Error list: 87 words, 178 occurrences (review 2026-10-25)
Browse files- README.md +79 -56
- occurrences.jsonl +37 -0
- words.jsonl +78 -55
README.md
CHANGED
|
@@ -23,7 +23,7 @@ code-switching, Jerusalem). Each word here is fed back into training as a **prio
|
|
| 23 |
recording app serves sentences with it first until there are enough takes of it. No transcript text,
|
| 24 |
audio or other content of the dictations is published, only the word, the wrong forms and the model.
|
| 25 |
|
| 26 |
-
-
|
| 27 |
- `models` / `model_line`: the Hebrish model that produced the error (Sabich, Shakshuka, …, with
|
| 28 |
version); `model_rev` is the exact weights' revision, recorded from app 0.7.3. Errors from before
|
| 29 |
the app recorded the model have none.
|
|
@@ -33,70 +33,93 @@ audio or other content of the dictations is published, only the word, the wrong
|
|
| 33 |
|
| 34 |
| Word | Heard as | Occurrences | Takes / target |
|
| 35 |
|---|---|---|---|
|
| 36 |
-
| ROCm | ROCM, Rockham, Rockin, RCM, Rock'em | 15 |
|
| 37 |
-
| LoRA | Lores, Loas, Laura, lores, Loa, LoRa, lore, lures | 14 |
|
| 38 |
-
| Claude Code | clawed code, clawed, claud, clawed coat, claw code, Core Code, claudcode |
|
| 39 |
-
| Corn | Coren, Cornak's, Eminent Corn, Corner, Korn, Corin | 6 |
|
| 40 |
-
| Hugging Face | Hooking Face, hugging face, hooking face, Hogging Face, hogging face | 6 |
|
| 41 |
| My Weird Prompts | MyWord Prompts, My WordPrompts, My Word Promise, mywordprompt | 6 | 9 / 20 |
|
| 42 |
-
| Hebrish | HeBrish, Hebris, keybrush, Hebrew Shop, Hebrew shtal | 5 |
|
| 43 |
-
|
|
| 44 |
-
|
|
| 45 |
-
|
|
|
|
|
|
|
|
| 46 |
| F-Droid | FDroid, Fdroid | 3 | 9 / 20 |
|
| 47 |
-
| Nemotron | Nematron, Nementron | 3 |
|
| 48 |
-
|
|
| 49 |
-
|
|
| 50 |
-
|
|
| 51 |
-
|
|
| 52 |
-
|
|
| 53 |
-
|
|
| 54 |
-
|
|
| 55 |
-
|
|
|
|
|
|
|
|
|
|
|
| 56 |
| ONNX | ONIX, onyx | 2 | 9 / 20 |
|
| 57 |
| Qwen | Qwen1, quen | 2 | 10 / 20 |
|
| 58 |
-
| series | Ceres, Siries | 2 |
|
| 59 |
-
|
|
| 60 |
-
|
|
| 61 |
-
|
|
| 62 |
-
|
|
| 63 |
-
|
|
| 64 |
-
|
|
| 65 |
-
|
|
| 66 |
-
|
|
| 67 |
-
| Bituach Leumi | betuachlomi | 1 | 0 / 20 |
|
| 68 |
| Boxty | E box d | 1 | |
|
| 69 |
-
| Carrot Cake AI | Karrot Cake AI | 1 |
|
| 70 |
| Chatterbox | Shatterbox | 1 | 9 / 20 |
|
| 71 |
-
|
|
| 72 |
-
|
|
| 73 |
-
|
|
| 74 |
-
|
|
|
|
|
| 75 |
| EXE | AXE | 1 | 9 / 20 |
|
| 76 |
-
| Ezra | Esther | 1 |
|
| 77 |
-
| fal | Fel | 1 |
|
| 78 |
-
| few-shot | fusil | 1 |
|
| 79 |
-
|
|
| 80 |
-
|
|
| 81 |
-
|
|
| 82 |
-
|
|
|
|
|
|
|
|
|
|
|
| 83 |
| Kdenlive | Caden Live | 1 | 9 / 20 |
|
| 84 |
-
|
|
| 85 |
-
|
|
| 86 |
-
|
|
| 87 |
-
|
|
| 88 |
-
|
|
| 89 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 90 |
| prosody | porosity | 1 | 9 / 20 |
|
| 91 |
-
|
|
| 92 |
-
|
|
| 93 |
-
|
|
| 94 |
-
|
|
| 95 |
-
|
|
| 96 |
-
|
|
| 97 |
-
|
|
| 98 |
-
|
|
| 99 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 100 |
|
| 101 |
## Release tests
|
| 102 |
|
|
|
|
| 23 |
recording app serves sentences with it first until there are enough takes of it. No transcript text,
|
| 24 |
audio or other content of the dictations is published, only the word, the wrong forms and the model.
|
| 25 |
|
| 26 |
+
- 87 words, 178 occurrences. Last review: 2026-10-25.
|
| 27 |
- `models` / `model_line`: the Hebrish model that produced the error (Sabich, Shakshuka, …, with
|
| 28 |
version); `model_rev` is the exact weights' revision, recorded from app 0.7.3. Errors from before
|
| 29 |
the app recorded the model have none.
