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Evidence Beats Certainty: Why My Classifier Refuses to Pretend Every Product Has an Answer

Batch 010 found a bug that looked like good news. The classification worker was finishing its work. Runs moved through the database. Product rows had candidate tariff codes. The regression suite was far enough along that a casual glance could have treated the classifier as alive. Then one test forced three uncomfortable cases through the loop: no candidate, weak confidence, and a near tie. All three came back looking too clean. The worker was persisting the run as classified , even when the evidence said the product needed review or had no supportable recommendation. That is the kind of bug I worry about in compliance software. Not the loud crash. The green row. A customs classifier can fail by throwing an exception. That failure is annoying, but honest. The operator sees it. The queue stops. The job gets retried. The audit trail can say, plainly, that classification did not happen. The worse failure is a result that looks complete while the evidence underneath is missing or contested. That was the real Batch 010 scar. The engine already carried the domain rule in its intent: classification is evidence, not a label. But the persistence path was still treating classification as if the only final state that mattered was success. The runtime could produce rejected candidates and confidence values. The database could store failure reasons. The tests could express review states. One narrow path still flattened doubt into completion. I was wrong about where the risk sat. I expected the hard part to be selecting the tariff code. The harder problem sat one layer later: making sure the code was not selected when the evidence did not deserve that much authority. Customs data makes that tension obvious. A product row is rarely a clean ontology entry. It is a SKU, a commercial name, a description written by someone under time pressure, a country of origin, a jurisdiction, maybe a material list, maybe an intended use. The difference between a good HS or HTS recommendation and a

2026-06-14 原文 →
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Tauri v2 Cheatsheet — The Commands I Use on Every Project

All tests run on an 8-year-old MacBook Air. All results from shipping 7 Mac apps as a solo developer. No sponsored opinion. After 7 Tauri apps, I type the same commands constantly. Here's the reference I wish existed when I started. Project setup # New project npm create tauri-app@latest # Add to existing project npm install --save-dev @tauri-apps/cli npx tauri init Development # Dev mode (hot reload) npm run tauri dev # Dev with specific log level RUST_LOG = debug npm run tauri dev # Dev with backend logs visible npm run tauri dev 2>&1 | grep -v "^$" Building # Standard build npm run tauri build # Universal binary (Intel + Apple Silicon) npm run tauri build -- --target universal-apple-darwin # Debug build (faster, no optimization) npm run tauri build -- --debug Plugins npm run tauri add global-shortcut npm run tauri add fs npm run tauri add shell npm run tauri add notification This updates both Cargo.toml and the plugin registration. Faster than doing it manually. Permissions (tauri.conf.json) { "app" : { "security" : { "capabilities" : [ { "identifier" : "main-capability" , "description" : "Main window capabilities" , "windows" : [ "main" ], "permissions" : [ "fs:read-all" , "fs:write-all" , "shell:execute" , "global-shortcut:allow-register" ] } ] } } } Tauri v2 requires explicit permission declarations. If a command silently does nothing, check permissions first. Common Rust patterns // Get app data directory let data_dir = app .path () .app_data_dir () .unwrap (); // Emit event to frontend app_handle .emit ( "event-name" , payload ) .ok (); // Get window let window = app .get_webview_window ( "main" ) .unwrap (); // App state app .manage ( MyState :: new ()); let state = app .state :: < MyState > (); Notarization (macOS) # Submit for notarization xcrun notarytool submit app.dmg \ --apple-id YOUR_APPLE_ID \ --team-id YOUR_TEAM_ID \ --password YOUR_APP_PASSWORD \ --wait # Staple after notarization xcrun stapler staple app.dmg Debugging # Check what's in the bundle

2026-06-13 原文 →
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Why SCORM Refuses to Die — And What AI Finally Changes About That

SCORM was built in the early 2000s for a world of CD-ROMs and Flash. It's 2026 and it still runs 80%+ of corporate e-learning. Here's why, and why generative AI might be the thing that finally breaks the cycle. SCORM Is Everywhere, and Nobody Is Happy About It If you work anywhere near corporate learning, you've encountered SCORM — the Sharable Content Object Reference Model. It's a set of standards that lets e-learning content talk to a Learning Management System: track completion, record scores, resume where you left off. SCORM 1.2 was released in 2001. SCORM 2004 followed a few years later. That's it. The spec hasn't meaningfully evolved in two decades. And yet, almost every LMS on the market — Moodle, Cornerstone, SAP SuccessFactors, Docebo, Absorb — still supports SCORM as a primary content format. Most Fortune 500 compliance training runs on it. Every major authoring tool, from Adobe Captivate to Articulate Storyline to Lectora, exports SCORM packages. It's the TCP/IP of corporate learning: unglamorous, creaky, universally understood. Why It Won't Die: The Network Effect Nobody Talks About People love to write "SCORM is dead" articles. I've been in e-learning engineering for 11 years and I've read that headline at least once a year since I started. SCORM isn't dead because it benefits from one of the strongest network effects in enterprise software. Consider the ecosystem: Authoring tools export SCORM because LMS platforms expect it. LMS platforms support SCORM because authoring tools export it. L&D teams require SCORM because their procurement processes mandate it. Procurement mandates SCORM because it's the only format every vendor supports. Breaking this cycle requires everyone to move simultaneously. That doesn't happen in enterprise software. It especially doesn't happen when "good enough" works and switching costs are invisible but enormous (repackaging thousands of courses, retraining content teams, renegotiating vendor contracts). xAPI (Tin Can) was su

2026-06-12 原文 →
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