perf(asr): speed up local Flow dictation and land CLM/keyboard refactor

Reduce perceived latency from key release to final text:
- Adaptive chunking: 2.5s first chunk + 5s follow-ups so short
  utterances start on-device recognition while still recording.
- Session-level ASR warmup and audio-format cache reuse to remove
  per-utterance cold-start of SpeechAnalyzer.
- Mirror live pipelined partials to the keyboard transcript line via
  a new flow.transcriptionPartial App Group key + Darwin ping.

Also commits the accumulated custom language model, Flow session,
keyboard extension restructure, and Xiaomi MiMo provider work in
progress on this branch.

Scripts/lexicon/build_v1_lexicon.py

@@ -1,15 +1,19 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-"""Build OSGKeyboard custom ASR lexicon v1 from Sogou-derived sources.
+"""Build OSGKeyboard domain ASR lexicon v1 from the computer-terms scel source.
 
-Sources (experimentation only — Sogou data is non-commercial):
-  1. ASC8384/SogouPopularDict accumulated pinyin TSV
-  2. Local 计算机词汇大全【官方推荐】.scel
-  3. Local 网络流行新词.scel
+Sources:
+  Local 计算机词汇大全【官方推荐】.scel (IT / computer vocabulary only)
+
+Casual network slang and Sogou popular-word dumps are intentionally excluded —
+they dilute custom LM phrase biasing without improving domain ASR accuracy.
 
 Output:
   OSGKeyboard/Resources/CustomLanguageModel/v1/phrases.tsv
   OSGKeyboard/Resources/CustomLanguageModel/v1/manifest.json
+
+The compiled .bin asset is exported separately to
+OSGKeyboardShared/Resources/CustomLanguageModel/v1/ via export_clm.swift.
 """
 
 from __future__ import annotations
@@ -17,43 +21,19 @@ from __future__ import annotations
 import argparse
 import json
 import sys
-import urllib.request
-from collections import Counter
 from dataclasses import dataclass
 from datetime import datetime, timezone
 from pathlib import Path
 
-from scel_parser import get_scel_info, load_pinyin_tsv, parse_scel_file
+from scel_parser import get_scel_info, parse_scel_file
 
 REPO_ROOT = Path(__file__).resolve().parents[2]
 DEFAULT_OUTPUT_DIR = REPO_ROOT / "OSGKeyboard/Resources/CustomLanguageModel/v1"
-SOGOU_ACCUMULATED_URL = (
-    "https://raw.githubusercontent.com/ASC8384/SogouPopularDict/main/"
-    "data/sogou_network_words_accumulated_pinyin.tsv"
-)
-
 DEFAULT_COMPUTER_SCEL = Path("/Users/rocky/Downloads/计算机词汇大全【官方推荐】.scel")
-DEFAULT_NETWORK_SCEL = Path("/Users/rocky/Downloads/网络流行新词.scel")
 
-
-@dataclass(frozen=True)
-class SourceSpec:
-    key: str
-    label: str
-    weight: int
-
-
-SOURCES = [
-    SourceSpec("computer_terms", "计算机词汇大全【官方推荐】", weight=5),
-    SourceSpec("network_slang_local", "网络流行新词.scel", weight=3),
-    SourceSpec("sogou_popular_accumulated", "SogouPopularDict accumulated", weight=1),
-]
-
-
-def download_accumulated_tsv(destination: Path) -> None:
-    destination.parent.mkdir(parents=True, exist_ok=True)
-    with urllib.request.urlopen(SOGOU_ACCUMULATED_URL, timeout=120) as response:
-        destination.write_bytes(response.read())
+SOURCE_KEY = "computer_terms"
+SOURCE_LABEL = "计算机词汇大全【官方推荐】"
+SOURCE_WEIGHT = 5
 