|
|
|
|
| 33 |
|
| 34 |
| Word | Heard as | Occurrences | Takes / target |
|
| 35 |
|---|---|---|---|
|
| 36 |
+
| ROCm | ROCM, Rockham, Rockin, RCM, Rock'em | 15 | 10 / 20 |
|
| 37 |
+
| LoRA | Lores, Loas, Laura, lores, Loa, LoRa, lore, lures | 14 | 4 / 20 |
|
| 38 |
+
| Claude Code | clawed code, clawed, claud, clawed coat, claw code, Core Code, claudcode, claud Code, the claud, clause claud code | 12 | 3 / 20 |
|
| 39 |
+
| Corn | Coren, Cornak's, Eminent Corn, Corner, Korn, Corin | 6 | 3 / 20 |
|
| 40 |
+
| Hugging Face | Hooking Face, hugging face, hooking face, Hogging Face, hogging face | 6 | 10 / 20 |
|
| 41 |
| My Weird Prompts | MyWord Prompts, My WordPrompts, My Word Promise, mywordprompt | 6 | 9 / 20 |
|
| 42 |
+
| Hebrish | HeBrish, Hebris, keybrush, Hebrew Shop, Hebrew shtal | 5 | 10 / 20 |
|
| 43 |
+
| Typst | TyPst, TIPS-type, tips toypt, TyPstip, tipst | 5 | 1 / 20 |
|
| 44 |
+
| NocoDB | No CodeDB, Nocode, NokoDB, Noko DB | 4 | 3 / 20 |
|
| 45 |
+
| CLAUDE.md | userlevelclaw.md, 4 MD, Claude NMD | 3 | 3 / 20 |
|
| 46 |
+
| Cloudflare | cloud fair, Cloud Claire, Cloud Clare | 3 | 3 / 20 |
|
| 47 |
+
| Colab | Collab, collab | 3 | 4 / 20 |
|
| 48 |
| F-Droid | FDroid, Fdroid | 3 | 9 / 20 |
|
| 49 |
+
| Nemotron | Nematron, Nementron | 3 | 10 / 20 |
|
| 50 |
+
| skill | scale | 3 | 0 / 20 |
|
| 51 |
+
| Unsloth | Unsoft, On Sloth, Onsloth | 3 | 4 / 20 |
|
| 52 |
+
| ab initio | a benissio, abenisio | 2 | 2 / 20 |
|
| 53 |
+
| DeepSeek | DeepSee, DeepC | 2 | 7 / 20 |
|
| 54 |
+
| disfluency | disluency, Dispuency | 2 | 2 / 20 |
|
| 55 |
+
| DMARC | GMARC, Dimark | 2 | 1 / 20 |
|
| 56 |
+
| evals | evalves, valves | 2 | 3 / 20 |
|
| 57 |
+
| Infisical | Infiniscal, Infinisco | 2 | 2 / 20 |
|
| 58 |
+
| Jupyter | Jupiter | 2 | 9 / 20 |
|
| 59 |
+
| Karpathy | Karpathi, Karpathi's | 2 | 9 / 20 |
|
| 60 |
+
| Netanyahu | Etanyahu, Etnia | 2 | 4 / 20 |
|
| 61 |
| ONNX | ONIX, onyx | 2 | 9 / 20 |
|
| 62 |
| Qwen | Qwen1, quen | 2 | 10 / 20 |
|
| 63 |
+
| series | Ceres, Siries | 2 | 2 / 20 |
|
| 64 |
+
| Z alias | ZALAS, Zed Elias | 2 | 2 / 20 |
|
| 65 |
+
| abliteration | Obliteration | 1 | 2 / 20 |
|
| 66 |
+
| agentic | agenda | 1 | 1 / 20 |
|
| 67 |
+
| AMD | MD | 1 | 2 / 20 |
|
| 68 |
+
| APK | ABK | 1 | 3 / 20 |
|
| 69 |
+
| ASR | ACR | 1 | 4 / 20 |
|
| 70 |
+
| Bezeq | Bezek | 1 | 0 / 20 |
|
| 71 |
+
| Bituach Leumi | betuachlomi | 1 | 5 / 20 |
|
|
|
|
| 72 |
| Boxty | E box d | 1 | |
|
| 73 |
+
| Carrot Cake AI | Karrot Cake AI | 1 | 9 / 20 |
|
| 74 |
| Chatterbox | Shatterbox | 1 | 9 / 20 |
|
| 75 |
+
| Claude in Chrome | Claude Grouping Chrome | 1 | 0 / 20 |
|
| 76 |
+
| co.il | co.al | 1 | 0 / 20 |
|
| 77 |
+
| do you want me | Dramat me | 1 | 0 / 20 |
|
| 78 |
+
| DSR Holdings | Vidyester Holdings | 1 | 5 / 20 |
|
| 79 |
+
| eSpeak | Espeak | 1 | 9 / 20 |
|
| 80 |
| EXE | AXE | 1 | 9 / 20 |
|
| 81 |
+
| Ezra | Esther | 1 | 3 / 20 |
|
| 82 |
+
| fal | Fel | 1 | 2 / 20 |
|
| 83 |
+
| few-shot | fusil | 1 | 2 / 20 |
|
| 84 |
+
| Gihon | Gichon | 1 | 0 / 20 |
|
| 85 |
+
| Hegseth | Hagsets | 1 | 2 / 20 |
|
| 86 |
+
| Hevrat HaHashmal | Chashmal Yisrael | 1 | 0 / 20 |
|
| 87 |
+
| inbound | for bound | 1 | 0 / 20 |
|
| 88 |
+
| Janglo | Danglo | 1 | 3 / 20 |
|
| 89 |
+
| Jupyter Notebook | Jupiter notebooks | 1 | 9 / 20 |
|
| 90 |
+
| Kaizen | Kaiser | 1 | 2 / 20 |
|
| 91 |
| Kdenlive | Caden Live | 1 | 9 / 20 |
|
| 92 |
+
| lav mic | Lav Mike | 1 | 0 / 20 |
|
| 93 |
+
| LLM | nelam | 1 | 6 / 20 |
|
| 94 |
+
| marking | marketing | 1 | 0 / 20 |
|
| 95 |
+
| Meorav | Maorab | 1 | 10 / 20 |
|
| 96 |
+
| Muse Spark | News Spark | 1 | 2 / 20 |
|
| 97 |
+
| Nvidia | NVideo | 1 | 3 / 20 |
|
| 98 |
+
| Ollama | ulama | 1 | 7 / 20 |
|
| 99 |
+
| OPNsense | OpenSense | 1 | 1 / 20 |
|
| 100 |
+
| Opus | Focus | 1 | 2 / 20 |
|
| 101 |
+
| populated | opulated | 1 | 0 / 20 |
|
| 102 |
+
| post-processing | PostgreSQL processing | 1 | 2 / 20 |
|
| 103 |
| prosody | porosity | 1 | 9 / 20 |
|
| 104 |
+
| pseudospam | Surasfam | 1 | 0 / 20 |
|
| 105 |
+
| Qdrant | Qdrint | 1 | 2 / 20 |
|
| 106 |
+
| rclone | Arclone | 1 | 0 / 20 |
|
| 107 |
+
| regex | RedJack | 1 | 3 / 20 |
|
| 108 |
+
| remote | ote | 1 | 0 / 20 |
|
| 109 |
+
| repo | Refo | 1 | 0 / 20 |
|
| 110 |
+
| SaaS | SAS | 1 | 3 / 20 |
|
| 111 |
+
| Sabich | Tzabich | 1 | 10 / 20 |
|
| 112 |
+
| SBC | SPCAdmin | 1 | 0 / 20 |
|
| 113 |
+
| Sonnet | Sommet | 1 | 5 / 20 |
|
| 114 |
+
| SSE | SSC | 1 | 2 / 20 |
|
| 115 |
+
| subscription | prescription | 1 | 0 / 20 |
|
| 116 |
+
| Superwhisper | Superwisper | 1 | 2 / 20 |
|
| 117 |
+
| system tray | system try | 1 | 0 / 20 |
|
| 118 |
+
| tab | top | 1 | 0 / 20 |
|
| 119 |
+
| tell her | Teller | 1 | 0 / 20 |
|
| 120 |
+
| TTS | CTS | 1 | 5 / 20 |
|
| 121 |
+
| vocab | Rocab | 1 | 0 / 20 |
|
| 122 |
+
| Whisperian | Wisperion | 1 | 3 / 20 |
|
| 123 |
|
| 124 |
## Release tests
|
| 125 |
|
occurrences.jsonl
CHANGED
|
@@ -98,6 +98,7 @@
|
|
| 98 |
{"word": "Unsloth", "heard_as": "On Sloth", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-23"}
|
| 99 |
{"word": "Unsloth", "heard_as": "Onsloth", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-23"}
|
| 100 |
{"word": "ab initio", "heard_as": "a benissio", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
|
|
| 101 |
{"word": "agentic", "heard_as": "agenda", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 102 |
{"word": "ASR", "heard_as": "ACR", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 103 |
{"word": "Claude Code", "heard_as": "clawed coat", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
@@ -111,6 +112,7 @@
|
|
| 111 |
{"word": "DeepSeek", "heard_as": "DeepSee", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 112 |
{"word": "disfluency", "heard_as": "disluency", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 113 |
{"word": "disfluency", "heard_as": "Dispuency", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
|
|
| 114 |
{"word": "evals", "heard_as": "valves", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 115 |
