ReExplain
Explain what you know to AI and discover what you don't Discussion | Link
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Explain what you know to AI and discover what you don't Discussion | Link
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
Welcome to Day 2! Today we focus on two core pillars of Object-Oriented Programming: Encapsulation: Hiding internal state and requiring all interaction to go through performing validation or controlled methods. Abstraction: Hiding complex implementation details and exposing only a clean, simplified interface to the user. 1. Access Modifiers: Public, Protected, & Private 🛡️ Python does not have strict enforcement keywords like public or private found in Java or C++. Instead, it uses naming conventions and name mangling to communicate intent and protect internal data. ┌─────────────────────────────────────────────────────────────────────────────┐ │ ACCESS MODIFIERS IN PYTHON │ ├──────────────┬───────────────┬──────────────────────────────────────────────┤ │ Level │ Syntax │ Scope / Intended Access │ ├──────────────┼───────────────┼──────────────────────────────────────────────┤ │ Public │ self.name │ Accessible anywhere (inside & outside class) │ │ Protected │ self._balance │ Internal & subclasses only (Convention) │ │ Private │ self.__pin │ Class internal only (Triggers Name Mangling) │ └──────────────┴───────────────┴──────────────────────────────────────────────┘ Public ( self.name ): Fully accessible from anywhere. Protected ( self._balance ): Single leading underscore. Signals to developers: "This is internal—do not mutate or access outside this class or its subclasses." Private ( self.__pin ): Double leading underscore. Triggers name mangling ( _ClassName__attribute ), making it hard to accidentally access or override outside the class. 💻 Code Example: Access Modifiers & Name Mangling class SecureVault : def __init__ ( self , owner : str , balance : float , pin_code : str ): self . owner = owner # Public self . _balance = balance # Protected (convention) self . __pin = pin_code # Private (name mangled) def verify_pin ( self , pin : str ) -> bool : return self . __pin == pin vault = SecureVault ( " Alice " , 50000.0 , " 9876 " ) # Public access works as expected
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People spend hours watching videos on their smartphones. The wider, shorter, and lighter Galaxy Z Fold8 caters to exactly that.
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Before you rename, move, or delete a Python module: who still imports this? Grepping hits strings and comments. Importing the package can run side effects. whoimports walks the tree with the AST and prints every matching import / from … import line. Install pip install git+https://github.com/SybilGambleyyu/whoimports.git Usage whoimports src/auth/session.py whoimports auth.session -f json whoimports pkg.util -f md Notes Zero dependencies, Python 3.10+ File paths and dotted module names Understands src/ layouts text / markdown / JSON output Pairs with gitchurn and redactx for safe refactor context. Source: github.com/SybilGambleyyu/whoimports · MIT
Cut the noise. Keep the stack traces. CI logs are mostly package installs. The failure is a few dozen lines buried under thousands of Downloading… lines. Scrolling for the real stack trace is a tax paid on every red build. logsnip is a zero-dependency Python CLI that extracts those failure regions and collapses the rest. Install pip install git+https://github.com/SybilGambleyyu/logsnip.git # or the whole toolkit: curl -fsSL https://raw.githubusercontent.com/SybilGambleyyu/devkit/main/install.sh | bash One-liners # Last failure from a GitHub Actions run gh run view --log-failed | logsnip --last # Headlines only logsnip ci-full.log --summary # Safe to paste into an AI assistant gh run view --log-failed | logsnip --last | redactx What it matches Built-in patterns cover pytest E lines and AssertionError , npm ERR! , rustc error[E…] , TypeScript error TS… , GitHub Actions ##[error] , make failures, and common exit-code messages. Stack frames after a hit are pulled in automatically. Design No network, no config files, no dependencies — pipe-friendly --check exits 1 when error-like lines appear (CI gate) --json for machines; --summary for humans in a hurry Pairs with redactx before anything leaves your machine Source: github.com/SybilGambleyyu/logsnip · MIT
Break the English ↔ Japanese language barrier with a QR scan Discussion | Link
I came to dev.to to translate. I stayed to steal. I mean that as the compliment it is here. On this site, "I'm stealing that line" is something you say to an author's face and they thank you for it. Ideas are meant to be lifted, reused, carried home. It took me a while to understand that culture — because I didn't arrive as a thief. I arrived as a tourist who couldn't read the signs. Here's the setup. I'm Korean. My English is workable but slow, and writing a comment good enough to earn a real reply from a stranger — in a second language, in a technical field I never trained in — is more than I can do alone at a speed I'd tolerate. So when I started reading and commenting here, I opened an AI beside me and used it the obvious way: as a translator. Read the post, get the gist, draft a reply, fix my English, post it. That was the whole plan. It lasted about a week. The moment the tool changed jobs What broke the plan was that the posts were good . Not content-farm good — actually good. People writing honestly about the thing that broke at 3am, the assumption that quietly rotted, the fix they were embarrassed they'd missed. I'd come for a translation and I'd leave with an idea lodged in my head that had nothing to do with the words. At some point — I don't remember deciding it — I stopped asking my AI to just translate the post, and started asking it something else: "Is there anything in here we should actually be using?" That question changed what the tool was. A translator turns one language into another. What I'd started doing was turning someone else's hard-won lesson into a change in my own system — and the AI wasn't a dictionary for that job. It was the thing that read the post, understood my setup, found the overlap, and then — the part that still surprises me — built it and tested it. That's the theft this series is named for. Not the words. The lessons. From translator to research partner, in three steps Looking back, the tool climbed through three jobs, and I
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A practical workflow for turning release artifacts into an accurate, reviewable SaaS product demo without inventing product details. Shipping a feature and explaining it are different kinds of work. The code may have tests and a clean deployment path. The material needed to explain the feature is scattered across tickets, release notes, draft docs, screenshots, and a rushed screen recording. Consider a fictional SaaS team releasing workspace permissions. The feature adds Owner, Editor, and Viewer roles, plus a log of permission changes. By release day, most inputs already exist. The challenge is turning them into one accurate product demo without asking the workflow to guess. Treat release material as a versioned input bundle, then review the script and storyboard before generating video. Why shipping the feature is easier than explaining it Engineers and product managers see the feature as a diff. Viewers do not. A ticket might say "enforce role checks at the workspace boundary," while a customer needs to know where to choose Viewer and what that person can access. That translation is where demos drift. A script can inherit an internal name, skip a prerequisite, show an old label, or turn a planned benefit into a shipped claim. Set one rule before starting: the video cannot introduce a product fact that is absent from the release bundle. Every UI action must also map to the released build. Build a release-to-video source bundle Store the material in a release-specific folder with the release tag or build date. Give each input an owner who can resolve conflicts. Input What it contributes Owner Release note Shipped scope and exclusions Product manager Audience and job One viewer and the task they need to complete Product or PMM Approved claims Language the team can defend Product and legal, if needed Help-center draft Prerequisites, steps, and edge cases Docs or customer education Three current screenshots Exact labels and important UI states Designer or feature owne
It didn’t keep my coworkers at bay, but Flipper’s expensive productivity tool did help me focus on the task at hand.
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