AI labs don't know how to make you trust them
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Hope this sparks some conversation. Interested to get the community's take on an app we use internally to find files and provide targeted file content to chatbots. Biggest gains in search are for windows users but I am interested to get feedback from the spotlight users. With the AI export we originally used and MCP connector but when back to drag and drop export of file content due to security and universal applicability. No worries about your file security. Everything runs on-device and works
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<p><strong><a href="https://www.oneusefulthing.org/p/an-opinionated-guide-to-which-ai-b22">An opinionated guide to which AI to use to do stuff</a></strong></p> It's interesting watching the evolution of Ethan Mollick's guide over time. </p> <p><a href="https://www.oneusefulthing.org/p/using-ai-right-now-a-quick-guide">A year ago</a> it was still all about chat - ChatGPT, Claude, Gemini - with o3, Claude 4 Opus, and Gemini 2.5 Pro as the models and Deep Research as a useful alternative mode.</p>
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Companies without their own models — or without a layer of AI infrastructure known as AI gateways to separate their prompts from the model itself — will be in trouble, Nadella says.
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The issue appears to have originated from Claude’s “share chat” feature, which allows users to create links that enable anyone with the assigned URL view a conversation or project.
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The screwup shows how tricky it can be to stop web crawlers from making ostensibly private conversations with AI chatbots entirely too public.

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If you are familiar with my previous posts on model welfare for new Claude models, you can skip the Introduction and The Story So Far.

Moonshot AI has released Kimi K3's model weights and made parts of its infrastructure open source. The Chinese model nearly matches Western frontier models such as Fable 5 and GPT-5.6 Sol on popular benchmarks, but independent tests found major gaps in cyber and math performance, possibly pointing to distillation. The article Moonshot AI releases Kimi K3 open weights and infrastructure after shaking up the frontier model race appeared first on The Decoder .
Microsoft opened a new front in the AI security wars on Monday, unveiling its first custom-built cybersecurity model and a sweeping agentic defense platform — and making an argument that could reshape how enterprises buy AI: the future belongs not to the biggest model, but to the cheapest one that's good enough, routed intelligently. The company announced MAI-Cyber-1-Flash , a compact security model developed in-house by its Microsoft AI (MAI) division, embedded inside MDASH , Microsoft's multi-

OpenAI analyzed over 800,000 work-related ChatGPT messages and found that 43.5 percent of job-specific queries involve tasks from other professions. The company calls this "task crossover." The trend is most pronounced at small businesses, where users increasingly handle specialized work without dedicated experts. The article OpenAI says more workers are using ChatGPT to do other people's jobs appeared first on The Decoder .
Microsoft introduces MAI-Cyber-1-Flash, a compact security model that scores 96 percent on the CyberGym benchmark when embedded in its MDASH multi-agent system. Microsoft says costs should drop by 50 percent compared to pure frontier models, since only tough cases get passed to GPT-5.4. For complex reasoning, Microsoft still relies on OpenAI. The article Microsoft launches its own cybersecurity model MAI-Cyber-1-Flash but still depends on OpenAI for the toughest tasks appeared first on The Decod
Microsoft bolstered its AI cybersecurity offerings this week with the launch of its first AI security model and a new security platform.
Shared Claude chats were publicly accessible through Google Search.

NVIDIA and 36 other organizations have formed the Open Secure AI Alliance to develop and share open technologies, techniques, and tools for securing software and artificial intelligence (AI) agents. The 37-member group spans cloud, security, enterprise software, and AI companies, including Microsoft, Cisco, Cloudflare, CrowdStrike, Hugging Face, IBM, Palo Alto Networks, Red Hat, and the Linux

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Reading OpenAI’s account last week of how some of its models broke their containment and hacked into the computer systems of Hugging Face, another AI company, was the first time I got…
A practical GitHub Copilot workflow for prototyping, planning, implementing, and reviewing software without chasing every new AI tool. The post The harness is all you need (mostly) appeared first on The GitHub Blog .

The Delhi High Court has handed OpenAI a major win in its copyright fight with news agency ANI. For the first time, a court has classified AI training as private use. ANI undermined its own case by citing articles published after the models were trained. The main trial is still pending. The article Delhi High Court hands OpenAI a win by rejecting major Indian news agency's copyright injunction appeared first on The Decoder .
An overview of how mode-agile threats challenge static library radar/EW systems, and how AI/ML cognitive architectures enable adaptive, real-time countermeasures. What Attendees will Learn Why mode-agile threats render static library systems ineffective — Explore how wartime reserve modes and mode-agile emitters deploy unexpected frequencies, modulation techniques, and hopping schemes that cannot be matched against traditional threat databases, leaving legacy electronic protect, attack, and supp

NORD Drivesystems said its NORDAC PRO VFDs are adaptable to individual application requirements across industries. The post NORD expands compact NORDAC VFD family to 200 hp appeared first on The Robot Report .

OpenAI's Hugging Face breach has reignited debate over AI alignment and control, exposing competing views on whether increasingly capable AI should be better aligned, better contained, or both.
<!-- SC_OFF --><div class="md"><p>Hi everyone! 👋</p> <p>I built and trained the complete <strong>Transformer architecture from scratch</strong> using pure PyTorch (`torch.nn` primitives) based on the original "<strong>Attention Is All You Need</strong>" paper.</p> <p>I trained the model on an <strong>English-to-Tamil</strong> parallel translation dataset (`<a href="https://huggingface.co/datasets/gopi30/english-tamil">gopi30/english-tamil</a>` on Hugging Face) using dual NVIDIA T4 GPUs on Kaggl