism",
            "Study notes"
          ],
          "markdownUrl": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes.md"
        }
      ]
    },
    {
      "name": "engineering practice",
      "slug": "engineering-practice",
      "url": "https://hydblog.xyz/en/topics/engineering-practice/",
      "articleCount": 1,
      "articles": [
        {
          "id": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code/",
          "title": "Three Claude Code implementations: an agent engineering notebook",
          "description": "Reading CoreCoder, Claude Code, and learn-claude-code side by side pulls Agent Loop, tools, context, concurrency, multi-agent, and security boundaries into a single line.",
          "llmSummary": "Agent engineering notes drawn from three implementations. CoreCoder is a minimal implementation written by hand in Python, Claude Code is the production implementation, and learn-claude-code is the teaching implementation. Coverage spans the Agent Loop's transition condition and interrupt repair, tool definitions and file-editing strategy, context injection and three tiers of compression, the long-lived state of QueryEngine, parallel tools and sub-agents, the Hook and Task extension points, multi-agent collaboration, memory / MCP / background tasks / scheduled tasks, and the security defenses around paths and commands.",
          "url": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code/",
          "pubDate": "2026-08-23T13:00:00.000Z",
          "tags": [
            "Agent",
            "Claude Code",
            "source reading",
            "engineering practice"
          ],
          "markdownUrl": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code.md"
        }
      ]
    },
    {
      "name": "source reading",
      "slug": "source-reading",
      "url": "https://hydblog.xyz/en/topics/source-reading/",
      "articleCount": 1,
      "articles": [
        {
          "id": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code/",
          "title": "Three Claude Code implementations: an agent engineering notebook",
          "description": "Reading CoreCoder, Claude Code, and learn-claude-code side by side pulls Agent Loop, tools, context, concurrency, multi-agent, and security boundaries into a single line.",
          "llmSummary": "Agent engineering notes drawn from three implementations. CoreCoder is a minimal implementation written by hand in Python, Claude Code is the production implementation, and learn-claude-code is the teaching implementation. Coverage spans the Agent Loop's transition condition and interrupt repair, tool definitions and file-editing strategy, context injection and three tiers of compression, the long-lived state of QueryEngine, parallel tools and sub-agents, the Hook and Task extension points, multi-agent collaboration, memory / MCP / background tasks / scheduled tasks, and the security defenses around paths and commands.",
          "url": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code/",
          "pubDate": "2026-08-23T13:00:00.000Z",
          "tags": [
            "Agent",
            "Claude Code",
            "source reading",
            "engineering practice"
          ],
          "markdownUrl": "https://hydblog.xyz/en/2026/08/23/agent-engineering-from-claude-code.md"
        }
      ]
    },
    {
      "name": "Study notes",
      "slug": "study-notes",
      "url": "https://hydblog.xyz/en/topics/study-notes/",
      "articleCount": 1,
      "articles": [
        {
          "id": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes/",
          "title": "Transformer learning notes: from the overall architecture to multi-head attention",
          "description": "Transformer study notes organized by mechanism: first how the data flows through the encoder and the decoder, then, one by one, Token, word embeddings, positional encoding, Q/K/V attention, the difference between training and inference, the causal mask, the feed-forward network, multi-head attention, and layer normalization.",
          "llmSummary": "Transformer study notes ordered whole-first and then part-by-part: the data flow through the encoder and the decoder; the three questions to answer before learning large models (neural networks, attention, what PyTorch is made of); what Token, the word embedding matrix, and positional encoding each solve; the meaning of Q, K, and V, why the Q·K dot product stands for relevance, and why the dot product is divided by the square root of the dimension before softmax; the difference between self-attention and cross-attention, and why cross-attention needs no mask; training predicts several tokens in parallel while inference generates one token at a time, which is why a causal mask is needed to prevent peeking, implemented by setting later positions to negative infinity so that softmax turns them into 0; the definitions of the feed-forward network, multi-head attention, and layer normalization, including how layer norm and batch norm differ in which dimension they normalize over.",
          "url": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes/",
          "pubDate": "2026-04-04T13:00:00.000Z",
          "tags": [
            "Transformer",
            "Deep learning",
            "Attention mechanism",
            "Study notes"
          ],
          "markdownUrl": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes.md"
        }
      ]
    },
    {
      "name": "Transformer",
      "slug": "transformer",
      "url": "https://hydblog.xyz/en/topics/transformer/",
      "articleCount": 1,
      "articles": [
        {
          "id": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes/",
          "title": "Transformer learning notes: from the overall architecture to multi-head attention",
          "description": "Transformer study notes organized by mechanism: first how the data flows through the encoder and the decoder, then, one by one, Token, word embeddings, positional encoding, Q/K/V attention, the difference between training and inference, the causal mask, the feed-forward network, multi-head attention, and layer normalization.",
          "llmSummary": "Transformer study notes ordered whole-first and then part-by-part: the data flow through the encoder and the decoder; the three questions to answer before learning large models (neural networks, attention, what PyTorch is made of); what Token, the word embedding matrix, and positional encoding each solve; the meaning of Q, K, and V, why the Q·K dot product stands for relevance, and why the dot product is divided by the square root of the dimension before softmax; the difference between self-attention and cross-attention, and why cross-attention needs no mask; training predicts several tokens in parallel while inference generates one token at a time, which is why a causal mask is needed to prevent peeking, implemented by setting later positions to negative infinity so that softmax turns them into 0; the definitions of the feed-forward network, multi-head attention, and layer normalization, including how layer norm and batch norm differ in which dimension they normalize over.",
          "url": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes/",
          "pubDate": "2026-04-04T13:00:00.000Z",
          "tags": [
            "Transformer",
            "Deep learning",
            "Attention mechanism",
            "Study notes"
          ],
          "markdownUrl": "https://hydblog.xyz/en/2026/04/04/transformer-learning-notes.md"
        }
      ]
    }
  ]
}