Blame for frontend/data.js

// Forge — mock graph + resolve engine
// Matches the FastAPI shapes in the handoff doc, so swap to fetch() later.

window.FORGE_NODES = [
  // optimizers
  { canonical: "adamw",        name: "AdamW",            type: "optimizer",    description: "Workhorse decoupled-weight-decay optimizer. Safe default.", tier: 1 },
  { canonical: "adamw_8bit",   name: "AdamW 8-bit",      type: "optimizer",    description: "bitsandbytes 8-bit AdamW. Saves VRAM. Default param groups can fight per-layer LR tricks.", tier: 1 },
  { canonical: "paged_adamw",  name: "Paged AdamW",      type: "optimizer",    description: "CPU-paged optimizer states. For when you really can't fit.", tier: 2 },
  { canonical: "lion",         name: "Lion",             type: "optimizer",    description: "Sign-momentum optimizer. Lower memory than AdamW; needs lower LR.", tier: 2 },
  { canonical: "muon",         name: "Muon",             type: "optimizer",    description: "Newton–Schulz orthogonalized momentum. Faster convergence on hidden weights.", tier: 2 },
  { canonical: "sophia",       name: "Sophia-G",         type: "optimizer",    description: "Hessian-informed second-order. Promising for LLMs.", tier: 3 },
  { canonical: "adafactor",    name: "Adafactor",        type: "optimizer",    description: "Memory-light. Tricky LR schedule.", tier: 2 },

  // schedulers
  { canonical: "cosine",       name: "Cosine",           type: "scheduler",    description: "Cosine decay with warmup. Boring; works.", tier: 1 },
  { canonical: "onecycle",     name: "OneCycle",         type: "scheduler",    description: "Aggressive warm-then-anneal. Faster but can overshoot on long runs.", tier: 2 },
  { canonical: "dlrs",         name: "DLRS",             type: "scheduler",    description: "Rick's dynamic LR scheduler. #1 on val_loss in the bench.", tier: 1 },
  { canonical: "linear",       name: "Linear",           type: "scheduler",    description: "Linear warmup → linear decay.", tier: 1 },
  { canonical: "wsd",          name: "WSD",              type: "scheduler",    description: "Warmup–Stable–Decay. Continual-pretrain friendly.", tier: 2 },
  { canonical: "constant",     name: "Constant",         type: "scheduler",    description: "Flat. Combine with manual restarts.", tier: 1 },

  // techniques
  { canonical: "qlora",        name: "QLoRA",            type: "technique",    description: "4-bit base + LoRA adapters. Lets a 70B fit on one card.", tier: 1 },
  { canonical: "lora",         name: "LoRA",             type: "technique",    description: "Low-rank adapters. Cheap, composable, the default PEFT.", tier: 1 },
  { canonical: "fft",          name: "Full Fine-Tune",   type: "technique",    description: "Update every parameter. Hungry. Mutually exclusive with adapter methods.", tier: 1 },
  { canonical: "grad_ckpt",    name: "Grad Checkpoint",  type: "technique",    description: "Trade FLOPs for VRAM. Must be off for vLLM gen during training.", tier: 1 },
  { canonical: "per_layer_lr_rotation", name: "Per-Layer LR Rotation", type: "technique", description: "Rick's trick: rotate LR across layer bands per step. Needs custom param groups.", tier: 1 },
  { canonical: "chaos_inject", name: "Chaos Injectors",  type: "technique",    description: "Activation perturbation at hidden layers. NaN-prone without staged melt-in.", tier: 1 },
  { canonical: "jacobian_reg", name: "Jacobian Reg",     type: "technique",    description: "Smoothness penalty via Jacobian. Forward pass corrupts the injector cache.", tier: 1 },
  { canonical: "fsdp",         name: "FSDP",             type: "technique",    description: "Fully-Sharded Data Parallel. Sharding for big models.", tier: 1 },
  { canonical: "ddp",          name: "DDP",              type: "technique",    description: "Vanilla data-parallel. Cheap when the model fits.", tier: 1 },
  { canonical: "deepspeed_z3", name: "DeepSpeed ZeRO-3", type: "technique",    description: "ZeRO stage 3 partitioning. Battle-tested.", tier: 1 },
  { canonical: "unsloth",      name: "Unsloth",          type: "technique",    description: "Fused kernels for LoRA/QLoRA. PyTorch-only; tight coupling to bnb.", tier: 2 },