 
 @dataclass
@@ -64,24 +44,26 @@ class LexiconEntry:
     weight: int
 
 
-def merge_entries(sources: list[tuple[SourceSpec, list[tuple[str, str]]]]) -> list[LexiconEntry]:
+def merge_entries(entries: list[tuple[str, str]]) -> list[LexiconEntry]:
     merged: dict[str, LexiconEntry] = {}
-    source_counts: Counter[str] = Counter()
 
-    for spec, entries in sources:
-        for word, pinyin in entries:
-            source_counts[spec.key] += 1
-            current = merged.get(word)
-            candidate = LexiconEntry(word=word, pinyin=pinyin, source=spec.key, weight=spec.weight)
-            if current is None or candidate.weight > current.weight:
-                merged[word] = candidate
-            elif current.weight == candidate.weight and not current.pinyin and pinyin:
-                merged[word] = candidate
+    for word, pinyin in entries:
+        current = merged.get(word)
+        candidate = LexiconEntry(
+            word=word,
+            pinyin=pinyin,
+            source=SOURCE_KEY,
+            weight=SOURCE_WEIGHT,
+        )
+        if current is None:
+            merged[word] = candidate
+        elif not current.pinyin and pinyin:
+            merged[word] = candidate
 
-    return sorted(merged.values(), key=lambda item: (item.weight * -1, item.word))
+    return sorted(merged.values(), key=lambda item: item.word)
 
 
-def write_outputs(entries: list[LexiconEntry], output_dir: Path, source_stats: dict[str, int]) -> None:
+def write_outputs(entries: list[LexiconEntry], output_dir: Path, raw_count: int) -> None:
     output_dir.mkdir(parents=True, exist_ok=True)
 
     phrases_path = output_dir / "phrases.tsv"
@@ -97,17 +79,16 @@ def write_outputs(entries: list[LexiconEntry], output_dir: Path, source_stats: d
         "entry_count": len(entries),
         "sources": [
             {
-                "key": spec.key,
-                "label": spec.label,
-                "weight": spec.weight,
-                "raw_count": source_stats.get(spec.key, 0),
+                "key": SOURCE_KEY,
+                "label": SOURCE_LABEL,
+                "weight": SOURCE_WEIGHT,
+                "raw_count": raw_count,
             }
-            for spec in SOURCES
         ],
         "notes": [
-            "Sogou-derived data is for internal ASR experimentation only.",
+            "Domain-specific computer/IT vocabulary only; casual network slang removed.",
             "PhraseCount weights map to SFCustomLanguageModelData relative frequencies.",
-            "Higher source weight wins on duplicate words.",
+            "Merged with ai-tech-brands seed at export time for the final .bin asset.",
         ],
         "files": {
             "phrases": phrases_path.name,
@@ -118,47 +99,19 @@ def write_outputs(entries: list[LexiconEntry], output_dir: Path, source_stats: d
     manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
 
 
-def build(
-    *,
-    computer_scel: Path,
-    network_scel: Path,
-    output_dir: Path,
-    skip_download: bool,
-) -> int:
-    cache_dir = REPO_ROOT / ".cache/lexicon"
-    cache_dir.mkdir(parents=True, exist_ok=True)
-    accumulated_tsv = cache_dir / "sogou_network_words_accumulated_pinyin.tsv"
-
-    if not skip_download and not accumulated_tsv.exists():
-        print(f"Downloading {SOGOU_ACCUMULATED_URL} …")
-        download_accumulated_tsv(accumulated_tsv)
-    elif not accumulated_tsv.exists():
-        print(f"Missing accumulated TSV: {accumulated_tsv}", file=sys.stderr)
-        return 1
-
+def build(*, computer_scel: Path, output_dir: Path) -> int:
     if not computer_scel.exists():
         print(f"Missing computer scel: {computer_scel}", file=sys.stderr)
         return 1
-    if not network_scel.exists():
-        print(f"Missing network scel: {network_scel}", file=sys.stderr)
-        return 1
 
     computer_info = get_scel_info(computer_scel)
-    network_info = get_scel_info(network_scel)
     print(f"Computer dict: {computer_info.name} ({computer_info.word_count} header count)")
-    print(f"Network dict: {network_info.name} ({network_info.word_count} header count)")
 
