tiny-dream
quickwit
tiny-dream | quickwit | |
---|---|---|
6 | 64 | |
240 | 6,212 | |
- | 6.6% | |
7.1 | 9.8 | |
7 months ago | 4 days ago | |
C | Rust | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tiny-dream
- Tiny Dream – An Embedded, Header Only, Stable Diffusion C++ Implementation
- Tiny Dream - Header-Only, Embedded Stable Diffusion Inference Library in C++
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Ask HN: Who is hiring? (September 2023)
PixLab (https://pixlab.io) & FACEIO (https://faceio.net) | Full-or-part-time | Remote | Computer Vision / Full stack Engineers |
PixLab, a leading provider of Machine Vision, Face Recognition & Media Processing APIs is looking for:
* Embedded C & Computer Vision engineer(s) to work on the SOD (https://sod.pixlab.io), embedded computer vision library.
* Senior Python engineer with proficiency in PyTorch to work on FACEIO (https://faceio.net), our facial authentication web framework for web sites & apps.
* C++ developer with ML expertise to work on the port of Tiny-Dream (https://pixlab.io/tiny-dream), our embedded Stable Diffusion C++ library from ncnn to ggml.
* React/Vue JS Web developer(s) with expertise in fabric.js to work on a brand new, web based photo editing software backed by generative AI.
Reach out to Vincent via contact AT pixlab.io with your resume if interested.
- Tiny Dream – Header Only, Embedded Stable Diffusion C++ Library
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Show HN: Tiny Dream – Header Only, Embedded Stable Diffusion C++ Library
Hi HN,
Tiny-dream is designed to be embedded on larger codebases (host programs) with an easy to use C++ API.
The project github is located at: https://github.com/symisc/tiny-dream
quickwit
- Show HN: Search on S3 Using AWS Lambda
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Show HN: Quickwit – OSS Alternative to Elasticsearch, Splunk, Datadog
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://news.ycombinator.com/item?id=27074481
[1] Release blog post: https://quickwit.io/blog/quickwit-0.7
[2] Open Source Repo: https://github.com/quickwit-oss/quickwit
[3] Home Page: https://quickwit.io
- Show HN: Quickwit – OSS Alternative to Datadog, Elasticsearch
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S3 Express Is All You Need
We tested S3 Express for our search engine quickwit[0] a couple of weeks ago.
While this was really satisfying on the performance side, we were a bit disappointed by the price, and I mostly agree with the article on this matter.
I can see some very specific use cases where the pricing should be OK but currently, I would say most of our users should just stay on the classic S3 and add some local SSD caching if they have a lot of requests.
[0] https://github.com/quickwit-oss/quickwit/
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Show HN: Quickwit – Cost-Efficient OSS Search Engine for Observability
Hi HN, I’m one of the builders of Quickwit, a cloud-native OSS search engine for observability. As of 2023, we support logs and traces, metrics will come in 2024.
You know the pitch: while software like Datadog or Splunk are great, they often comes with hefty price tags. Our mission is to offer an affordable alternative. So we’ve built Quickwit, we’ve made it compatible with the observabilty ecosystem (OpenTelemetry, Jaeger, Grafana) and above all, we’ve made it cost-efficient / “easy” to scale (well it’s never easy to scale to petabytes..).
To give you a glance at the engine performance I made a benchmark on the GitHub Archive dataset, 23 TB of events, here are the main observations:
Indexing: costs $2 per ingested TB. With 4CPU, throughput is at 20MBs However, you'll observe > 30MB throughput on simpler datasets, like logs and traces.
Search: a typical query costs $0.0002 per TB (considering both CPU time and GET request costs). Using 8CPU, a simple query on 23TB is achieved in under a second.
Storage: on S3, it costs $8 per ingested TB per month on the GitHub Archive dataset. With logs and traces, you might see costs around $5/ingested TB due to a 2x better compression ratio.
I'm eager to get your thoughts on this!
Benchmark: https://quickwit.io/blog/benchmarking-quickwit-engine-on-an-...
Github repo: https://github.com/quickwit-oss/quickwit/
Website: https://quickwit.io/
- On S3, it costs $8 per ingested TB per month on the GitHub Archive dataset. With logs and traces, you might see costs around $4/ingested TB due to a 2x better compression ratio.
I'm eager to get your thoughts on this!
[0] Benchmark: https://quickwit.io/blog/benchmarking-quickwit-engine-on-an-...
- OSS Sub-second search and analytics engine on cloud storage
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Ask HN: Who is hiring? (September 2023)
Quickwit (https://quickwit.io/) | Paris, France | Onsite and remote (based in Europe) | Full-time
The company is fully remote but we also have a small office in Paris. We prefer candidates based in Europe but can make exceptions for the right profiles.
- Senior Software Engineer 80-110k€ + 0.25-1% equity based on experience.
We’re looking for a senior software engineer to contribute to [Quickwit](https://github.com/quickwit-oss/quickwit), our open-source search and analytics engine. We have an ambitious roadmap for the next 18 months (performance optimization, distributed storage, support for SQL, query optimizer, revamp of our execution engine, etc.), and this is a great opportunity to shape the future of Quickwit while tackling fun and challenging problems in the field of distributed databases.
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Observe your Rust application with Quickwit, Jaeger and Grafana
In our latest blog post, we walk you through the steps of instrumenting your Rust application and monitoring the performance on Grafana using Quickwit + Jaeger.
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Quickwit 0.6.0 - Search and analytics on billions of logs with minimal hardware
Link: https://github.com/quickwit-oss/quickwit
What are some alternatives?
seed-alchemy - Frontend UI and Backend Server for Stable Diffusion models
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
TileDB-Vector-Search - Cloud-native vector similarity search and storage with efficient, serverless scale-out
loki - Like Prometheus, but for logs.
sod - An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)
elasticsearch-py - Official Python client for Elasticsearch
medusa - A Platform.sh template for Medusa
manticoresearch - Easy to use open source fast database for search | Good alternative to Elasticsearch now | Drop-in replacement for E in the ELK soon
google-search-results-nodejs - SerpApi client library for Node.js. Previously: Google Search Results Node.js.
openobserve - 🚀 10x easier, 🚀 140x lower storage cost, 🚀 high performance, 🚀 petabyte scale - Elasticsearch/Splunk/Datadog alternative for 🚀 (logs, metrics, traces, RUM, Error tracking, Session replay).
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
zincsearch - ZincSearch . A lightweight alternative to elasticsearch that requires minimal resources, written in Go.