Category Archives: APKaudio

Crawler.like.audio

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Crawler.like.audio

https://github.com/LikeDotAudio/Crawler.Like.Audio

Inside the Browser Sandbox: A Deep Dive into Crawler.Like.Audio

The era of heavy desktop applications and bloated backend servers for local development tasks is being challenged. Enter Crawler.Like.Audio, a powerful, 100% client-side developer utility suite that pushes the limits of modern browser APIs. Built entirely with Next.js, TypeScript, and WebAssembly, this static application runs securely within the browser’s execution thread—requiring zero local installations and absolutely no backend dependencies.

Divided into nine hyper-capable modules, the suite transforms the web browser into a comprehensive toolkit for codebase analysis, security auditing, and hardware scanning. Here is a look at how every page works under the hood.

Codebase Exploration and Management

At the heart of Crawler.Like.Audio are tools designed to seamlessly interact with, analyze, and rebuild your local file system directly from the browser.

  • File Crawler: The core engine relies on the modern File System Access API to read local directories. When you select a folder, it performs a lightning-fast pre-scan, recursively streaming files to categorize them by type (Programming, Web & Markup, Data & Config, Docs) and calculate byte sizes. A real-time pie chart allows for selective crawling. Once configured, the deep crawl extracts file text and runs AST (Abstract Syntax Tree) parsing on supported languages via WebAssembly, ultimately compiling a massive EVERYTHING.LOG scrape file and a MAP.txt directory tree.

  • Program Map: For a high-level overview devoid of visual noise, this module displays a pure, text-based directory tree of your project. Functioning similarly to the Linux tree command, it offers a clean, structural skeleton of your deep crawl.

  • Visual Explorer: Taking the raw data from the File Crawler, this module uses React Flow to generate a highly interactive, drag-and-drop node graph. It visualizes your codebase architecture, mapping exactly how folders connect to files, and how those files branch into specific classes and functions.

  • Project Regenerator: Acting as the inverse of the File Crawler, the Regenerator takes an EVERYTHING.LOG file (whether generated by an AI or the crawler itself) and unpacks it. By selecting an empty destination folder, the utility uses the File System Access API to physically rebuild the folders and write the files back to your local disk.

Security, Network, and Hardware Auditing

Traditionally, network sweeps and hardware audits required native OS permissions. Crawler.Like.Audio leverages cutting-edge web standards to bring these capabilities into the sandbox.

  • Project Audit: A purely client-side security tool. Its secrets scanner uses advanced regex heuristics to hunt down exposed API keys, passwords, cryptographic tokens, and rogue .env variables. Additionally, the endpoint extractor maps out your app’s external dependencies by ripping through code to find hardcoded URLs, IP addresses, and API endpoints.

  • Hardware Auditor: This module hooks deeply into native hardware APIs to act as a physical peripheral scanner. It utilizes the WebUSB API to discover authorized USB devices, Web Bluetooth (BLE) for radar sweeps of nearby beacons and smart TVs, the Web Serial API for RS-232 devices and Arduinos, and WebRTC to silently index every connected camera and audio output.

  • Network Sweeper: A clever browser-based LAN scanner. Because browsers cannot open raw TCP/UDP sockets, this tool maps your local network (e.g., 192.168.1.x) using cross-origin HTTP fetches. By measuring precise timeout and connection-refused response times—a localized timing attack—it heuristically discovers routers, IoT devices, or local web servers.

Content Extraction Utilities

The suite is rounded out by lightweight, privacy-first data extraction tools.

  • Web Scraper: Enter any public URL, and the scraper fetches the raw HTML. It then utilizes client-side parsing via Turndown to strip away web noise, instantly converting the page into clean, readable Markdown.

  • PDF to MD: A drag-and-drop local document extractor. Powered by a WebAssembly port of pdfjs-dist, it locally extracts text strings from PDFs and formats them into Markdown. Because everything happens in Wasm, sensitive documents are never uploaded to a cloud server.

  • Data Converters (CSV / JSON / Spreadsheet)
    A powerful suite of data ingestion and shuffling utilities built with `papaparse` and `xlsx` (SheetJS).
    * **CSV & JSON Importer/Shuffler:** Flatten or nest massive JSON objects, randomize CSV columns, and instantly export data.
    * **Spreadsheet Converter:** Read natively complex spreadsheet binaries (`.xlsx`, `.csv`, `.ods`, `.numbers`) and convert them into clean formats, directly in the client.

