specification
gerev
| specification | gerev | |
|---|---|---|
| 6 | 28 | |
| 689 | 2,601 | |
| 1.0% | 0.3% | |
| 7.2 | 8.5 | |
| about 1 month ago | 4 months ago | |
| TypeScript | Python | |
| Apache License 2.0 | MIT License |
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.
specification
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How to Use JSON Path
Here's a discussion with more comparisons: https://github.com/serverlessworkflow/specification/issues/2...
- FLaNK Stack Weekly 27 March 2023
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No-code or Workflow as code? Better both
Once you have defined the workflow on a diagram, a JSON, following the Serverless workflow specification, is stored in KuFlow. With this specification, we can use all the great tools/libs that are developed.
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Lessons Learned from Developing Serverless Workflow Runtime Implementation
Among workflow languages out there, we choose Serverless Workflow. It's a vendor-neutral, open-source and community-driven workflow ecosystem. The workflow definition can be written in JSON or YAML format. And then there are SDKs available in various programming languanges, like Java, Go, TypeScript, .NET, Python.
- Serverless Workflow Specification
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Temporal - the iPhone of System Design
This example happens to be from Google, but you can compare similar config-driven syntaxes from Argo, Amazon, and Airflow. The bottom line is you ultimately find yourself hand-writing the Abstract Syntax Tree of something you can read much better in code anyway:
gerev
- A FOSS chat bot trained on docs/ansible?
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Show HN: GPT-4-powered web searches for developers on Phind.com
https://github.com/gerevai/gerev to see for yourself.
Or you could try our sweet little demo: https://demo.gerev.ai
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Show HN: I “leaked” W23 YC internal pages and made them searchable
Some legit funny stuff is hiding here.
You can use gerev to host your own workplace search engine: <https://github.com/gerevai/gerev>
Disclaimer: no I didn't leak YC's internal intranet! all output by partners was generated by ChatGPT. don't sue me!
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What do you self host that has replaced paid services?
gerev - self hosted search engine
- FLaNK Stack Weekly 27 March 2023
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Show HN: Google-like search for workplace knowledge
Or easily bring up important docs in real-time during meetings.
I believe private data should remain private. All too often, AI products send private data to cloud-based LLMs. I believe that AI should assist users without breaching their privacy.
Feel free to check it out <https://github.com/gerevai/gerev>
- ChatGPT-like workplace search engine
- Show HN: Google-Like Search for Workplaces
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A new tool - webdev workplace search engine
https://github.com/gerevai/gerev - growing super fast.
What are some alternatives?
spec - CloudEvents Specification
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
gateway - Manages Envoy Proxy as a Standalone or Kubernetes-based Application Gateway
gerevai
wg-serverless - CNCF Serverless WG
pgvector - Open-source vector similarity search for Postgres
aquarium - AI-controlled Linux Containers
spring-boot-startup-report - Spring Boot Startup Report library generates an interactive Spring Boot application startup report that lets you understand what contributes to the application startup time and perhaps helps to optimize it.
smi-spec - Service Mesh Interface
dotfiles - The best and strongest dotfiles. Editor: Neovim; Shell: zsh(zinit, powerlevel10k); Terminal: wezterm; Desktop: hyprland/sway, ulauncher, dunst; OS: ArchLinux (Ubuntu/Fedora/CentOS)
eu-dcc-hcert-spec - Electronic Health Certificates Specification
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.