Software engineering as a whole is going to explode in coming years, far from dying. (imho) We'll keep finding harder, tougher problems to solve that were too complex & time taking to undertake before. New fields & niches may emerge while current ones get automated. There's no denying that tech market is cooked & very hard right now, just coz of how difficult it's become for many corpos to adapt to post-ai landscape. Teams with lean numbers will outperform bigger ones just coz of less overhead & bloat. But we'll have more of these teams, not less, to ship out more & more software for orgs that can build software that was out of their reach coz of budget, manpower & complexity. They'll ship more, sell more & grow more to undertake more work & employ more in new areas than before. I could definitely be wrong & we get the "bad ending" (it is what is then sadly) But there's nothing wrong in being optimistic & keep pushing forward, coz this is the best time to go do & build whatever you'd in your mind coz intelligence has gotten way cheaper than before & it'll only get better.

I have discovered $50 amazon photo frames to be the PERFECT home display screen They can be rooted, updated to run a modern browser, have WiFi and can be powered from USB 1280×800 resolution with a nice touch screen I detailed how to hack these yourself in my latest video wesbos.com/hacking-smart-pho…

Grace Hopper Celebration Canceled: A Big Loss For Women In Tech This Year buff.ly/ZwOSpB6

I’ll never forget this guy on X who said, “AI is replacing software engineers. We’re all cooked!” I replied, “We’re not. You are.” 😂 Then he played the age card, telling me I was too young to understand and that he’d been in this industry longer than I’d been alive. I just don’t see it that way… We’re entering a whole new era of software development. And personally, I couldn’t be more excited about it. One day, we’ll look back at how we used to build software and think, “How the hell did we put up with all that?” All the boilerplate, plumbing, wiring, and countless hours spent debugging things that had nothing to do with the actual problem we were trying to solve. I never forget the days I used to spend hours fixing broken tests. I don’t want to go back. AI is changing the way we build software, freeing us to spend more time on what really matters: solving problems and building great products people actually want. But here’s the thing. The better you understand software, the better you can guide AI. Knowing what to ask, how things fit together, and when something doesn’t make sense matters more than ever. If you think software development is dead, you're welcome to believe that. Less competition for the rest of us. 😉 We’re just getting started.

JetBrains is so lost. They've added a cursor-like full screen agent mode, but the puck is already moving onward. Always one step behind. In the meantime, we could all use some help with markdown editing, huge diff views, summarization, change visualization, worktrees, git management, ... They could become a brilliant agentic dev companion tool. Instead, they chose to chase after being a shitty last-gen agentic dev tool.

something that keeps bothering me is how some people see slop as something that is happening to them, like they're some kind of helpless bystander powerless to do anything about it excuse me but you are a software engineer, you literally learned how to make sand think and make computers do your bidding?! and now you can't do anything about it? please the 2 things you should know to cut through all the noise: 1. you need to give your agent a way to verify its own work (i just posted about this so won't rehash) 2. your codebase is memory the 2nd is not new either, we have always had lint rules, compilers with good diagnostics and strong type systems, LSPs, etc to help us write better code. there is no difference when agents write the code instead, you just need more of them, and stronger ones. forget rules and skills. you can add those later. yes, your codebase is a form of memory to agents! agents love extending what already exists. so every nit and every annoyance you've ever seen, every mistake and bad pattern you observe from agents, needs to turn into a constraint, or the agents will just keep repeating those same mistakes like a virus in your code. then you keep observing and repeating this process until your codebase is locked down enough that even agents with little context can one shot a high quality PR. an even better direction is to refactor/rewrite your code such that agent anti-patterns become categorically impossible. use type systems to your advantage, use libraries/frameworks that reduce the architectural search space, or heck, create your own framework like we did for grok bot and design high quality primitives and well thought out APIs

Introducing SubQ Snap, the first long-context decision model for real enterprise work. Snap is the only decision model or LLM where compute per token stays constant as context grows. • 384K tokens of multimodal context, 12x larger than Jev. • 61.7 on Decision Index v0.2.1 vs Jev’s 57.9, outperforming on 13 of 16 enterprise benchmarks. •Processes 100K tokens and returns a decision in under one second. •$0.05/MM input tokens. Output is free. Available today to anyone. The first 10,000 sign-ups get 100M free input tokens. We’ll likely hit capacity fast and will add users as we add compute. Compute offers welcome 🙂. Latency will be variable as we tune the platform to this new model and category of users. Model speeds are 80ms for minimal context, 250ms for 32k, 660ms for 100k, and 2.5 seconds for 384k. Visit: subq.ai/