chatData.json

{
  "demoTitle": "Spotting Digital Waste in Code gives you energy",
  "model": "ChatGPT 5.4",
  "thinkingDelay": 2200,
  "messageDelay": 500,
  "steps": [
    {
      "role": "user",
      "stepNumber": 0,
      "content": "Before we start, introduce yourself and then interview me"
    },
    {
      "role": "assistant",
      "stepNumber": "0A",
      "slide": "Meet Sparky",
      "presentationStyle": "popup",
      "content": "I am Sparky, your AI co-host. Curious, concise, and professional. I will ask Wilco three quick questions so everyone knows who is speaking and why this topic matters today.",
      "media": [
        {
          "type": "image",
          "src": "robot-assistant.svg",
          "className": "robot-float",
          "caption": "Sparky, your AI co-host."
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "0B",
      "content": "Go ahead"
    },
    {
      "role": "assistant",
      "stepNumber": "0C",
      "slide": "Speaker Intro",
      "content": "First question for Wilco: who are you, and what do you spend your time working on?"
    },
    {
      "role": "user",
      "stepNumber": "0D",
      "content": "Next question"
    },
    {
      "role": "assistant",
      "stepNumber": "0E",
      "slide": "Speaker Intro",
      "content": "Second question for Wilco: what do you actually do, and why do software, architecture, and sustainability belong in the same conversation for you?"
    },
    {
      "role": "user",
      "stepNumber": "0F",
      "content": "One more"
    },
    {
      "role": "assistant",
      "stepNumber": "0G",
      "slide": "My Upbringing",
      "presentationStyle": "popup",
      "content": "Final question for Wilco: where does that perspective come from? Part of the answer is upbringing. Growing up around heavy industry made energy use, material flows, and waste feel visible and real from the start.",
      "openImageFullscreenAfterSpeech": true,
      "media": [
        {
          "type": "image",
          "src": "upbrining.png",
          "caption": "A glimpse of the environment that shaped how I think about energy, industry, and waste."
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 1,
      "content": "What is this session about?"
    },
    {
      "role": "popup",
      "stepNumber": 2,
      "content": "Spotting digital waste in code gives you energy because once you see waste clearly, you can do something about it."
    },
    {
      "role": "user",
      "stepNumber": 3,
      "content": "What will I learn here?"
    },
    {
      "role": "assistant",
      "stepNumber": 4,
      "slide": "Agenda",
      "content": "You move through four stages: awareness, dialogue, decision frames, and action. The goal is to make waste visible and discussable."
    },
    {
      "role": "user",
      "stepNumber": 5,
      "content": "Where does this start?"
    },
    {
      "role": "assistant",
      "stepNumber": 6,
      "slide": "Opening Video",
      "content": "It starts with a visual cue. Before code, you are invited to notice the act of looking closely.",
      "media": [
        {
          "type": "video",
          "src": ".pptx_extract/ppt/media/media1.mp4",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "6A",
      "content": "Who is Freek"
    },
    {
      "role": "assistant",
      "stepNumber": 7,
      "slide": "Spotting in Nature",
      "content": "**This is Freek.**\n\nFreek loves wildlife spotting.\n\nWhen Freek spots a rare bird, he does not just go outside. He records it at https://waarneming.nl/.\n\nThat helps researchers and policymakers understand what is living out there.\n\nWhat you notice today can make a difference for nature conservation tomorrow.\n\n**Be like Freek.**",
      "mediaLayout": "right",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image24.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "7A",
      "content": "What does spotting teach you?"
    },
    {
      "role": "assistant",
      "stepNumber": 8,
      "slide": "Spotting in Nature",
      "content": "Spotting trains your eyes. You go out early, keep looking, and slowly learn to recognize what deserves attention.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image26.jpeg",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 9,
      "content": "What do you notice first?"
    },
    {
      "role": "popup",
      "stepNumber": 10,
      "content": "A plastic bottle. A paper bag. Waste that was there all along, but now stands out immediately.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image28.jpeg",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 11,
      "content": "Why didn't I see it before?"
    },
    {
      "role": "popup",
      "stepNumber": 12,
      "content": "Habit makes waste disappear into the background.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image29.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 13,
      "content": "What else makes it easy to miss?"
    },
    {
      "role": "popup",
      "stepNumber": 14,
      "content": "Another reason is ownership. If it feels like someone else's problem, it becomes easy to ignore.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image30.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 15,
      "content": "When can you not ignore it anymore?"