-    loaded_sources: list[tuple[SourceSpec, list[tuple[str, str]]]] = [
-        (SOURCES[0], parse_scel_file(computer_scel)),
-        (SOURCES[1], parse_scel_file(network_scel)),
-        (SOURCES[2], load_pinyin_tsv(accumulated_tsv)),
-    ]
+    raw_entries = parse_scel_file(computer_scel)
+    merged = merge_entries(raw_entries)
+    write_outputs(merged, output_dir, raw_count=len(raw_entries))
 
-    source_stats = {spec.key: len(entries) for spec, entries in loaded_sources}
-    merged = merge_entries(loaded_sources)
-    write_outputs(merged, output_dir, source_stats)
-
-    print(f"Raw counts: {source_stats}")
+    print(f"Raw count: {len(raw_entries)}")
     print(f"Merged unique entries: {len(merged)}")
     print(f"Wrote {output_dir / 'phrases.tsv'}")
     print(f"Wrote {output_dir / 'manifest.json'}")
@@ -166,18 +119,11 @@ def build(
 
 
 def main() -> int:
-    parser = argparse.ArgumentParser(description="Build OSGKeyboard custom ASR lexicon v1")
+    parser = argparse.ArgumentParser(description="Build OSGKeyboard domain ASR lexicon v1")
     parser.add_argument("--computer-scel", type=Path, default=DEFAULT_COMPUTER_SCEL)
-    parser.add_argument("--network-scel", type=Path, default=DEFAULT_NETWORK_SCEL)
     parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR)
-    parser.add_argument("--skip-download", action="store_true")
     args = parser.parse_args()
-    return build(
-        computer_scel=args.computer_scel,
-        network_scel=args.network_scel,
-        output_dir=args.output_dir,
-        skip_download=args.skip_download,
-    )
+    return build(computer_scel=args.computer_scel, output_dir=args.output_dir)
 
 
 if __name__ == "__main__":

Scripts/lexicon/export_clm.swift

@@ -15,7 +15,7 @@ import Speech
 // MARK: - CLI
 
 struct CLIOptions {
-    var sogouTSV: URL
+    var domainTSV: URL
     var aiTechTSV: URL
     var outputBin: URL
     var localeID: String
@@ -29,14 +29,14 @@ struct CLIOptions {
             .deletingLastPathComponent()
             .deletingLastPathComponent()
 
-        var sogou = repoRoot.appendingPathComponent(
+        var domain = repoRoot.appendingPathComponent(
             "OSGKeyboard/Resources/CustomLanguageModel/v1/phrases.tsv"
         )
         var aiTech = repoRoot.appendingPathComponent(
             "OSGKeyboard/Resources/CustomLanguageModel/ai-tech-brands/v1/phrases.tsv"
         )
         var output = repoRoot.appendingPathComponent(
-            "OSGKeyboard/Resources/CustomLanguageModel/v1/OSGKeyboardCLM.bin"
+            "OSGKeyboardShared/Resources/CustomLanguageModel/v1/OSGKeyboardCLM.bin"
         )
         var localeID = "zh_CN"
         var modelID = "com.osgkeyboard.custom-lm.v1"
@@ -46,8 +46,8 @@ struct CLIOptions {
         var iterator = CommandLine.arguments.dropFirst().makeIterator()
         while let flag = iterator.next() {
             switch flag {
-            case "--sogou-tsv":
-                sogou = URL(fileURLWithPath: iterator.next() ?? "")
+            case "--domain-tsv", "--sogou-tsv":
+                domain = URL(fileURLWithPath: iterator.next() ?? "")
             case "--ai-tech-tsv":
                 aiTech = URL(fileURLWithPath: iterator.next() ?? "")
             case "--output":
@@ -71,7 +71,7 @@ struct CLIOptions {
         }
 
         return CLIOptions(
-            sogouTSV: sogou,
+            domainTSV: domain,
             aiTechTSV: aiTech,
             outputBin: output,
             localeID: localeID,
@@ -86,7 +86,7 @@ struct CLIOptions {
         export_clm.swift — build SFCustomLanguageModelData .bin on macOS
 
