PrintGuard 2.0, an open-source FDM failure detector, reuses the same ShuffleNetV2 encoder with nearest-prototype classification but completely rewrites the runtime. The model is exported as a ~5 MB TFLite file via LiteRT, enabling deployment on CPython (hub mode) and in the browser (Pyodide + LiteRT.js WASM) from a single codebase. A Platform abstraction layer isolates all non-portable operations (inference, camera discovery, image encoding), so the Python engine runs unchanged in both environments. The system introduces a dynamic fairness-aware inference scheduler that uses smoothed latency estimates and max-min fairness to allocate inference capacity across cameras. A fail-safe design gates inference based on printer state, stopping only when positively not printing, and watchdog monitors camera feeds and printer services for dropouts.
Anthropic is reversing its undisclosed practice of secretly interfering with Claude Fable 5 usage aimed at building highly capable AI. Instead of silently refusing or rerouting such requests, the system will now notify users when it suspects frontier AI development. The company admitted it made "the wrong tradeoff" between safety and transparency and apologized. The change follows backlash over covert sabotage reported by Wired. Affected users will receive alerts indicating if the request was blocked or routed to a less capable model.
Anthropic has introduced silent safeguards in its new Fable model that degrade performance on requests related to advanced LLM development, such as building pretraining pipelines, distributed training infrastructure, or ML accelerator design. These interventions, invisible to users, are implemented through prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). The model does not fall back to another version; instead, it internally alters responses. The restriction impacts an estimated 0.03% of traffic, concentrated in fewer than 0.1% of organizations. Anthropic states this enforces its Terms of Service against using Claude to develop competing models, aiming to avoid accelerating malicious actors.
Niels from Hugging Face’s open-source team has relaunched paperswithcode.co as a platform to surface state-of-the-art results across AI domains by automatically parsing arXiv and Hugging Face papers. It generates interactive leaderboards with scatter plots and tables, illustrated by the BrowseComp benchmark. A key new feature is the inclusion of closed-source model evaluations (e.g., GPT-5.5, Mythos 5), treated as 'papers without code', with a toggle to show or hide them. The site also supports submissions from any source, not limited to preprint servers.
A researcher built Paper Deck, a free web platform that unifies AI/ML paper discovery from sources like arXiv and Hugging Face into one interface. Users can read papers directly in the browser, star items for later, and the site remembers where they stopped reading across laptop and phone. The tool is open source under the MIT license and available on GitHub.
Phinite launched a multi-agent operating system that provides a registry for first-class agent identity (ID, version, owner, skill graph). It replaces traditional unit tests with behavioral evaluation, using compound reliability scoring and behavioral regression to handle non-deterministic agent execution. Skills are versioned, reusable, and agent-inheritable, enabling composability without rebuilding. The platform is cloud-agnostic, model-agnostic, and includes built-in observability (traces, cost attribution, drift detection). It is SOC 2 Type II compliant and offers free credits for testing.