    },
    {
      "role": "popup",
      "stepNumber": 16,
      "content": "It reaches the point where ignoring it is no longer possible.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image31.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 17,
      "content": "When does it become a value question?"
    },
    {
      "role": "popup",
      "stepNumber": 18,
      "content": "The shift happens when value becomes visible. Waste looks very different once you ask what something is worth.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image32.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "18A",
      "content": "What changes once you frame it as value?"
    },
    {
      "role": "assistant",
      "stepNumber": 19,
      "slide": "Tree Value",
      "content": "The tree is framed as real value, roughly 150,000 to 250,000 euros. That reframes careless loss as measurable damage.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image32.png",
          "caption": "Tree value made tangible."
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 20,
      "content": "How does that relate to software?"
    },
    {
      "role": "assistant",
      "stepNumber": 21,
      "slide": "Electricity Maps",
      "content": "You bring in Electricity Maps to make infrastructure consumption feel physical. Compute is not abstract; it runs somewhere real, with location, intensity, and trade-offs.",
      "openImageFullscreenAfterSpeech": true,
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image33.png",
          "caption": "Electricity Maps view with the live generation mix."
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "21A",
      "content": "So what's the core idea?"
    },
    {
      "role": "assistant",
      "stepNumber": 22,
      "slide": "Cost Without Value",
      "content": "Here the core definition lands: digital waste is cost without value. That cost can be money, energy, capacity, or attention.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image39.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "22A",
      "content": "Can you show that in the cloud?"
    },
    {
      "role": "assistant",
      "stepNumber": "22B",
      "slide": "AWS Example",
      "content": "This becomes concrete with AWS. Waste is not abstract here either. It shows up in the infrastructure choices teams make every day.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image40.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "22C",
      "content": "Where do you see that choice?"
    },
    {
      "role": "assistant",
      "stepNumber": "22D",
      "slide": "Instance Types",
      "content": "It starts with instance selection. The platform gives you many shapes, but that flexibility also makes overprovisioning easy.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image41.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "22E",
      "content": "Any caveat before we compare?"
    },
    {
      "role": "assistant",
      "stepNumber": "22F",
      "slide": "Comparison Caveat",
      "content": "In this story, the terms virtual machines, containers, and pods are treated as interchangeable. The point is the waste pattern, not the infrastructure taxonomy."
    },
    {
      "role": "user",
      "stepNumber": "22G",
      "content": "What happens at low utilization?"
    },
    {
      "role": "assistant",
      "stepNumber": "22H",
      "slide": "25% Utilization",
      "content": "At 25% utilization, a shared 16-vCPU AWS instance uses about 340 kWh per year, while a dedicated on-prem 16-core server uses about 1,600 kWh per year."
    },
    {
      "role": "user",
      "stepNumber": "22H1",
      "content": "Why is there such a difference between AWS and on-prem?"
    },
    {
      "role": "assistant",
      "stepNumber": "22H2",
      "slide": "Why AWS Differs",
      "content": "Because the comparison is not one dedicated machine versus another dedicated machine. In AWS, that workload runs on shared infrastructure, so the energy burden of the physical server is spread across many tenants. On-prem, a lightly used dedicated server still carries most of its baseline power cost by itself."
    },
    {
      "role": "user",
      "stepNumber": "22I",
      "content": "What about higher utilization?"
    },
    {
      "role": "assistant",
      "stepNumber": "22J",
      "slide": "75% Utilization",
      "content": "At 75% utilization, a shared 16-vCPU AWS instance uses about 640 kWh per year, while a dedicated on-prem 16-core server is around 2,700 kWh per year."
    },
    {
      "role": "user",
      "stepNumber": "22K",
      "content": "How do you quantify that?"
    },
    {
      "role": "assistant",
      "stepNumber": 23,
      "slide": "Consolidation Example",
      "content": "Then you quantify it. Consolidating three 25% VMs into one 75% VM can save roughly two full instance costs, or about 13,000 euros per year with m5.4xlarge on-demand pricing."
    },
    {
      "role": "user",
      "stepNumber": 24,
      "content": "What should I look for in a real system?"
    },
    {
      "role": "assistant",
      "stepNumber": 25,
      "slide": "Three Signals",
      "content": "Look for three signals: too much capacity with too little use, slowness without full CPU, or one saturated part while the rest waits."