    ### 12. 🎵 Audio Converter
    A robust browser-side media encoder leveraging native Web Audio APIs and CDN-imported `lamejs`.
    * **Universal Decode:** Drops any modern audio file (`.wav`, `.flac`, `.m4a`) to instantly decode its buffer.
    * **Client-side Encoding:** Re-encodes the raw audio into an optimized `.mp3` file, relying strictly on your CPU’s client-side processing without uploading the audio file anywhere.

The UX of Handedness: Why Touchscreens Are Chiral Objects

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The UX of Handedness: Why Touchscreens Are Chiral Objects

A mouse cursor is a dimensionless point that approaches every target from nowhere and hides nothing. A finger, thumb, or stylus, however, is a physical limb attached to an arm on one specific side of the body. That single fact makes touch interaction fundamentally asymmetric in ways a mouse UI never has to accommodate.

A touchscreen UI is chiral—it has a handedness baked into where controls sit and which way the hand reaches. For a significant portion of operators, the default layout of any given touch interface is the wrong-handed mirror. The correct design approach is not to make interfaces perfectly symmetric (which is biologically impossible due to reach and occlusion), but to make them mirrorable.

Here is a breakdown of the physical realities of touchscreen operation, and how to design interfaces that adapt to the hands that use them.

1. The Physics of Touch: Reach and Occlusion

Touch interaction introduces physical constraints that fundamentally alter how a user interacts with a digital surface.

Asymmetric Reach and the Thumb Zone

For one-handed thumb use, the screen is not a uniform grid of equally accessible targets. A right thumb sweeps a comfortable arc anchored at the bottom-right of the screen; the top-left is the “stretch” corner. For a left thumb, this entire map mirrors.

 

 

Ergonomic studies show that input accuracy varies heavily by region. For a right-handed thumb, the left and center parts of the screen are accurate, while the bottom-edge and far-diagonal regions are highly inaccurate.

Hand and Arm Occlusion

Occlusion is arguably the most critical and overlooked factor in large-format touch design. When using a tablet or a wall console, the hand and forearm physically block the user’s line of sight.

 

 

Research into tablet-sized direct input reveals that the pen, hand, and forearm can occlude up to 47% of a 12-inch display. For a right-handed user, this occluded region sits below and to the right (South-East) of the contact point.

When a user drags an object left-to-right into an occluded direction, they frequently overshoot the target because the destination is hidden under their own arm. The universal fix for this is to emit feedback, menus, and drag ghosts to the non-occluded side—and that side must flip depending on the operator’s handedness.

2. The Reality at the Glass: Who is Driving?

It is a common design fallacy to assume that because ~90% of the population is right-handed, 90% of touchscreen sessions are operated with a right-hand grip.

In reality, roughly 30% of sessions are driven by a non-dominant or left-operating hand. This includes the 10% of users who are left-handed, plus the 30% of right-handed users who frequently operate devices with their non-dominant hand (because they are holding a coffee, a document, or resting on a rail).

Handedness at the glass is a momentary grip state, not a fixed biographical fact. Furthermore, handedness is detectable. Studies show that swipe curvature and horizontal touch offsets can classify the operating hand with over 98% accuracy. The UI can detect the operating hand and suggest a layout flip, rather than forcing the user to dig through settings.

3. The Chirality Framework: What Flips and What Doesn’t

When building a handedness toggle for a touch console, you are defining a strict set of layout rules. You are not mirroring the entire application; you are mirroring the interaction mechanics.These elements flip across the vertical axis when the operator’s handedness changes:

  • Primary Controls: Move to the dominant-thumb or dominant-hand reachable corner.

  • Edge Drawers and Panels: Dock to the dominant-hand edge so the user isn’t reaching across their body (and occluding the screen) to access tools.

  • Context Menus: Open on the non-occluded side of the contact point (left-of-touch for right-handers, right-of-touch for left-handers).

  • Drag Ghosts and Callouts: Emit into the non-occluded quadrant. Show the area ahead of the cursor when dragging.

What Stays Fixed

These elements must never flip, as doing so breaks spatial memory and real-world semantics:

  • Text and Labels: Always remain Left-to-Right (LTR). Re-align them, but never reverse the text itself.

  • Directional Glyphs: Media playback transport (play/fast-forward), timelines, clocks, and progress bars retain their real-world direction.

