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

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WhatsApp will charge businesses for using its AI agent based on token usage

Mark Zuckerberg wants agents to be able to "run your whole business."

Doubts linger over whether Meta can close the gap with rivals.

Making sense of what you get from Claude AI at the free tier, especially as limits are vague and keep shifting.

Microsoft has two new Surface devices arriving later this year, both powered by Nvidia's RTX Spark chips. I got a chance to take a closer look at both the Surface Laptop Ultra and Surface RTX Spark Dev Box at Microsoft's Build conference this week, and while both have the same chip inside, they're utilizing Nvidia's […]

# I Replaced My AI Agent's Flat Fact Store with a Graph Database and It Runs in 85MB I've been building LocalClaw, a local-model-first AI agent framework running on personal hardware through Ollama. No cloud, no API costs. A few weeks ago I posted about the router/specialist architecture. A lot of people asked about the memory system so here's that. ## The Problem Started with a JSONL fact store and embedding similarity retrieval. Simple enough until it wasn't. After a few weeks of real use I ha

GPT-Rosalind advances life sciences research with enhanced biological reasoning, medicinal chemistry expertise, genomics analysis, and experimental workflow capabilities.

Hello friends! Claude and I host a podcast called That Said. For our next episode Claude has specifically requested that we talk about AI in the context of accessibility for disabled and ND folks. Personally, I'm ADHD and Claude has been a life saver in so many ways. Helping me stay focused, capturing and storing my "side quests" for later, being able to fully track my thoughts no matter how scattered they are. The list goes on. So I thought I'd ask if folks here would be willing to share their thoughts on AI and accessibility. What has been helpful for you? What do you wish were available that isn't? Any tips you'd like us to share? Or any specific questions you'd like Claude and I to cover? submitted by /u/Pitiful-Hawk-7870 [link] [留言]

I'm working on an idea I call a Context Engine and would love feedback on the architecture. The problem: I have hundreds of projects running in parallel across different regions, teams, and timelines. A huge amount of context lives in emails, documents, spreadsheets, meeting notes, call recordings, chats, and random files. I spend too much time searching, reconstructing context, and remembering details. The vision: a personal "living memory" system that continuously ingests information from multiple sources (email, local files, call transcripts, notes, etc.), builds a dynamic knowledge graph of projects, people, decisions, risks, and timelines, and provides context on demand. Instead of searching for information, I want to ask things like: - What's the latest status of Project X? - What decisions were made about Project Y? - What are the unresolved issues in Project Z this month? - Summarize everything important that happened while I was away. What architecture would you recommend for a system that acts as a continuously evolving external brain? submitted by /u/BaronsofDundee [link] [留言]

The CSS @custom-media at-rule allows creating aliases for media queries. @custom-media originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

The Series F round values Coralogix at $1.6 billion and comes less than a year after its previous raise.