Sinan Can
@s1nancanResearch scientist @amazon @awscloud | Physics PhD @UCBerkeley | EE & Physics @unibogazici Data science/blog: https://t.co/HfY87oBpFm
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Announcing Twitter '95, an AI simulation of Twitter, if it had existed in 1995. - LLaMA 3.1 405B + @FastAPI on @modal_labs ✅ - @nextjs app on @vercel ✅ - @PostgreSQL on @supabase ✅ - Jordan dunking on Clinton ✅ - MVP written in 26 hours at crossover hackathon/marathon ✅
Data science/ML problemi cozerken, meseleyi ayni sonuca goturecek baska bir cerceveden bakip, problemi yeniden tanimlamak, "reframing" uzerine hatirlatma babinda kisa bir not. link.medium.com/KFm4DmuixGb
Burda mesele github olmasi degil. Githuba koyacak biseyler yapmis olmak. Isi yaptiktan sonra ister githuba koy ister blog yaz mediuma koy nereye koyarsan koy.
Github ve LinkedIn kullanmayan yazılımcılar ne bekliyor işlerin onların ayağına mı gelmesini, üniversite zamanından beri kendi tayfama aşılamaya çalışıyorum daha bugün biri gelmiş"bana github konusunda yardımcı olur musun"? diyor☺️
Cografya kaderdir mi diyolardi nasildi. Pek de oyle degil aslinda, ben de 10 sene yasadim orda, mal gibi bir tane ai meetup a gitmedim. Net salaklik.
evidence we are in a dirt-cheap simulation: at the corner of @MistralAI and Harrison @hwchase17 in @swyx's Arena sits @NotionHQ i have attended at least a dozen AI meetups within a minute walk
🚀 Introducing AlphaCodium - A first-of-its-kind open-source code generation tool that surpasses most human competitors in code contests ⭐️ Inspired by DeepMind's AlphaCode❤️🔥, but beats it (judge by yourself!) 1/
If you are good at software/system design, there is an “easy” path into MLE from software development. If you lack those, sticking to business focused data science is way to go for “outsider”/non-software developers.
Machine Learning Engineering (MLE) interviews are 20% about ML, and 80% about Software Engineering/System Design. Plan accordingly.
1998 de yayinlanmis "10 yilda yazilim ogrenin", ve 25 sene onceki ipuclarini hala farkedememis olanlar var. Benim en begendigim: "Talk to other programmers; read other programs. This is more important than any book or training course." web.archive.org/web/1998020622…
“Self-criticism” tabirini gördüm bugün. Kendini eleştirmek. Bir işe başlarken bunu ertele diyordu. Muhtemelen çoğu işi erken bırakmamıza sebep şeylerin başında geliyor. Başkalarını umursamamak self-criticism den daha kolay benim için, bazen kendimi çok kafaya takıyorum belki de.
I wonder if “love what you do” is specifically chosen over “do what you love”. What i love may not turn out to be great/interesting, but i can learn to love working on things with bright future.
Sr/jr tartismasina katilmis :) Bence tek etken sadece AI degil, yas/hayat cizgisinde ilerledikce risk alip yeni seyler deneme arzusu ister istemez azaliyor. Bulundugu yeri koruma vs yeni seyler ogrenme denklemi yavasca birinciye dogru kayiyor, cok normal bir sosyal davranis.
Learned something interesting from the CEO of a fairly big tech co. Usually 28 year olds are more productive programmers than 22 year olds, because they have more experience. But apparently 22 year olds are now as good as 28 year olds because they're more at ease using AI.
Fantastik isler basarmis insanlara bakip, onlarin calisma rutinleri ya da “verimlilik” hacklerini uygulamaya calismayi, basarililarin hirs ve doyumsuzluklarina sahip olmadan yapmak muhtemelen eldeki gonul rahatligi ve ferahligini da kaybeder.
You can learn a lot from ambitious, hungry overachievers, but you won’t learn much about peace of mind and contentment, they got where they are precisely because they were never satisfied with what they had.
Daha az is/mesai fikirleri kendi motive ya da hayatta ne yapmak istedigini bilen insanlar icin cok iyi, fakat kalan zamani youtube/twitter/netflix de heba etme potansiyeli yuksek olan buyuk populasyon icin “bos” yatma riskiyle beraber geliyor.
The workweek archetypes: • 40-hour workweek, the mainstream • 80-hour workweek, the martyr • 4-hour workweek, the minimizer • 20/20 workweek, the happy medium — the new aspiration
Dashboard review edip, hata analizi yapmak >= fantastik model optimizasyonlarina ugrasmak
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