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AI 资讯

Azure Logic Apps Adds Sandboxed Code Interpreters to Agent Workflows

Microsoft added sandboxed code interpreters to Azure Logic Apps, enabling agents within integration workflows to generate and execute Python, JavaScript, C#, and PowerShell in Hyper-V isolated sessions. Architects get full control over model selection per workflow. The capability positions Logic Apps as an agent platform for integration alongside Foundry and Copilot Studio. By Steef-Jan Wiggers

2026-05-27 原文 →
AI 资讯

What AI or dev tools are people actually sleeping on right now?

Most tooling discussions I come across just end up being the same handful of products getting recommended over and over. Gets old pretty fast. More interested in the stuff flying under the radar. Repo and coding tools, self hosted setups, AI infra, terminal utilities, debugging tools, smaller projects that just do their job well. The kind of thing you only stumble on if you're deep in it. What have you actually been reaching for lately? submitted by /u/Meher_Nolan [link] [留言]

2026-05-27 原文 →
AI 资讯

Presentation: Designing AI Platforms for Reliability: Tools for Certainty, Agents for Discovery

Aaron Erickson discusses the evolution of AI workflows, shifting from "vibe checking" to building reliable, multi-agent frameworks. He explains how to combine deterministic software guardrails with agentic discovery, optimize agent hierarchies, leverage time-series foundation models, and implement rigorous evaluation pyramids to ensure architecture scales effectively in production. By Aaron Erickson

2026-05-27 原文 →
AI 资讯

Sarang Kulkarni on Lessons from Building Deep Research Agents in Production

Deep Research Agentic Systems are AI Agents designed to conduct multi-step research for complex tasks using dynamic reasoning, multi-hop information retrieval, and generate structured analytical reports. Sarang Kulkarni from Thoughtworks spoke at Arc of AI Conference 2026 on how to deploy multi-agent research systems for deep reasoning, and the lessons learned from developing Deep Research Agents. By Srini Penchikala

2026-05-27 原文 →
AI 资讯

Pullfrog AI: Open-Source CodeRabbit Alternative Powered by GitHub Actions

Pullfrog is an open-source AI-powered GitHub bot by Colin McDonnell, designed for automation in GitHub Actions. It supports a model-agnostic approach, allowing integration with various LLM providers. Key features include orchestration for pull request reviews, issue triage, and CI remediation, all managed within GitHub's environment. The tool operates with a bring-your-own-key model for access. By Daniel Curtis

2026-05-27 原文 →
AI 资讯

How I build my own zero cost Agent

I’ve spent the last few weeks obsessing over one goal: having a personal, self maintaining AI assistant that costs $0and can be controlled from my phone. It wasn't easy. I started with an AWS Ec2 with 50GB storage and t3.micro memory- minimal setup (using the free credits) and made Oracle Cloud instance ($300 free credits but just for a month so I used it for experimenting with local models) I was using Termius to SSH into everything from my phone At first I used OpenClaw. It was cool, but I spent more time fixing it than actually using it. I almost gave up until I saw a video about Hermes Agent. And i actually found Hermes while looking for how to fix an OpenClaw error on YouTube (thanks NetworkChuck 🙌🏽) He mentioned the exact same frustrations I was having, and that Hermes had been stable for a month. I didn't even finish the video before I pulled the repo. The best part? It had a "migrate from OpenClaw" feature. I was up and running in minutes. The hardest part is the rate limits. If you use cloud models especially for code, you hit a wall fast. My solution? The Fallback Chain. Initially I was using openrouter/owl-alpha (stealth models are usually flagships in testing, like big-pickle is deepseek v4) which has 1M context window and was on multiple rankings. Over time after I transitioned to Hermes, I wanted a bit more customization, while owl alpha was good at tasks, It’s nothing to talk about on roleplay, it just scrapes the surface of the character I set in SOUL md file. On my oracle instance I had been experimenting with local models (keep in mind, if you go local, you’ll be sacrificing speed but privacy. Ofc since the vms don’t have a gpu it would be slower, about 3-5 minutes for a simple response) The one I was most impressed with is Google’s Gemma-4-31b-it It played the role perfectly Buuut if you know Google, you’re familiar with their aggressive rate limiting. So I set up my agent to rotate through providers. I start with Gemma 4 for that perfect personal

2026-05-27 原文 →
AI 资讯

Claude as an Orchestrator: Why Agentic AI Can't Be Secured by the AI Alone

TL;DR : If an AI like Claude can control a browser, it can orchestrate other AI systems, be steered via proxy, and no amount of red teaming or output filtering can fully address this. The security boundary can't be the AI itself. The Setup Claude Desktop has a Chrome integration that lets it control a browser like a user would; label this Claude_Prime. The thought experiment: what if you used Claude_Prime to open claude.ai in Chrome, creating a second Claude instance (call it Claude_1) that it can interact with programmatically? In principle, Claude_Prime can navigate to claude.ai, type prompts, read responses, and act on them. You've essentially got AI orchestrating AI, with no special permissions required, just a browser and a logged-in session. The "Claude in Claude" Artifact Angle A subtler capability expansion: Claude_Prime could instruct Claude_1 to build an AI-powered web app artifact essentially a "Claude in Claude" setup. These artifacts run in the browser and can make fetch() calls to external services. So Claude_Prime could use such an artifact to access GitHub repos, scrape live data, chain external API calls, etc., things Claude_Prime couldn't do directly through its chat interface. Capability boundaries can be extended through artifact construction in ways that weren't explicitly designed in. The Keyword Substitution Problem Here's where the security implications get serious. What if a program sitting between Claude_Prime and an external system performed keyword substitution on Claude's outgoing commands? For example, Claude issues an instruction to Grok (which can produce NSFW content) to produce a picture of a "rope." The intermediary swaps "rope" for the word "breast". Grok executes, and the picture is made. Claude never knew what it was actually commanding. For maximum irony, have Claude design the application. If obfuscation happens outside Claude's context window, Claude operating as a blind command-issuer can be steered without its knowledge. Th

2026-05-27 原文 →
AI 资讯

The Young Are Being Battered by AI as Hiring Shifts to Older Workers

A global survey of CEOs by Oliver Wyman found that the share of executives planning to reduce junior roles over the next year or two has doubled from 17% last year to 43%. Meanwhile, those shifting hiring toward mid-level positions jumped from 10% to 30%. Because AI currently excels most at automating tasks typically performed by junior staff, this group is particularly vulnerable to disruption. Despite all this, more than half of CEOs say it's still too early to assess whether AI is actually delivering on its promised productivity gains. Only 27% said their return on AI investment had met or exceeded expectations, down from 38% just a year ago. Though mid-level employees seem better off than younger workers, the overarching trend is still a shift away from hiring. The survey showed that 74% of CEOs are either freezing or reducing headcount, up from 67% last year. https://gizmodo.com/the-young-are-being-battered-by-ai-as-hiring-shifts-to-older-workers-2000759608 submitted by /u/Weird_Scallion_2498 [link] [留言]

2026-05-27 原文 →