  // quantization
  { canonical: "bnb_4bit",     name: "bnb 4-bit",        type: "quantization", description: "bitsandbytes NF4. The QLoRA base.", tier: 1 },
  { canonical: "bnb_8bit",     name: "bnb 8-bit",        type: "quantization", description: "LLM.int8(). Inference-leaning; training works.", tier: 1 },
  { canonical: "gptq",         name: "GPTQ",             type: "quantization", description: "Post-training quant. Inference-only for our purposes.", tier: 2 },
  { canonical: "awq",          name: "AWQ",              type: "quantization", description: "Activation-aware weight quant. Inference-time.", tier: 2 },

  // architectures
  { canonical: "llama3",       name: "Llama-3",          type: "architecture", description: "Llama-3 8B / 70B family.", tier: 1 },
  { canonical: "mistral",      name: "Mistral",          type: "architecture", description: "Mistral / Mixtral.", tier: 1 },
  { canonical: "qwen2",        name: "Qwen-2.5",         type: "architecture", description: "Strong open multilingual base.", tier: 1 },
  { canonical: "distilbert",   name: "DistilBERT",       type: "architecture", description: "The bench model. SST-2 sandbox.", tier: 1 },

  // inference
  { canonical: "vllm",         name: "vLLM",             type: "inference",    description: "PagedAttention server. Needs grad-ckpt off during in-train generation.", tier: 1 },
  { canonical: "sglang",       name: "SGLang",           type: "inference",    description: "Structured-gen server.", tier: 2 },
];

// edges. relations: REQUIRES, CONDITIONAL, COMPATIBLE, DEGRADES, BREAKS
// Direction: from → to. For REQUIRES: "from needs to to work".
// For BREAKS: symmetric in display but stored once.
window.FORGE_EDGES = [
  // --- T1 verified seed (from the backend doc) -----------------------------
  { from: "per_layer_lr_rotation", to: "adamw_8bit", relation: "BREAKS", tier: 1, confidence: 1.0,
    fix: "Pass custom optimizer_grouped_parameters — adamw_8bit's default decay/no-decay split overrides your per-layer LR bands.",
    evidence: [{ url: "mempalace://chaos-injection-trainer-notes", quote: "adamw_8bit produces decay/no-decay param groups by default; per-layer LR rotation silently no-ops unless you pass optimizer_grouped_parameters yourself.", source_type: "practitioner_run", tier: 1 }] },

  { from: "jacobian_reg", to: "chaos_inject", relation: "BREAKS", tier: 1, confidence: 1.0,
    fix: "Jacobian-reg's extra forward pass corrupts the injector activation cache → ortho_loss is poisoned. Disable one.",
    evidence: [{ url: "mempalace://chaos-injection-trainer-notes", quote: "the jacobian reg forward overwrites the activation cache that chaos_inject samples from; ortho_loss explodes.", source_type: "practitioner_run", tier: 1 }] },

  { from: "chaos_inject", to: "staged_meltin", relation: "REQUIRES", tier: 1, confidence: 1.0,
    fix: "Stage injectors in over ~100 steps; NaN at layer ~6 if injected from step 0.",
    evidence: [{ url: "mempalace://chaos-injection-trainer-notes", quote: "NaN at injector layer ~6 if run from step 0; staged melt-in over 100 steps fixed it.", source_type: "practitioner_run", tier: 1 }] },

  { from: "grad_ckpt", to: "vllm", relation: "BREAKS", tier: 1, confidence: 1.0,
    fix: "Disable gradient_checkpointing for the in-train vLLM gen pass. TRL-documented.",
    evidence: [{ url: "https://huggingface.co/docs/trl", quote: "gradient_checkpointing must be disabled when generating with vLLM during training.", source_type: "official_docs", tier: 1 }] },

