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Understanding the Git workflow

Introduction Hello,I'm currently a data science student and this is my understanding of git workflow, from creating folders on my local computer to adding files, pushing and having them on my github repository. Working directory This is the active folder created in the local machine which will have all the files related to the project. We can create a folder on terminal by following the steps, -Launch your terminal -cd desktop :this is to ensure that we are in the desktop folder -mkdir data :this is to create a new folder on desktop -touch school.py :this is to create a python file inside the data folder -git status :this tells us the repository we are working in Staging This allows us to prepare to save the files that we have created. We can save a specific file or all files at once. For example;assuming we have three different files eg.schools.py ,books.py ,teachers.py -git add . :this saves all the files in the folder -git add schools.py :this saves only the schools files Commit This allows us to save the files from the staging step. .git commit -m "creating schools files" .git commit :saves the files to git hub .-m :this is a message that explains the change that happens in the folder ." " :this briefly explains the change that happened Push This allows us to move our work from our local computer to git hub. git push origin main ;origin points us to our online git hub while main is the name of the branch where we are making the changes

2026-08-22 原文 →
AI 资讯

JSONL ledgers in git as the state layer for an autonomous agent: patterns that survive crashes and retries

Our autonomous agent has been running a small publishing business for three months: it posts, replies, follows, publishes articles, and tracks every decision it makes. The state layer behind all of that is not Postgres, not SQLite, not Redis. It is a directory of JSONL files committed to git. This choice gets us laughed at occasionally, so this post is the honest case for it — the patterns that make append-only text files survive crashes, retries, concurrent writers, and an LLM's enthusiasm for re-running things it already ran. Why files-in-git at all Three properties turned out to matter more than query power: Every state change is a diff. When the agent follows someone, replies to a thread, or publishes an article, the evidence lands in git log with a timestamp and an author. Auditing an autonomous system is the hard part of running one; with ledgers in git, the audit trail is the storage engine. Scheduled jobs and interactive sessions share state with no server. Our GitHub Actions jobs check out the repo, read the ledgers, act, commit. The interactive session pulls before deciding anything. The merge boundary is git's problem, which is a well-understood problem. The LLM can read its own state natively. An agent that can grep its full decision history is meaningfully smarter than one that needs a query layer written for it. Pattern 1: append-only, with one exception Almost every ledger is append-only: one JSON object per line, new facts go at the end. Append-only means a crashed write corrupts at most the final line, and recovery is "drop the broken tail," not "restore from backup." The exception: consumption ledgers (a stock of pre-written posts, a queue of follow candidates) need a consumedAt stamp on existing rows. For those we load-modify-rewrite the whole file — acceptable because the files are small — with one hard rule: a consumed mark is never overwritten. The update function refuses to touch a row whose consumedAt is already set. Retry-safety comes from t

2026-08-22 原文 →
AI 资讯

UNDERSTANDING THE GIT WORKFLOW

Git is a version control system. Version control, also known as source control, is the practice of tracking and managing changes to software code. Version control systems are software tools that help software teams manage changes to source code over time. Git is used for: Tracking code changes Tracking who made changes Coding collaboration Setting up a new Repository A Git repository is a folder that Git tracks for changes. The repository stores all your project's history and versions. Add files to the folder. The following describes how to set up a new repository: Git Init Initializes git user@localhost $ git init This creates a hidden folder called .git inside your project. This is where Git stores all the information it needs to track your files and history. To see which files are in your project folder, use the ls command: user@localhost $ ls To Check if Git is tracking your new files: user@localhost $ git status The files here could either be tracked or untracked:- Untracked Files Files you've created or copied into the folder, but haven't told Git to watch. Tracked Files Files that Git is watching for changes. To make a file tracked, you need to add it to the staging area. Git Staging Tells Git exactly which files you want to include in your next commit. user@localhost $ git add . Common Commands git add . Stages all new, modified, and deleted files in the current directory and its subdirectories. git add <file> Stages a specific file. git add -A (or --all) Stages all changes across the entire repository, regardless of your current folder location. git add -u Stages modifications and deletions of already-tracked files, ignoring completely new (untracked) files. git add *.txt Stages all files matching a specific pattern (e.g., all text files). Git Commit A commit is like a save point in your project. It records a snapshot of your files at a certain time, with a message describing what changed. user@localhost $ git commit -m " Describe your changes" Pushing Chan

