How to make your engineering job application stand out (from the perspective of someone looking at hundreds of resumes):
1. Your resume should be one page. If you really need more space, link to a website. You don't need 10+ bullets for each job.
2. You will immediately stand out >90% of applications if you link a personal website that has some intentionality behind it.
3. If you are going to link your X, you might want to clean up your posts? Seems obvious but... people post some wild stuff.
4. You should link your GitHub. Please avoid doing a profile README that looks like a MySpace profile with the badges and images. I'm trying to look at code and your ability to build interesting ideas.
5. You should try to customize your application to the company. If you're applying to a startup, the courses you took in college probably don't matter as much. Maybe more if you're trying to make it through the ATS screening for FAANG.
6. I'm seeing a surprising number of resumes which don't talk about AI or agents at all. Software engineering is changing and it's a pretty fair assumption that you will be expected to learn or understand coding with AI for your job. That should be reflected on your resume and projects (and I'm not just saying this because I'm at Cursor).
7. Take your LinkedIn seriously. Most devs are here hanging out on X but surprisingly still most people will send around your LinkedIn internally.
8. Find ways to show your unique strengths/tastes/interests. It's nice to see people are smart, well-rounded, and thoughtful. Maybe this is a collection of books you enjoyed and why. Or some writing you've done. Or films you liked. At the end of the day, people want to work with other people they like and respect. If nothing else, it will be a good conversation starter ("oh I love [book] as well!").
9. Do not use AI to write your cover letter or resume text. It's incredibly obvious, especially if you are applying to an AI company. You can still use it to ideate on ideas or phrases, but write it by hand (don't fall victim to the overused in-the-distribution-AI-phrases). See: /humanizer skill.
10. No photos on resumes. Save those for whatever you link out to.
11. Quality over quantity. 3 really good, thoughtful, detailed, interesting projects versus a wall of 27 AI-slop ones.
Remember that hiring managers / recruiters are getting hundreds or thousands of applications for a role. They're not going to spend 20 minutes on every single application. You need to cut the cruft and get to the point. I hope this helps you stand out!
اگر علاقه مند به کتاب و کتابخونی هستین
این بچه ها تو دل روستا یک کتابخونه درست کردن با فقط ۵ کتاب
اگر کتابی دارین که مناسب سنشون باشه یا دوست دارید کمک کنین دریغ نکنین
یا حداقل این لینک رو برای کسایی که فکر میکنین میتونن کمک کنن بفرستین
https://t.co/4u94NGjPLG
Fully Automated Trading by Large Language Models in Financial Markets
(With a Focus on Bitcoin and Gold)
As always, I've put this project on GitHub purely for educational purposes and to share it — there's no guarantee it'll actually be profitable. But I'll say this to you as a friend: this project is genuinely worth a look, because depending on your own risk tolerance, you might be able to put in the time and turn it into something personally profitable. It doesn't hurt to try, and I do recommend it!
Three months ago I worked on a project (research only, not a full production system) aimed at fully automating the problem of portfolio management in financial markets and making the process more efficient. The approach I chose was to hand off clearly defined, structured financial decisions to a language model with very strong reasoning ability.
Where do you think the main challenge was? Language models don't necessarily produce a single, fixed output for identical inputs. As you know, there's no such thing as an objectively "best" financial decision, precisely because it isn't unique. Also, in practice, when you want to train and test the whole model and pipeline you've built, you have to run the language model over and over again to see how it would have performed in a hypothetical past — and that adds up to very high costs. The third challenge is the perennial challenge of financial markets: overfitting! If a model performed well in the past, that's no guarantee whatsoever that it'll be profitable in the future.
So in this GitHub repo, I tried to tackle these three challenges with a set of ideas — though I don't claim to have necessarily picked the best solutions for them. I'd recommend checking out the code for educational and research purposes.
I'd also appreciate a #repost/#retweet. Thanks!
#LLM #AI #Quant #Quantitative_Finance #Crypto #Bitcoin #Gold #BTC #Finance #Trading #Portfolio_Optimization #Github #Open_Souce
https://t.co/RFTstTHRjR
@about_nafise متوجهم چقدر اذیت شدی. ولی خوب شد که نشد. چون همچین جای درهم برهمی اگه شروع به کار میکردی به مراتب بیشتر اذیت میشدی و مرسی که ا��نجا عنوان کردی تا بقیه نجات پیدا کنند
سوگ اینطوریه که زمان درمانت نمیکنه.
فقط راه رفتن توی mazeی که یه روزی ازش میترسیدی رو یادت میده.
یه روز میفهمی هنوز اون اتاقها وجود دارن،
فقط دیگه اونجا زندگی نمیکنی.