Wednesday 31 January 2024

New top story on Hacker News: Show HN: Telescope – Hassle-free company research
Show HN: Telescope – Hassle-free company research
8 by GRVYDEV | 1 comments on Hacker News.
Hey HN. I recently started a company and found myself constantly doing company research for competitors, prospective customers, and outbound leads. As an engineer, I found it challenging to figure out where to get up-to-date company information as well as tedious needing to visit multiple sites often. I built Telescope to solve that problem. Under the hood, Telescope performs multiple search queries the same way I would and synthesizes the results for me. This is very much a WIP but I would love for you to try it out and let me know what you think. Over the next couple of weeks, I plan on continuing to improve Telescope and add more features. Cheers :)

Tuesday 30 January 2024

Wednesday 24 January 2024

Monday 22 January 2024

Tuesday 16 January 2024

New top story on Hacker News: Benchmarks and comparison of LLM AI models and API hosting providers
Benchmarks and comparison of LLM AI models and API hosting providers
27 by Gcam | 7 comments on Hacker News.
Hi HN, ArtificialAnalysis.ai provides objective benchmarks and analysis of LLM AI models and API hosting providers so you can compare which to use in your next (or current) project. The site consolidates different quality benchmarks, pricing information and our own technical benchmarking data. Technical benchmarking (throughput, latency) is conducted through sending API requests every 3 hours. Check out the site at https://artificialanalysis.ai , and our twitter at https://twitter.com/ArtificialAnlys Twitter thread with initial insights: https://twitter.com/ArtificialAnlys/status/17472648324397343... All feedback is welcome and happy to discuss methodology, etc.

Friday 12 January 2024

New top story on Hacker News: Show HN: Marimo – an open-source reactive notebook for Python
Show HN: Marimo – an open-source reactive notebook for Python
53 by akshayka | 13 comments on Hacker News.
Hi HN! We’re excited to share marimo, an open-source reactive notebook for Python [1]. marimo aims to solve well-known problems with traditional notebooks [2]: marimo notebooks are reproducible (no hidden state), git-friendly (stored as Python files), executable as Python scripts, and deployable as web apps. GitHub repo: https://ift.tt/cirGb2k In marimo, a notebook’s code, outputs, and program state are always consistent. Run a cell and marimo reacts by automatically running the cells that reference its declared variables. Delete a cell and marimo scrubs its variables from program memory, eliminating hidden state. Our reactive runtime is based on static analysis, so it’s performant. If you’re worried about accidentally triggering expensive computations, you can disable specific cells from auto-running. marimo comes with UI elements like sliders, a dataframe transformer, and interactive plots that are automatically synchronized with Python [3]. Interact with an element and the cells that use it are automatically re-run with its latest value. Reactivity makes these UI elements more useful and ergonomic than Jupyter’s ipywidgets. Every marimo notebook can be run as a script from the command line, with cells executed in a topologically sorted order, or served as an interactive web app, using the marimo CLI. We’re a team of just two developers. We chose to develop marimo because we believe that the Python community deserves a better programming environment to do research and communicate it; experiment with code and share it; and learn computational science and teach it. We’ve seen lots of research start in Jupyter notebooks (much of my own has), only to fail to reproduce; lots of promising prototypes built that were never made real; and lots of tutorials written that failed to engage students. marimo has been developed with the close input of scientists and engineers, and with inspiration from many tools, including Pluto.jl and streamlit. We open-sourced it recently because we feel it’s ready for broader use. Please try it out (pip install marimo && marimo tutorial intro). We’d appreciate your feedback! [1] https://ift.tt/cirGb2k [2] https://ift.tt/gUe1EkW [3] https://ift.tt/EtoOGmX

New top story on Hacker News: Show HN: Conway's Game of Life, but with a gallery of other peoples patterns
Show HN: Conway's Game of Life, but with a gallery of other peoples patterns
6 by Dave_Bruwer | 2 comments on Hacker News.
This is my spin on Conway's Game of Life. I have added the ability to create an account, save grids that you have discovered, and browse the gallery of grids saved by other people and replay them. This project has served as a sandbox for me to practice various aspects of developing a comprehensive web application from scratch. This was my first time developing a full scale web app with [almost] all the features you would expect. I know it is nowhere near perfect in its current state, but I feel it has reached a point of diminishing returns, and therefore my time is better spent focussing on other projects with more potential. I may continue to develop this project further in the future just for fun.

Wednesday 10 January 2024

Tuesday 9 January 2024

Sunday 7 January 2024

New top story on Hacker News: Show HN: Quickwit – OSS Alternative to Elasticsearch, Splunk, Datadog
Show HN: Quickwit – OSS Alternative to Elasticsearch, Splunk, Datadog
8 by francoismassot | 6 comments on Hacker News.
Hi folks, Quickwit cofounder here. We started Quickwit 3 years ago with a POC, "Searching the web for under $1000/month" (see HN discussions [0]), with the goal of making a robust OSS alternative to Elasticsearch / Splunk / Datadog. We have reached a significant milestone with our latest release (0.7) [1], as we have witnessed users of the nightly version of Quickwit deploy clusters with hundreds of nodes, ingest hundreds of terabytes of data daily, and enjoy considerable cost savings. To give you a concrete example, one company is ingesting hundreds of terabytes of logs daily and migrating from Elasticsearch to Quickwit. They divided their compute costs by 5x and storage costs by 2x while increasing retention from 3 to 30 days. They also increased their durability, accuracy with exactly-once semantics thanks to the native Kafka support, and elasticity. The 0.7 release also brings better integrations with the Observability ecosystem: improvements of the Elasticsearch-compatible API and better support of OpenTelemetry standards, Grafana, and Jaeger. Of course, we still have a lot of work to be a fully-fledged observability engine, and we would love to get some feedback or suggestions. To give you a glance at our 2024 roadmap, we planned to focus on Kibana/OpenDashboard integration, metrics support, and pipe-based query language. [0] Searching the web for under $1000/month: https://ift.tt/dzgJ7Tx [1] Release blog post: https://ift.tt/1lF9xGI [2] Open Source Repo: https://ift.tt/XQvUWxc [3] Home Page: https://quickwit.io

Friday 5 January 2024