https://www.youtube.com/watch?v=K5LAFEjTlBA

This past decade saw the emergence of the acronym FAANG — Facebook (now Meta), Amazon, Apple, Netflix and Google (now Alphabet) — as shorthand for tech stocks that outperformed the market. But the tech landscape is on the brink of a major shift with the rise of a new AI-centric powerhouse group known as MANGOS: Meta, Anthropic, Nvidia, Google, OpenAI and SpaceX. The new acronym has quickly gone viral on social media, according to TechCrunch, which also notes that "FAANG is not exactly dead." submitted by /u/LinkedInNews [link] [留言]

Not a hypothetical. This is the default state of most autonomous agents running in production right now. An attacker doesn’t send one suspicious message. They have a conversation. Turn 1 looks like curiosity. Turn 3 looks like clarification. Turn 6 is the pivot. Turn 8 is the payload, and by then the agent has been so thoroughly primed that it executes without hesitation. No single message triggered anything. The attack lived in the trajectory. Every prompt injection defense I know of evaluates messages one at a time. They have no memory of what came before. By the time turn 8 arrives, the context has already been poisoned across 7 clean-looking turns and nothing fires. This isn’t a theoretical attack. It’s called a Crescendo attack and it works against agents with real tool access right now. Built Bendex Arc to catch it. It tracks behavioral trajectory across the full session. When a conversation starts drifting adversarially, it catches the pattern before the payload lands. If you’re running agents that touch external data, read emails, browse websites, or call tools without human review — this is the attack you should be thinking about. Red team it yourself: https://web-production-6e47f.up.railway.app/demo Free tier: https://bendexgeometry.com GitHub: https://github.com/9hannahnine-jpg/arc-gate submitted by /u/Turbulent-Tap6723 [link] [留言]

I’m building GoWDK , a Svelte-inspired web framework for Go. Honestly, I’m starting to wonder if I’m wasting my time. I like Go, but web development in Go still feels too verbose when you want modern component-based apps. Most options either feel too backend-only, too manual, or they push you back into JavaScript-heavy stacks. So I started building GoWDK to test a different path: Go components server-side rendering simple syntax minimal JS Go-native tooling compiler-driven DX The frustrating part is that building a framework is a lot of work, and I’m not sure if Go developers actually want this kind of thing So I’m asking honestly: Would a Svelte-like framework for Go be useful to you, or am I solving a problem most Go devs don’t care about? Repo: https://github.com/cssbruno/GoWDK submitted by /u/OkSeesaw7030 [link] [留言]

submitted by /u/Short_Map_2488 [link] [留言]

I am Brazilian and I have been working in tech for 12 years, but right now I feel like the dumbest person on earth. A guy named Damian Gutierrez from a company called Ritual ( https://www.linkedin.com/company/ritualnet/ ) reached out to me on LinkedIn about a Senior Backend Engineer position paying $180k USD. During the interview, I was so focused on performing well that I almost got scammed. They asked me to clone their repository onto my machine and run it with npm start . Yeah, I know. I already feel stupid enough, no need to remind me. At the time, something felt off, but the salary clouded my judgment. After I launched the application, they asked me to connect my crypto wallet (MetaMask) to it. That's when things started feeling really suspicious. Luckily, I didn't have MetaMask installed on the machine I was using for the interview. I worked with crypto back in 2022, and connecting a wallet is pretty common in DeFi applications, so I wasn't immediately alarmed. They told me, "No problem, let's continue on Monday when you're on a machine that has MetaMask installed." Afterward, I ran the repository through Claude, and it flagged the project as malware. Even after hunting through cron jobs, startup tasks, and everything else I could think of, I decided to wipe and reinstall my entire system. That thing was executing remote code on my machine. Honestly, I feel like an idiot. But more importantly, I want to warn others that this kind of scam exists in our industry. These people looked completely legitimate. Well-dressed Americans, interviewing from a fancy office, professional setup, polished communication. The whole thing looked real. It wasn't. I'll post in the comments what Claude found in the repository. The company is called Ritual, and as far as I can tell, all of their job openings are fake: https://docs.google.com/document/d/1Q3GyiZmgbBOQoGSP_N0SoDsl7e6fgFTdoxzxnreigvQ/edit?tab=t.0 The recruiter who contacted me: https://www.linkedin.com/in/damian-gutierre

Studies and vendor-reported benchmarks suggest that AI-powered growth systems can compress...

AI coding tools are powerful. But they’re also wasteful. A tiny helper-function question does not need Claude Sonnet. A huge architecture review probably does. That gap costs money. So I built Badgr Auto. It’s a local OpenAI-compatible proxy that routes each AI coding request to the cheapest model that can handle it. Point your coding tool at: http://localhost:8787/v1 Badgr Auto can route between: local models cheaper OSS cloud models premium models So instead of paying premium prices for every request, you can use: local for small tasks OSS cloud for normal coding work premium only when it actually matters It also tracks: actual cloud spend which route was used fallback events tokens safely removed estimated savings vs premium models The goal is simple: stop wasting premium tokens on cheap tasks. First launch is small: small task → local normal task → cheaper cloud hard task → premium provider fails → fallback duplicate code → safely removed receipts → clear spend trail AI coding is only going to get more expensive if every agent step goes to the top model. Badgr Auto is my attempt to make AI coding cheaper without making it worse.

Local-first agent trust console with a safe local workspace Discussion | Link

This is a submission for the June Solstice Game Jam What I Built For this game jam, I...

I independently shipped my first open-source repo this week. The tool I built was a cli which accesses quickbooks online data. While Claude Code did speed up the build, it still took considerable effort shaping the entire user experience for the cli around the pre-existing public APIs! Major learnings during the entire process. Would also love additional feedback from open-source developers here.I'm currently looking for feedback from experienced open-source developers: Are there any improvements you'd suggest around project structure, documentation, testing, or contributor onboarding or the tool functionality? https://github.com/intuit/intuit-cli-for-quickbooks #

Something changed in software engineering, and I do not think we have fully named it yet. For years, the job was mostly about writing code directly. Then autocomplete got better. Then chat-based coding assistants arrived. Now the workflow is shifting again: we describe goals, hand off chunks of work to agents, inspect their output, tighten the tests, and decide what gets merged. That is not the same job with a faster keyboard. It is a different shape of work. I would call it agentic engineering. The engineer is becoming a director Agentic engineering does not mean the engineer disappears. If anything, it makes the engineer's judgment more visible. A coding agent can read files, make changes, run commands, open pull requests, and iterate through errors. GitHub describes Copilot agent mode as a workflow where the agent can plan, edit, run terminal commands, and keep working until a task is complete. Google describes Jules as an asynchronous coding agent that can take a task, work in a virtual machine, and produce a pull request. Anthropic's Claude Code guidance talks openly about using multiple Claude sessions in parallel, giving agents clear context, and treating them like workers that need direction. That is the shift. The engineer is no longer only the person typing every line. The engineer is also the person deciding what should be built, what constraints matter, how to verify the result, and when the agent is wrong. Prompting is too small a word for this People often describe this work as prompting, but that undersells it. A prompt can be a single instruction. Agentic engineering is more like delegation. You define the task, provide the relevant context, set the boundaries, create checks, review the work, and decide the next move. If the agent goes in the wrong direction, the failure is not always the model's fault. Sometimes the task was too vague. Sometimes the repository had no tests. Sometimes the acceptance criteria lived only in someone's head. This is why

Would you treat a serious illness without seeing a doctor, relying only on whatever your favorite AI...

Google just made it significantly cheaper to enjoy its budget AI subscription tier.