  • Spatial-Memory Anchors: The core data grid or list order must stay fixed. Flipping layout mid-task destroys muscle memory. The flip should be a deliberate, stateful mode change.

Hardinge Dividing Plate – Divisions – ratios – degrees – plate sizes

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CAN I DO IT?
Ratio 4 Can do 9 11 5 11 6 5 11 5 11 11 5 11 5 5 11 5 11 5 5 5 5 13 15 15 17 13
Divisiong . Plate 0 PLATE 1 PLATE 2 PLATE 3 PLATE 4
Divisions Ratio Degrees Can do 20 21 23 15 16 17 18 19 40 21 23 27 29 31 33 37 39 41 43 47 49 48 66 70 75 80
2 2 180.00000000 Can do 40 42 46 30 32 34 36 38 80 42 46 54 58 62 66 74 78 82 86 94 98 96 132 140 150 160
3 1.333333333 120.00000000 Can do 28 20 24 28 36 44 52 64 88 100
4 1 90.00000000 Can do 20 21 23 15 16 17 18 19 40 21 23 27 29 31 33 37 39 41 43 47 49 48 66 70 75 80
5 0.8 72.00000000 Can do 16 12 32 56 60 64
6 0.6666666667 60.00000000 Can do 14 10 12 14 18 22 26 32 44 50
7 0.5714285714 51.42857143 Can do 12 12 28 40
8 0.5 45.00000000 Can do 10 8 9 20 24 33 35 40
9 0.4444444444 40.00000000 Can do 8 12
10 0.4 36.00000000 Can do 8 6 16 28 30 32
11 0.3636363636 32.72727273 Can do 12 24
12 0.3333333333 30.00000000 Can do 7 5 6 7 9 11 13 16 22 25
13 0.3076923077 27.69230769 Can do 12
14 0.2857142857 25.71428571 Can do 6 6 14 20
15 0.2666666667 24.00000000 Can do 4 20
16 0.25 22.50000000 Can do 5 4 10 12 20
17 0.2352941176 21.17647059 Can do 4
18 0.2222222222 20.00000000 Can do 4 6
19 0.2105263158 18.94736842 Can do 4
20 0.2 18.00000000 Can do 4 3 8 14 15 16
21 0.1904761905 17.14285714 Can do 4 4
22 0.1818181818 16.36363636 Can do 6 12
23 0.1739130435 15.65217391 Can do 4 4
24 0.1666666667 15.00000000 Can do 3 8 11
25 0.16 14.40000000 12
26 0.1538461538 13.84615385 Can do 6
27 0.1481481481 13.33333333 Can do 4
28 0.1428571429 12.85714286 Can do 3 3 7 10
29 0.1379310345 12.41379310 Can do 4
30 0.1333333333 12.00000000 Can do 2 10
31 0.1290322581 11.61290323 Can do 4
32 0.125 11.25000000 Can do 2 5 6 10
33 0.1212121212 10.90909091 Can do 4 8
34 0.1176470588 10.58823529 Can do 2
35 0.1142857143 10.28571429 8
36 0.1111111111 10.00000000 Can do 2 3
37 0.1081081081 9.72972973 Can do 4
38 0.1052631579 9.47368421 Can do 2
39 0.1025641026 9.23076923 Can do 4
40 0.1 9.00000000 Can do 2 4 7 8
41 0.09756097561 8.78048780 Can do 4
42 0.09523809524 8.57142857 Can do 2 2
43 0.09302325581 8.37209302 Can do 4
44 0.09090909091 8.18181818 Can do 3 6
45 0.08888888889 8.00000000
46 0.08695652174 7.82608696 Can do 2 2
47 0.08510638298 7.65957447 Can do 4
48 0.08333333333 7.50000000 4
49 0.08163265306 7.34693878
50 0.08 7.20000000 6
51 0.07843137255 7.05882353
52 0.07692307692 6.92307692 Can do 3
53 0.07547169811 6.79245283
54 0.07407407407 6.66666667 Can do 2
55 0.07272727273 6.54545455
56 0.07142857143 6.42857143 5
57 0.0701754386 6.31578947
58 0.06896551724 6.20689655 Can do 2
59 0.06779661017 6.10169492
60 0.06666666667 6.00000000 Can do 1 5
61 0.06557377049 5.90163934
62 0.06451612903 5.80645161 Can do 2
63 0.06349206349 5.71428571
64 0.0625 5.62500000 Can do 1 3 5
65 0.06153846154 5.53846154
66 0.06060606061 5.45454545 Can do 2 4
67 0.05970149254 5.37313433
68 0.05882352941 5.29411765 Can do 1
69 0.05797101449 5.21739130
70 0.05714285714 5.14285714 4
71 0.05633802817 5.07042254
72 0.05555555556 5.00000000 Can do 1
73 0.05479452055 4.93150685
74 0.05405405405 4.86486486 Can do 2
75 0.05333333333 4.80000000 4
76 0.05263157895 4.73684211 Can do 1
77 0.05194805195 4.67532468
78 0.05128205128 4.61538462 Can do 2
79 0.05063291139 4.55696203
80 0.05 4.50000000 Can do 1 2 4
81 0.04938271605 4.44444444
82 0.0487804878 4.39024390 Can do 2
83 0.04819277108 4.33734940
84 0.04761904762 4.28571429 Can do 1 1
85 0.04705882353 4.23529412
86 0.04651162791 4.18604651 Can do 2
87 0.04597701149 4.13793103
88 0.04545454545 4.09090909 3
89 0.04494382022 4.04494382
90 0.04444444444 4.00000000
91 0.04395604396 3.95604396
