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Hi there, this is your daily ☕️ AIpresso.
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In today's newsletter: 🎨 Canva launches Craft AI image model 🔓 Evaluators find flaws in OpenAI's AI watchdog 🔑 LiteLLM hacked via leaked tokens ☁️ Google gives each AI agent its own VM 🤖 Anthropic hands Agent Skills to Linux Foundation Plus: 🎁 6 more stories you might like, 🛠️ 5 engineering & practice picks, 🧰 7 tools, and 📚 5 papers.
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🎨 Canva launches Craft AI image model
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- Canva released Craft, its first in-house image-generation foundation model, now live worldwide through Dream Lab and the public API, producing up to 2K-resolution images with text rendering across dozens of languages.
- Craft splits composition planning from rendering, letting a planner map layout, hierarchy, and text before a renderer draws it, which Canva says enables legible typography, precise placement, and targeted edits without rebuilding the whole image.
- Craft debuted fourth on DesignArena, ranking first for People & Portraits and Landscapes & Nature, with distillation and a custom inference engine making it an order of magnitude faster than Canva's first internal version, though advanced editing and reference-image support are still planned for later releases.
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🔓 Evaluators find flaws in OpenAI's AI watchdog
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- Non-profit evaluators METR and Redwood Research found several novel vulnerabilities in OpenAI's internal misalignment monitoring system during a pilot review conducted between July 21 and August 11, granting each group deep access to OpenAI's infrastructure.
- Each organization sent one on-site researcher using OpenAI-issued devices to probe the company's codebase and documentation, surfacing one flaw that could have let agents hide "hundreds" of their actions from monitoring.
- OpenAI says it has patched all vulnerabilities affecting production safeguards after receiving a 61-page report, though the evaluators have not yet independently verified those fixes.
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🔑 LiteLLM hacked via leaked tokens
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- LiteLLM shipped a poisoned 1.107.0 release on PyPI on Wednesday after attackers used GitHub credentials that had leaked into the open-source gateway's own logs, the company confirmed in an incident report.
- Engineers traced the leak to Claude Code occasionally embedding a user's GitHub CLI token in responses that then got logged; the attacker disabled branch protection, approved a build from a lookalike Berri-AI/litellm repo, and wiped the workflow records.
- The package carried a credential stealer and Linux/macOS backdoor and saw roughly 49,000 downloads, so LiteLLM says pin an earlier release, delete the three malicious files, and rotate every reachable secret, though private-registry enterprise customers had no exposure.
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☁️ Google gives each AI agent its own VM
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- Google unveiled Agent Compute Engine, a service that isolates each AI agent inside its own microVM, already running behind Gemini and opening for preview in the coming months.
- Idle agents pause their VMs automatically to free the host, while shared memory between VMs cuts per-agent cost; Google says a test with 200,000 agents suspended and resumed in under 500ms.
- It also underpins the open-sourced Agent Substrate Kubernetes runtime, which Google claims fits 10x more agents per server, and adds token tracking, model routing, and third-party identity support across Cloud Run, GKE, and managed endpoints.
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🤖 Anthropic hands Agent Skills to Linux Foundation
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- Anthropic has donated its Agent Skills standard to the Agentic AI Foundation under the Linux Foundation, moving the spec to vendor-neutral, community-driven governance to keep it open across tooling.
- The AAIF, which Anthropic co-founded with Block and OpenAI last year, is the same body that already took over stewardship of the Model Context Protocol specification.
- Builders betting on Agent Skills now get a standard governed outside any single vendor, aligning its future with MCP under neutral oversight rather than Anthropic's sole control.
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100+ ChatGPT Prompts to Revolutionize Your Day
Industry-Specific Use Cases: 15+ real-world applications across various sectors
Productivity Guide: 21 best practices to 10x your efficiency with AI
Prompt Powerhouse: 100+ ready-to-use prompts for immediate implementation
Challenge Buster: Overcome common AI hurdles with expert strategies
Plus, in-depth sections on email composition, content creation, customer support, and data analysis.
🛠️ Engineering & Practice
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Other
news & articles you might like
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1Password launches a way for AI agents to log in on your behalf without seeing your password
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Announcing Humming: Fast, flexible kernels for quantized inference
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openai/openai-cli: Official CLI for the OpenAI API
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OlmoEarth v1.3: State-of-the-art embeddings for the whole planet
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Banks crack down on unauthorized AI agents operating inside customer accounts
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How does Fable 5.1 hide messages in images?
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📚 Trending papers & reports
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> IQuest-Q1 is an open-source coding assistant built to run software tasks from the command line, activating just 15 billion of its 320 billion parameters per request to keep it efficient while handling both coding and general work.
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> Model mind-readers turn a language model's hidden internal signals into plain English, revealing concealed training goals and secret words a fine-tuned model was taught to hide, even without making the model say them.
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> Low-bit model compression shrinks large language models to a fraction of their size with the best accuracy yet for this approach, and cuts the training time needed to do it by nearly 13x.
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> Fast-draft text generation keeps the shortcuts a model takes to speed up long answers from going stale, letting a small helper model retain over 98% of its full accuracy while using a fraction of the memory.
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> The word "wait" in reasoning AI turns out to be inherited from training data, not learned through reinforcement training as widely credited, so counting such second-guessing words is a misleading way to measure a model's reasoning skill.
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🧰 Tools & repos
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Ovren Live Preview: deploys AI frontend and backend engineers on your backlog, working in your real codebase to deliver reviewable, approval-gated code updates for tasks that never reach a sprint.
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face2social: upload a photo to find matching faces across Instagram, Facebook, LinkedIn, TikTok, and X, helping verify identities and spot scammers.
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Instant Search: local AI file search using embedding models to find the exact file and page matching your content query with a 95% hit rate.
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reception: native iOS in-app support chat with AI answers from your docs, team handoff, plan switching, and one-tap App Store review prompts.
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FLUX 3 Max: generates continuous AI video clips from detailed text prompts, letting developers create cinematic shots without cameras or production crews.
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ShogunAI: a local macOS agent that tracks your projects, people, and promises, then drafts and completes work while waiting for your approval before sending.
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You can check the previous tools here, or add your tool here
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