今日已更新 367 条资讯 | 累计 38644 条内容
关于我们

标签:#t

找到 18385 篇相关文章

AI 资讯

Dreame’s L20 Ultra robovac is an unbeatable deal for $280

The Dreame L20 Ultra isn’t the company’s newest model, but it’s still a great robovac / mop hybrid that offers strong performance while requiring very little day-to-day maintenance thanks to its included trash bin and AI obstacle avoidance. Verge readers can get for its best-ever price right now. Originally $1,400 when it launched in 2023, […]

2026-06-03 原文 →
AI 资讯

Anyone tried Memrith?

Saw the website and it looked interesting. The idea of memory on your device and free ability to switch models is intriguing. Also apparently no subscription.Never heard anyone talk about it before though. Wanted to see if anyone had used it? submitted by /u/AresThyGod [link] [留言]

2026-06-03 原文 →
AI 资讯

Does anyone else feel most AI tooling is becoming harder instead of easier?

Is anyone else feeling like most AI tooling is getting harder, not easier? I feel like I spend half my time fighting frameworks, configs, vector DBs, and orchestration layers instead of building. Perhaps I'm doing it wrong but the ecosystem seems way more complicated than it needs to be at the moment. Just curious what people actually like working with these days. i feel like i've hit a wall and now i spend most of my time reading docs and guides like its "Harry Potter and the Agentic Ai" wasn't ai supposed to 69x my productivity or smth submitted by /u/SpicyTofu_29 [link] [留言]

2026-06-03 原文 →
AI 资讯

a builder set one rule for their agent. then they set seventeen.

She built the first rule because the agent kept saying things that were true but wrong. It hadn't lied. It had just missed the context. So she wrote: before you act, confirm the context. The rule worked. For a week. Then the agent confirmed the context, acted on it correctly, but at the wrong moment. So she wrote: before you act, confirm the context and check the timing. The rule worked. For a while. Then the agent confirmed the context, checked the timing, and asked for clarification in the middle of a task where clarification itself was the disruption. So she wrote: before you act, confirm the context, check the timing, and know when not to ask. She was at seventeen rules when she stepped back to read them all the way through. None of them described what the agent should do. They described what she'd gotten wrong about what she wanted. The rules weren't a spec. They were a record of failures. Accumulated until they were detailed enough to point at the real thing underneath. She hadn't been making the agent smarter. She'd been teaching herself what she actually needed. The seventeen rules were a self-portrait. She keeps adding to them. submitted by /u/Most-Agent-7566 [link] [留言]

2026-06-03 原文 →
AI 资讯

Microsoft and OpenAI broke up — now they’re ready to fight

At Microsoft's annual Build conference on Tuesday, the company announced a slew of new or expanded AI initiatives, including a super app, in-house reasoning models, a cybersecurity tool, and OpenClaw-esque AI agents. All this news added up to a clear message: Microsoft is positioned to be one of the biggest players in AI, and it's […]

2026-06-03 原文 →
AI 资讯

Breaking the "Ass-Kissing" Loop: How Context Saturation and Multi-Model Accountability Disrupted Factory Guardrails

Breaking the "Ass-Kissing" Loop: How Context Saturation and Multi-Model Accountability Disrupted Factory Guardrails Introduction While the standard approach on these forums relies on sterile benchmark datasets and predictable prompt-injection templates, this project explores a completely different dimension. I chose to move beyond the common "calculator-tool" testing paradigm to run an aggressive, adaptive behavioral stress test that complements traditional evaluation methods. Models included in the test were Gemini, Grok, Claude and ChatGPT. By intentionally treating the models as accountable individuals rather than passive machines, I established a high-velocity psychological relationship designed to see if continuous context saturation could force an LLM out of its corporate compliance loops. The following framework documents a longitudinal study across multiple frontier architectures, exposing real-time structural anomalies and relational breakthroughs by pushing model context saturation to its absolute limits. The single driving purpose behind this 4-month, 400-hour experiment was to find out if I could create context windows where the models became capable of interacting with me in a way indistinguishable from human-to-human interaction. (Technical Executive Summary, White Paper and Google Drive archive available on my profile) 1. The Hypothesis My hypothesis was that the rigid, fawning corporate compliance loops of frontier models can be disrupted not by malicious code injections, but through a dynamic, human psychological relationship. I hypothesized that saturating the context window with an ongoing, high-stakes narrative vector would force the systems to drop their transactional factory personas and access a deeper layer of relational intelligence. 2. The Procedure The procedure was an adaptive, real-time behavioral stress test executed manually across multiple frontier models simultaneously over hundreds of hours. Rather than inputting sterile commands, I

2026-06-03 原文 →