标签：#ci

Most developers use Git every day and understand almost none of it. That's not an insult, it's just the reality of how most people learn tools. You pick up the commands that get you through the day, you memorize the ones that fix the situations you keep breaking, and you build a working mental model that is almost entirely wrong at the mechanical level. The mental model most people carry looks something like this: Git tracks changes to files. When you commit, it saves a snapshot of what changed. Branches are pointers to different lines of work. That's roughly correct at a surface level, but it skips over the actual machinery in a way that leaves you confused every time something unexpected happens. Why does rebasing rewrite history? Why are commits immutable? Why does detached HEAD state exist? Why can you lose work in ways that feel impossible if Git is just tracking changes? The answers are all in the object model, and the object model is surprisingly simple once you sit with it. Git is a content-addressable filesystem Before any of the version control concepts, Git is a key-value store. You put content in, you get a hash back. You use that hash later to retrieve the content. That's the entire foundation, and everything else is built on top of it. The hash Git uses is SHA-1, producing a 40-character hexadecimal string. When you run git hash-object on a file, Git takes the content, prepends a small header describing the object type and size, and runs SHA-1 over the whole thing. The resulting hash is both the key and the identity of that content. Two files with identical content will always produce the same hash. A file whose content changes even slightly will produce a completely different hash. This is the first thing that breaks people's mental models. In most storage systems, identity is location: a file is "that file" because it lives at that path. In Git's object store, identity is content. The path a file lives at is separate metadata, not the file's identity

Chemicals from accidents that injured or killed people increased by nearly 50 percent in recent years.

Wildfire survivors call fire-prediction markets “morally reprehensible” and worry they could increase the risk of arson.

The heat of the Hadean may have come from impacts as well as the interior.

A large-scale clinical trial has shown that even long-term consumption of DHA—an omega-3 fatty acid found in abundance in oily fish—may not lead to improvements in cognitive function.

A new study has succeeded in mapping, on a global scale, the fungal network that supports plant life and helps regulate our planet’s climate.

You can minimize the risk of films and shows being spoiled for you by muting comments, conversations, and keywords on various platforms.

In modern software development, Continuous Integration and Continuous Deployment (CI/CD) pipelines...

Every big social network locks audience data behind auth walls and anti-bot systems. Bluesky went the other way. The AT Protocol is open by design, so public profile data (bios, follower counts, full follower and following lists) is queryable through a documented API without logging in. The whole surface is basically two endpoints: GET https://api.bsky.app/xrpc/app.bsky.actor.getProfile?actor=HANDLE GET https://api.bsky.app/xrpc/app.bsky.graph.getFollowers?actor=HANDLE&limit=100 There's also getProfiles for batching 25 handles per call. Follower lists paginate with a normal cursor , which still works on the graph endpoints. Search is a different story, cursor pagination 403s there now, but that's a topic for another post. For one-off lookups, curl is honestly all you need. Where it gets tedious Bulk. Thousands of profiles, follower exports that run into six figures, weekly snapshots for tracking. Pagination, rate-limit backoff, and stitching the pages together is boring code that has to run reliably. I packaged that part as an Apify actor: Bluesky Profile Scraper . Paste handles or profile URLs, optionally turn on follower/following export, and you get JSON or CSV back with a sourceProfile field linking each follower record to the profile it belongs to. $2 per 1,000 records, runs on a schedule if you want snapshots over time. What people use this for Vetting an influencer's real audience before paying them. Exporting who follows a competitor and what their bios say. Charting follower growth from weekly runs. And enrichment: find who's talking about you with a mentions monitor , then profile those authors to see their actual reach. Bluesky is the only major network right now where any of this is straightforward and stable. Worth using while it lasts.

I wanted to know when people mention a brand on Bluesky. Simple ask. Turns out Brandwatch, Mention, Hootsuite, basically every social listening tool, still doesn't cover it. They're all busy with X and Instagram while Bluesky sits at 27M+ monthly users. So I looked at doing it myself and found out something most people miss: you don't need to scrape anything. Bluesky runs on the AT Protocol, which is open by design. Public posts are searchable through a documented endpoint. No login, no API key. GET https://api.bsky.app/xrpc/app.bsky.feed.searchPosts?q=YOUR_BRAND&sort=latest&limit=100 That returns full post objects. Text, author handle, timestamps, like/repost/reply counts, embedded links, hashtags. Everything you need. Two things that broke my first version Worth writing down because most tutorials get this wrong now: The public.api.bsky.app host that older guides point to returns 403 for search. Use api.bsky.app instead. As of July 2026, unauthenticated search rejects cursor pagination. Page one works fine, page two gets you a 403 with "request forbidden by administrative rules". The nasty part is it looks like rate limiting, but it isn't. The workaround: paginate by time. Use sort=latest , then pass until= with the createdAt of the oldest post from the previous page. Dedupe on uri because the boundary post shows up twice. If you don't want to maintain any of that I packaged the whole job as an Apify actor: Bluesky Mentions Scraper . Keywords in, clean JSON out. It handles the pagination and retry stuff above, filters replies if you want, scores basic sentiment, and can pull follower counts for each author so you can sort mentions by reach. Runs on a schedule, exports CSV, plugs into Slack or n8n through Apify's integrations. It also works as an MCP tool inside Claude or Cursor. Pricing is per result, $4 per 1,000 mentions. No subscription. What I actually monitor Brand and product names plus the common misspellings. Competitor names, because share of voice on Blu

