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AI 资讯 InfoQ

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

Steef-Jan Wiggers 2026-05-27 17:45 14 原文
AI 资讯 Reddit r/artificial

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]

/u/Meher_Nolan 2026-05-27 17:33 4 原文
AI 资讯 InfoQ

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

Aaron Erickson 2026-05-27 17:04 18 原文
AI 资讯 Product Hunt

Parastore

Simulate real store with LLM-powered synthetic consumer Discussion | Link

KYEONGEOP LIM 2026-05-27 16:53 3 原文
AI 资讯 Reddit r/webdev

serving static data to the front-end

I am trying to build a guess-the-x sort of game/quiz, a full stack application, the stack doesn't matter, I was trying to find a way to send an API payload to my front-end securely without users being able to cheat by inspecting the payload using the requests devtools tab. the idea of this quiz is users are given a pixelated image, and many game details like title, summary, devs, pubs, platforms etc... depending on the difficulty I reveal to the users a percentage of the hints early, e.g. easy they can 30% of [devs, pubs, summary, etc...] 15% at medium, and 0% at hard. the image cannot be unpixelated manually however, at each wrong guess it unpixelated automatically, but for other indicators users can "unredact" them using hint points, (3 in total) obviously if not designed properly this is very each to cheat if a user is even slighly tech savvy since the backend sends the entire payload as-is to the client, what i did was my payload response looks something like this: { summary: { { text: "xxxxxxxxxxxxxxxxxxxxxx", revealded: false, text: "something something", revealed : true } } } and i store game state in redis that way i dont have to hit my database for every post request the client makes to fetch the game data; the redis state looks something like this { answer : game.title, hints_left: 3, guesses_made: 0, full_game_data : game, } then the other issue is the game cover, the data isnt mine, prior to this i had built an ETL pipeline to ingest data from IGDB's api, i only save their cover id to my database, something like 1234abc. so on the initial GET request, i hit their CDN endpoint, get the cover, process it in the backend, (pixelization), base64 encode it, which im aware that encoding binaries to base64 is a 1.37x increase over the original data, but i found no way of transmitting images securely to the front end except this one; so im asking for recommendations. right now this is a little slow, even on local host it could reach 2s for image load, mainly beca

/u/MEHDII__ 2026-05-27 16:27 3 原文
AI 资讯 InfoQ

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

Srini Penchikala 2026-05-27 15:45 14 原文