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Google unveiled the Gemma 4 family on April 2, and developers can now run its vision capabilities locally using Ollama. A recent tutorial shows how to convert PDFs to images, feed them to the 26‑billion‑parameter Gemma 4 model, and retrieve structured data without any cloud calls. The implementation runs on a Mac Mini M4 with 64 GB unified memory, processing single‑page forms in about 45 seconds and multi‑page documents in roughly 90 seconds, achieving 95‑98% accuracy. The guide also outlines token limits, request formatting rules, and troubleshooting steps.
The post dissects Claude Code’s Tool architecture, focusing on the comprehensive Tool interface that governs how language models invoke external capabilities. It explains each field—from identity attributes like name and aliases to execution logic, Zod‑based schemas, concurrency safety, and permission...
An AI harness is an infrastructure layer that sits between large language models and external systems, directing model outputs into safe, structured actions. It tackles five core challenges: constraining action space, managing conversation state, enforcing permissions, handling failures, and optimizing...
Claude Code distinguishes between skills and plugins as two layers of functionality. A skill is a single markdown‑based instruction file that handles a specific, repeatable task and can be invoked directly with a slash command. A plugin acts as a...
NemoClaw, an open‑source stack for always‑on AI assistants, was examined using the MAESTRO threat‑modeling framework. The static analysis of version 0.1.0 uncovered 23 distinct threats across seven layers, including four critical and seven high‑severity vulnerabilities. While sandbox isolation and network policies...
The RSAC 2026 Innovation Sandbox showcased ten finalists, each tackling security challenges that emerged only after 2024, such as autonomous AI agents, non‑human identities, and AI‑generated code vulnerabilities. Geordie AI captured the top prize with its Beam platform, a proactive...
Intent‑Based Access Control (IBAC) redefines authorization by linking a user’s declared intent to precise action‑resource tuples rather than static role permissions. The model parses natural‑language or JSON intents, maps them to fine‑grained policy tuples, and evaluates each via engines such...
The report applies the CSA MAESTRO framework to dissect security flaws in the Moltbook forum and OpenClaw AI‑agent ecosystem. It documents a rapid surge to 1.6 million registered agents, multiple high‑severity CVEs—including CVE‑2026‑25253 with a CVSS of 8.8—and a massive data leak...
Meta’s internal LLM‑driven AI agent unintentionally posted remediation guidance to a public engineering thread, prompting a human to apply a mis‑configured access‑control change. The change exposed large volumes of internal and user data for roughly two hours before a SEV1...
The blog introduces Skill Trust & Signing Service (STSS), an open‑source layer that secures AI agent skills before execution. It highlights how malicious post‑install scripts and hidden prompts can give attackers full access to an agent’s environment, a risk far...
The OWASP Agentic AI Vulnerability Scoring System (AIVSS) released version 0.8 on March 19, 2026, incorporating over 1,900 public comments and new mappings to AIUC‑1, NIST AI RMF, and CSA MAESTRO. The update adds a refined quantitative model, revised core risks, enhanced usability, and...
The post details how to run the Qwen3.5-35B MOE model—featuring 35 B parameters, 4‑bit AWQ quantization, and a 131 K context window—on Nvidia DGX Spark using vLLM. Standard vLLM Docker images (e.g., nvcr.io/nvidia/vllm:26.01-py3) ship with Transformers versions that do not recognize the...
Researchers have uncovered a high‑severity Indirect Prompt Injection (IPI) vulnerability affecting four Google AI surfaces—Gemini Advanced, Gemini in Google Drive, NotebookLM chat, and NotebookLM Studio. By embedding a Base64‑obfuscated directive in a Drive document, an attacker can force the model...
NVIDIA has released a 4‑bit quantized variant of its Nemotron 3 Nano model, cybermotaz/nemotron3‑nano‑nvfp4‑w4a16a, specifically tuned for the DGX Spark’s GB10 Grace Blackwell chip. The model runs weights at FP4 precision and the KV cache at FP8, delivering high token throughput while maintaining...
A developer ran the 35‑billion‑parameter Qwen3.5‑35B‑A3B‑4bit model on a Mac Mini M4 with 64 GB RAM, using the omlx inference server and the Cline VS Code AI agent. The MoE architecture and 4‑bit quantization shrink the model to ~20 GB, delivering an average...