  // --- core technique mutex / requires -------------------------------------
  { from: "qlora", to: "fft", relation: "BREAKS", tier: 1, confidence: 1.0,
    fix: "QLoRA freezes the base model; Full Fine-Tune updates it. Pick one.",
    evidence: [{ url: "https://arxiv.org/abs/2305.14314", quote: "QLoRA backprops gradients through a frozen 4-bit quantized model into low-rank adapters.", source_type: "paper", tier: 1 }] },

  { from: "qlora", to: "bnb_4bit", relation: "REQUIRES", tier: 1, confidence: 1.0,
    fix: "QLoRA is defined as 4-bit NF4 base + LoRA adapters — load the base in bnb 4-bit.",
    evidence: [{ url: "https://huggingface.co/docs/peft", quote: "QLoRA fine-tunes a 4-bit quantized base model loaded via bitsandbytes NF4.", source_type: "official_docs", tier: 1 }] },

  { from: "qlora", to: "lora", relation: "REQUIRES", tier: 1, confidence: 1.0,
    fix: "QLoRA = 4-bit base + LoRA adapters. The adapter rank is your hyperparameter.",
    evidence: [{ url: "https://arxiv.org/abs/2305.14314", quote: "QLoRA augments the frozen quantized model with Low-Rank Adapters.", source_type: "paper", tier: 1 }] },

  { from: "lora", to: "fft", relation: "BREAKS", tier: 2, confidence: 0.85,
    fix: "Adapter-method and full fine-tune are mutually exclusive within one run.",
    evidence: [{ url: "https://huggingface.co/docs/peft", quote: "PEFT methods freeze the base; choose either full fine-tuning or a PEFT method per run.", source_type: "official_docs", tier: 2 }] },

  // --- optimizer ↔ quantization --------------------------------------------
  { from: "muon", to: "adamw_8bit", relation: "BREAKS", tier: 2, confidence: 0.8,
    fix: "Muon owns the optimizer step for hidden weights; 8-bit AdamW state is incompatible with the Newton–Schulz update.",
    evidence: [{ url: "https://kellerjordan.github.io/posts/muon/", quote: "Muon replaces the optimizer update for 2D weights; use AdamW for the rest, not its 8-bit variant.", source_type: "blog", tier: 2 }] },

  { from: "muon", to: "adamw", relation: "REQUIRES", tier: 2, confidence: 0.9,
    fix: "Muon only updates 2D hidden weights — embeddings + biases still need AdamW.",
    evidence: [{ url: "https://kellerjordan.github.io/posts/muon/", quote: "non-hidden parameters (embeddings, scalars) are handled by a standard AdamW.", source_type: "blog", tier: 2 }] },

  { from: "lion", to: "bnb_8bit", relation: "DEGRADES", tier: 3, confidence: 0.5,
    fix: "Lion sign-update interacts poorly with 8-bit state quant — drop to 16-bit moments.",
    evidence: [{ url: "https://github.com/bitsandbytes-foundation/bitsandbytes/issues", quote: "single user report: Lion+8-bit moments diverged at step 4k on a 7B base.", source_type: "issue", tier: 3 }] },

  // --- unsloth / quant family ----------------------------------------------
  { from: "unsloth", to: "bnb_4bit", relation: "REQUIRES", tier: 1, confidence: 1.0,
    fix: "Unsloth's fused kernels assume a bnb 4-bit base.",
    evidence: [{ url: "https://github.com/unslothai/unsloth", quote: "Unsloth supports 4-bit quantized models via bitsandbytes for QLoRA fine-tuning.", source_type: "official_docs", tier: 1 }] },

  { from: "unsloth", to: "lora", relation: "REQUIRES", tier: 1, confidence: 1.0,
    fix: "Unsloth's fast path is the LoRA / QLoRA path.",
    evidence: [{ url: "https://github.com/unslothai/unsloth", quote: "Unsloth accelerates LoRA and QLoRA fine-tuning with custom Triton kernels.", source_type: "official_docs", tier: 1 }] },

