LLM Based Spark Profiler
Hey HN, Spark event logs run into 100s of MBs and offer a wealth of insight into your workloads but making sense of them has always been quite a bit prohibitive. We’ve recently built a lightweight tool that automatically parses Spark event logs and surfaces targeted insights to help you optimize your data jobs. Whether you’re chasing down a bottleneck or balancing performance vs. cost, the profiler got you covered with real-time configuration recommendations, data skew analysis, and more. Curious how it works in action? Check out this quick Loom video for a walk-through:…
In plain words
LLM Based Spark Profiler is a tool that automatically analyzes Spark event logs to help data engineers optimize their jobs. It parses large event logs and provides targeted insights including data skew analysis and configuration recommendations to identify bottlenecks and balance performance against cost. The tool is designed for teams working with Apache Spark who need to understand and improve their data workload performance without manually parsing hundreds of megabytes of log files.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN, Spark event logs run into 100s of MBs and offer a wealth of insight into your workloads but making sense of them has always been quite a bit prohibitive. We’ve recently built a lightweight tool that automatically parses Spark event logs and surfaces targeted insights to help you optimize your data jobs. Whether you’re chasing down a bottleneck or balancing performance vs. cost, the profiler got you covered with real-time configuration recommendations, data skew analysis, and more. Curious how it works in action? Check out this quick Loom video for a walk-through: https://www.loom.com/share/07348eb54f6b440da93f96753937792a?... We’d love your feedback — check it out at https://app.datasre.ai and let us know what you think!
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, April 2025
the whole month →- IB
Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
Dev tools · 2025 · runpyxl.com
- UC
Life & fun · 2025 · filiph.github.io
- IB
https://the-pocket.github.io/Tutorial-Codebase-Knowledge/
AI · 2025 · github.com