{"word": "Ezra", "heard_as": "Esther", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 116 |
{"word": "fal", "heard_as": "Fel", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
@@ -125,17 +127,52 @@
|
|
| 125 |
{"word": "LoRA", "heard_as": "Laura", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 126 |
{"word": "LoRA", "heard_as": "lore", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 127 |
{"word": "LoRA", "heard_as": "lures", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
|
|
| 128 |
{"word": "Muse Spark", "heard_as": "News Spark", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 129 |
{"word": "My Weird Prompts", "heard_as": "MyWord Prompts", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 130 |
{"word": "Ollama", "heard_as": "ulama", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 131 |
{"word": "ONNX", "heard_as": "onyx", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
|
|
| 132 |
{"word": "post-processing", "heard_as": "PostgreSQL processing", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 133 |
{"word": "Qdrant", "heard_as": "Qdrint", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
|
|
|
|
|
| 134 |
{"word": "series", "heard_as": "Ceres", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 135 |
{"word": "series", "heard_as": "Siries", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
|
|
| 136 |
{"word": "SSE", "heard_as": "SSC", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 137 |
{"word": "Superwhisper", "heard_as": "Superwisper", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 138 |
{"word": "Typst", "heard_as": "TyPst", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 139 |
{"word": "Typst", "heard_as": "TIPS-type", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
|
|
| 140 |
{"word": "Z alias", "heard_as": "ZALAS", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 141 |
{"word": "Z alias", "heard_as": "Zed Elias", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 98 |
{"word": "Unsloth", "heard_as": "On Sloth", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-23"}
|
| 99 |
{"word": "Unsloth", "heard_as": "Onsloth", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-23"}
|
| 100 |
{"word": "ab initio", "heard_as": "a benissio", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 101 |
+
{"word": "ab initio", "heard_as": "abenisio", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 102 |
{"word": "agentic", "heard_as": "agenda", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 103 |
{"word": "ASR", "heard_as": "ACR", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 104 |
{"word": "Claude Code", "heard_as": "clawed coat", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
|
|
| 112 |
{"word": "DeepSeek", "heard_as": "DeepSee", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 113 |
{"word": "disfluency", "heard_as": "disluency", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 114 |
{"word": "disfluency", "heard_as": "Dispuency", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 115 |
+
{"word": "DMARC", "heard_as": "Dimark", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 116 |
{"word": "evals", "heard_as": "valves", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 117 |
{"word": "Ezra", "heard_as": "Esther", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 118 |
{"word": "fal", "heard_as": "Fel", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
|
|
|
| 127 |
{"word": "LoRA", "heard_as": "Laura", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 128 |
{"word": "LoRA", "heard_as": "lore", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 129 |
{"word": "LoRA", "heard_as": "lures", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 130 |
+
{"word": "marking", "heard_as": "marketing", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 131 |
{"word": "Muse Spark", "heard_as": "News Spark", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 132 |
{"word": "My Weird Prompts", "heard_as": "MyWord Prompts", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 133 |
{"word": "Ollama", "heard_as": "ulama", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 134 |
{"word": "ONNX", "heard_as": "onyx", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 135 |
+
{"word": "OPNsense", "heard_as": "OpenSense", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 136 |
{"word": "post-processing", "heard_as": "PostgreSQL processing", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 137 |
{"word": "Qdrant", "heard_as": "Qdrint", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 138 |
+
{"word": "repo", "heard_as": "Refo", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 139 |
+
{"word": "SBC", "heard_as": "SPCAdmin", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 140 |
{"word": "series", "heard_as": "Ceres", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 141 |
{"word": "series", "heard_as": "Siries", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 142 |
+
{"word": "skill", "heard_as": "scale", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 143 |
{"word": "SSE", "heard_as": "SSC", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 144 |
{"word": "Superwhisper", "heard_as": "Superwisper", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 145 |
{"word": "Typst", "heard_as": "TyPst", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 146 |
{"word": "Typst", "heard_as": "TIPS-type", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 147 |
+
{"word": "vocab", "heard_as": "Rocab", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 148 |
{"word": "Z alias", "heard_as": "ZALAS", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 149 |
{"word": "Z alias", "heard_as": "Zed Elias", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-24"}
|
| 150 |
+
{"word": "Bezeq", "heard_as": "Bezek", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 151 |
+
