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奥特曼:PC上装内存太浪费,最好留给AI数据中心

华尔街见闻OpenAI CEO奥特曼语出惊人:本地运行AI极度浪费,全球内存应集中云端,他预警AI将引爆史无前例的“漏洞潮”,底层架构亟待重构;更透露“AI自我复制建厂”已在掌握。比起万亿IQ单脑,他更期待万亿AI智能体共建社会,全面颠覆未来。
· AI应用,OpenAI,Agent智能体,基础设施
AI 资讯

DeepSeek发布全新Agent架构

华尔街见闻
· 大模型,AI应用,DeepSeek,Agent智能体,模型发布

DeepSeek公布下一代agent harness理念

华尔街见闻告别被动响应,Agent正加速进化为全天候“数字同事”。DeepSeek公布下一代agent harness理念,凭双层架构、事件驱动与存算分离强势破局多任务并发瓶颈,实现跨会话自主推进。彻底解放人类注意力,领跑商业与人机协作新范式。
· 大模型,AI应用,DeepSeek,Agent智能体

已进入第二版!傅里叶帮Meta研发机器人原型,打造“Muse物理化身”

华尔街见闻Meta与中国机器人公司傅里叶联合开发人形机器人原型“Max”,并启动第二代研发,计划将消费级AI智能体Muse延伸至家庭场景。新原型将升级操控与感知能力,Meta同步在上海、深圳招募量产人才,但目前暂无销售计划,消费级人形机器人布局仍处研发阶段。
· AI应用,具身智能,Meta,Agent智能体,招聘HR,产品更新
AI 资讯

DeepSeek又开源了!公布Super agent架构

网易科技
2026-11-01T00:30:27+08:00 · 大模型,AI应用,开源,DeepSeek,Agent智能体

Marelleo, a story box I built for my kids

Lobsters

Comments

2026-10-15T00:00:00+00:00 · 大模型,算力芯片,AI应用,具身智能,Google,Anthropic,Meta,搜索RAG,招聘HR,网络安全,榜单评测
AI 资讯

Enlightenment 0.28.0 Release

Lobsters

Their alternative to Qt/GTK (EFL) also got a new release: https://www.enlightenment.org/news/2026-10-31-efl-1.29.0

Comments

· 算力芯片,AI应用,Google,Meta,搜索RAG,强化学习,提示工程,端侧AI,招聘HR,榜单评测
AI 资讯

Novellas with groff

Lobsters

Comments

· 算力芯片,AI应用,Google,搜索RAG,扩散模型,强化学习,招聘HR,榜单评测
AI 资讯

Avoid Public Functions Calling Other Public Functions

Lobsters

Comments

· AI应用,推理思考,搜索RAG,强化学习,招聘HR,开发者生态
AI 资讯

The Kluever Maneuver

Lobsters

Comments

· AI应用,开源,搜索RAG,扩散模型,强化学习,招聘HR

I built a scikit-learn for RSI [P]

Reddit r/MachineLearning
I built a scikit-learn for RSI [P]

After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search:

  1. Sampling: How do we find promising changes efficiently?
  2. Evaluation: How do we know a new variant actually improves the agent?
  3. Diversity: How do we avoid converging too early?

That motivated us to build Gear, an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management.

Gear separates search decisions from execution and evaluation. Our goal is a scikit-learn for RSI, making approaches such as GEPA, RRSI, and Harness² easier to implement and compare. We currently have a working GEPA variant with shared failure diagnosis and staged evaluation.

With GPT 5.6 Luna held at medium reasoning effort, we observed these task pass rates:

Benchmark Base DSH Gear-optimized DSH
AutomationBench Marketing 27.0% 40.0%
AutomationBench Support 17.0% 25.0%
Terminal-Bench 2.1 52.8% 62.9%

At max effort on Marketing, Luna with the optimized harness reached 88.88% objective completion, versus 84.08% for Astra + Codex. Strict task pass rates were 53% versus 57%, so the advantage was in partial completion.

Separately, with both models using the same optimized DSH at max effort, Luna’s estimated API cost averaged $0.0568 per task versus $1.1858 for Astra—95.21% lower, excluding optimization and infrastructure costs.

We’d welcome feedback on candidate evaluation and diversity preservation under limited budgets.

Code · Paper

submitted by /u/Pale-Training-6944
[link] [comments]
2026-10-31 16:33:26 · 大模型,AI应用,开源,Meta,代码生成,Agent智能体,推理思考,搜索RAG,扩散模型,强化学习,模型评测,提示工程,招聘HR,开发者生态
AI 资讯

telemetry.dev

Product Hunt

LLM observability for AI agents

Discussion | Link

2026-10-29 00:13:46 · 大模型,AI应用,Agent智能体,招聘HR
AI 资讯

SpaceO

Product Hunt

Give AI agents their own screen on your Mac

Discussion | Link

2026-10-04 08:20:17 · AI应用,Agent智能体,招聘HR
AI 资讯

Backenly

Product Hunt

The autonomous backend for AI coding agents

Discussion | Link

2026-10-29 21:07:32 · AI应用,Agent智能体,招聘HR
AI 资讯

GitHub Copilot Fusion

Product Hunt

GitHub's AI coding agent that picks and combines models

Discussion | Link

2026-10-29 05:39:35 · AI应用,开源,Microsoft,代码生成,Agent智能体,招聘HR
AI 资讯

Pluto Door

Product Hunt

SSH client with an AI agent and a desktop for every server

Discussion | Link

2026-10-30 22:10:44 · AI应用,Agent智能体,招聘HR,榜单评测

Surviving "The Graduate"