         Options:
-          --sogou-tsv <path>     Sogou merged phrases TSV
+          --domain-tsv <path>    Domain phrases TSV (computer/IT terms)
           --ai-tech-tsv <path>   AI/tech seed phrases TSV
           --output <path>        Output .bin path
           --locale <id>          Locale identifier (default: zh_CN)
@@ -126,7 +126,7 @@ enum TSVLoader {
             }
 
             // Formats:
-            // sogou: word, pinyin, source, weight
+            // domain: word, pinyin, source, weight
             // ai-tech: word, pinyin, source, category, weight, canonical
             let weight: Int
             if parts.count >= 6, let parsed = Int(parts[4]) {
@@ -171,17 +171,17 @@ enum ExportCLM {
         let options = CLIOptions.parse()
         let fm = FileManager.default
 
-        guard fm.fileExists(atPath: options.sogouTSV.path) else {
-            throw ExportError.missingInput(options.sogouTSV.path)
+        guard fm.fileExists(atPath: options.domainTSV.path) else {
+            throw ExportError.missingInput(options.domainTSV.path)
         }
         guard fm.fileExists(atPath: options.aiTechTSV.path) else {
             throw ExportError.missingInput(options.aiTechTSV.path)
         }
 
         fputs("Loading phrases…\n", stderr)
-        let sogou = try TSVLoader.load(from: options.sogouTSV, sourceLabel: "sogou_v1")
+        let domain = try TSVLoader.load(from: options.domainTSV, sourceLabel: "computer_terms")
         let aiTech = try TSVLoader.load(from: options.aiTechTSV, sourceLabel: "ai_tech_seed")
-        var merged = TSVLoader.merge([sogou, aiTech])
+        var merged = TSVLoader.merge([domain, aiTech])
 
         if let cap = options.maxEntries, merged.count > cap {
             merged = Array(merged.prefix(cap))
@@ -189,7 +189,7 @@ enum ExportCLM {
         }
 
         fputs(
-            "Merged \(merged.count) unique phrases (sogou=\(sogou.count), ai-tech=\(aiTech.count))\n",
+            "Merged \(merged.count) unique phrases (domain=\(domain.count), ai-tech=\(aiTech.count))\n",
             stderr
         )
         fputs("Locale=\(options.localeID) identifier=\(options.modelID) version=\(options.modelVersion)\n", stderr)
@@ -236,7 +236,7 @@ enum ExportCLM {
             "version": options.modelVersion,
             "phrase_count": merged.count,
             "sources": [
-                "sogou_v1": sogou.count,
+                "computer_terms": domain.count,
                 "ai_tech_seed": aiTech.count,
             ],
             "bin_file": outputURL.lastPathComponent,

Scripts/lexicon/prepare_clm.swift

@@ -29,7 +29,7 @@ struct PrepareOptions {
             .deletingLastPathComponent()
 
         var input = repoRoot.appendingPathComponent(
-            "OSGKeyboard/Resources/CustomLanguageModel/v1/OSGKeyboardCLM.bin"
+            "OSGKeyboardShared/Resources/CustomLanguageModel/v1/OSGKeyboardCLM.bin"
         )
         var output = repoRoot.appendingPathComponent(
             "OSGKeyboard/Resources/CustomLanguageModel/v1/compiled"