{"word": "Claude Code", "heard_as": "clawed code", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 152 |
+
{"word": "Claude Code", "heard_as": "claud Code", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 153 |
+
{"word": "Claude Code", "heard_as": "clawed code", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 154 |
+
{"word": "Claude Code", "heard_as": "the claud", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 155 |
+
{"word": "Claude Code", "heard_as": "clause claud code", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 156 |
+
{"word": "Claude in Chrome", "heard_as": "Claude Grouping Chrome", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 157 |
+
{"word": "CLAUDE.md", "heard_as": "Claude NMD", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 158 |
+
{"word": "co.il", "heard_as": "co.al", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 159 |
+
{"word": "DeepSeek", "heard_as": "DeepC", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 160 |
+
{"word": "do you want me", "heard_as": "Dramat me", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 161 |
+
{"word": "Gihon", "heard_as": "Gichon", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 162 |
+
{"word": "Hevrat HaHashmal", "heard_as": "Chashmal Yisrael", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 163 |
+
{"word": "inbound", "heard_as": "for bound", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 164 |
+
{"word": "lav mic", "heard_as": "Lav Mike", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 165 |
+
{"word": "Opus", "heard_as": "Focus", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 166 |
+
{"word": "populated", "heard_as": "opulated", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 167 |
+
{"word": "pseudospam", "heard_as": "Surasfam", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 168 |
+
{"word": "rclone", "heard_as": "Arclone", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 169 |
+
{"word": "remote", "heard_as": "ote", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 170 |
+
{"word": "skill", "heard_as": "scale", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 171 |
+
{"word": "skill", "heard_as": "scale", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 172 |
+
{"word": "subscription", "heard_as": "prescription", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 173 |
+
{"word": "system tray", "heard_as": "system try", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 174 |
+
{"word": "tab", "heard_as": "top", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 175 |
+
{"word": "tell her", "heard_as": "Teller", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 176 |
+
{"word": "Typst", "heard_as": "tips toypt", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 177 |
+
{"word": "Typst", "heard_as": "TyPstip", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
| 178 |
+
{"word": "Typst", "heard_as": "tipst", "model": "sabich Sabich v2.0", "model_line": "Sabich", "model_version": "v2.0", "model_rev": "eebf26d8c3f9139f6edb88e0d7a22375a0e25976", "source": "dictate", "date": "2026-10-25"}
|
words.jsonl
CHANGED
|
@@ -1,64 +1,87 @@
|
|
| 1 |
-
{"word": "ROCm", "heard_as": ["ROCM", "Rockham", "Rockin", "RCM", "Rock'em"], "kinds": ["tech", "linux"], "meaning": "AMD's open-source GPU compute stack (like CUDA)", "occurrences": 15, "models": ["sabich Sabich v2.0", "shakshuka Shakshuka v2.0"], "first_seen": "2026-10-21", "last_seen": "2026-10-23", "takes":
|
| 2 |
-
{"word": "LoRA", "heard_as": ["Lores", "Loas", "Laura", "lores", "Loa", "LoRa", "lore", "lures"], "kinds": ["tech", "ai_interactions"], "meaning": "low-rank adapter fine-tuning", "occurrences": 14, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-24", "takes":
|
| 3 |
-
{"word": "Claude Code", "heard_as": ["clawed code", "clawed", "claud", "clawed coat", "claw code", "Core Code", "claudcode"], "kinds": ["tech", "ai_interactions"], "meaning": "Anthropic's coding agent CLI", "occurrences":
|
| 4 |
-
{"word": "Corn", "heard_as": ["Coren", "Cornak's", "Eminent Corn", "Corner", "Korn", "Corin"], "kinds": ["personal", "name"], "meaning": "co-host on Daniel's podcast My Weird Prompts", "occurrences": 6, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-24", "takes":
|
| 5 |
-
{"word": "Hugging Face", "heard_as": ["Hooking Face", "hugging face", "hooking face", "Hogging Face", "hogging face"], "kinds": ["tech", "ai_interactions"], "meaning": "the AI model and dataset hub", "occurrences": 6, "models": ["sabich Sabich v2.0", "shakshuka Shakshuka v2.0"], "first_seen": "2026-10-21", "last_seen": "2026-10-24", "takes":
|
| 6 |
{"word": "My Weird Prompts", "heard_as": ["MyWord Prompts", "My WordPrompts", "My Word Promise", "mywordprompt"], "kinds": ["personal"], "meaning": "Daniel's AI podcast", "occurrences": 6, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-24", "takes": 9, "target": 20, "status": "training"}
|
| 7 |
-
{"word": "Hebrish", "heard_as": ["HeBrish", "Hebris", "keybrush", "Hebrew Shop", "Hebrew shtal"], "kinds": ["personal", "tech"], "meaning": "Daniel's Hebrew-English code-switching ASR project, app and Hugging Face org", "occurrences": 5, "models": ["sabich Sabich v2.0", "shakshuka Shakshuka v2.0"], "first_seen": "2026-10-21", "last_seen": "2026-10-24", "takes":
|
| 8 |
-
{"word": "
|
| 9 |
-
{"word": "
|
| 10 |
-
{"word": "
|
|
|
|
|
|
|
| 11 |
{"word": "F-Droid", "heard_as": ["FDroid", "Fdroid"], "kinds": ["tech"], "meaning": "the open-source Android app store", "occurrences": 3, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-21", "last_seen": "2026-10-23", "takes": 9, "target": 20, "status": "training"}
|
| 12 |
-
{"word": "Nemotron", "heard_as": ["Nematron", "Nementron"], "kinds": ["tech", "ai_interactions"], "meaning": "NVIDIA's Nemotron model family (a streaming ASR model here)", "occurrences": 3, "models": ["sabich Sabich v2.0", "shakshuka Shakshuka v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes":
|
| 13 |
-
{"word": "
|
| 14 |
-
{"word": "
