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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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Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed
Perplexity details its GPU embedding serving stack (Ivy, Tulip, ROSE), which reuses LLM prefill/decode kernels, CUDA graphs, and LazyTensors to cut launch overhead.
Perplexity engineers published a deep dive on the serving infrastructure behind pplx-embed, used across Perplexity Search and its API platform. The stack comprises Ivy (Rust HTTP gateway), Tulip (gRPC scheduling and batching), and ROSE (Runtime-Optimized Serving Engine), which reuses LLM prefill and decode kernels rather than running a separate embedding engine. Optimizations include whole-model CUDA graphs with lazy capture and a LazyTensor abstraction that overlaps CPU batch preparation with in-flight GPU work. Benchmarks are reported against vLLM v0.22.0 in BF16, with FlashAttention 4 generally fastest but FlashInfer 3 winning on Qwen-based models at very long sequence lengths.
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Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend
MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.
The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.
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Show HN: Engrim – A universal, local-first SQLite memory engine for AI CLIs
Developer launches Engrim, an open-source local-first SQLite memory engine giving AI CLI agents persistent memory.
Engrim, shared on Hacker News (89 points), is a universal, local-first memory layer built on SQLite for AI CLI tools. It targets agent-style CLI applications that need durable cross-session memory without cloud dependencies. Details beyond the repository description were not provided in the source text.
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MathKernel: An evidence-aware multi-engine mathematics kernel and MCP server
MathKernel is an open-source, evidence-aware multi-engine mathematics kernel that exposes verification workflows to AI agents via an MCP server.
MathKernel, published on GitHub, is a mathematics kernel that combines multiple computation engines with evidence-aware outputs. It ships as an MCP server, enabling AI agents and coding assistants to perform and verify calculations. The project drew moderate attention on Hacker News.
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Mastercard's new API based digital platform integrates fintech solutions and Mastercard capabilities
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Rapidly scaling online storage to serve over 1 billion ChatGPT users
OpenAI's Habitat online storage platform now handles over 70 million requests per second and 500 PB of data for 1 billion users.
OpenAI details the evolution of Habitat, its online storage platform backing ChatGPT and other products, which began in mid-2024 as a Python client-side library over Azure Cosmos DB. Habitat now processes more than 70 million requests per second, serves over 500 petabytes of data across nearly 40 geographic regions, and supports over 1 billion users weekly. By mid-2025 the client library approach became brittle, so OpenAI moved Habitat into a standalone service to centralize deployments, observability, and multi-tenancy reliability. This is part one of a two-part series; a future post will cover read optimization and scaling the Azure Cosmos DB partnership.
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Edge Case launches Guardian, an AI platform for tracking risk across autonomous systems
Edge Case launched Guardian, an AI platform creating a Digital Safety Twin to continuously track risk across autonomous and defense systems.
Edge Case launched Guardian, an AI-driven platform that fuses safety analysis, engineering data, and operational signals into a continuous Digital Safety Twin. It targets advanced autonomous platforms in commercial and defense markets, with six customer agreements signed at launch. The platform tracks hazards, mitigations, and source-linked evidence across the development lifecycle, shifting safety from one-time review to continuous capability.
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Akuity gives AI agents operational context to safely ship software
Akuity launches Agentic Control Plane and MCP Server to govern AI agent actions inside production software delivery pipelines.
Akuity introduced an Agentic Control Plane and MCP Server that lets AI agents such as Claude, Codex, and Cursor access deployment and cluster context under existing identity, permission, and audit controls. MLB reported the product identified over 100 degraded applications within hours. Akuity positions it against open-source MCP servers granting raw access with no identity inheritance or policy layer.
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Ask HN: How do you manage skills files?
A Hacker News thread debates whether agent skill files are worth managing, citing 2–4x output-token reductions on flagship models in one company's testing.
Commenters argue skills are stored prompts that help less-technical users compensate for weak prompting, while one participant reports company testing found skills reduce flagship-model output tokens by roughly 2–4x, a gap growing with newer models. Others note skills can bundle reusable scripts and inline commands for deterministic context building, and that harnesses now execute backticked commands before the agent sees the skill. Some argue improving model capability makes downloadable skills redundant.
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The Evolution of the Agent Harness
Latent Space essay argues late-2025 agent gains came from models and harnesses maturing together, with harness logic absorbed into model weights.
The piece defines the agent harness as everything beyond model weights—tools, context, memory, guardrails—and charts its evolution from ReAct prompting (October 2022) through AutoGPT's premature autonomy, Cursor/Copilot's human-in-the-loop retreat, and Devin's roughly 15% success rate, to o1's capability overhang and Claude Code's February 2025 terminal agent with permission rules. It argues the Christmas 2025 jump cited by Transformer co-inventor Lukasz Kaiser reflected model and harness curves crossing, and that remaining harnesses will serve human attention rather than the model.
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