标签：#c

1:00 AM to 2:27 AM. One bug, three root causes, zero clean error messages. It started with a simple complaint: an admin sends a push notification, and the user never receives it. No crash, no red error in the console, nothing obviously broken. Just silence on the other end. That kind of bug is the most frustrating kind. Everything looks like it's working. The permission prompt shows up fine. The admin panel says "sent." And yet nothing arrives. By 1 AM, after a long day already spent on a fairly large project, this was the last thing left to fix before calling it a night. It turned into an hour and a half of tracing one silent failure into another. Layer One: The CSP Was Blocking the Fix Before It Could Even Start The first clue showed up in the browser console: a Content Security Policy violation, quietly blocking a script that OneSignal's SDK needed to complete its own initialization. The permission popup looked completely normal, so it was easy to assume the subscription step was working. It wasn't. The script that OneSignal used internally to finish setting up the subscription was being blocked by the site's own security headers. The fix was small: add the missing domain to the script-src directive. But finding it meant not trusting what the UI looked like it was doing, and instead reading the actual network requests line by line. Layer Two: "Sent" and "Delivered" Are Not the Same Thing Once the CSP was fixed, notifications appeared to send successfully. The API returned a success response, an ID was created, and the admin panel showed a "sent" confirmation. Except the user still got nothing. This turned out to be a subtler problem. OneSignal's newer API doesn't return a recipient count in that initial response, so a message could be "created" successfully by OneSignal's servers while still reaching zero actual devices. The code was treating message creation as proof of delivery, which is not the same thing at all. The fix involved polling OneSignal's delivery-s

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

Okay so. Story time. Grab a coffee; this one's got a body count. A few months back I got assigned a...

As someone who is not affiliated with any of the big tech companies, I find it particularly difficult to have the confidence or enthusiasm to approach any ML problem with an attitude that my professors probably had at my stage in life. I'm sure I am not the only one having the following thoughts: "My research is currently being done better at companies." "ML problem I set out to solve is already solved and in fact turned into products and sold for millions at companies X, Y, Z. There is no need for further research." "Industry is not interested in theoretical ideas and there is plenty of evidence for that, starting with their hiring practice." "Companies wouldn't have millions of dollars in funding or revenues if their models weren't working." "Research is like Darwinian evolution. Evolution aims to produce the fittest model. After decades of evolution, the fittest model is already in industry, why should I explore other evolutionary dead-ends?" "There may not be a next big thing after LLM. If there were, it would be simply incorporated as a function or a subroutine that LLM simply calls when needed, and the average person would be none the wiser. My contribution would be invisible." Seems like research outside of big tech companies is pointless (unless you are a prof who is making big $$ while doing it). Because whatever they are working on might be lightyears ahead of whatever you are doing, but you wouldn't know because their model is simultaneously closed-source and omnipotent. There are tons of people sharing their resumes on other ML/CS subreddits and occasionally you see that their projects are along the lines of "linear regression for Titanic dataset" or "YOLO for pedestrian detection" and they are wondering out loud why nobody is hiring them. Everyone with more ML experience can see because there is zero need for people with this skillset. But what if my very research also looks the same to people in industry? What if my "deep geometric autoencoding variati