|
| 15 |
-
{"word": "
|
| 16 |
-
{"word": "
|
| 17 |
-
{"word": "
|
| 18 |
-
{"word": "
|
| 19 |
-
{"word": "
|
| 20 |
-
{"word": "
|
|
|
|
|
|
|
|
|
|
| 21 |
{"word": "ONNX", "heard_as": ["ONIX", "onyx"], "kinds": ["tech", "ai_interactions"], "meaning": "the Open Neural Network Exchange model format", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-21", "last_seen": "2026-10-24", "takes": 9, "target": 20, "status": "training"}
|
| 22 |
{"word": "Qwen", "heard_as": ["Qwen1", "quen"], "kinds": ["tech", "ai_interactions"], "meaning": "Alibaba's Qwen model family", "occurrences": 2, "models": ["sabich Sabich v2.0", "shakshuka Shakshuka v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-23", "takes": 10, "target": 20, "status": "training"}
|
| 23 |
-
{"word": "series", "heard_as": ["Ceres", "Siries"], "kinds": ["other"], "meaning": null, "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes":
|
| 24 |
-
{"word": "
|
| 25 |
-
{"word": "
|
| 26 |
-
{"word": "
|
| 27 |
-
{"word": "
|
| 28 |
-
{"word": "
|
| 29 |
-
{"word": "
|
| 30 |
-
{"word": "
|
| 31 |
-
{"word": "
|
| 32 |
-
{"word": "Bituach Leumi", "heard_as": ["betuachlomi"], "kinds": ["hebrew"], "meaning": "National Insurance Institute", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 0, "target": 20, "status": "training"}
|
| 33 |
{"word": "Boxty", "heard_as": ["E box d"], "kinds": ["personal"], "meaning": null, "occurrences": 1, "models": [], "first_seen": "2026-10-21", "last_seen": "2026-10-21", "takes": null, "target": null, "status": "not-listed"}
|
| 34 |
-
{"word": "Carrot Cake AI", "heard_as": ["Karrot Cake AI"], "kinds": ["personal"], "meaning": "a company/brand name Daniel uses, linked from his profiles", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes":
|
| 35 |
{"word": "Chatterbox", "heard_as": ["Shatterbox"], "kinds": ["tech", "ai_interactions"], "meaning": "Resemble AI's open-source TTS model", "occurrences": 1, "models": ["shakshuka Shakshuka v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 9, "target": 20, "status": "training"}
|
| 36 |
-
{"word": "
|
| 37 |
-
{"word": "
|
| 38 |
-
{"word": "
|
| 39 |
-
{"word": "
|
|
|
|
| 40 |
{"word": "EXE", "heard_as": ["AXE"], "kinds": ["tech"], "meaning": "the Windows executable file format, said as letters", "occurrences": 1, "models": [], "first_seen": "2026-10-21", "last_seen": "2026-10-21", "takes": 9, "target": 20, "status": "training"}
|
| 41 |
-
{"word": "Ezra", "heard_as": ["Esther"], "kinds": ["personal", "name"], "meaning": "
|
| 42 |
-
{"word": "fal", "heard_as": ["Fel"], "kinds": ["tech", "ai_interactions"], "meaning": "generative media API platform (fal.ai)", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes":
|
| 43 |
-
{"word": "few-shot", "heard_as": ["fusil"], "kinds": ["ai_interactions"], "meaning": "prompting with a few examples", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes":
|
| 44 |
-
{"word": "
|
| 45 |
-
{"word": "
|
| 46 |
-
{"word": "
|
| 47 |
-
{"word": "
|
|
|
|
|
|
|
|
|
|
| 48 |
{"word": "Kdenlive", "heard_as": ["Caden Live"], "kinds": ["linux"], "meaning": "the KDE video editor", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 9, "target": 20, "status": "training"}
|
| 49 |
-
{"word": "
|
| 50 |
-
{"word": "
|
| 51 |
-
{"word": "
|
| 52 |
-
{"word": "
|
| 53 |
-
{"word": "
|
| 54 |
-
{"word": "
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 55 |
{"word": "prosody", "heard_as": ["porosity"], "kinds": ["other"], "meaning": "the rhythm, stress and intonation of speech, as in TTS", "occurrences": 1, "models": ["shakshuka Shakshuka v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 9, "target": 20, "status": "training"}
|
| 56 |
-
{"word": "
|
| 57 |
-
{"word": "
|
| 58 |
-
{"word": "
|
| 59 |
-
{"word": "
|
| 60 |
-
{"word": "
|
| 61 |
-
{"word": "
|
| 62 |
-
{"word": "
|
| 63 |
-
{"word": "
|
| 64 |
-
{"word": "
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"word": "ROCm", "heard_as": ["ROCM", "Rockham", "Rockin", "RCM", "Rock'em"], "kinds": ["tech", "linux"], "meaning": "AMD's open-source GPU compute stack (like CUDA)", "occurrences": 15, "models": ["sabich Sabich v2.0", "shakshuka Shakshuka v2.0"], "first_seen": "2026-10-21", "last_seen": "2026-10-23", "takes": 10, "target": 20, "status": "training"}
|
| 2 |
+
{"word": "LoRA", "heard_as": ["Lores", "Loas", "Laura", "lores", "Loa", "LoRa", "lore", "lures"], "kinds": ["tech", "ai_interactions"], "meaning": "low-rank adapter fine-tuning", "occurrences": 14, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-24", "takes": 4, "target": 20, "status": "training"}
|
| 3 |
+
{"word": "Claude Code", "heard_as": ["clawed code", "clawed", "claud", "clawed coat", "claw code", "Core Code", "claudcode", "claud Code", "the claud", "clause claud code"], "kinds": ["tech", "ai_interactions"], "meaning": "Anthropic's coding agent CLI", "occurrences": 12, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-25", "takes": 3, "target": 20, "status": "training"}
|
| 4 |
+
{"word": "Corn", "heard_as": ["Coren", "Cornak's", "Eminent Corn", "Corner", "Korn", "Corin"], "kinds": ["personal", "name"], "meaning": "co-host on Daniel's podcast My Weird Prompts", "occurrences": 6, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-24", "takes": 3, "target": 20, "status": "training"}
|
| 5 |
+
{"word": "Hugging Face", "heard_as": ["Hooking Face", "hugging face", "hooking face", "Hogging Face", "hogging face"], "kinds": ["tech", "ai_interactions"], "meaning": "the AI model and dataset hub", "occurrences": 6, "models": ["sabich Sabich v2.0", "shakshuka Shakshuka v2.0"], "first_seen": "2026-10-21", "last_seen": "2026-10-24", "takes": 10, "target": 20, "status": "training"}
|
| 6 |
{"word": "My Weird Prompts", "heard_as": ["MyWord Prompts", "My WordPrompts", "My Word Promise", "mywordprompt"], "kinds": ["personal"], "meaning": "Daniel's AI podcast", "occurrences": 6, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-24", "takes": 9, "target": 20, "status": "training"}