Hacker NewsComments
2026-10-31T10:00:00.000Z · AI应用,Google,快手,文生视频,Agent智能体,搜索RAG,Transformer,扩散模型,强化学习,招聘HR,榜单评测,开发者生态

HackerTube: YouTube built from videos recommended by HN

Hacker NewsComments
· 大模型,AI应用,开源,Google,Anthropic,Meta,代码生成,Agent智能体,扩散模型,招聘HR,榜单评测,开发者生态

EqualAI CEO: Your AI assistant is acting in your name. Who signed off?

Fortune

This summer, investor Katie Jacobs Stanton said her AI assistant, Instinct, sent an email on her behalf without consulting her first, according to TechCrunch. She described the email as innocuous. The breach of trust was not.

Next time, it could be the email that costs someone a job, a marriage or a lawsuit.

The appeal of personal AI agents is compelling. An assistant that can identify priority emails and draft responses, remember your mother-in-law’s birthday and buy a present based on your correspondence is enticing. For people stretched by demanding jobs, caregiving, or a disability, that help can be transformative. But Stanton’s experience raises an important question: when we give an assistant access to our lives, how do we retain control? Assistants such as Instinct and Meta’s Muse act for us rather than simply answering questions or making recommendations. A mistaken purchase may be refundable. An intimate disclosure cannot be taken back.

The harms are already emerging. Forbes reported that when investor Jason Yeh asked Instinct to find open dinner reservations, it booked a table instead, leaving him with a $200 cancellation fee he never approved. TechCrunch reported that one tester couldn’t get his data deleted, and another received an inbox summary hours after disconnecting her account. And in September, security researcher Patrick Wardle exposed a flaw in Muse’s Mac app that could let malware already on a computer hijack the assistant, which Meta quickly patched.

The problem now reaches our bank accounts, too. Last week, major financial institutions reported that personal AI agents were operating inside online accounts. In one case, an agent called a bank without authorization to file a complaint. JPMorgan Chase CEO Jamie Dimon said the bank would not allow unvetted “outside agents to come in inside the company”, and Schwab’s CEO said they’re actively blocking unauthorized agents. White House adviser David Sacks countered that data was shared willingly and these are “in no way cyberattacks.” Each side has a point, which is precisely the difficulty. We have not yet settled on what authorization means when the party acting is software.

The failures are also hard to see. A public record launched this week by the security firm Manifold counts 59 incidents since July 2025, forty-two of them disclosed in the past four months alone. The median incident took 70 days to come to light, and only 11 of the 59 were detected by a security control.

At EqualAI, we have long maintained that good AI governance is good business because trust will ultimately determine whether people adopt these tools at all. We recently convened senior executives who develop and deploy agents to work through a simulated crisis. The exercise exposed the same gaps repeatedly. In our mock company, without sufficient AI governance, audit trails were too thin to reconstruct what an agent had done. Vendor contracts were silent on who answers when an agent errs. Responsibility for oversight was assumed rather than assigned. These gaps were most acute where speed had outrun precaution. Those gaps shaped the roadmap that came out of the exercise.

The legal landscape is moving in the same direction. California law now prevents companies from avoiding civil liability by arguing that the AI acted autonomously. Senators Hawley and Murphy proposed extending federal anti-hacking law to developers and operators whose agents recklessly cause damage.

Those same principles should apply to the assistants people are bringing into their homes. For consumers, they translate into four commitments.

Make permission specific. An assistant should receive only the access a task requires. Permission to read your inbox should not automatically become permission to send emails in your name. Before an assistant spends money, changes who can access an account or shares sensitive data, there should be safeguards in place, which are known to users.

Assign responsibility in advance. If an assistant wrongly cancels your flight, the remedy must be clear. Companies should determine in advance of deployment who investigates an assistant’s unauthorized actions, who can halt them and how users are made whole.

Test with users’ perspective. Independent testing should confirm that assistants ask before acting, respect their limits and fully disconnect when access is revoked. Because models change constantly, testing must be continuous. Wardle’s discovery was valuable. Work like his should be the norm.

Report and study failures. Aviation became one of the safest forms of travel in part because pilots and airlines report near misses and investigators learn from them. Companies should be expected to identify an “AI incident”, preserve the records, notify those affected and correct the underlying cause, while policymakers can establish reporting requirements for serious cases.

Consumers also have a role. They need to understand permissions they’ve granted and how to revoke its access. But the greater responsibility rests with companies selling the tools to define their limits and set reasonable expectations. We shouldn’t need a cybersecurity background to use these tools safely.

We board planes without understanding how they fly because industry and regulators answer for our safety. We need a similar framework for AI assistants. If companies want our inboxes, accounts and errands, they must earn our trust by clarifying what we’re authorizing, how to regain control, and answer for mistakes their assistants make in our name.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

This story was originally featured on Fortune.com

2026-10-31 12:00:00 · AI应用,Meta,Agent智能体,搜索RAG,扩散模型,招聘HR,版权诉讼
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