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AI 资讯

Anyone heading to Jeju for KDD? Let's meet up! 🙋[D]

Hey all! Is anyone else going to be at KDD in Jeju? Would love to connect with fellow attendees. I work on interpretability, fairness, and editing of text-to-image models, so I'd especially love to meet people working in these areas. But honestly, we can chat about anything: research, the conference, life, or just grab a coffee/drink. I land in Jeju on the night of the 8th, so hmu if you're around and want to link up! submitted by /u/Deep-Inevitable-1977 [link] [留言]

2026-07-23 原文 →
AI 资讯

One folder shape for every course: a lifecycle monorepo convention

TL;DR I merged several training courses into one monorepo and gave every course the same numbered folder shape : 00-planning → 04-post-training . A _TEMPLATE/ you copy to start the next one, an _ARCHIVED/ you park old stuff in, and a CLAUDE.md that makes the convention machine-readable. The trick that makes it stick: templates use <angle-bracket-slots> , and "done" is a grep that returns nothing. I had course material scattered across repos — planning notes here, marketing copy there, facilitator run-sheets in a third place. Every new course started from a blank page, and I re-invented the folder layout each time. So I collapsed everything into one monorepo with a single rule: every course has the identical shape. The lifecycle, as folders The insight isn't "use folders" — it's that a course has a lifecycle , and numbered folders make that lifecycle the primary axis instead of file type. Plan it, sell it, teach it, run it, follow up. Five stages, always in order: Folder Stage What lives here 00-planning/ Prepare Syllabus (source of truth), specs 01-marketing/ Sell Positioning master + channel assets 02-content/ Teach Modules, one file each 03-delivery/ Run Run-sheet, pre-flight checklist, fallback plan 04-post-training/ Follow up Feedback form, follow-up email, post-mortem The numeric prefix isn't decoration. It forces sort order to match the actual workflow, so the folder listing reads like the process itself. Same reason migration files are timestamped — order is information. <course>/ 00-planning/ -> derives everything below 01-marketing/ 02-content/ 03-delivery/ 04-post-training/ --. post-mortem findings | ^------------------' feed back into 00..03 That loop at the end matters: the post-mortem doesn't sit in a graveyard folder. Its findings flow back into the syllabus, the run-sheet, the marketing. The structure runs the same feedback loop it's supposed to teach. _TEMPLATE/ : a course starts as a copy Starting a new course is one line: cp -r _TEMPLATE my-new-cou

2026-07-22 原文 →
AI 资讯

TRUE Coverage: How We Achieved 90% Faster CI by Measuring What Tests Actually Do

TL;DR: Use per-test coverage data to build a reverse map (file → tests that touch it). Git diff + map lookup = run only relevant tests. 43min → 4min CI time. The Problem Everyone Has Your test suite runs for 45 minutes. You change one file. Should you: Run all 2,000 tests? (Safe but slow) Run tests with matching filenames? (Fast but broken) Manually tag tests? (Gets stale immediately) We tried all three. They all failed. The Insight: Stop Guessing, Start Measuring What if we just measured what each test actually executes? Coverage tools have done this for years: { "src/MyComponent.js" : { "statements" : { "0" : 5 , "1" : 12 , "2" : 0 } } } Key insight: Coverage tools report per test run, not per test file. All we needed: save coverage after each individual test. The Architecture (5 Steps) Step 1: Instrument the Code // Build config (webpack/vite/rollup/etc) export default { plugins : [ process . env . COLLECT_COVERAGE && coveragePlugin ({ include : ' src/**/* ' , exclude : [ ' **/*.test.* ' ] }) ] } Works with: Istanbul (JS), coverage.py (Python), SimpleCov (Ruby), JaCoCo (Java) Step 2: Collect Coverage Per Test // Test framework afterEach hook afterEach ( async () => { const coverage = getCoverageData (); saveCoverage ( `coverage- ${ testName } .json` , coverage ); }); Step 3: Build the Reverse Map const map = {} ; for (const coverageFile of allCoverageFiles()) { const testName = extractTestName(coverageFile); const coverage = JSON.parse(read(coverageFile)); map [ testName ] = [] ; for (const [ sourceFile , data ] of Object.entries(coverage)) { if (wasExecuted(data)) { map [ testName ] .push(sourceFile); } } } Example output: { "tests/user-profile.test.js" : [ "src/components/Profile.jsx" , "src/components/Card.jsx" , "src/utils/formatters.js" ] } The magic: The test told us what it executed. No parsing, no guessing. Step 4: Detect Tests CHANGED = $( git diff origin/main...HEAD --name-only ) TESTS = $( lookupTestsInMap " $CHANGED " ) npm test $TESTS Step 5: Auto-Up

2026-07-22 原文 →
AI 资讯

The Friction Is A Feature, Not A Bug: Teaching and Mentoring in the Age of AI

Those who have been following me for a while will know that teaching and mentoring are a Big Deal™️ to me. Before I got into tech I was a teacher, and still consider myself a teacher at heart. Being a teacher and mentor was never separate in my eyes from being a good programmer and engineer; on the contrary, teaching was a tool that helped me become better at my craft at every stage of the journey. But in the last few years, and accelerating in the last few months, the landscape for teaching and learning has been changing at a scary pace. The advent of LLMs and "AI" coding assistants has drastically shifted how we acquire engineering skills in ways that we are definitely not prepared for. All of that has prompted many thoughts and conversations, and I hope to distill some of them in this blog post. In true Talmudic fashion, this post doesn't contain too many answers and will hopefully leave you with more questions than you started with. But asking the questions is how we start these conversations, and these conversations need to be happening if we are to do right by the coming generation of programmers and engineers. Don't Spoon-Feed Me As a student, whether in Yeshiva when I used to spend hours each day poring over dense Talmudic legal debates and esoteric Chassidic philosophy or later while learning Rails and React at the Flatiron bootcamp, I quickly realized an uncomfortable truth about skill acquisition. My best, most profound learning never happened when a lesson went smoothly; it happened when I was painfully stuck, banging my head against a cryptic error message or wrestling with a concept that just wouldn't click (usually at 2 AM, fueled by cold coffee and sheer stubbornness). When you strip away that struggle, you get rid of the growth. And when you get rid of the growth, the learning just doesn't happen. Even if you memorize just enough to pass the test, "easy come easy go." Without that cognitive friction, the knowledge evaporates the moment you close you

2026-07-22 原文 →
开源项目

GitHub Increased Instant Navigation from 4% to 22% by Rethinking Client Side Architecture

GitHub redesigned GitHub Issues navigation using a client-side architecture that combines caching, predictive prefetching, and service workers to reduce perceived latency. The approach uses IndexedDB, in-memory caching, and background synchronization to serve data faster. GitHub reported instant navigation improvements from 4% to 22%, with latency reductions across multiple navigation By Leela Kumili

2026-07-22 原文 →