Scripts/lexicon/seeds/ai_tech_brands_seed.tsv

@@ -379,3 +379,242 @@ embodied AI		具身智能	tech_term	80
 数字游民	shu zi you min	digital nomad	tech_term	75
 远程办公	yuan cheng ban gong	remote work	tech_term	78
 副业	fu ye	side hustle	tech_term	72
+
+# --- 2026 AI models, agents & coding tools ---
+GPT-5		gpt five|GPT five|ChatGPT 5	ai_model	98
+GPT-5.5		gpt five five|GPT five point five|gpt-5.5	ai_model	98
+GPT-4o		gpt four o|GPT four oh|gpt4o	ai_model	92
+Claude Code		claude code|ClaudeCode|克劳德代码	dev_tool	96
+Claude Sonnet		claude sonnet|Sonnet|Sonnet 4|Sonnet 4.6	ai_model	94
+Claude Opus		claude opus|Opus|Opus 4|Opus 4.7	ai_model	94
+Composer		cursor composer|Composer 2|Composer 2.5	dev_tool	92
+Cursor Composer		cursor composer|Composer	dev_tool	92
+OpenAI Codex		openai codex|Codex|codex cli	dev_tool	92
+Codex CLI		codex cli|OpenAI Codex	dev_tool	90
+Gemini CLI		gemini cli	dev_tool	88
+Gemini 2.5 Pro		gemini two five pro|Gemini Pro	ai_model	90
+Gemini 3 Pro		gemini three pro|Gemini Pro	ai_model	92
+Gemini Flash		gemini flash|Gemini 3 Flash|Gemini 2.5 Flash	ai_model	88
+DeepSeek V3		deepseek v three|DeepSeek-V3	ai_model	92
+DeepSeek R1		deepseek r one|DeepSeek-R1	ai_model	95
+DeepSeek V4		deepseek v four|DeepSeek-V4|DeepSeek V4 Pro	ai_model	92
+Qwen Coder		qwen coder|千问 coder|通义千问 coder	ai_model	90
+Qwen Max		qwen max|Qwen3 Max|通义千问 Max	ai_model	88
+Qwen Thinking		qwen thinking|通义千问 thinking	ai_model	86
+Kimi K2		kimi k two|Kimi K2.6|Kimi K2.5	ai_model	90
+Kimi Code		kimi code|Moonshot Kimi Code	dev_tool	88
+GLM Coding Plan		glm coding plan|智谱 coding plan	dev_tool	84
+GLM-4.6		glm four six|GLM 4.6	ai_model	84
+GLM-5		glm five|GLM 5	ai_model	86
+Grok 4		grok four|Grok 4.3|Grok Build	ai_model	86
+Grok Build		grok build|Grok Build CLI	dev_tool	84
+Llama 4		llama four|Llama Maverick|Llama Scout	ai_model	86
+Llama Maverick		llama maverick|Llama 4 Maverick	ai_model	84
+Mistral Large		mistral large|Mistral Large 3	ai_model	84
+Devin		devin|Cognition Devin	dev_tool	88
+Cognition		cognition ai|Cognition AI	ai_brand	82
+Kiro		kiro|Amazon Kiro	dev_tool	84
+Amazon Q		amazon q|AWS Q|Q Developer	dev_tool	84
+Q Developer		q developer|Amazon Q Developer	dev_tool	82
+OpenCode		open code|opencode|OpenCode AI	dev_tool	88
+OpenHands		open hands|OpenHands AI	dev_tool	84
+Roo Code		roo code|RooCode	dev_tool	80
+Continue		continue dev|Continue.dev	dev_tool	78
+Zed		zed editor|Zed AI	dev_tool	80
+Trae		trae|Trae AI|Trae CN	dev_tool	84
+TRAE		trae|字节 Trae	dev_tool	82
+Lovable		lovable|Lovable AI	dev_tool	84
+Bolt.new		bolt new|Bolt AI|StackBlitz Bolt	dev_tool	84
+v0		vercel v0|v zero|V0	dev_tool	86
+Replit Agent		replit agent|Replit AI	dev_tool	84
+Manus		manus|Manus AI	ai_brand	86
+AutoGen		autogen|Microsoft AutoGen	ai_platform	82
+CrewAI		crew ai|Crew AI	ai_platform	82