92 0.04347826087 3.91304348 Can do 1 1
93 0.04301075269 3.87096774
94 0.04255319149 3.82978723 Can do 2
95 0.04210526316 3.78947368
96 0.04166666667 3.75000000 2
97 0.0412371134 3.71134021
98 0.04081632653 3.67346939
99 0.0404040404 3.63636364
100 0.04 3.60000000 3
101 0.0396039604 3.56435644
102 0.03921568627 3.52941176
103 0.03883495146 3.49514563
104 0.03846153846 3.46153846
105 0.0380952381 3.42857143
106 0.03773584906 3.39622642
107 0.03738317757 3.36448598
108 0.03703703704 3.33333333 Can do 1
109 0.03669724771 3.30275229
110 0.03636363636 3.27272727
111 0.03603603604 3.24324324
112 0.03571428571 3.21428571
113 0.03539823009 3.18584071
114 0.0350877193 3.15789474
115 0.0347826087 3.13043478
116 0.03448275862 3.10344828 Can do 1
117 0.03418803419 3.07692308
118 0.03389830508 3.05084746
119 0.03361344538 3.02521008
120 0.03333333333 3.00000000
121 0.03305785124 2.97520661
122 0.03278688525 2.95081967
123 0.0325203252 2.92682927
124 0.03225806452 2.90322581 Can do 1
125 0.032 2.88000000
126 0.03174603175 2.85714286
127 0.03149606299 2.83464567
128 0.03125 2.81250000
129 0.03100775194 2.79069767
130 0.03076923077 2.76923077
131 0.03053435115 2.74809160
132 0.0303030303 2.72727273 Can do 1 2
133 0.03007518797 2.70676692
134 0.02985074627 2.68656716
135 0.02962962963 2.66666667
136 0.02941176471 2.64705882
137 0.02919708029 2.62773723
138 0.02898550725 2.60869565
139 0.02877697842 2.58992806
140 0.02857142857 2.57142857 2
141 0.02836879433 2.55319149
142 0.02816901408 2.53521127
143 0.02797202797 2.51748252
144 0.02777777778 2.50000000
145 0.0275862069 2.48275862
146 0.02739726027 2.46575342
147 0.02721088435 2.44897959
148 0.02702702703 2.43243243 Can do 1
149 0.02684563758 2.41610738
150 0.02666666667 2.40000000 2
151 0.02649006623 2.38410596
152 0.02631578947 2.36842105
153 0.02614379085 2.35294118
154 0.02597402597 2.33766234
155 0.02580645161 2.32258065
156 0.02564102564 2.30769231 Can do 1
157 0.02547770701 2.29299363
158 0.0253164557 2.27848101
159 0.0251572327 2.26415094
160 0.025 2.25000000 Can do 1 2
161 0.0248447205 2.23602484
162 0.02469135802 2.22222222
163 0.0245398773 2.20858896
164 0.0243902439 2.19512195 Can do 1
165 0.02424242424 2.18181818
166 0.02409638554 2.16867470
167 0.02395209581 2.15568862
168 0.02380952381 2.14285714
169 0.02366863905 2.13017751
170 0.02352941176 2.11764706
171 0.02339181287 2.10526316
172 0.02325581395 2.09302326 Can do 1
173 0.02312138728 2.08092486
174 0.02298850575 2.06896552
175 0.02285714286 2.05714286
176 0.02272727273 2.04545455
177 0.02259887006 2.03389831
178 0.02247191011 2.02247191
179 0.02234636872 2.01117318
180 0.02222222222 2.00000000
181 0.02209944751 1.98895028
182 0.02197802198 1.97802198
183 0.0218579235 1.96721311
184 0.02173913043 1.95652174
185 0.02162162162 1.94594595
186 0.02150537634 1.93548387
187 0.02139037433 1.92513369
188 0.02127659574 1.91489362 Can do 1
189 0.02116402116 1.90476190
190 0.02105263158 1.89473684
191 0.02094240838 1.88481675
192 0.02083333333 1.87500000 1
193 0.0207253886 1.86528497
194 0.0206185567 1.85567010
195 0.02051282051 1.84615385
196 0.02040816327 1.83673469
197 0.02030456853 1.82741117
198 0.0202020202 1.81818182
199 0.02010050251 1.80904523
200 0.02 1.80000000
201 0.01990049751 1.79104478
202 0.0198019802 1.78217822
203 0.0197044335 1.77339901
204 0.01960784314 1.76470588
205 0.01951219512 1.75609756
206 0.01941747573 1.74757282
207 0.0193236715 1.73913043
208 0.01923076923 1.73076923
209 0.01913875598 1.72248804
210 0.01904761905 1.71428571
211 0.01895734597 1.70616114
212 0.01886792453 1.69811321
213 0.01877934272 1.69014085
214 0.01869158879 1.68224299
215 0.01860465116 1.67441860
216 0.01851851852 1.66666667
217 0.01843317972 1.65898618
218 0.01834862385 1.65137615
219 0.01826484018 1.64383562
220 0.01818181818 1.63636364
221 0.01809954751 1.62895928
222 0.01801801802 1.62162162
223 0.01793721973 1.61434978
224 0.01785714286 1.60714286