|
| 7 |
+
{"word": "Hebrish", "heard_as": ["HeBrish", "Hebris", "keybrush", "Hebrew Shop", "Hebrew shtal"], "kinds": ["personal", "tech"], "meaning": "Daniel's Hebrew-English code-switching ASR project, app and Hugging Face org", "occurrences": 5, "models": ["sabich Sabich v2.0", "shakshuka Shakshuka v2.0"], "first_seen": "2026-10-21", "last_seen": "2026-10-24", "takes": 10, "target": 20, "status": "training"}
|
| 8 |
+
{"word": "Typst", "heard_as": ["TyPst", "TIPS-type", "tips toypt", "TyPstip", "tipst"], "kinds": ["tech"], "meaning": "Markup-based typesetting system", "occurrences": 5, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-25", "takes": 1, "target": 20, "status": "training"}
|
| 9 |
+
{"word": "NocoDB", "heard_as": ["No CodeDB", "Nocode", "NokoDB", "Noko DB"], "kinds": ["tech"], "meaning": "open-source no-code database", "occurrences": 4, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 3, "target": 20, "status": "training"}
|
| 10 |
+
{"word": "CLAUDE.md", "heard_as": ["userlevelclaw.md", "4 MD", "Claude NMD"], "kinds": ["ai_interactions", "tech"], "meaning": "Claude Code instructions file", "occurrences": 3, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-25", "takes": 3, "target": 20, "status": "training"}
|
| 11 |
+
{"word": "Cloudflare", "heard_as": ["cloud fair", "Cloud Claire", "Cloud Clare"], "kinds": ["tech"], "meaning": "CDN and network provider", "occurrences": 3, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 3, "target": 20, "status": "training"}
|
| 12 |
+
{"word": "Colab", "heard_as": ["Collab", "collab"], "kinds": ["tech"], "meaning": "Google's hosted notebook service", "occurrences": 3, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 4, "target": 20, "status": "training"}
|
| 13 |
{"word": "F-Droid", "heard_as": ["FDroid", "Fdroid"], "kinds": ["tech"], "meaning": "the open-source Android app store", "occurrences": 3, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-21", "last_seen": "2026-10-23", "takes": 9, "target": 20, "status": "training"}
|
| 14 |
+
{"word": "Nemotron", "heard_as": ["Nematron", "Nementron"], "kinds": ["tech", "ai_interactions"], "meaning": "NVIDIA's Nemotron model family (a streaming ASR model here)", "occurrences": 3, "models": ["sabich Sabich v2.0", "shakshuka Shakshuka v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 10, "target": 20, "status": "training"}
|
| 15 |
+
{"word": "skill", "heard_as": ["scale"], "kinds": ["ai_interactions"], "meaning": "Agent skill (Claude Code)", "occurrences": 3, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 16 |
+
{"word": "Unsloth", "heard_as": ["Unsoft", "On Sloth", "Onsloth"], "kinds": ["tech", "ai_interactions"], "meaning": "fine-tuning library and platform", "occurrences": 3, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 4, "target": 20, "status": "training"}
|
| 17 |
+
{"word": "ab initio", "heard_as": ["a benissio", "abenisio"], "kinds": ["other"], "meaning": "Latin: from the beginning", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 18 |
+
{"word": "DeepSeek", "heard_as": ["DeepSee", "DeepC"], "kinds": ["ai_interactions", "tech"], "meaning": "Chinese LLM provider", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-25", "takes": 7, "target": 20, "status": "training"}
|
| 19 |
+
{"word": "disfluency", "heard_as": ["disluency", "Dispuency"], "kinds": ["tech"], "meaning": "fillers and false starts in speech", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 20 |
+
{"word": "DMARC", "heard_as": ["GMARC", "Dimark"], "kinds": ["tech"], "meaning": "Email authentication policy", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-24", "takes": 1, "target": 20, "status": "training"}
|
| 21 |
+
{"word": "evals", "heard_as": ["evalves", "valves"], "kinds": ["tech", "ai_interactions"], "meaning": "AI model evaluations", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-24", "takes": 3, "target": 20, "status": "training"}
|
| 22 |
+
{"word": "Infisical", "heard_as": ["Infiniscal", "Infinisco"], "kinds": ["tech"], "meaning": "secrets manager", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 23 |
+
{"word": "Jupyter", "heard_as": ["Jupiter"], "kinds": ["tech"], "meaning": "Open-source project behind Jupyter Notebook and JupyterLab; pronounced like the planet Jupiter", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-23", "takes": 9, "target": 20, "status": "training"}
|
| 24 |
+
{"word": "Karpathy", "heard_as": ["Karpathi", "Karpathi's"], "kinds": ["name", "tech"], "meaning": "Andrej Karpathy, AI researcher and educator (ex-OpenAI, ex-Tesla AI)", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 9, "target": 20, "status": "training"}
|
| 25 |
+
{"word": "Netanyahu", "heard_as": ["Etanyahu", "Etnia"], "kinds": ["name", "hebrew"], "meaning": "Israeli prime minister", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 4, "target": 20, "status": "training"}
|
| 26 |
{"word": "ONNX", "heard_as": ["ONIX", "onyx"], "kinds": ["tech", "ai_interactions"], "meaning": "the Open Neural Network Exchange model format", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-21", "last_seen": "2026-10-24", "takes": 9, "target": 20, "status": "training"}
|
| 27 |
{"word": "Qwen", "heard_as": ["Qwen1", "quen"], "kinds": ["tech", "ai_interactions"], "meaning": "Alibaba's Qwen model family", "occurrences": 2, "models": ["sabich Sabich v2.0", "shakshuka Shakshuka v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-23", "takes": 10, "target": 20, "status": "training"}
|
| 28 |
+
{"word": "series", "heard_as": ["Ceres", "Siries"], "kinds": ["other"], "meaning": null, "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 29 |
+
{"word": "Z alias", "heard_as": ["ZALAS", "Zed Elias"], "kinds": ["linux"], "meaning": "zoxide shortcut to a repo", "occurrences": 2, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 30 |
+
