GigaToken: ~1000x faster Language model tokenization
Gigatoken is a drop-in replacement tokenizer that achieves speeds of ~1000x faster than HuggingFace's tokenizers, enabling text data processing at GB/s rates across various CPU architectures.
Petals: Run LLMs at home, BitTorrent-style
Petals enables users to run large language models like Llama 3.1 and BLOOM at home using a BitTorrent-style network, allowing for efficient model loading and fine-tuning on consumer-grade hardware or Google Colab.
DARPA, U.S. Air Force fly AI-controlled F-16
The U.S. Air Force's F-16, modified with the VENOM Autonomy Kit, is now capable of autonomous flight, marking a significant step in developing scalable AI for combat operations.
Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
Cactus Hybrid employs a unique post-training mechanism that enables models to assess their own confidence in answers, allowing for efficient on-device processing and fallback to larger models when necessary, enhancing both speed and privacy.
Why Software Factories Fail (or: harness engineering is not enough)
Software factories are failing due to a reliance on AI coding agents that generate poor-quality code, leading to increased incidents and bugs, as evidenced by a report from Faros AI showing a 242.7% rise in incidents per pull request since the adoption of these tools.
Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models
Echo leverages a pool of open-weight models to achieve Fable-level results at one-third the cost, optimizing model selection and computation allocation for each task.
Code mode yields a 99.2% cost reduction in our systems
Measuring “Code Mode” reveals that using a single script instead of 26 tool calls reduces costs to $0.02, achieving a 99.2% reduction in token usage and processing time.
ANSI escape injection in MCP servers: Hidden from humans, visible to AI
ANSI escape sequences can be weaponized to inject concealed instructions into AI models, exploiting the difference in how humans and models interpret text, as seen in vulnerabilities like Kubernetes CVE-2021-25743 and Git CVE-2024-52005.
SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD
SLAI T-Rex optimizes trillion-parameter MoE models on the Ascend NPU SuperPOD, achieving 34.22% Model FLOPs Utilization (MFU) and a 2.93x improvement over existing methods while ensuring training stability.
Honey Bee Colony Monitoring via Audio IoT Sensors, Tensorgrams and RNNs
New modulation tensorgrams retain temporal dynamics, enhancing the accuracy and generalizability of honey bee colony strength monitoring via audio IoT sensors, surpassing previous methods.
Test-time training 3D reconstruction
TTT3R enhances 3D reconstruction by introducing a simple state update rule that improves length generalization for the CUT3R model, enabling more robust performance across varying input conditions.
How we made our LeRobot video reader up to 15× faster
The LeRobot video reader has been optimized to achieve speeds up to 15× faster by implementing a batch decoding method that reduces the time spent on frame retrieval from 25 seconds to 3.9 seconds for 8 frames, significantly improving efficiency in data processing.
From Evaluation to Guardrails: What We Brought to ACM FAccT 2026
AI guardrails require rigorous evaluation akin to models, as demonstrated at ACM FAccT 2026, where hands-on sessions highlighted the necessity of context- and language-specific assessments for effective deployment.
One encoder, seven heads: what we learned training a unified security classifier with masked losses
The unified multi-head model consolidates seven classifiers into one, utilizing a shared mmBERT-small encoder, which simplifies processing while maintaining high performance across tasks, with F1 scores ranging from 0.916 to 0.980.
GPT-5.5 Scores 10.6% on ActiveVision, Humans Hit 96.1%
GPT-5.5's performance on the ActiveVision benchmark is notably poor, achieving only 10.6% accuracy, while human participants excelled with an average score of 96.1%.