1/2 This is obviously not a comprehensive benchmark, but it’s clear that we finally have an open model that can be trusted and depended upon on difficult research tasks. You can easily run autoresearch yourself with GLM 5.2 by changing ‘arxiv’ to ‘autoarxiv’ for any arXiv URL:
AUTOMATION
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Airwallex targets cross-border payments for AI agents with Airi and T:0
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Everyone's teaching AI agents to shop. Almost nobody is solving the boring part:
— Chubby♨️ (@kimmonismus) 25 juin 2026
letting them move the money, pay across borders, keep the books, stay compliant.
That's the gap Airwallex is going after, with Airi (faster agent checkout today, a real wallet in the works) and T:0… https://t.co/TdPBxF4o5TEveryone's teaching AI agents to shop. Almost nobody is solving the boring part: letting them move the money, pay across borders, keep the books, stay compliant. That's the gap Airwallex is going after, with Airi (faster agent checkout today, a real wallet in the works) and T:0
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Hyperagent gives each agent its own dedicated cloud machine
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We spent two years calling things agents that fall over the second nobody's watching.
— Chubby♨️ (@kimmonismus) 25 juin 2026
A setup tied to one laptop, one wifi, and one person awake at 1am to restart it when it breaks is closer to a pager than to autonomy.
Hyperagent gives every agent its own cloud machine that… https://t.co/VAkGZuAUDoWe spent two years calling things agents that fall over the second nobody's watching. A setup tied to one laptop, one wifi, and one person awake at 1am to restart it when it breaks is closer to a pager than to autonomy. Hyperagent gives every agent its own cloud machine that
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Genspark Design: AI Tool Creates Production-Ready Assets from Ideas
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Workflows Are More Efficient Than Agents for Many Tasks
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A workflow can solve more than people think. Before you reach for an agent, look at the task. Every move from workflow to single agent to multi-agent costs you 4-15x more tokens, more latency, and more time spent debugging.
So make sure the task actually needs all that before -
Andrew Ng explains self-improving loops for AI agents
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/4 Andrew Ng says the next step for AI agents is self-improving loops.
— AlphaSignal (@AlphaSignalAI) 25 juin 2026
In this 31-minute interview, he explains how to build agents that can run tasks, evaluate their own outputs, and improve through feedback instead of relying on better prompting alone.
It is a clear starting…/4 Andrew Ng says the next step for AI agents is self-improving loops. In this 31-minute interview, he explains how to build agents that can run tasks, evaluate their own outputs, and improve through feedback instead of relying on better prompting alone. It is a clear starting
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14-Step Guide: Moving from Hand Prompting to Automated Loop Engineering Systems
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/5 Loop engineering has been everywhere lately. This 14-step guide shows what it actually means to move from prompting coding agents by hand to designing systems that prompt, verify, remember state, and keep running without you babysitting every turn. It covers the basics of
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Autoresearch lets agents run experiments on GitHub repos with one URL change
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/1 Research artifacts are no longer just papers.
— AlphaSignal (@AlphaSignalAI) 25 juin 2026
Autoresearch now lets you point an agent at a GitHub repo, have it orient itself inside the codebase, resolve setup problems, and help run experiments with one URL change.
It's pretty easy. Change GitHub to ARGithub in any repo…/1 Research artifacts are no longer just papers. Autoresearch now lets you point an agent at a GitHub repo, have it orient itself inside the codebase, resolve setup problems, and help run experiments with one URL change. It's pretty easy. Change GitHub to ARGithub in any repo
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Manufacturers shift AI decisions from cloud to edge for low latency
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This is why many manufacturers are rethinking where AI decisions happen, not just how good those models are. The shift is moving decision-making to the edge of the network, inside the factory itself. That means:
→ No dependency on distant cloud infrastructure
→ Ultra-low -
Manufacturers Improve AI Outcomes with Operational Responsiveness
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The manufacturers seeing the strongest AI outcomes are often the ones improving operational responsiveness, not just model performance.
— Ronald van Loon (@Ronald_vanLoon) 25 juin 2026
In many environments, latency can directly impact automation efficiency, waste reduction, and production precision.
Here’s what I learned…… pic.twitter.com/X6UA5qCezKThe manufacturers seeing the strongest AI outcomes are often the ones improving operational responsiveness, not just model performance. In many environments, latency can directly impact automation efficiency, waste reduction, and production precision. Here’s what I learned…