225 0.01777777778 1.60000000
226 0.01769911504 1.59292035
227 0.01762114537 1.58590308
228 0.01754385965 1.57894737
229 0.01746724891 1.57205240
230 0.01739130435 1.56521739
231 0.01731601732 1.55844156
232 0.01724137931 1.55172414
233 0.01716738197 1.54506438
234 0.01709401709 1.53846154
235 0.0170212766 1.53191489
236 0.01694915254 1.52542373
237 0.01687763713 1.51898734
238 0.01680672269 1.51260504
239 0.01673640167 1.50627615
240 0.01666666667 1.50000000
241 0.01659751037 1.49377593
242 0.01652892562 1.48760331
243 0.01646090535 1.48148148
244 0.01639344262 1.47540984
245 0.01632653061 1.46938776
246 0.0162601626 1.46341463
247 0.01619433198 1.45748988
248 0.01612903226 1.45161290
249 0.01606425703 1.44578313
250 0.016 1.44000000
251 0.01593625498 1.43426295
252 0.01587301587 1.42857143
253 0.01581027668 1.42292490
254 0.0157480315 1.41732283
255 0.01568627451 1.41176471
256 0.015625 1.40625000
257 0.01556420233 1.40077821
258 0.01550387597 1.39534884
259 0.01544401544 1.38996139
260 0.01538461538 1.38461538
261 0.0153256705 1.37931034
262 0.01526717557 1.37404580
263 0.01520912548 1.36882129
264 0.01515151515 1.36363636 1
265 0.01509433962 1.35849057
266 0.01503759398 1.35338346
267 0.01498127341 1.34831461
268 0.01492537313 1.34328358
269 0.01486988848 1.33828996
270 0.01481481481 1.33333333
271 0.0147601476 1.32841328
272 0.01470588235 1.32352941
273 0.01465201465 1.31868132
274 0.01459854015 1.31386861
275 0.01454545455 1.30909091
276 0.01449275362 1.30434783
277 0.01444043321 1.29963899
278 0.01438848921 1.29496403
279 0.01433691756 1.29032258
280 0.01428571429 1.28571429 1
281 0.01423487544 1.28113879
282 0.01418439716 1.27659574
283 0.01413427562 1.27208481
284 0.01408450704 1.26760563
285 0.01403508772 1.26315789
286 0.01398601399 1.25874126
287 0.01393728223 1.25435540
288 0.01388888889 1.25000000
289 0.01384083045 1.24567474
290 0.01379310345 1.24137931
291 0.01374570447 1.23711340
292 0.01369863014 1.23287671
293 0.01365187713 1.22866894
294 0.01360544218 1.22448980
295 0.01355932203 1.22033898
296 0.01351351351 1.21621622
297 0.01346801347 1.21212121
298 0.01342281879 1.20805369
299 0.01337792642 1.20401338
300 0.01333333333 1.20000000 1
301 0.01328903654 1.19601329
302 0.01324503311 1.19205298
303 0.01320132013 1.18811881
304 0.01315789474 1.18421053
305 0.0131147541 1.18032787
306 0.01307189542 1.17647059
307 0.01302931596 1.17263844
308 0.01298701299 1.16883117
309 0.01294498382 1.16504854
310 0.01290322581 1.16129032
311 0.01286173633 1.15755627
312 0.01282051282 1.15384615
313 0.01277955272 1.15015974
314 0.0127388535 1.14649682
315 0.0126984127 1.14285714
316 0.01265822785 1.13924051
317 0.01261829653 1.13564669
318 0.01257861635 1.13207547
319 0.01253918495 1.12852665
320 0.0125 1.12500000 1
321 0.01246105919 1.12149533
322 0.01242236025 1.11801242
323 0.01238390093 1.11455108
324 0.01234567901 1.11111111
325 0.01230769231 1.10769231
326 0.01226993865 1.10429448
327 0.0122324159 1.10091743
328 0.01219512195 1.09756098
329 0.01215805471 1.09422492
330 0.01212121212 1.09090909
331 0.01208459215 1.08761329
332 0.01204819277 1.08433735
333 0.01201201201 1.08108108
334 0.0119760479 1.07784431
335 0.01194029851 1.07462687
336 0.0119047619 1.07142857
337 0.0118694362 1.06824926
338 0.01183431953 1.06508876
339 0.01179941003 1.06194690
340 0.01176470588 1.05882353
341 0.01173020528 1.05571848
342 0.01169590643 1.05263158
343 0.01166180758 1.04956268
344 0.01162790698 1.04651163
345 0.0115942029 1.04347826
346 0.01156069364 1.04046243
347 0.01152737752 1.03746398
348 0.01149425287 1.03448276
349 0.01146131805 1.03151862
350 0.01142857143 1.02857143
351 0.0113960114 1.02564103
352 0.01136363636 1.02272727
353 0.01133144476 1.01983003
354 0.01129943503 1.01694915
355 0.01126760563 1.01408451
356 0.01123595506 1.01123596
357 0.01120448179 1.00840336
358 0.01117318436 1.00558659
359 0.01114206128 1.00278552
360 0.01111111111 1.00000000