{"word": "abliteration", "heard_as": ["Obliteration"], "kinds": ["tech", "ai_interactions"], "meaning": "removing refusal behaviour from model weights", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 2, "target": 20, "status": "training"}
|
| 31 |
+
{"word": "agentic", "heard_as": ["agenda"], "kinds": ["ai_interactions"], "meaning": null, "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 1, "target": 20, "status": "training"}
|
| 32 |
+
{"word": "AMD", "heard_as": ["MD"], "kinds": ["tech"], "meaning": "GPU and CPU maker", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 2, "target": 20, "status": "training"}
|
| 33 |
+
{"word": "APK", "heard_as": ["ABK"], "kinds": ["tech"], "meaning": "Android app package", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 3, "target": 20, "status": "training"}
|
| 34 |
+
{"word": "ASR", "heard_as": ["ACR"], "kinds": ["tech"], "meaning": "automatic speech recognition", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 4, "target": 20, "status": "training"}
|
| 35 |
+
{"word": "Bezeq", "heard_as": ["Bezek"], "kinds": ["hebrew", "name"], "meaning": "Israeli telecom / internet provider", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 36 |
+
{"word": "Bituach Leumi", "heard_as": ["betuachlomi"], "kinds": ["hebrew"], "meaning": "National Insurance Institute", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 5, "target": 20, "status": "training"}
|
|
|
|
| 37 |
{"word": "Boxty", "heard_as": ["E box d"], "kinds": ["personal"], "meaning": null, "occurrences": 1, "models": [], "first_seen": "2026-10-21", "last_seen": "2026-10-21", "takes": null, "target": null, "status": "not-listed"}
|
| 38 |
+
{"word": "Carrot Cake AI", "heard_as": ["Karrot Cake AI"], "kinds": ["personal"], "meaning": "a company/brand name Daniel uses, linked from his profiles", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 9, "target": 20, "status": "training"}
|
| 39 |
{"word": "Chatterbox", "heard_as": ["Shatterbox"], "kinds": ["tech", "ai_interactions"], "meaning": "Resemble AI's open-source TTS model", "occurrences": 1, "models": ["shakshuka Shakshuka v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 9, "target": 20, "status": "training"}
|
| 40 |
+
{"word": "Claude in Chrome", "heard_as": ["Claude Grouping Chrome"], "kinds": ["ai_interactions"], "meaning": "Claude browser extension", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 41 |
+
{"word": "co.il", "heard_as": ["co.al"], "kinds": ["tech"], "meaning": "Israeli domain suffix", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 42 |
+
{"word": "do you want me", "heard_as": ["Dramat me"], "kinds": ["other"], "meaning": "Plain English phrase", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 43 |
+
{"word": "DSR Holdings", "heard_as": ["Vidyester Holdings"], "kinds": ["personal", "name"], "meaning": "Daniel's company", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 5, "target": 20, "status": "training"}
|
| 44 |
+
{"word": "eSpeak", "heard_as": ["Espeak"], "kinds": ["linux"], "meaning": "the open-source speech synthesizer", "occurrences": 1, "models": ["shakshuka Shakshuka v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 9, "target": 20, "status": "training"}
|
| 45 |
{"word": "EXE", "heard_as": ["AXE"], "kinds": ["tech"], "meaning": "the Windows executable file format, said as letters", "occurrences": 1, "models": [], "first_seen": "2026-10-21", "last_seen": "2026-10-21", "takes": 9, "target": 20, "status": "training"}
|
| 46 |
+
{"word": "Ezra", "heard_as": ["Esther"], "kinds": ["personal", "name"], "meaning": "the speaker's baby son, a toddler born in 2025; sentences about family life, never work", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 3, "target": 20, "status": "training"}
|
| 47 |
+
{"word": "fal", "heard_as": ["Fel"], "kinds": ["tech", "ai_interactions"], "meaning": "generative media API platform (fal.ai)", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 48 |
+
{"word": "few-shot", "heard_as": ["fusil"], "kinds": ["ai_interactions"], "meaning": "prompting with a few examples", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 2, "target": 20, "status": "training"}
|
| 49 |
+
{"word": "Gihon", "heard_as": ["Gichon"], "kinds": ["hebrew", "name"], "meaning": "Jerusalem water utility", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 50 |
+
{"word": "Hegseth", "heard_as": ["Hagsets"], "kinds": ["name"], "meaning": "US Secretary of Defense", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 2, "target": 20, "status": "training"}
|
| 51 |
+
{"word": "Hevrat HaHashmal", "heard_as": ["Chashmal Yisrael"], "kinds": ["hebrew", "name"], "meaning": "Israel Electric Corporation", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 52 |
+
{"word": "inbound", "heard_as": ["for bound"], "kinds": ["other"], "meaning": "Incoming (email)", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 53 |
+
{"word": "Janglo", "heard_as": ["Danglo"], "kinds": ["name"], "meaning": "English-speakers' community site in Israel", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 3, "target": 20, "status": "training"}
|
| 54 |
+
{"word": "Jupyter Notebook", "heard_as": ["Jupiter notebooks"], "kinds": ["tech"], "meaning": "Interactive Python notebook environment (.ipynb files) for data science and ML", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 9, "target": 20, "status": "training"}
|
| 55 |
+
{"word": "Kaizen", "heard_as": ["Kaiser"], "kinds": ["other"], "meaning": "continuous small-step improvement", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 56 |
{"word": "Kdenlive", "heard_as": ["Caden Live"], "kinds": ["linux"], "meaning": "the KDE video editor", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 9, "target": 20, "status": "training"}
|
| 57 |
+
{"word": "lav mic", "heard_as": ["Lav Mike"], "kinds": ["other"], "meaning": "Lavalier microphone", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 58 |
+
{"word": "LLM", "heard_as": ["nelam"], "kinds": ["ai_interactions"], "meaning": "large language model", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 6, "target": 20, "status": "training"}
