Simon Willison reported on a challenge where 2,000 people attempted to extract secrets from an AI assistant, OpenClaw, via email. Despite 6,000 attempts and significant token spend, no one succeeded in leaking the secret. The assistant, powered by Opus 4.6, was protected by robust anti-prompt-injection rules.
→ This highlights the effectiveness of well-crafted anti-prompt-injection techniques, crucial for securing RAG implementations and protecting local LLMs like Gemma or Llama from data exfiltration.
A recent German court ruling held Google liable for errors in its AI overviews, prompting discussion on AI agent liability. Bruce Schneier and Nathan Sanders argue that organizations deploying AI should be legally responsible for its outputs, similar to human agents. They suggest that exempting companies from liability for AI errors would create harmful incentives for corporate misbehavior.
→ This ruling sets a precedent for holding companies accountable for AI-generated content, impacting how developers approach RAG and factual accuracy in their own applications.
Hugging Face has introduced a new feature allowing users to deploy a vLLM server on HF Jobs with a single command. This integration simplifies the process of running large language models, offering a streamlined solution for developers. The vLLM server can now be easily set up for various AI applications directly within the Hugging Face ecosystem.
→ This is a significant win for local LLM deployment, making it far easier to spin up vLLM instances for projects like the Gemma 4 contest or custom RAG applications.
Simon Willison reported on a challenge where 2,000 people attempted to extract secrets from an AI assistant, OpenClaw, via email. Despite 6,000 attempts and significant token spend, no one succeeded in leaking the secret. The assistant, powered by Opus 4.6, was protected by robust anti-prompt-injection rules.
→ This highlights the effectiveness of well-crafted anti-prompt-injection techniques, crucial for securing RAG implementations and protecting local LLMs like Gemma or Llama from data exfiltration.
Hugging Face has introduced a new feature allowing users to deploy a vLLM server on HF Jobs with a single command. This integration simplifies the process of running large language models, offering a streamlined solution for developers. The vLLM server can now be easily set up for various AI applications directly within the Hugging Face ecosystem.
→ This is a significant win for local LLM deployment, making it far easier to spin up vLLM instances for projects like the Gemma 4 contest or custom RAG applications.
Hack Your Summer is a new 4-week production sprint for undergraduate and graduate students, and recent graduates, designed to help them build tangible, public-facing projects. The initiative aims to address the current internship crisis by providing mentorship and a structured environment for students to create work they can showcase to future employers. Participants will learn project identification, progress management, and receive peer and mentor support.
→ This initiative could cultivate future talent for open-source AI projects and local LLM development, potentially feeding into communities like the Gemma 4 hackathon.
A recent German court ruling held Google liable for errors in its AI overviews, prompting discussion on AI agent liability. Bruce Schneier and Nathan Sanders argue that organizations deploying AI should be legally responsible for its outputs, similar to human agents. They suggest that exempting companies from liability for AI errors would create harmful incentives for corporate misbehavior.
→ This ruling sets a precedent for holding companies accountable for AI-generated content, impacting how developers approach RAG and factual accuracy in their own applications.
Hugging Face research explores how hybrid models, combining a small language model with a larger one, predict tokens. They found that smaller models excel at predicting common tokens, while larger models are better at less frequent, more complex tokens. This suggests a potential for efficiency gains by leveraging the strengths of both model sizes.
→ This research on hybrid models could inform strategies for optimizing local LLMs like Gemma and Llama, potentially improving performance and efficiency for on-device applications.
Simon Willison reported on a challenge where 2,000 people attempted to extract secrets from an AI assistant, OpenClaw, via email. Despite 6,000 attempts and significant token spend, no one succeeded in leaking the secret. The assistant, powered by Opus 4.6, was protected by robust anti-prompt-injection rules.
→ This highlights the effectiveness of well-crafted anti-prompt-injection techniques, crucial for securing RAG implementations and protecting local LLMs like Gemma or Llama from data exfiltration.
A recent German court ruling held Google liable for errors in its AI overviews, prompting discussion on AI agent liability. Bruce Schneier and Nathan Sanders argue that organizations deploying AI should be legally responsible for its outputs, similar to human agents. They suggest that exempting companies from liability for AI errors would create harmful incentives for corporate misbehavior.
→ This ruling sets a precedent for holding companies accountable for AI-generated content, impacting how developers approach RAG and factual accuracy in their own applications.