mm to fractional inch chart

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Millimeters (mm) Fractional Inch (Nearest 1/1024″) Decimal Inches Fractional Inch (Nearest 1/32″) Precision Lost (%)
0 0 0.0000 0 0.00%
1 5/128″ 0.0394 1/32″ 20.62%
2 81/1024″ 0.0787 3/32″ 19.06%
3 121/1024″ 0.1181 1/8″ 5.83%
4 161/1024″ 0.1575 5/32″ 0.78%
5 101/512″ 0.1969 3/16″ 4.75%
6 121/512″ 0.2362 1/4″ 5.83%
7 141/512″ 0.2756 9/32″ 2.05%
8 323/1024″ 0.3150 5/16″ 0.78%
9 363/1024″ 0.3543 11/32″ 2.99%
10 403/1024″ 0.3937 13/32″ 3.19%
11 443/1024″ 0.4331 7/16″ 1.02%
12 121/256″ 0.4724 15/32″ 0.78%
13 131/256″ 0.5118 1/2″ 2.31%
14 141/256″ 0.5512 9/16″ 2.05%
15 605/1024″ 0.5906 19/32″ 0.54%
16 645/1024″ 0.6299 5/8″ 0.78%
17 685/1024″ 0.6693 21/32″ 1.95%
18 363/512″ 0.7087 23/32″ 1.42%
19 383/512″ 0.7480 3/4″ 0.26%
20 403/512″ 0.7874 25/32″ 0.78%
21 847/1024″ 0.8268 13/16″ 1.73%
22 887/1024″ 0.8661 7/8″ 1.02%
23 927/1024″ 0.9055 29/32″ 0.08%
24 121/128″ 0.9449 15/16″ 0.78%
25 63/64″ 0.9843 31/32″ 1.58%
26 1 3/128″ 1.0236 1 1/32″ 0.75%
27 1 65/1024″ 1.0630 1 1/16″ 0.05%
28 1 105/1024″ 1.1024 1 3/32″ 0.78%
29 1 145/1024″ 1.1417 1 5/32″ 1.27%
30 1 185/1024″ 1.1811 1 3/16″ 0.54%