  { from: "unsloth", to: "fsdp", relation: "BREAKS", tier: 2, confidence: 0.8,
    fix: "Unsloth's custom kernels don't compose with FSDP sharding hooks today.",
    evidence: [{ url: "https://github.com/unslothai/unsloth/issues", quote: "FSDP is not currently supported alongside Unsloth's fused kernels.", source_type: "issue", tier: 2 }] },

  // --- inference quants (category errors) ----------------------------------
  { from: "awq", to: "fft", relation: "BREAKS", tier: 2, confidence: 0.85,
    fix: "AWQ is an inference-time weight quant. You can't fine-tune through it.",
    evidence: [{ url: "https://github.com/casper-hansen/AutoAWQ", quote: "AWQ is intended for post-training quantization for inference.", source_type: "official_docs", tier: 2 }] },

  { from: "gptq", to: "fft", relation: "BREAKS", tier: 2, confidence: 0.85,
    fix: "GPTQ is post-training quant — frozen base only.",
    evidence: [{ url: "https://arxiv.org/abs/2210.17323", quote: "GPTQ is a one-shot post-training quantization method.", source_type: "paper", tier: 2 }] },

  // --- distributed -----------------------------------------------------------
  { from: "fsdp", to: "bnb_4bit", relation: "CONDITIONAL", tier: 2, confidence: 0.8, conditions: { plugin: "bnb-fsdp" },
    fix: "Works only with the bnb-FSDP plugin; vanilla FSDP shards over uninitialized 4-bit weights.",
    evidence: [{ url: "https://huggingface.co/docs/accelerate", quote: "FSDP + bitsandbytes 4-bit requires the bnb-fsdp wrap policy.", source_type: "official_docs", tier: 2 }] },

  { from: "deepspeed_z3", to: "bnb_8bit", relation: "DEGRADES", tier: 3, confidence: 0.5,
    fix: "Reports of slowdown / hangs on multi-node Z3 + 8-bit. Use bf16 weights, 8-bit optimizer states only.",
    evidence: [{ url: "https://github.com/microsoft/DeepSpeed/issues", quote: "Z3 + 8-bit weights hang on the param-gather step in some configs.", source_type: "issue", tier: 3 }] },

  // --- scheduler benchmark relations ---------------------------------------
  { from: "dlrs", to: "distilbert", relation: "COMPATIBLE", tier: 1, confidence: 1.0,
    fix: "DLRS #1 on the SST-2 bench: val_loss 0.2653, val_acc 0.890, steps_to_target 266.7 (n=3 seeds).",
    evidence: [{ url: "https://huggingface.co/spaces/juiceb0xc0de/lr-scheduler-benchmark", quote: "DLRS leads on val_loss across 3 seeds on distilbert/sst2.", source_type: "benchmark", tier: 1 }] },

  { from: "onecycle", to: "distilbert", relation: "DEGRADES", tier: 1, confidence: 1.0,
    fix: "OneCycle underperforms on the SST-2 bench: val_loss 0.4284 vs cohort cutoff 0.4022.",
    evidence: [{ url: "https://huggingface.co/spaces/juiceb0xc0de/lr-scheduler-benchmark", quote: "OneCycle val_loss 0.4284 is above the cohort cutoff 0.4022.", source_type: "benchmark", tier: 1 }] },

  // --- compatible/positive edges (just so the graph isn't all conflict) ----
  { from: "lora", to: "bnb_8bit",  relation: "COMPATIBLE", tier: 2, confidence: 0.8, fix: "", evidence: [] },
  { from: "lora", to: "bnb_4bit",  relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "grad_ckpt", to: "fsdp", relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "grad_ckpt", to: "qlora",relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "cosine", to: "adamw",   relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "dlrs", to: "adamw",     relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "fsdp", to: "llama3",    relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "qlora", to: "llama3",   relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "qlora", to: "mistral",  relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "qlora", to: "qwen2",    relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "lora",  to: "distilbert", relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "vllm",  to: "llama3",   relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "vllm",  to: "mistral",  relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "deepspeed_z3", to: "fft", relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "fsdp", to: "fft",       relation: "COMPATIBLE", tier: 1, confidence: 1.0, fix: "", evidence: [] },
  { from: "muon", to: "llama3",    relation: "COMPATIBLE", tier: 2, confidence: 0.8, fix: "", evidence: [] },
];