+LangGraph		lang graph|LangGraph	ai_platform	84
+DSPy		dspy|DSPy AI	ai_platform	78
+LlamaIndex		llama index|LlamaIndex	ai_platform	82
+Haystack		haystack ai|Deepset Haystack	ai_platform	76
+ComfyUI		comfy ui|ComfyUI	ai_platform	78
+Fooocus		fooocus|Focus AI	ai_platform	72
+LM Studio		lm studio|LMStudio	ai_platform	82
+Open WebUI		open web ui|OpenWebUI	ai_platform	82
+AnythingLLM		anything llm|Anything LLM	ai_platform	78
+
+# --- Agent, MCP & AI engineering terms ---
+Agent Mode		agent mode|代理模式|智能体模式	ai_term	92
+智能体模式	zhi neng ti mo shi	Agent Mode	ai_term	90
+multi-agent		multi agent|多智能体	ai_term	90
+多智能体	duo zhi neng ti	multi-agent	ai_term	88
+subagent		sub agent|子智能体	ai_term	86
+子智能体	zi zhi neng ti	subagent	ai_term	84
+tool use		tool-use|工具使用	ai_term	88
+tool calling		tool-calling|工具调用	ai_term	90
+structured output		structured outputs|结构化输出	ai_term	86
+结构化输出	jie gou hua shu chu	structured output	ai_term	84
+context engineering		context engineering|上下文工程	ai_term	92
+上下文工程	shang xia wen gong cheng	context engineering	ai_term	90
+prompt caching		prompt cache|提示词缓存	ai_term	84
+提示词缓存	ti shi ci huan cun	prompt caching	ai_term	82
+semantic search		语义搜索|semantic retrieval	ai_term	84
+语义搜索	yu yi sou suo	semantic search	ai_term	82
+vector search		向量搜索|vector retrieval	ai_term	84
+向量搜索	xiang liang sou suo	vector search	ai_term	82
+hybrid search		混合搜索|hybrid retrieval	ai_term	82
+reranker		rerank|重排序模型	ai_term	82
+重排序模型	chong pai xu mo xing	reranker	ai_term	80
+evals		evaluation|模型评测|评测集	ai_term	86
+模型评测	mo xing ping ce	evals	ai_term	84
+SWE-agent		swe agent|SWE Agent	ai_term	80
+WebDev Arena		web dev arena|WebDevArena	ai_term	76
+LLM harness		llm harness|agent harness	ai_term	80
+agent harness		agentic harness|智能体框架	ai_term	80
+reasoning effort		reasoning effort|推理强度	ai_term	78
+推理强度	tui li qiang du	reasoning effort	ai_term	76
+test-time compute		test time compute|测试时计算	ai_term	78
+上下文压缩	shang xia wen ya suo	context compression	ai_term	82
+context compression		上下文压缩	ai_term	80
+memory bank		memory bank|记忆库	ai_term	78
+AI workflow		ai workflow|AI 工作流	ai_term	82
+工作流编排	gong zuo liu bian pai	workflow orchestration	ai_term	82
+workflow orchestration		工作流编排	ai_term	80
+MCP server		mcp server|模型上下文协议服务器	ai_term	90
+MCP client		mcp client|模型上下文协议客户端	ai_term	86
+MCP Inspector		mcp inspector|MCP 调试器	dev_tool	82
+Streamable HTTP		streamable http|MCP HTTP	ai_term	78
+stdio transport		stdio transport|标准输入输出传输	ai_term	76
+
+# --- Developer platforms, databases & infra ---
+Neon		neon database|Neon Postgres	tech_company	84
+PlanetScale		planet scale|PlanetScale MySQL	tech_company	82
+Turso		turso|libSQL	tech_company	80
+libSQL		lib sql|Turso	tech_term	78
+Convex		convex dev|Convex database	tech_company	80
+Clerk		clerk auth|Clerk	dev_tool	80
+Auth0		auth zero|Auth Zero	dev_tool	78