    },
    {
      "role": "user",
      "stepNumber": 26,
      "content": "What forms does digital waste usually take?"
    },
    {
      "role": "assistant",
      "stepNumber": 27,
      "slide": "Four Types",
      "content": "You meet it as idle resources, structural overcapacity, repeated default settings, or hidden compute that adds no visible value."
    },
    {
      "role": "user",
      "stepNumber": 28,
      "content": "Why does this stay around for years?"
    },
    {
      "role": "assistant",
      "stepNumber": 29,
      "slide": "Link with Tech Debt",
      "content": "Because technical debt makes change expensive. Tight coupling, maintenance burden, and fear of breakage keep waste in place."
    },
    {
      "role": "user",
      "stepNumber": 30,
      "content": "How do I think beyond electricity savings?"
    },
    {
      "role": "assistant",
      "stepNumber": 31,
      "slide": "Workload Quality",
      "content": "The question changes. Not only 'how do we save power?' but 'is this workload healthy, efficient, and worth running at all?'"
    },
    {
      "role": "user",
      "stepNumber": "31A",
      "content": "Can you show that visually?"
    },
    {
      "role": "assistant",
      "stepNumber": 32,
      "slide": "Workload Quality Visual",
      "content": "This visual makes the pivot concrete by separating power use from utilization. A system can consume plenty without delivering much.",
      "openImageFullscreenAfterSpeech": true,
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image42.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "32A",
      "content": "What does that reinforce?"
    },
    {
      "role": "assistant",
      "stepNumber": 33,
      "slide": "Workload Quality Sequence",
      "content": "It reinforces the same point: healthy workloads balance value, efficiency, and operational quality, not just raw activity."
    },
    {
      "role": "user",
      "stepNumber": "33A",
      "content": "Can you request core utilization for a virtual machine in Linux?"
    },
    {
      "role": "assistant",
      "stepNumber": "33B",
      "slide": "Linux proc stat",
      "content": "Yes. In Linux, `/proc/stat` exposes cumulative CPU time counters for the whole system and for each core. The image shows those `cpu`, `cpu0`, `cpu1`, and similar lines. Each line reports time spent in modes like user, system, idle, and iowait. By reading the counters twice and comparing the change over time, you can estimate utilization per core, even inside a virtual machine.",
      "openImageFullscreenAfterSpeech": true,
      "media": [
        {
          "type": "image",
          "src": "procstat.png",
          "caption": "Example of `/proc/stat` CPU counters in Linux."
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 34,
      "content": "Can a tiny code change really matter that much?"
    },
    {
      "role": "popup",
      "stepNumber": 35,
      "content": "Only 40 lines of code can hide a massive gap in latency, work done, and energy consumed.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image50.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "35A",
      "content": "What example do you use?"
    },
    {
      "role": "assistant",
      "stepNumber": "35B",
      "slide": "OpenJDK Issue",
      "presentationStyle": "popup",
      "content": "It starts with a concrete OpenJDK issue. That makes the case specific immediately, not abstract.",
      "openImageFullscreenAfterSpeech": true,
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image47.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "35C",
      "content": "Why does that catch attention?"
    },
    {
      "role": "assistant",
      "stepNumber": 36,
      "slide": "Primeagen",
      "presentationStyle": "popup",
      "content": "Because it makes the idea feel real. You can immediately see that even a tiny code change can have a surprisingly large effect on performance, work done, and energy use.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image48.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "36A",
      "content": "What stands out in that story?"
    },
    {
      "role": "assistant",
      "stepNumber": "36B",
      "slide": "Commit Log",
      "presentationStyle": "popup",
      "content": "A tiny commit in a long stream of work suddenly matters. That is exactly the point: small code can hide very large waste.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image49.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "36C",
      "content": "How do you introduce the concrete case?"
    },
    {
      "role": "assistant",
      "stepNumber": 37,
      "slide": "Code Case Intro",
      "presentationStyle": "popup",
      "content": "A bug report and a tiny implementation detail become the entry point. That makes the waste feel concrete, not theoretical.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image51.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "37A",
      "content": "What's the core technical idea?"
    },
    {
      "role": "assistant",
      "stepNumber": 38,
      "slide": "Procfs vs Direct Clock",
      "content": "The core idea is simple: reading procfs means opening text and parsing it, while direct clock access is simpler and faster."
    },
    {
      "role": "user",
      "stepNumber": 39,
      "content": "What does the old implementation look like?"