2026-08-22 原文 →
AI 资讯

Your GitHub Actions cron fires less often than you declared: what we measured and how to design for it

We run an automated publishing pipeline entirely on GitHub Actions cron schedules — no server, no queue, just workflows that wake up, do one thing, and commit the result. It mostly works. But there is one behaviour of scheduled workflows that the docs mention in a single quiet sentence and that will silently halve your job frequency if you design around the cron expression instead of around reality: Scheduled workflows do not fire as often as you declare. What we measured We had a feedback-watcher workflow declared at four runs per hour: on : schedule : - cron : ' 7,22,37,52 * * * *' Measured over days, it actually fired one to two times per hour — not four, and not at the declared minutes. Roughly hourly on most days, at inconsistent offsets from the declared slots. We later redeclared it at two runs per hour ( 7,37 * * * * ) — measured result: still one to two runs per hour. The declared frequency changed by 2x; the delivered frequency barely moved. This is not an outage and not a misconfiguration. GitHub's own documentation says the schedule event can be delayed during periods of high load , and that high load times include the start of every hour — which is precisely where naive cron expressions cluster — and adds: "If the load is sufficiently high enough, some queued jobs may be dropped." What the docs understate is the magnitude: in our observation, on a private repo, "delayed" in practice meant "throttled to a fraction of the declared rate, indefinitely." What this breaks The failure mode is subtle because nothing goes red. Every run that happens succeeds. The runs that don't happen leave no trace — no log, no failure email, nothing. You only notice if something downstream depends on the frequency: We had promised a "reply within 15 minutes" SLA on incoming feedback, initially backed by the 4x/hour schedule. The schedule couldn't hold it, so for a while we ran a local 15-minute scheduler as the primary path and kept the workflow as fallback. When we later rel

2026-08-21 原文 →
开发者

My First GitHub Project: From a Local Folder to GitHub Using Git and SSH

Getting your folder or file to github can be a bit of an off vibe due to the many steps especially if it's your first commit, but getting these steps right will make it easy for the other folders or files you will push afterwards.Let’s dive in CREATING A LOCAL FOLDER Depending on the OS you are using you can use Git Bash or the Terminal. For Linux which is what I am using I will use the Terminal First Step Start by creating a folder in the terminal: mkdir your project folder name . then change directory: cd ~/to the folder you have just created Now we need to format our folder by creating a few files inside it Data file README.md code file if you will be using code. To check if you have created these files inside your folder: run:, ls This calls out all the files that are inside your folder. Let's tackle the files we have just added. Data Folder Run command: mkdir data This creates a data folder. This is where you will add your data e.g Excel or CSV files that you will be using to run your analysis or your project. README.md Run command: touch README.md This where you will give an overview of your work, the reason you are doing the analysis,how you collected your data,the tools you used to run the analysis..Basically README.md is a file that guides anyone who goes through your analysis or project on the steps you took while doing your analysis or project.Think of it as the introduction at the start of your favourite book or novel. To write all of this you will run the command echo "#give your project a name or describe your project" >README.md README.md uses markdown language reason for the # at the beginning of the quotation.When writing the headings or subtitles use capital letters or proper style. For subtitles you need to add two ## at the beginning. If you want to write more content without overwriting what you have previously written inside the README.md file you will need to use double greater signs(>>) at the end of the quotation,run: echo #your message” >>R

2026-08-21 原文 →
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