+WorkOS		work os|Work OS	dev_tool	78
+Clerk Auth		clerk auth|Clerk	dev_tool	78
+Drizzle		drizzle orm|Drizzle ORM	dev_tool	82
+Prisma		prisma orm|Prisma ORM	dev_tool	84
+Drizzle ORM		drizzle orm|Drizzle	dev_tool	82
+Prisma ORM		prisma orm|Prisma	dev_tool	82
+Postgres		postgres|PostgreSQL|postgre sql	tech_term	88
+PostgreSQL		postgresql|Postgres|postgre sql	tech_term	88
+SQLite		sqlite|SQLite	tech_term	84
+DuckDB		duck db|Duck DB	tech_term	80
+ClickHouse		click house|ClickHouse	tech_term	80
+Kafka		kafka|Apache Kafka	tech_term	82
+Redpanda		red panda|Redpanda	tech_company	76
+OpenTelemetry		open telemetry|OTel	tech_term	84
+OTel		otel|OpenTelemetry	tech_term	82
+Grafana		grafana	dev_tool	78
+Prometheus		prometheus	dev_tool	78
+Sentry		sentry	dev_tool	82
+SST		sst|Serverless Stack	dev_tool	76
+Tailscale		tailscale	tech_company	78
+Fly.io		fly io|Fly	tech_company	78
+Render		render.com|Render	tech_company	76
+Railway		railway app|Railway	tech_company	78
+Modal		modal labs|Modal	tech_company	78
+Modal Labs		modal labs|Modal	tech_company	76
+Hugging Face Spaces		hugging face spaces|Spaces	ai_platform	78
+Nix		nix|NixOS	tech_term	78
+NixOS		nixos|Nix OS	tech_term	76
+Bun		bun js|Bun runtime	dev_tool	82
+Deno		deno|Deno Deploy	dev_tool	80
+Biome		biome js|Biome	dev_tool	78
+Turborepo		turbo repo|Turborepo	dev_tool	78
+Nx		nx monorepo|Nx	dev_tool	76
+pnpm		pnpm	dev_tool	76
+shadcn/ui		shadcn ui|shadcn|shad cn	dev_tool	84
+Tailwind CSS		tailwind css|Tailwind	dev_tool	84
+React Server Components		react server components|RSC	dev_tool	82
+RSC		react server components	dev_tool	80
+Server Actions		server actions|React Server Actions	dev_tool	78
+Astro		astro js|Astro	dev_tool	80
+SvelteKit		svelte kit|SvelteKit	dev_tool	80
+Remix		remix run|Remix	dev_tool	78
+Nuxt		nuxt|Nuxt.js	dev_tool	78
+Hono		hono js|Hono	dev_tool	78
+FastAPI		fast api|FastAPI	dev_tool	82
+Ktor		ktor|Ktor server	dev_tool	76
+
+# --- Apple, Swift & on-device speech stack ---
+SwiftData		swift data|SwiftData	dev_tool	88
+Swift Testing		swift testing|Testing framework	dev_tool	86
+App Intents		app intents|AppIntents	dev_tool	84
+WidgetKit		widget kit|WidgetKit	dev_tool	82
+ActivityKit		activity kit|ActivityKit	dev_tool	82
+Live Activities		live activities|灵动岛实时活动	dev_tool	82
+App Group		app group|App Groups	dev_tool	86
+Core ML		core ml|CoreML	dev_tool	88
+Create ML		create ml|CreateML	dev_tool	82
+SpeechAnalyzer		speech analyzer|Speech Analyzer	dev_tool	92
+DictationTranscriber		dictation transcriber|Dictation Transcriber	dev_tool	92
+SpeechTranscriber		speech transcriber|Speech Transcriber	dev_tool	86
+SFCustomLanguageModelData		custom language model data|SF Custom Language Model Data	dev_tool	90
+SFSpeechLanguageModel		speech language model|SF Speech Language Model	dev_tool	88
+AssetInventory		asset inventory|Speech AssetInventory	dev_tool	84
