sudo_navendu

https://navendu.me

Weights on different metrics. From https://github.com/twitter/the-algorithm/blob/ec83d01dcaebf3...

private def getLinearRankingParams: ThriftRankingParams = { ThriftRankingParams( `type` = Some(ThriftScoringFunctionType.Linear), minScore = -1.0e100, retweetCountParams = Some(ThriftLinearFeatureRankingParams(weight = 20.0)), replyCountParams = Some(ThriftLinearFeatureRankingParams(weight = 1.0)), reputationParams = Some(ThriftLinearFeatureRankingParams(weight = 0.2)), luceneScoreParams = Some(ThriftLinearFeatureRankingParams(weight = 2.0)), textScoreParams = Some(ThriftLinearFeatureRankingParams(weight = 0.18)), urlParams = Some(ThriftLinearFeatureRankingParams(weight = 2.0)), isReplyParams = Some(ThriftLinearFeatureRankingParams(weight = 1.0)), favCountParams = Some(ThriftLinearFeatureRankingParams(weight = 30.0)), langEnglishUIBoost = 0.5, langEnglishTweetBoost = 0.2, langDefaultBoost = 0.02, unknownLanguageBoost = 0.05, offensiveBoost = 0.1, inTrustedCircleBoost = 3.0, multipleHashtagsOrTrendsBoost = 0.6, inDirectFollowBoost = 4.0, tweetHasTrendBoost = 1.1, selfTweetBoost = 2.0, tweetHasImageUrlBoost = 2.0, tweetHasVideoUrlBoost = 2.0, useUserLanguageInfo = true, ageDecayParams = Some(ThriftAgeDecayRankingParams(slope = 0.005, base = 1.0)) ) }

I think humans will be able to innovate much faster by building on top of existing knowledge that tools like GPT give access to. It might be different from what we have experienced so far. But there is very less chance that humans stop innovating.

It is perfectly fine if the code actually works. At least for now, getting the exact code you want from an AI is a legit skill. This was pure spam.

I wrote more about what actually happened here: https://navendu.me/posts/ai-generated-spam-prs/

It can help set some context to the discussions.

I wrote more about what actually happened here: https://navendu.me/posts/ai-generated-spam-prs/

It can help set some context to the discussions.

TLDR:

Recently, a person has been using AI tools to generate code and open pull requests to open source projects I contribute to.

The code is entirely wrong and doesn’t work, and it is evident that the person making these pull requests doesn’t understand the code.

The person also copied explanations (which was an obvious giveaway as it sounded like a typical <popular AI tool> response) into the pull request and attempted to explain the code and answer questions from the reviewers.

We were polite and when it didn’t work, reported the person to GitHub.

I don’t want to shame the person publicly. But I want to make other open source maintainers aware that this is a thing and prevent them from wasting time and effort chasing such people down.

Actually, we were sure that it was spam. GitHub gives the option to "approve workflow run for first-time contributors". I guess none of the maintainers thought to approve it because they thought it might be spam. Still, a lot of time and effort spent to review it.

Now they clearly won't because we will be reporting them to GitHub.

This is how you use AI tools to write code.

In our project, the user just copy-pasted the output from the AI tool and called it a day. They did not even bother to build the project and test it.

I have also started using AI tools and it has made me much more efficient. I could have done a task without AI but with it, it is much more faster.

As long as it does what it intends to do, plain old HTML would suffice.

I love using RSS to subscribe to blogs. Many of my favorite personal developer blogs have RSS feeds, and I have them on my blog. I'm currently using Readwise to get all my feeds in one place. This filters out a lot of noise prevalent in social media like Twitter.

I love using RSS to subscribe to blogs. Many of my favorite personal developer blogs have RSS feeds, and I have them on my blog. I'm currently using Readwise to get all my feeds in one place. This filters out a lot of noise prevalent in social media like Twitter.

Looking back at my career, there have always been points where I felt I hit rock bottom and anything I did wasn't helping.

When I quit my job to start a new career in software engineering, I hoped to find a job after a couple of months of preparation. Even after 8 months and close to 200 applications, I still couldn't find a job.

I felt like I made the wrong decision by quitting my previous job and whether I actually had any skills to work in tech. I almost decided to stop and take a different job when I received a life-changing opportunity as a software engineer.

The good thing about hitting rock bottom is that the only place you have left to go is high. It will be hard, but slow progress will surely help you reach your goal. If things aren't working out as you expected, it might be time to switch your goals and try something different.

Anyway, this is undoubtedly not the end. You were clearly determined to pursue what you wanted, but maybe it wasn't aligned. I genuinely wish you all the best for your future, and I hope to see you reach your goals.

SkyWalking definitely does it all. But like you mentioned Prometheus plus Grafana is the most popular solution. But it does not compare to the complete observability solution provided by SkyWalking.

GitHub usually asks the user to provide permissions explicitly. When you go through the OAuth flow, GitHub will show what permissions the app require and you have to provide them explicitly.

This is an unfortunate event and I hope GitHub will lift the ban from the ones affected and enforce ban on the people misusing this. But always check what permissions you are giving.

I have been using AI tools since a couple of months to help my writing. It does not mean my entire job is replaced by an AI, but it makes it much easier to do my job.

There might be a future where AI can do a lot of work and we might shift our jobs to work on top of these AIs. We might have new jobs that did not exist till now in the future.

This is a good argument. A lot of problems that live chat aims to solve can be solved by documenting and making sure that the documentation is discoverable. I’m definitely talking to my team about this.

Thanks for sharing!

This tutorial uses Apache APISIX as Kubernetes Ingress. Although using APISIX custom resources means you are tied to APISIX, it makes it much easier to work with its full features.

The new Kubernetes Gateway API promises to make it more vendor neutral without having to compromise features and usability.

Same for me. I tried waiting for a bit but still nothing.

I enjoy spending my free time working on my pet projects. Some of them are good, most are stupid. But I enjoy it.

But I also understand that a lot of people only program as part of their job and I respect that. You don't need to program for fun to be a "real" developer. Having hobbies outside programming is great.

The reason I started contributing to open source was to learn new skills. It was a great way to work on real world projects and gain experience.

The project I was working on was community driven to a large extent. We had users and contributors from around the world and being one of the core maintainers, I basically had no quitting time. I was working around the clock and did not have any boundaries. I had a lot of work in the project. I was writing code, reviewing PRs, hosting community meetings, discussing project roadmaps etc.

After some time, I started working on open source full-time. I was paid by a company to work on their open source project. We had a bigger team and it made my life a lot easier. I had well defined jobs and I did not have to worry about other aspects of the project like I did before.

It is the same for me. A tutorial to get me started and then docs once I'm comfortable using it.

This is a solid way to support open source maintainers. Another way I have seen companies support open source is by hiring these maintainers as employees or paying an employee to work on the open source project.

Of course, right now this is happening at popular open source projects like Kubernetes and is only followed by big tech companies.

But, giving some time for internal developers to contribute back to the open source project directly can have a huge positive impact as well.

This is not an alternate solution to actually sponsoring money but something that could complement it.