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Every team with a real Postgres schema eventually hits the same wall: you need a populated database to develop, test, and demo โ but you can't use production data. Either it's regulated (PII, PCI, HIPAA), or the company doesn't exist yet, or your distributed team isn't cleared to touch it.
The existing options are all bad:
test@test.comSeedfast: realistic, relational data generated from your schema alone, no production access required.
Point Seedfast at your live Postgres, give it a natural-language scope (for example, "fintech app with 100 accounts, transactions, and varied balances"), and it fills your database in one CLI command. Foreign keys resolve automatically. Values are domain-appropriate โ names look like names, transactions look like transactions, dates make sense. The same tool works for 10 rows in a unit test and hundreds of thousands in a load test.
Because nothing ever connects to production, there's no PII pipeline to maintain, no security review to clear, no compliance risk to mitigate. The compliance problem doesn't exist instead of being solved.
Teams that can't use production data:
Also a strong fit for any team with 20+ tables and a seed file that's quietly become tech debt nobody wants to own.
Postgres-first CLI, runs anywhere you can run a binary.
seedfast seed !
GitHub SeedfastSeedfast's answer:
Seedfast is schema-driven by design. Point it at your Postgres database and it generates realistic, relational data โ written directly into your live tables, with triggers firing correctly, constraints staying valid, and foreign keys resolved across the entire database. Complex schema features (views, JSON fields, enums, deep multi-level relations) are handled natively. One CLI command. No production access required, no seed scripts to maintain, no PII risk.
Seedfast's answer:
Simplicity. One CLI command, no config files, no dashboards, nothing to maintain. Connect your database, describe what you need in plain English, and the data lands in your tables โ usually under two minutes from install to a seeded database. Same command whether you need 10 rows for a unit test or 500,000 for a load testing.
Seedfast's answer:
Developers in compliance-regulated environments who can't use production data, and anyone tired of writing and maintaining seed scripts. The common thread: they want realistic, relational data without the setup tax.
Seedfast's answer:
We were tired of maintaining seed scripts. Every migration broke them, the data looked fake, and we were burning hours every week on something that should've been a non-problem.
Based on our record, GitHub seems to be more popular. It has been mentiond 2473 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
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:- Source: Hacker News / 4 days agoHost *.
All of this assumes you can actually inspect what the agent did โ 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
# 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
This is why eval and observability ship as a unit, not as separate purchases. agent-eval scores and gates the output โ 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
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
GitLab - Create, review and deploy code together with GitLab open source git repo management software | GitLab
Mockaroo - A realistic data generator to test your app
BitBucket - Bitbucket is a free code hosting site for Mercurial and Git. Manage your development with a hosted wiki, issue tracker and source code.
Tonic AI - The fake data company
VS Code - Build and debug modern web and cloud applications, by Microsoft
Claude Code - Transform hours of debugging into seconds with a single command. Experience coding at thought-speed with Claude's AI that understands your entire codebaseโno more context switching, just breakthrough results.