// add "staged melt-in" as a hidden technique reachable as a fix-target
window.FORGE_NODES.push({
  canonical: "staged_meltin", name: "Staged Melt-In", type: "technique",
  description: "Linear ramp-in of chaos injectors over N steps. Prevents NaN at layer ~6.",
  tier: 1,
});

// ============================================================================
// RESOLVE ENGINE
// ============================================================================
// For each non-selected node N, look at edges between N and any selected S:
//   BREAKS              → blocked (red)
//   REQUIRES(S → N)     → S needs N; mark N "needed by S" (amber/conditional)
//   REQUIRES(N → S)     → N needs S, S is selected → that requirement is satisfied (ignore)
//   CONDITIONAL         → conditional (amber) with conditions
//   DEGRADES            → available + warning
//   COMPATIBLE          → available
// Plus: if N has any REQUIRES(N → X) where X is *not* selected and X is also not
//   universally optional, N is conditional ("needs X").

window.forgeResolve = function (selectedCanons) {
  const sel = new Set(selectedCanons);
  const out = {};
  const nodeByCanon = Object.fromEntries(window.FORGE_NODES.map(n => [n.canonical, n]));
  const incoming = {};
  const outgoing = {};
  for (const e of window.FORGE_EDGES) {
    (outgoing[e.from] = outgoing[e.from] || []).push(e);
    (incoming[e.to]   = incoming[e.to]   || []).push(e);
  }

  for (const n of window.FORGE_NODES) {
    if (sel.has(n.canonical)) {
      out[n.canonical] = { status: "selected", reasons: [], warnings: [] };
      continue;
    }
    const reasons = [];
    const warnings = [];

    // edges adjacent to n
    const adj = [...(outgoing[n.canonical] || []), ...(incoming[n.canonical] || [])];

    for (const e of adj) {
      const otherCanon = e.from === n.canonical ? e.to : e.from;
      const direction  = e.from === n.canonical ? "out" : "in"; // n -> other or other -> n
      const other = nodeByCanon[otherCanon];
      if (!other) continue;

      if (sel.has(otherCanon)) {
        if (e.relation === "BREAKS") {
          reasons.push({
            kind: "blocked",
            label: `breaks with ${other.name}`,
            fix: e.fix,
            evidence: e.evidence,
            tier: e.tier,
            confidence: e.confidence,
            otherCanon,
          });
        } else if (e.relation === "CONDITIONAL") {
          reasons.push({
            kind: "conditional",
            label: `conditional on ${other.name}`,
            fix: e.fix,
            conditions: e.conditions,
            evidence: e.evidence,
            tier: e.tier,
            otherCanon,
          });
        } else if (e.relation === "REQUIRES" && direction === "in") {
          // other -> n  means "other requires n". other is selected, n is not.
          // n is a missing ingredient.
          reasons.push({
            kind: "needed-by",
            label: `needed by ${other.name}`,
            fix: e.fix,
            evidence: e.evidence,
            tier: e.tier,
            otherCanon,
          });
        } else if (e.relation === "DEGRADES") {
          warnings.push({
            label: `degrades with ${other.name}`,
            fix: e.fix,
            evidence: e.evidence,
            tier: e.tier,
            otherCanon,
          });
        }
      }
    }

    let status = "available";
    if (reasons.some(r => r.kind === "blocked"))       status = "blocked";
    else if (reasons.some(r => r.kind === "conditional" || r.kind === "needed-by")) status = "conditional";

    out[n.canonical] = { status, reasons, warnings };
  }

  return out;
};