Introduction I used to think "connecting to a database" was one skill. Turns out it's two: connecting to a database chilling quietly on your own laptop, and connecting to one living in the cloud, behind a login, in this case, an SSL certificate that will not let you in until you treat it with respect. This week I did both. Same tool (Power BI), same dataset, two very different vibes. Grab a coffee, here's the full walkthrough local PostgreSQL first, then Aiven's cloud version, side by side, screenshots and all. Part 1: Local PostgreSQL → Power BI Step 1 : Create a schema Nothing fancy, just giving my table a home: CREATE SCHEMA powerbi ; Step 2 : Import the dataset Right-click the new schema → Import Data in DBeaver, point it at your CSV, and let the wizard do its thing. Step 3 : Check the table landed properly A quick peek at the columns to make sure nothing got mangled on the way in. Step 4 : Connect Power BI In Power BI Desktop: Get Data → Database → PostgreSQL database. In the Server field, type localhost (or 127.0.0.1 ) and your database name. localhost Choose Import , hit OK, and log in with your local username and password. Click Load . That's it. That's the whole local experience. Part 2: Aiven PostgreSQL (Cloud) → Power BI Now for the part that actually taught me something. Step 1 : Grab your connection details Everything you need lives on Aiven's Overview page: Host, Port, Database name, User, SSL mode. Your service URI will look something like this (don't worry, this isn't a real password, Aiven masks it in the console): postgres : // avnadmin : •••••••• @ pg - xxxxxxxx - yourproject . c . aivencloud . com : 22016 / defaultdb ? sslmode = require Step 2 : Import the dataset into Aiven Same DBeaver wizard as before, just pointed at the Aiven connection instead of local. CREATE SCHEMA powerbi ; Step 3 : Aiven's certificate. Download the CA cert from the Overview page: Now here's the part that actually tripped me up: Power BI's PostgreSQL connector doesn't ha

This week's science news.

The Swift Observatory was launched in 2004, but recent solar storms have pushed its orbit lower, and it's in danger of burning up in Earth's atmosphere as soon as this year. To try and stave off its demise, NASA has enlisted Katalyst Space Technologies. The company's Link spacecraft launched Friday with the goal of intercepting […]

The US Department of Energy reportedly deleted about 6,000 pages related to energy conservation as a historic heatwave tears across the country. The deletion was suspiciously timed, following Republican outrage over Mayor Zohran Mamdani asking New Yorkers to help reduce strain on the grid by setting their AC to 78 degrees. Republicans like Ted Cruz […]

Why Your SOE Resume Needs a Structural Overhaul Chinese state-owned enterprises (SOEs) often have deep hierarchical structures and a culture of collective achievement. But Western tech companies want to see individual impact, autonomy, and data-driven results. Continuing to lead with your former employer's prestige or your rank (e.g., "Senior Engineer Grade 7") wastes valuable space. The solution: reshape every section to answer the question "What did you personally accomplish with data?" The Core Shift: From Hierarchy to Impact In a Chinese SOE resume, it's tempting to list departments you led or teams you oversaw. In a Western senior data scientist resume, focus on the problems you defined, the algorithms you deployed, and the revenue, cost savings, or user metrics that improved. For example, instead of "Led the data analytics team of 10 people," write "Designed and deployed a demand-forecasting model that reduced inventory costs by 15% (¥12M annually)." Three Resume Sections That Require Full Rewriting Professional Summary: From 'Accomplished Engineer' to 'Data Science Leader' Start with your total years of experience, your technical stack, and the types of business problems you solve. Example: "Senior Data Scientist with 10+ years applying machine learning to supply chain and logistics. Expertise in Python, TensorFlow, and Spark. Reduced operational costs by 15-30% through predictive models deployed at [SOE name]." Work Experience: From Role Descriptions to Metric-Driven Bullets For each role, list 3-5 bullets. Every bullet should have a verb, a task, a technology (if relevant), and a quantified result. Avoid vague phrases like "responsible for." Use specific numbers: "Improved forecast accuracy from 70% to 85% by building an ensemble of ARIMA and XGBoost models." Education & Certifications: Emphasize Transferable Skills Your Chinese degree is fine, but add relevant certifications (AWS, TensorFlow, Coursera) to show adaptability. Consider a "Technical Skills" se

Disturbances in this critical sense are often linked to problems with brain health.

Biology could explain the find, but there are other potential explanations.

Aa large-scale study demonstrates that preservatives widely used in everyday processed foods may exacerbate common health risks.

Ambystoma quetzalcoatli is the first fossil salamander to be formally identified in Mexico, revealing that axolotls have inhabited the country for millions of years.

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.