Gretel AI Beta² Reviews

GitHub Reviews

1. Reinhard

   · Boss at CLOUD Meister · over 5 years ago
   perfect 4 open Source

Gretel AI Beta² mentions (6)

• The Shift to Synthetic Data Markets: How to Prepare Your C# Applications for 2026

  4. Third-Party Tools Gretel and Hazy offer enterprise solutions with C# SDKs for complex synthetic data needs. - Source: dev.to / 9 months ago

• Assessing the Quality of Synthetic Data with Data-centric AI

  If you are working with synthetic data and would like to learn more, check out the blogpost that demonstrates how to automatically detect issues in synthetic customer reviews data generated from the http://Gretel.ai LLM synthetic data generator. Source: about 3 years ago

• Synthetic Data

  I was chatting with the founder of GretelAI recently and learned a lot about the area of synthetic data. Source: over 3 years ago

• Generating Synthetic Data from Prod systems into Dev/UAT

  The requirement is to build out synthetic data with very similar size and shape to our production data and ideally have a framework to do this, maybe at some level use our data and "de-productionize" it. gretel.ai looks like it may be a fit, but from what I see I need to upload production data to their environment and that's a no-go. Source: over 3 years ago

• Ask HN: Will AI-generated images flooding the web pollute future training data?

  > https://gretel.ai/ Where were you the last decade of my professional life? I couldn’t find anyone to take my money for exactly this. - Source: Hacker News / almost 4 years ago

GitHub mentions (2473)

• Superlogical – Mitchell Hashimoto

  Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:

          Host *.

  - Source: Hacker News / 4 days ago

• Your Agent's Confidence Score Is Not a Probability

  All of this assumes you can actually inspect what the agent did — the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 5 days ago

• Foreman 101: agentic coding as Kubernetes resources

  # git: the API token, plus the credential used for the push Kubectl create secret generic foreman-github \ --from-literal=GITHUB_TOKEN="$GITHUB_TOKEN" -n foreman-system Kubectl create secret generic foreman-git-credentials \ --from-literal=token="$GITHUB_TOKEN" -n foreman-system Helm upgrade foreman llmkube/foreman -n foreman-system --reuse-values \ --set agent.githubToken.secretName=foreman-github \ ... - Source: dev.to / 5 days ago

• Stop Judging Every Run: Eval Sampling Is a Budget Decision, Not a Coverage One

  This is why eval and observability ship as a unit, not as separate purchases. agent-eval scores and gates the output — the tiers above, drift, hallucination. AgentLens captures the trace of how the agent got there: every model step and tool call, the resolved inputs, the raw outputs, the trajectory. Two things fall out of that:. - Source: dev.to / 15 days ago

• Claude Code permission rules: how allow, deny, and ask actually match

  The real fragility is in trying to constrain arguments. The docs are explicit that a pattern like Bash(curl http://github.com/ *) fails to do what it looks like it does. It won't match curl -X GET http://github.com/... (option before the URL), curl https://github.com/... (different protocol), curl -L http://bit.ly/xyz (redirects to GitHub), URL=http://github.com && curl $URL (variable), or curl http://github.com... - Source: dev.to / 16 days ago