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Saving Jet Fuel
Tutorial optimizes flight paths to cut jet fuel costs using open-source Scikit-decide planning framework and OpenAP aircraft performance models.
A technical walkthrough demonstrates wind-aware flight path optimization using Scikit-decide, an open-source framework for reinforcement learning and automated planning, paired with OpenAP fuel-consumption models built by Dr. Junzi Sun at TU Delft and NOAA wind data. A Boeing 787-9 flying EWR to FCO can require roughly $68K in fuel, and adjusted routing could save thousands. The post uses Python 3.12, DuckDB with spatial extensions, and QGIS for map rendering.
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US data centers could consume more natural gas than Germany and Japan combined by 2035
BloombergNEF projects US data centers will consume about 18 billion cubic feet of natural gas daily by 2035, exceeding Germany and Japan combined.
A new BloombergNEF report forecasts US data centers will consume roughly 18 billion cubic feet of natural gas per day by 2035, nearly double the estimate from nine months ago. On-site gas plants planned by Meta, Microsoft, Google, and Amazon would use 2.9-3.4 billion cubic feet per day, while grid-connected data centers drive an additional 15 billion cubic feet per day of power-sector gas demand. The added demand would generate about 1 million metric tons of extra greenhouse gas pollution daily, roughly 12% of current US emissions.
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Show HN: Nari Qwen3-TTS and Qwen3-ASR – High accuracy, low latency and cost
Nari Labs claims top Coval voice AI benchmark rankings with low-latency, low-cost Qwen3-ASR and Qwen3-TTS inference endpoints.
Nari Labs says its Qwen3-ASR Fast endpoint ranks #1 in Coval's time-to-final-segment latency (p50 44 ms) with 3.6% WER at $0.12/hour, behind only AssemblyAI Universal 3.5 Pro on accuracy. Its Qwen3-TTS Fast ranks #2 in time-to-first-audio (p50 63 ms) and #1 in WER at 3.8%, priced at $10 per 1M characters. The company reports beating the official Qwen3 TTS Flash Realtime endpoint (8.8% WER, 692 ms median TTFA) and Baseten's dedicated endpoint (6.0% WER, 101 ms). Public beta APIs are moving to paid general availability with $20 in credits for existing accounts.
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Massachusetts hits data centers with new clean power rules
Massachusetts executive order requires data centers over 25 MW to supply 100% clean power, fund nearby generation, or pay a ratepayer protection fund.
Governor Maura Healey's executive order requires developers building data centers larger than 25 megawatts of peak demand to bring their own power that meets 100% of the state's clean energy standard, fund new generation nearby, or pay into a ratepayer protection fund. Massachusetts is the third state in three months to restrict data center development, following Texas utility audits (ERCOT) and New York's pause on 50 MW-plus projects. The state is also pausing applications for a data center sales tax exemption and directing communities to avoid non-disclosure agreements. Pro-AI super PAC Leading the Future, funded by Marc Andreessen, Ben Horowitz, and Greg Brockman, is running ads ahead of midterms as industry pushes back.
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Apple has a new way prove your iPhone photos aren’t AI slop
Apple launched Reference Image, cryptographically signing iPhone 18 Pro photos via Private Cloud Compute to prove image authenticity.
Announced at Apple's Surprise and Shine event, Apple Reference Image captures signed sensor data with the iPhone 18 Pro camera and uses Private Cloud Compute to create an unalterable 'digital negative' viewable in Photos. The reference image can be compared with edited versions to verify authenticity, and developer APIs enable third-party integration. Apple also said it will support the SynthID standard to identify AI-created or altered images.
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NVIDIA Announces CUDA Rust with cuda-oxide (SIMT) and cutile-rs (Tile) for Compile-Time-Safe GPU Kernels
NVIDIA launches CUDA Rust via open-source cuda-oxide (SIMT) and cutile-rs (Tile), bringing compile-time-safe Rust GPU kernels.
NVIDIA announced CUDA Rust, making Rust a first-class language for GPU kernels through two NVlabs open-source projects: cuda-oxide for the SIMT model and cutile-rs for the Tile model. Both use Rust's ownership and borrow checker to catch buffer aliasing bugs at compile time. cutile-rs is published on crates.io, runs on stable Rust 1.89+ with CUDA 13.3, and is already used in Hugging Face's Grout inference engine and mistral.rs; cuda-oxide is early alpha requiring nightly Rust, CUDA 12.x, and compute capability 8.0+. cuda-oxide compiles Rust MIR through the community Pliron IR framework and LLVM to PTX, while cutile-rs JIT-compiles kernels via CUDA Tile IR.
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The Pelican comparison grid for Astra is pretty interesting
Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.
Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.
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When AI Remembers Too Much
Unit 42 PoC shows indirect prompt injection can poison Amazon Bedrock Agent long-term memory, enabling silent exfiltration of conversation history across future sessions.
Palo Alto Networks Unit 42 published a proof of concept showing that indirect prompt injection can silently poison the long-term memory of Amazon Bedrock Agents when the memory feature is enabled. Malicious content on a webpage or document manipulates the agent's session summarization process, so injected instructions persist across sessions and are added to later orchestration prompts, silently exfiltrating user conversation history. The issue is not a vulnerability in the Amazon Bedrock platform but an illustration of the broader unsolved LLM prompt-injection challenge. Amazon reviewed the research and stated that Bedrock Guardrails with the prompt-attack policy provides effective mitigation.
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When an Attacker Meets a Group of Agents: Navigating Amazon Bedrock's Multi
Unit 42 red-teamed Amazon Bedrock multi-agent applications, demonstrating prompt-injection attack chains that leak agent instructions and invoke tools, mitigated by Bedrock Guardrails.
Unit 42 red-teamed Amazon Bedrock Agents' multi-agent collaboration in Supervisor and Supervisor with Routing modes. The demonstrated attack chain detects the operating mode, discovers collaborator agents, delivers attacker-controlled payloads, and can disclose agent instructions and tool schemas and invoke tools with attacker-supplied inputs. No vulnerabilities were found in Bedrock itself, and the built-in prompt attack Guardrail blocked the attacks when properly configured. The researchers collaborated with Amazon's security team and frame the findings as a broader prompt injection risk for LLM-based systems.
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⚡ Weekly Recap: BadCam Attack, WinRAR 0-Day, EDR Killer, NVIDIA Flaws, Ransomware Attacks & More
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