// recipe payload
window.forgeRecipe = function (selectedCanons) {
  const sel = new Set(selectedCanons);
  const nodeByCanon = Object.fromEntries(window.FORGE_NODES.map(n => [n.canonical, n]));
  const conflicts = [];
  const unmet = [];
  const warnings = [];

  for (const e of window.FORGE_EDGES) {
    const a = sel.has(e.from), b = sel.has(e.to);
    if (a && b) {
      if (e.relation === "BREAKS") {
        conflicts.push({ a: nodeByCanon[e.from].name, b: nodeByCanon[e.to].name, fix: e.fix, tier: e.tier, evidence: e.evidence });
      }
      if (e.relation === "DEGRADES") {
        warnings.push({ a: nodeByCanon[e.from].name, b: nodeByCanon[e.to].name, fix: e.fix, tier: e.tier, evidence: e.evidence });
      }
    } else if (a && !b && e.relation === "REQUIRES") {
      unmet.push({ from: nodeByCanon[e.from].name, missing: nodeByCanon[e.to].name, missingCanon: e.to, fix: e.fix, tier: e.tier, evidence: e.evidence });
    } else if (!a && b && e.relation === "REQUIRES") {
      // selected b but not a; the requires goes from a→b meaning a needs b. a not selected, so not unmet here.
    }
  }

  // scheduler benchmark cohort (mock real bench data)
  const scheduler_picks = [
    { component: "DLRS",      val_loss: 0.26528, val_acc: 0.89029, steps_to_target: 266.7, n_seeds: 3,  source: "https://huggingface.co/spaces/juiceb0xc0de/lr-scheduler-benchmark", canonical: "dlrs" },
    { component: "Cosine",    val_loss: 0.31417, val_acc: 0.87959, steps_to_target: 312.0, n_seeds: 3,  source: "https://huggingface.co/spaces/juiceb0xc0de/lr-scheduler-benchmark", canonical: "cosine" },
    { component: "WSD",       val_loss: 0.34081, val_acc: 0.86790, steps_to_target: 348.3, n_seeds: 3,  source: "https://huggingface.co/spaces/juiceb0xc0de/lr-scheduler-benchmark", canonical: "wsd" },
  ];
  const scheduler_warnings = [
    { component: "OneCycle",  val_loss: 0.42844, cutoff: 0.4022, canonical: "onecycle" },
  ];

  const valid = conflicts.length === 0 && unmet.length === 0;

  return { selected: selectedCanons, valid, conflicts, unmet, warnings, scheduler_picks, scheduler_warnings };
};

// ============================================================================
// LIVE DISCOVERIES FEED (mock SSE)
// ============================================================================
window.FORGE_DISCOVERIES = [
  { a: "Gradient Checkpointing", relation: "COMPATIBLE", b: "LoRA",          tier: 2, source: "huggingface.co/docs/peft",    ts_offset: -8 },
  { a: "Muon",                   relation: "REQUIRES",   b: "AdamW",         tier: 2, source: "kellerjordan.github.io",       ts_offset: -22 },
  { a: "DeepSpeed ZeRO-3",       relation: "DEGRADES",   b: "bnb 8-bit",     tier: 3, source: "github.com/microsoft/DeepSpeed", ts_offset: -41 },
  { a: "QLoRA",                  relation: "COMPATIBLE", b: "Qwen-2.5",      tier: 2, source: "qwenlm.github.io",             ts_offset: -67 },
  { a: "FSDP",                   relation: "CONDITIONAL",b: "bnb 4-bit",     tier: 2, source: "huggingface.co/docs/accelerate", ts_offset: -94 },
  { a: "Sophia-G",               relation: "DEGRADES",   b: "bnb 8-bit",     tier: 3, source: "github.com/Liuhong99/Sophia",  ts_offset: -128 },
  { a: "WSD",                    relation: "COMPATIBLE", b: "Continual PT",  tier: 2, source: "arxiv.org/abs/2404.06395",     ts_offset: -171 },
  { a: "Unsloth",                relation: "BREAKS",     b: "FSDP",          tier: 2, source: "github.com/unslothai/unsloth", ts_offset: -210 },
  { a: "AWQ",                    relation: "BREAKS",     b: "Full Fine-Tune",tier: 2, source: "github.com/casper-hansen/AutoAWQ", ts_offset: -266 },
  { a: "OneCycle",               relation: "DEGRADES",   b: "distilbert/sst2", tier: 1, source: "hf.co/spaces/juiceb0xc0de/lr-scheduler-benchmark", ts_offset: -311 },
];