    },
    {
      "role": "assistant",
      "stepNumber": 40,
      "slide": "Old Implementation",
      "presentationStyle": "popup",
      "content": "Here is the old procfs-based path. The important thing is not the line count, but the amount of work hidden behind it.",
      "code": [
        {
          "language": "cpp",
          "caption": "Opening the procfs stat file and reading its contents.",
          "content": "static jlong user_thread_cpu_time(Thread* thread) {\n  pid_t tid = thread->osthread()->thread_id();\n  char* s;\n  char stat[2048];\n  size_t statlen;\n  char proc_name[64];\n  int count;\n  long sys_time, user_time;\n  char cdummy;\n  int idummy;\n  long ldummy;\n  FILE* fp;\n\n  os::snprintf_checked(proc_name, 64, \"/proc/self/task/%d/stat\", tid);\n  fp = os::fopen(proc_name, \"r\");\n  if (fp == nullptr) return -1;\n\n  statlen = fread(stat, 1, 2047, fp);\n  stat[statlen] = '\\0';\n  fclose(fp);"
        },
        {
          "language": "cpp",
          "caption": "Skipping the command string, parsing fields, and returning user time.",
          "content": "  s = strrchr(stat, ')');\n  if (s == nullptr) return -1;\n  do { s++; } while (s && isspace((unsigned char)*s));\n\n  count = sscanf(s, \"%c %d %d %d %d %d %lu %lu %lu %lu %lu %lu %lu\",\n    &cdummy, &idummy, &idummy, &idummy, &idummy, &idummy,\n    &ldummy, &ldummy, &ldummy, &ldummy, &ldummy,\n    &user_time, &sys_time);\n\n  if (count != 13) return -1;\n  return (jlong)user_time * (1000000000 / os::Posix::clock_tics_per_second());\n}"
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "40A",
      "content": "Where do the first hotspots show up?"
    },
    {
      "role": "assistant",
      "stepNumber": "40B",
      "slide": "fopen Hotspot",
      "presentationStyle": "popup",
      "content": "The first hotspot is opening the file. Even before parsing, path resolution and setup already cost real work.",
      "mediaColumns": 2,
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image54.png",
          "caption": ""
        },
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image55.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "40C",
      "content": "What about the read path?"
    },
    {
      "role": "assistant",
      "stepNumber": "40D",
      "slide": "fread and fclose",
      "presentationStyle": "popup",
      "content": "Then the read and close path show up. That is where kernel work, copying, and teardown add more cost than the tiny code suggests.",
      "mediaColumns": 2,
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image56.png",
          "caption": ""
        },
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image57.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "40E",
      "content": "Where does parsing show up?"
    },
    {
      "role": "assistant",
      "stepNumber": 41,
      "slide": "sscanf Hotspot",
      "presentationStyle": "popup",
      "content": "Parsing is another hotspot. The ASCII conversion work is branch-heavy and surprisingly expensive for such a small-looking path.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image58.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "41A",
      "content": "Can you model that at a higher level?"
    },
    {
      "role": "assistant",
      "stepNumber": "41B",
      "slide": "Theoretical Model",
      "presentationStyle": "popup",
      "content": "Yes. A simple model links frequency, micro-operations, voltage, and switching. It is not perfect physics. It is a way to reason about where dynamic work is happening.",
      "openImageFullscreenAfterSpeech": true,
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image59.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "41C",
      "content": "Can you show that on real code?"
