标签：#RAM

I understand why so many people are questioning software engineering right now. Every week there’s another headline saying AI will replace developers. Junior engineers are worried there won’t be jobs. Senior engineers are wondering how long their experience will stay valuable. And honestly, if you spend enough time on tech Twitter or LinkedIn, it can start feeling like the industry is collapsing in real time. But after using AI heavily in my day-to-day work as a software engineer, I’ve started seeing things differently. AI didn’t make me feel less useful. It made me feel more capable. Before AI became part of my workflow, a lot of engineering time disappeared into things that were mentally draining but necessary: repetitive refactoring debugging small issues writing boilerplate digging through documentation trying to remember syntax cleaning up legacy code writing SQL queries optimizing simple functions translating vague tickets into technical tasks None of these tasks were impossible. They were just time-consuming. Now, a lot of that friction is reduced dramatically. One of the biggest changes I noticed was backlog cleanup. Tasks that used to sit untouched because nobody wanted to deal with them suddenly became manageable. Not because AI magically solved everything. But because it helped reduce the “mental startup cost” of difficult tasks. Sometimes all you need is: a starting point a refactored example help understanding unfamiliar code a faster debugging path quick documentation summaries That momentum matters more than people realize. A task that feels overwhelming at 9AM suddenly becomes achievable when AI helps break it down. I also noticed we started delivering faster as a team. Not in a “replace developers with AI” kind of way. More in a: less context switching faster research quicker prototyping fewer hours stuck on repetitive problems better ticket breakdowns improved communication kind of way. The interesting part is that AI didn’t just help with coding.

I am a software developer who has been using LLMs extensively at work. I wanted to develop a foundational understanding of LLMs, but have no background in machine learning or statistics. So, I started to read and take notes with the goal to eventually write up a developer's guide to the foundations of LLMs. The article kept growing, so I have split it into four parts. This is the first in the series. Hope this helps! submitted by /u/Normal-Tangelo-7120 [link] [留言]

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After announcing tests of premium subscriptions for Facebook, Instagram, WhatsApp earlier this year, TechCrunch and Bloomberg report that Meta is launching a global rollout over the next few weeks and is also starting to test subscriptions for Meta AI. With the new offerings, Meta joins many other tech companies in changing up its subscription plans […]

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I interviewed Samy about the MySpace worm, being approached by Epstein's team, reverse engineering games as a teenager, internet privacy, and building Openpath. Some sections people here may find interesting: 07:54 — Counter-Strike cheats & game hacking 16:00 — Creating the Samy Worm 22:00 — Secret Service raid 31:00 — Manipulating Google Maps traffic 50:00 — Selling Openpath to Motorola 55:55 — Being contacted by Jeffrey Epstein 1:18:15 — Security in the vibe-coding era submitted by /u/rorfm [link] [留言]

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WebAssembly started as a technology tailored to web browsers and is becoming popular as a server-side technology as well. The next step is for Wasm to become a powerful tool for cloud-native applications. When combined with Kubernetes, WebAssembly can revolutionize application deployment, security, and resource efficiency in ways traditional containers cannot. submitted by /u/goto-con [link] [留言]

InfoQ expands its online certification portfolio with new AI Engineering and Organizational Architecture cohorts, giving senior practitioners a confidential peer group to pressure-test production AI, platform, team design, and architecture decisions. By Artenisa Chatziou

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