Hugging Face has introduced a new feature allowing users to deploy a vLLM server on HF Jobs with a single command. This integration simplifies the process of running large language models, offering a streamlined solution for developers. The vLLM server can now be easily set up for various AI applications directly within the Hugging Face ecosystem.
→ This is a significant win for local LLM deployment, making it far easier to spin up vLLM instances for projects like the Gemma 4 contest or custom RAG applications.
Hugging Face research explores how hybrid models, combining a small language model with a larger one, predict tokens. They found that smaller models excel at predicting common tokens, while larger models are better at less frequent, more complex tokens. This suggests a potential for efficiency gains by leveraging the strengths of both model sizes.
→ This research on hybrid models could inform strategies for optimizing local LLMs like Gemma and Llama, potentially improving performance and efficiency for on-device applications.
Hack Your Summer is a new 4-week production sprint for undergraduate and graduate students, and recent graduates, designed to help them build tangible, public-facing projects. The initiative aims to address the current internship crisis by providing mentorship and a structured environment for students to create work they can showcase to future employers. Participants will learn project identification, progress management, and receive peer and mentor support.
→ This initiative could cultivate future talent for open-source AI projects and local LLM development, potentially feeding into communities like the Gemma 4 hackathon.
Only 200 AI Engineer tickets left - on track to sell out in the next 24 hours. Grab now for over $60k in sponsor credits! Add this to the WTF Happened in 2025? files: OpenAI Economic Research is reporting that token usage for everything outside coding is exploding: Through August
Stop losing money on separate AI subscriptions. Get ChatGPT, Claude, Gemini, and 200+ models in one place for one price with Nexos. Get 50% off with my code: https://nexos.ai/wolf Sakana AI just released a model that beats Fable on some benchmarks, ties it on others, and loses on
Human Agent in the loop I dislike the phrase “human in the loop” because it cedes authority to the machines. Let’s flip the narrative. It’s our loop, we work the same way we always have, now we recruit agents to join the team. An agent-assisted process need not be a black box tha
This is a bad state of affairs. Consider, in particular, some industry dynamics: Frontier models are trained at an enormous cost, and a significant fraction of that cost is recouped in the few post-release months that they are broadly available. After that period elapses, the mod
Incident Report: CVE-2026-LGTM Spectacular hypothetical incident report by Andrew Nesbitt. Day 2, 16:00 UTC --- Two AI review agents from competing vendors, both attached to a downstream pull request bumping foxhole-lz4 , enter a disagreement loop over whether the package is mali
We're beginning a limited preview of the GPT‑5.6 series: Sol, our flagship model; Terra, a balanced model for everyday work; and Luna, a fast and affordable model. Terra has competitive performance to GPT‑5.5 while being 2x cheaper and Luna brings strong capability at our lowest
HP Inc. scales its OpenAI Frontier partnership to deploy AI across customer experiences, software development, and enterprise operations.
OpenAI previews GPT-5.6 Sol, a next-generation model with stronger capabilities in coding, science, and cybersecurity, paired with its most advanced safety stack.
Here's the truth about that MIT AI study everyone's been referencing. Yes, leaning on AI too early or too completely can make you "dumber." But the full study reveals a much more nuanced reality: human skill + AI performs better than human skill alone. When you already know how t
This week’s AI Weekly Brief looks at the emerging government-limited rollout process for frontier models, from Mythos to GPT-5.6, and why an opaque, customer-by-customer access regime could be bad for everyone. It also covers Claude Tag, open model momentum, CEO-led AI ROI, and t
Headlines cover OpenAI's Jalapeño ASIC, GPT‑5.5 Instant updates for free users, and Anthropic's Fable rumors and Claude Tag rollout. KPMG's quarterly survey finds CEO ownership of AI strategy yields three times the reported ROI and shifts priorities toward human‑AI collaboration
This is like saying there's no learning curve to being a manager because your employees will just do whatever you tell them to do. — Timothy B. Lee , on the idea that LLMs take no skill and have no learning curve Tags: llms , ai , generative-ai
Release: datasette-export-database 0.3a2 An embarrassingly tiny release. The pyproject.toml had pinned to datasette==1.0a27 , inadvertently making this plugin incompatible with all other Datasette versions. It's now datasette>=1.0a27 instead. Tags: datasette
tp3_memories_localgemma3:4b)