pinkfluffymochi
Joined 4 years ago
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Member since
Mar 22
data fullstacks building tools to enable more data fullstacks at fleak.ai
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Comment · 1 year ago
Fleak.ai | Full Stack | REMOTE or onsite (San Jose, California)
About us: we build semantic translation layer for machine generated data streams. The "google translate" for the machine universe to make every device on the planet off the planet AI-ready. If your ambition is not just building another workflow AI agent but bringing AI into the mission critical backbone of our society.
Please apply here: https://amazing-diadem-1ef.notion.site/Full-Stack-Engineer-2...
Comment · 1 year ago
We've built an app that solves one of the most tedious problems in security engineering - normalizing diverse log formats into a standard schema (OCSF). Security teams typically spend months manually mapping proprietary log formats to work with their tools, costing organizations millions annually in integration costs.
Our approach:
AI-assisted mapping that analyzes your logs and suggests appropriate OCSF mappings Support for both structured JSON logs and unstructured text logs Real-time validation against sample logs as you develop Export portable config files that can run in production with our open-source execution engine
The app is free for the community: https://app.ocsf.fleak.ai/
Break our AI, Win a Chair: We've also launched a challenge with our log classifier AI. Try to stump our classifier with unusual security logs at https://ocsf-classifier.fleak.ai. Winners who successfully "break" our AI will receive a camping chair at our RSAC booth (ESE-65) next week! This helps us improve our models while you test your security knowledge.
We've open-sourced the execution engine (ZephFlow) that powers the production deployments: https://github.com/fleaktech/zephflow-core
Looking for feedback on usability, especially from security engineers who've dealt with log normalization. We're also building a library of templates for common security logs - if you have particular log types that need mapping, we'd love to help create templates for them.
Comment · 2 years ago
This is gold! practicing this pitch in front of a mirror right now. Agree on the AI part, time has changed....(we figured it was a great buzz word 6 months ago...now not so sure any more) T_T
Comment · 2 years ago
The common use cases we found so far are two: data tagging see https://docs.fleak.ai/1.0/tutorials/sentiment-labeling
and embedding pipeline into vector store, see: https://docs.fleak.ai/1.0/tutorials/pinecone-embedding
Comment · 2 years ago
Fleak | Fullstack Engineer | Remote (European timezone) |Full time| https://fleak.ai/alpha-test
Fleak Explained: A serverless AI workflow builder. Fleak offers a user-friendly visual orchestrator, allowing users to craft their Directed Acyclic Graphs (DAGs) by seamlessly connecting data sources, AI models(GPU/CPU), and user defined functions. With a single click, the visual designs evolve into distributed, high-performance workloads and automated API endpoints. Our core engine stands out with its ability to deploy multi-model real-time workflows without data storage
Our founding team has built Netflix DataMesh, Splunk Core Search, and a quant VC firm with over $1B AUM. We have closed a $1.3M Preseed within 2 months from valley's top infrastructure and cyber security funds. For more details, please apply here: https://amazing-diadem-1ef.notion.site/Full-Stack-Engineer-2...
Comment · 2 years ago
Fleak (fleak.ai)|Founding Engineer(Java/compilers/distributed systems)|Full-time|Remote/Onsite|San Jose, CA.
Fleak Explained: A compound AI system for real time data streams. Fleak offers a user-friendly visual orchestrator, allowing users to craft their Directed Acyclic Graphs (DAGs) by seamlessly connecting data sources, AI models(GPU/CPU), and user defined functions. With a single click, the visual designs evolve into distributed, high-performance workloads and automated API endpoints. Our core engine stands out with its ability to deploy multi-model real-time data pipelines without a centralized data store over 100 times faster than patching python libraries onto Spark/Flink frameworks.
Our founding team has built Netflix DataMesh, Splunk Core Search, and a quant VC firm with over $1B AUM. We have closed a $1.3M Preseed within 2 months from valley's top infrastructure and cyber security funds. For more details, please apply here: https://amazing-diadem-1ef.notion.site/Founding-Engineer-e34...