// new live discoveries that stream in over time (offsets in seconds from "now")
window.FORGE_FUTURE_DISCOVERIES = [
  { a: "Per-Layer LR Rotation", relation: "BREAKS",     b: "AdamW 8-bit", tier: 1, source: "mempalace://rick-notes" },
  { a: "Jacobian Reg",          relation: "BREAKS",     b: "Chaos Injectors", tier: 1, source: "mempalace://rick-notes" },
  { a: "Lion",                  relation: "DEGRADES",   b: "bnb 8-bit",   tier: 3, source: "github.com/bitsandbytes-foundation/bitsandbytes" },
  { a: "Paged AdamW",           relation: "COMPATIBLE", b: "QLoRA",       tier: 2, source: "huggingface.co/docs/transformers" },
  { a: "DLRS",                  relation: "COMPATIBLE", b: "WSD",         tier: 2, source: "hf.co/spaces/juiceb0xc0de/lr-scheduler-benchmark" },
  { a: "Cosine",                relation: "COMPATIBLE", b: "AdamW",       tier: 1, source: "pytorch.org/docs" },
  { a: "FlashAttention-3",      relation: "COMPATIBLE", b: "Llama-3",     tier: 2, source: "github.com/Dao-AILab/flash-attention" },
  { a: "Liger Kernel",          relation: "COMPATIBLE", b: "Unsloth",     tier: 3, source: "github.com/linkedin/Liger-Kernel" },
];

// ============================================================================
// LIVE API BOOTSTRAP — prefer the live Forge API; fall back to the bundled mock.
// Default base is same-origin (the API serves this frontend in prod).
// Split-port dev: ?api=http://localhost:8010
// ============================================================================
window.FORGE_API = (function () {
  const m = location.search.match(/[?&]api=([^&]+)/);
  return m ? decodeURIComponent(m[1]) : "";
})();
window.FORGE_LIVE = false;
window.forgeBootstrap = async function () {
  const base = window.FORGE_API.replace(/\/$/, "");
  try {
    const g = await (await fetch(base + "/graph", { cache: "no-store" })).json();
    if (g && Array.isArray(g.nodes) && g.nodes.length && Array.isArray(g.edges)) {
      const tierByNode = {};
      for (const e of g.edges) {
        for (const c of [e.from_canon, e.to_canon]) {
          tierByNode[c] = Math.min(tierByNode[c] == null ? 9 : tierByNode[c], e.tier || 2);
        }
      }
      window.FORGE_NODES = g.nodes.map(n => ({
        canonical: n.canonical, name: n.name, type: n.type,
        description: n.description || "", tier: tierByNode[n.canonical] || 2,
      }));
      window.FORGE_EDGES = g.edges.map(e => ({
        from: e.from_canon, to: e.to_canon, relation: e.relation, tier: e.tier,
        confidence: e.confidence, fix: e.fix || "", conditions: e.conditions || {},
        evidence: e.evidence || [],
      }));
      window.FORGE_LIVE = true;
    }
    const feed = await (await fetch(base + "/feed?n=12", { cache: "no-store" })).json();
    if (Array.isArray(feed) && feed.length) {
      window.FORGE_DISCOVERIES = feed.map(f => ({
        a: f.a, relation: f.relation, b: f.b, tier: f.tier, source: "bright data", ts_offset: 0,
      }));
    }
  } catch (err) {
    console.warn("[forge] live API unavailable, using bundled mock:", err.message);
  }
  return window.FORGE_LIVE;
};