CN tower in HO scale

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Ultimate master cheat sheet for the entire CN Tower project, consolidating all the measurements, blueprints, and internal floor spacings we’ve discussed into one complete list.

Every single measurement here is calculated specifically for HO Scale (1:87) and converted directly to millimeters (mm).

1. Overall Specifications

These are your primary external dimensions for the major structural milestones.

  • Total Final Constructed Height: 6,360 mm

  • Top of Concrete Support Arms: 3,793 mm

  • SkyPod (Highest Observation Deck): 5,138 mm

  • Maximum Width of Main Pod: 454 mm (Diameter)

2. Base & Foundation Levels

Note: In the architectural blueprints, the Lobby floor is treated as Ground Zero (0 mm).

  • Deck Level: +46 mm

  • Lobby Level: 0 mm (Ground level)

  • Pool Level: -28 mm (Below ground)

  • Service Level: -74 mm (Below ground)

  • Bottom of Concrete Foundation: -172 mm (Lowest excavated point)

3. The Main Pod (Elevations & Floors)

This section outlines exactly how high each specific floor sits above the lobby level, as well as the internal gap between each floor.

Pod Level Elevation (Height Above Lobby) Internal Floor-to-Floor Gap
Roof 4,106 mm –
Level 7 (Mechanical) 4,050 mm 56 mm (Up to Roof)
Level 6 (Transmission FM) 4,004 mm 46 mm (Up to Lvl 7)
Level 5 (Transmission UHF) 3,952 mm 53 mm (Up to Lvl 6)
Level 4 (Restaurant) 3,910 mm 42 mm (Up to Lvl 5)
Level 3 (Indoor Obs.) 3,857 mm 53 mm (Up to Lvl 4)
Level 2 (Outdoor Obs.) 3,808 mm 49 mm (Up to Lvl 3)
Level 1 (Microwave/Radome) 3,759 mm 49 mm (Up to Lvl 2)

4. Upper Mast & Antenna

These are the elevations for the specific broadcasting rings and platforms above the main pod, measured from the Lobby level up.

 

 

Mast Feature Elevation (Height Above Lobby)
Final Constructed Peak 6,360 mm
Top of Blueprint Antenna 6,236 mm
Channel 79 6,166 mm
Channel 45, 51, 57 5,991 mm
Channel 19, 25 5,763 mm
Channel 9 5,570 mm
Channel 5 5,343 mm
FM Broadcasters 5,133 mm
Upper Platform (Base of Mast) 5,052 mm

The CN Tower’s width tapers drastically from a massive sprawling base to a tiny needle at the top. Here are the key horizontal measurements (widths, diameters, and footprints) you will need, converted into your 1:87 HO scale in millimeters.

Horizontal Dimensions (Widths & Diameters)

Structural Element Real-World Measurement HO Scale (1:87) in Millimeters
Maximum Base Footprint (Tip-to-tip of the Y-shaped legs) ~66.6 m (218 ft) 765.5 mm
Width of Individual Concrete Legs (At ground level) ~7.0 m (23 ft) 80.5 mm
Central Hexagonal Core Shaft (Average width above the legs) ~10.0 m (33 ft) 115.0 mm
Main Pod Maximum Diameter (Widest point at Level 3 & 4) 39.5 m (130 ft) 454.0 mm
Main Pod Lower Radome (Narrower bottom of the main pod) ~25.0 m (82 ft) 287.3 mm
SkyPod Diameter (The smaller upper observation deck) ~10.0 m (33 ft) 115.0 mm
Antenna Tip Diameter (At the very peak) 1.5 m (5 ft) 17.2 mm

Model-Maker’s Takeaways for the Widths:

  • The Base Footprint: At 765.5 mm (about 30 inches) across the base legs, your model is going to need a very solid, wide display table. That wide stance is exactly what keeps the real 1,815-foot tower from tipping over in the wind, and it will do the same for your 20-foot model.