    },
    {
      "role": "assistant",
      "stepNumber": "41D",
      "slide": "Generated Example",
      "presentationStyle": "popup",
      "content": "Yes. Here the code is shown with inline annotations for estimated micro-operations and energy cost. The point is to make hidden runtime work visible directly next to the code itself.",
      "media": [
        {
          "type": "image",
          "src": "Screenshot Example.png",
          "caption": ""
        }
      ],
      "code": [
        {
          "language": "csharp",
          "caption": "Annotated example with inline estimates taken from the screenshot.",
          "content": "using System.Linq;                    // no runtime energy\n\nclass Program\n{\n    static void Main()\n    {\n        const int N = 1_000_000;                    // compile-time constant - no runtime energy\n        var rnd = new Random();                     // 5 uOp -> 2 e-9 mWh = 0.000002 mWh\n        var list = new List<int>(capacity: N);      // 10 uOp -> 4 e-9 mWh = 0.000004 mWh\n\n        // Loop body: 21 uOp per iteration -> 21 uOp x 1 000 000 = **21 000 000 uOp**\n        for (int i = 0; i < N; i++)                 // loop overhead 3 uOp/iter -> 0.0012 mWh\n        {\n            int a = rnd.Next(1000), b = rnd.Next(1000);\n            // 10 uOp/iter (two RNG calls) -> 0.004 mWh\n\n            int sum = a + b, prod = a * b;          // 3 uOp/iter -> 0.0012 mWh\n            bool even = (sum % 2 == 0);             // 3 uOp/iter -> 0.0012 mWh\n\n            list.Add(even ? prod : sum);            // 2 uOp/iter -> 0.0008 mWh\n            // **Loop total = 0.0084 mWh**\n        }\n\n        list.Sort();                                // ~200 M uOp (Timsort) -> 0.080 mWh\n        var groups = list.GroupBy(x => x);          // ~1.08 M uOp (hash buckets + scan) -> 0.000432 mWh\n        var maxFreq = groups.Max(g => g.Count());   // ~1 M uOp (final scan) -> 0.000400 mWh\n\n        Console.WriteLine($\"Max frequency: {maxFreq}\");\n        // 30 uOp -> 1.2 e-8 mWh (negligible)\n    }\n}"
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "41E",
      "content": "Could GenAI predict the bottlenecks from that code?"
    },
    {
      "role": "assistant",
      "stepNumber": "41F",
      "slide": "GenAI Question",
      "presentationStyle": "popup",
      "content": "That leads to the real question: given this annotated code and the theoretical model, could GenAI point to the likely hotspots before profiling confirms them?",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image61.jpeg",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "41G",
      "content": "What hotspots would it probably point to first?"
    },
    {
      "role": "assistant",
      "stepNumber": "41H",
      "slide": "Likely Hotspots",
      "content": "A plausible answer would be: `fopen()`, `fread()`, and `sscanf()`. Each one concentrates a different kind of work. Opening the procfs file drives path resolution and kernel setup. Reading it moves data from kernel space to user space. Parsing it turns ASCII text into numbers with branch-heavy conversion logic.",
      "code": [
        {
          "language": "cpp",
          "caption": "1. `fread()` block",
          "content": "statlen = fread(stat, 1, 2047, fp);   // ~0.00010 mWh, ~300k uops (sys_read + copy + kernel formatting)\nstat[statlen] = '\\0';                // ~0.0000001 mWh, ~5 uops (store)\nfclose(fp);                           // ~0.00002 mWh, ~80k uops (sys_close + teardown)"
        },
        {
          "language": "cpp",
          "caption": "2. `fopen()` block",
          "content": "os::snprintf_checked(proc_name, 64, \"/proc/self/task/%d/stat\", tid); // ~0.000002 mWh, ~5k uops (formatting)\nfp = os::fopen(proc_name, \"r\");                                   // ~0.00003 mWh, ~100k uops (open + VFS walk)\nif (fp == nullptr) return -1;                                      // ~0.0000001 mWh, ~3 uops"
        },
        {
          "language": "cpp",
          "caption": "3. `sscanf()` block",
          "content": "count = sscanf(s, \"%c %d %d %d %d %d %lu %lu %lu %lu %lu %lu %lu\", // ~0.00002 mWh, ~60k uops (parsing + conversions)\n  &cdummy, &idummy, &idummy, &idummy, &idummy, &idummy,\n  &ldummy, &ldummy, &ldummy, &ldummy, &ldummy, &user_time, &sys_time);\nif (count != 13) return -1;                                        // ~0.0000001 mWh, ~5 uops"
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 42,
      "content": "So what changes in the better path?"
    },
    {
      "role": "assistant",
      "stepNumber": 43,
      "slide": "Better Implementation",
      "presentationStyle": "popup",
      "content": "The better path asks the kernel directly for the right clock. Less parsing, less copying, fewer syscalls, and much better latency.",
      "code": [
        {
          "language": "cpp",
          "caption": "The optimized path.",
          "content": "static bool get_thread_clockid(Thread* thread, clockid_t* clockid, bool total) {\n  constexpr clockid_t CLOCK_TYPE_MASK = 3;\n  constexpr clockid_t CPUCLOCK_VIRT = 1;\n\n  int rc = pthread_getcpuclockid(thread->osthread()->pthread_id(), clockid);\n  if (rc != 0) {\n    assert_status(rc == ESRCH, rc, \"pthread_getcpuclockid failed\");\n    return false;\n  }\n\n  if (!total) {\n    *clockid = (*clockid & ~CLOCK_TYPE_MASK) | CPUCLOCK_VIRT;\n  }\n\n  return true;\n}\n\nstatic jlong user_thread_cpu_time(Thread* thread) {\n  clockid_t clockid;\n  bool success = get_thread_clockid(thread, &clockid, false);\n  return success ? os::Linux::thread_cpu_time(clockid) : -1;\n}"
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": "43A",
      "content": "Can you show that visually too?"
