utsav

@badlogicgames @0xblacklight I'm curious, who are the SWEs you look up to?

@0xblacklight i believe for someone to be a great SWE, they need to love the underlying craft. which means the tedious coding part. that's the part that trains your spidey senses. i don't have the biggest sample on earth, but all the great SWEs i look up to are actually great craftspeople.

recommended reading. this is our field's version of "i hoped AI would be able to do my laundry, but instead it does my art" kabir.au/blog/the-ctf-s…

x algo is wilddd

miss the good old days of raymond hettinger's talks

I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them. I can't name any specific people because they include personal friends I deeply respect, but I worry about how this plays out. I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now its... the whole software development industry (maybe the whole world, really). It's frightening, because the psychosis folks operate under an almost absolute "MTTR is all you need" mentality: "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!" We learned in infrastructure that MTTR is great but you can't yeet resilient systems entirely. The main issue is I don't even know how to bring this up to people I know personally, because bringing this topic up leads to immediately dismissals like "no no, it has full test coverage" or "bug reports are going down" or something, which just don't paint the whole picture. We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happens so fast that nobody notices the underlying architecture decaying. I worry.

@VicVijayakumar holy c rewrite when?

holy shit bye bye zig and rust

lgtm

This is very cool! kyunghyuncho.me/teaching-funda…

@GergelyOrosz @ashtom as long as one has their expectations in check stuff like "review is the bottleneck now" doesn't help when you are using a llm like a slot machine

“People claiming that AI is slowing them down because of all the slop are not all that different from people that claimed DevOps is slowing them down.” - from @ashtom A new tool is just a tool, and there are many ways to use it, including productively and counter-productively!

@StefanTMD for a second i wondered why rewrite opencode in go and not rust 🤦

opencode GO now has 69,000 subscribers very nice

time to take a hard look at all your npm and pypi dependencies, use them clankers to re-implement and minimise those dependencies tl;dr tanstack, mistralai, guardrailsai compromised

@art_zucker only if the folks holding the reins understood this nuance

This is going to be a little bit long, but I want to give hope to my fellow anxious ML engineers. We see a lot of propaganda on how this or that AI one shotted something, about how incredibly strong the models are getting and how we don't even need to review PRs and we can just ship to production. Although this can be true for some cases, its also far from being representative of all the challenges we have to face. I started using claude code 4 month ago, and quickly realized how it really does change the way we work. I can experiment 10x faster, fix small issues without coding and refactor code without sweating. BUT, these tasks were "just" tedious and not hard. The challenge in my day to day work is to take a research code and integrate it into transformers using our standards. Its challenging because code beauty is abstract and subjective just like a philosophy. By relying too much on claude, and on how seemingly good the code it produces look, I pushed the deepseekv4 integration without realizing that claude really did not understand the model. I gave it access to `transformers`, the original paper, the original code, the different blog posts and my past chats and skills created to add a model, a b200 node node and a LOT of tokens, but it did NOT nail it. It did not understand the eager attention path, it did not understand the basics of causal attention. It was even wrong implementing the manifold constrained hyper connections. It helped to reduce the burden of exploring implementation and debugging but it did not help reason around the model. I am not a doomer, I think our job as Software Engineers has never been this great, I am just saying that we still have a job, and we should still be a bit careful when it looks to good to be true 😉

@teortaxesTex try github.com/0xku/kon, economical than both

Can confirm, pi is shockingly economical hermes isn't far, somehow. others seem worse

@alexellisuk 27b or 35B-A3B? also the hardware and tps you get?

Local Qwen 3.6 (Q6 quant) hooked up to opencode just diagnosed and fixed a broken CLI recipe in arkade. Completely hands-off - something that local models made a complete hash of as recently as Jan 2026. github.com/alexellis/arka…

highly recommended read this was doing the rounds recently, just got around to reading it ngrok.com/blog/quantizat…

@coolcoder56 0xku.github.io/leetcode-compe… this should help folks looking for more data points this particular post is the 2nd one there btw

Uber offering ₹1.3Cr+ TC for SSE (L5-A) in India now ₹81.3L base $102K RSUs ₹15.3L bonus Candidate is already at ~₹1Cr+ TC and expecting promotion to PE soon Tech definitely pays better than any other field

CEO: “We should integrate AI into everything.” AI 5 minutes later:

@badlogicgames casey muratori's wading through ai is also pretty grounded

recommended viewing. in anders we trust. youtu.be/CPrePbvbbic?is…

if it was not obvious already, here is how /goal works in codex