+AVAudioEngine		av audio engine|AVAudioEngine	dev_tool	84
+AVAudioSession		av audio session|AVAudioSession	dev_tool	84
+TestFlight		test flight|TestFlight	dev_tool	84
+App Store Connect		app store connect|AppStoreConnect	dev_tool	84
+XcodeGen		xcode gen|XcodeGen	dev_tool	82
+Tuist		tuist	dev_tool	78
+Swift Package Manager		swift package manager|SPM	dev_tool	82
+SPM		swift package manager	dev_tool	80
+SF Symbols		sf symbols|SFSymbols	dev_tool	82
+visionOS		vision os|Vision Pro	dev_tool	82
+RealityKit		reality kit|RealityKit	dev_tool	80
+Metal		metal|Metal Performance Shaders	dev_tool	80
+
+# --- China AI, creator tools & current tech buzzwords ---
+可灵	ke ling	Kling|Kling AI	ai_brand	86
+Kling		kling ai|可灵	ai_brand	86
+海螺AI	hai luo AI	Hailuo|MiniMax Video	ai_brand	84
+Hailuo		hailuo ai|海螺AI	ai_brand	82
+即梦	ji meng	Jimeng|即梦AI	ai_brand	84
+Jimeng		jimeng ai|即梦	ai_brand	82
+剪映	jian ying	CapCut	tech_company	84
+CapCut		cap cut|剪映	tech_company	84
+腾讯元宝	teng xun yuan bao	Yuanbao	ai_brand	84
+元宝	yuan bao	腾讯元宝|Yuanbao	ai_brand	82
+纳米AI	na mi AI	Nami AI	ai_brand	78
+秘塔AI	mi ta AI	Metaso	ai_brand	80
+Metaso		metaso|秘塔AI	ai_brand	78
+夸克AI	kua ke AI	Quark AI	ai_brand	80
+Quark		quark ai|夸克	ai_brand	78
+天工	tian gong	tiangong|昆仑万维天工	ai_brand	78
+商量	shang liang	SenseChat	ai_brand	76
+杭州六小龙	hang zhou liu xiao long	Hangzhou Six Little Dragons	tech_term	84
+AI治理	AI zhi li	AI governance	tech_term	84
+AI governance		AI治理	tech_term	82
+世界模型	shi jie mo xing	world model	ai_term	84
+world model		世界模型	ai_term	82
+端到端	duan dao duan	end-to-end	tech_term	82
+end-to-end		端到端	tech_term	80
+端侧模型	duan ce mo xing	on-device model	ai_term	84
+on-device model		端侧模型	ai_term	82
+本地模型	ben di mo xing	local model	ai_term	84
+local model		本地模型	ai_term	82
+具身智能体	ju shen zhi neng ti	embodied agent	ai_term	82
+embodied agent		具身智能体	ai_term	80
+人形机器人	ren xing ji qi ren	humanoid robot|humanoid	tech_term	86
+humanoid robot		人形机器人	tech_term	84
+新质生产力	xin zhi sheng chan li	new quality productive forces	tech_term	82
+数字分身	shu zi fen shen	digital avatar	tech_term	78
+digital avatar		数字分身	tech_term	76
+AI视频	AI shi pin	AI video	tech_term	82
+AI video		AI视频	tech_term	80
+文生图	wen sheng tu	text-to-image	tech_term	82
+图生视频	tu sheng shi pin	image-to-video	tech_term	82
+文生视频	wen sheng shi pin	text-to-video	tech_term	82
+小红书	xiao hong shu	Xiaohongshu|RedNote	tech_company	86
+Xiaohongshu		xiao hong shu|小红书|RedNote	tech_company	84
+RedNote		red note|Xiaohongshu|小红书	tech_company	82
+活人感	huo ren gan	authentically human|real person vibe	tech_term	80
+情绪价值	qing xu jia zhi	emotional value	tech_term	80
+赛博对账	sai bo dui zhang	cyber reconciliation	tech_term	78
+赛博	sai bo	cyber	tech_term	76
+村咖	cun ka	village coffee	tech_term	72
+拉布布	la bu bu	Labubu	tech_term	72
+Labubu		labubu|拉布布	tech_term	72