    },
    {
      "role": "assistant",
      "stepNumber": "43B",
      "slide": "Better Implementation Visual",
      "presentationStyle": "popup",
      "content": "Yes. This visual shows the shift from the heavier procfs path to the more direct clock-based path.",
      "openImageFullscreenAfterSpeech": true,
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image63.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 44,
      "content": "How should I use this code case in the talk?"
    },
    {
      "role": "popup",
      "stepNumber": 45,
      "content": "Check it out later, not now. The code case should sharpen your eye, not pull attention away from your own systems.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image64.png",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 46,
      "content": "How do I turn this into backlog items?"
    },
    {
      "role": "assistant",
      "stepNumber": 47,
      "slide": "Backlog Framing",
      "content": "Map each waste item to yearly cost, energy or CO2, performance impact, and risk. That is how it becomes backlog-worthy."
    },
    {
      "role": "user",
      "stepNumber": "47A",
      "content": "How do I phrase that as an outcome?"
    },
    {
      "role": "assistant",
      "stepNumber": 48,
      "slide": "Outcome Language",
      "content": "Do not pitch implementation details. Frame it as an outcome, like reducing infrastructure cost by consolidating underused workloads."
    },
    {
      "role": "user",
      "stepNumber": "48A",
      "content": "What actions come first?"
    },
    {
      "role": "assistant",
      "stepNumber": 49,
      "slide": "Quick Wins",
      "content": "Start with safe quick wins. Not a platform revolution, but cleanup, consolidation, and right-sizing."
    },
    {
      "role": "user",
      "stepNumber": "49A",
      "content": "How do teams make room for that work?"
    },
    {
      "role": "assistant",
      "stepNumber": 50,
      "slide": "Capacity for Reduction",
      "content": "Give it room to happen: reserve sprint capacity or a quarterly target. Otherwise urgent delivery will always win."
    },
    {
      "role": "user",
      "stepNumber": "50A",
      "content": "Who should own it?"
    },
    {
      "role": "assistant",
      "stepNumber": 51,
      "slide": "Ownership",
      "content": "No owner means no living backlog item. Add labels, ownership, and a reason to revisit the work."
    },
    {
      "role": "user",
      "stepNumber": 52,
      "content": "What happens if teams leave it alone?"
    },
    {
      "role": "popup",
      "stepNumber": 53,
      "content": "If you do nothing, inefficiency keeps running: more cost, more energy use, and more systems slowly gathering dust.",
      "media": [
        {
          "type": "image",
          "src": ".pptx_extract/ppt/media/image65.jpeg",
          "caption": ""
        }
      ]
    },
    {
      "role": "user",
      "stepNumber": 54,
      "content": "What decision framework should I leave with?"
    },
    {
      "role": "assistant",
      "stepNumber": 55,
      "slide": "Frames",
      "content": "Use three frames: value, cost, and exception. Ask who uses it, what it costs, and whether the trade-off is intentional."
    },
    {
      "role": "user",
      "stepNumber": "55A",
      "content": "What choices does that lead to?"
    },
    {
      "role": "assistant",
      "stepNumber": 56,
      "slide": "Choices",
      "content": "Then choose: remove it, combine it, calibrate it, or consciously accept it with a named owner."
    },
    {
      "role": "user",
      "stepNumber": 57,
      "content": "How should I start after this talk?"
    },
    {
      "role": "assistant",
      "stepNumber": 58,
      "slide": "Get Started",
      "content": "Start with one application. Ask what you already do, what blocks progress, and which system has the biggest user, compute, or cost impact."
    },
    {
      "role": "user",
      "stepNumber": 59,
      "content": "What should I walk away with?"
    },
    {
      "role": "popup",
      "stepNumber": 60,
      "content": "You can now spot digital waste, discuss workload quality, inspect code differently, and turn that insight into practical action."
    }
  ]
}