|
| 59 |
+
{"word": "marking", "heard_as": ["marketing"], "kinds": ["other"], "meaning": "As in marking complete", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 0, "target": 20, "status": "training"}
|
| 60 |
+
{"word": "Meorav", "heard_as": ["Maorab"], "kinds": ["personal", "hebrew"], "meaning": "Daniel's TTS voice-clone model; Hebrew for 'mixed'", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 10, "target": 20, "status": "training"}
|
| 61 |
+
{"word": "Muse Spark", "heard_as": ["News Spark"], "kinds": ["ai_interactions"], "meaning": "LLM", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 62 |
+
{"word": "Nvidia", "heard_as": ["NVideo"], "kinds": ["tech"], "meaning": "GPU maker", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 3, "target": 20, "status": "training"}
|
| 63 |
+
{"word": "Ollama", "heard_as": ["ulama"], "kinds": ["tech", "ai_interactions"], "meaning": "local LLM runner", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 7, "target": 20, "status": "training"}
|
| 64 |
+
{"word": "OPNsense", "heard_as": ["OpenSense"], "kinds": ["tech", "linux"], "meaning": "Open-source firewall OS", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 1, "target": 20, "status": "training"}
|
| 65 |
+
{"word": "Opus", "heard_as": ["Focus"], "kinds": ["ai_interactions"], "meaning": "Claude Opus model, as in Opus 5.5", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 2, "target": 20, "status": "training"}
|
| 66 |
+
{"word": "populated", "heard_as": ["opulated"], "kinds": ["other"], "meaning": "Filled in", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 67 |
+
{"word": "post-processing", "heard_as": ["PostgreSQL processing"], "kinds": ["tech"], "meaning": "cleanup applied to ASR output", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 68 |
{"word": "prosody", "heard_as": ["porosity"], "kinds": ["other"], "meaning": "the rhythm, stress and intonation of speech, as in TTS", "occurrences": 1, "models": ["shakshuka Shakshuka v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 9, "target": 20, "status": "training"}
|
| 69 |
+
{"word": "pseudospam", "heard_as": ["Surasfam"], "kinds": ["personal"], "meaning": "Daniel's term for generic non-spam bulk email", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 70 |
+
{"word": "Qdrant", "heard_as": ["Qdrint"], "kinds": ["tech"], "meaning": "open-source vector database", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 71 |
+
{"word": "rclone", "heard_as": ["Arclone"], "kinds": ["tech", "linux"], "meaning": "Cloud storage sync CLI", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 72 |
+
{"word": "regex", "heard_as": ["RedJack"], "kinds": ["tech"], "meaning": "regular expression", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 3, "target": 20, "status": "training"}
|
| 73 |
+
{"word": "remote", "heard_as": ["ote"], "kinds": ["tech"], "meaning": "As in remote agent", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 74 |
+
{"word": "repo", "heard_as": ["Refo"], "kinds": ["tech"], "meaning": "Repository", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 0, "target": 20, "status": "training"}
|
| 75 |
+
{"word": "SaaS", "heard_as": ["SAS"], "kinds": ["tech"], "meaning": "software as a service", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 3, "target": 20, "status": "training"}
|
| 76 |
+
{"word": "Sabich", "heard_as": ["Tzabich"], "kinds": ["personal", "hebrew"], "meaning": "Daniel's main ASR fine-tune, named after the Israeli sandwich", "occurrences": 1, "models": [], "first_seen": "2026-10-21", "last_seen": "2026-10-21", "takes": 10, "target": 20, "status": "training"}
|
| 77 |
+
{"word": "SBC", "heard_as": ["SPCAdmin"], "kinds": ["tech", "linux"], "meaning": "Single-board computer, e.g. Raspberry Pi", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 0, "target": 20, "status": "training"}
|
| 78 |
+
{"word": "Sonnet", "heard_as": ["Sommet"], "kinds": ["tech", "ai_interactions"], "meaning": "Anthropic's mid-tier Claude model", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 5, "target": 20, "status": "training"}
|
| 79 |
+
{"word": "SSE", "heard_as": ["SSC"], "kinds": ["tech"], "meaning": "server-sent events (MCP transport)", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 80 |
+
{"word": "subscription", "heard_as": ["prescription"], "kinds": ["other"], "meaning": "Subscription (Claude Max)", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 81 |
+
{"word": "Superwhisper", "heard_as": ["Superwisper"], "kinds": ["tech"], "meaning": "macOS dictation app", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 2, "target": 20, "status": "training"}
|
| 82 |
+
{"word": "system tray", "heard_as": ["system try"], "kinds": ["linux"], "meaning": "Desktop notification area", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 83 |
+
{"word": "tab", "heard_as": ["top"], "kinds": ["tech"], "meaning": "Browser tab", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 84 |
+
{"word": "tell her", "heard_as": ["Teller"], "kinds": ["other"], "meaning": "Plain English phrase", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-25", "last_seen": "2026-10-25", "takes": 0, "target": 20, "status": "training"}
|
| 85 |
+
{"word": "TTS", "heard_as": ["CTS"], "kinds": ["tech", "ai_interactions"], "meaning": "text-to-speech", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-23", "last_seen": "2026-10-23", "takes": 5, "target": 20, "status": "training"}
|
| 86 |
+
{"word": "vocab", "heard_as": ["Rocab"], "kinds": ["other"], "meaning": "Vocabulary", "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-24", "last_seen": "2026-10-24", "takes": 0, "target": 20, "status": "training"}
|
| 87 |
+
{"word": "Whisperian", "heard_as": ["Wisperion"], "kinds": ["tech"], "meaning": null, "occurrences": 1, "models": ["sabich Sabich v2.0"], "first_seen": "2026-10-22", "last_seen": "2026-10-22", "takes": 3, "target": 20, "status": "training"}
|