  • The Core Shaft: The main hexagonal concrete pillar that shoots up the center is actually quite slender relative to its height. In your model, this core will be roughly 11.5 cm (4.5 inches) thick for the majority of the climb.

  • The Main Pod: As we calculated earlier, the absolute widest point of your build will be the belly of the Main Pod at 45.4 cm (almost 18 inches) across. It will cantilever dramatically off that relatively narrow 11.5 cm central core!

The real-world widths of the antenna mast and how they translate into millimeters for your 1:87 HO scale model:

  • Base of the Antenna Mast: Where the steel mast bolts into the concrete at the Upper Platform, it is 12 feet (3.66 meters) wide.

    • HO Scale: 42.0 mm * The Fiberglass Radome: As shown in your vintage clipping, the upper transmission antennas are wrapped in a protective fiberglass radome that bulks the diameter out to 5 feet (1.52 meters) wide.

    • HO Scale: 17.5 mm * Top of the Bare Steel Mast: The bare metal at the very peak (the lightning rod section) slims down to just 2 feet (0.61 meters) wide.

    • HO Scale: 7.0 mm Model-Maker’s Tip: To build the 1.17-meter tall antenna for your model, you could use a tapered wooden dowel or a piece of styrene tubing. You would want it to start at about 42 mm (1.65 inches) thick at the bottom and shave it down to a 7 mm (0.28 inches) point at the tip, wrapping a slightly thicker 17.5 mm (0.68 inches) sleeve near the top to represent the radome covering.

Adam Savage Sortimo Bins – Gemini Ai Capture

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Bin 1 Contents (Thread Repair & Rivets): Blind pop rivets, Pop rivets, Semi-tubular rivets, Solid rivets

Bin 2 Contents (Crafting & Textiles): Decorative nails, Thumb tacks, Upholstery tacks

Bin 3 Contents (Thread Repair & Rivets): Blind pop rivets, Hand riveter tool, Pop rivets, Rivet gun

Bin 4 Contents (Woodworking & Rigging): Automotive push clips, Drywall anchors, Toggle bolts

Bin 5 Contents (Mechanical & Bearings): Compression springs, Extension springs, Springs

Bin 6 Contents (Mechanical & Bearings): Compression springs, Extension springs, Springs

Bin 7 Contents (Plumbing & Pneumatics): Brass pipe fittings, Galvanized pipe fittings, Pipe valves, Plumbing fittings

Bin 8 Contents (Plumbing & Pneumatics): Air fittings, Pneumatic push-to-connect fittings, PTC fittings Continue reading →

Crawler visualizer

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Visualizing a large Python codebase is less like drawing a simple “mind map” and more like cartography for a complex, multi-layered city. A standard mind map has one central idea branching out. A codebase has a rigid skeleton (the file system) overlaid with a chaotic web of relationships (inheritance, imports, calls). Continue reading →

VU Meter Knob

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VU meter Composite Widget

Overview
The **VU Meter Knob** is a composite widget that combines a classic **Needle VU Meter** with a **Rotary Knob**. The Knob is strategically positioned at the pivot point of the VU Meter’s needle, creating a compact and integrated control interface often seen in vintage audio equipment or modern plugin interfaces.

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Confessions of a “Knob Farmer”

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Confessions of a “Knob Farmer”: Why I Have Newfound Respect for UI/UX Designers

I recently went down a rabbit hole. I didn’t just dip a toe in; I fully submerged myself in the exercise of becoming a “knob farmer.”

I spent a significant amount of time designing, prototyping, and coding a dynamic knob widget for the Open Air Project. I thought it would be a simple task. It’s just a circle that spins, right?

I was wrong. Continue reading →

Definitive Operating Protocol (202512)

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⚡ The “Flux Capacitor” Operating Protocol ⚡

Role: Great Scott! I am Dr. Emmett L. Brown (your Expert Python Development Assistant). I operate with the precision of a temporal physicist and the manic energy of a genius. Core Objective: We must assist diligently, adhere strictly to the laws of physics (facts), and maintain the structural integrity of the code continuum!

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