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SecondStack gives an entire organization access to AI without handing its data to third-party clouds.
The platform ships three end-user workspaces โ Chat for everyday work, Code for engineering teams, and Agent for automation โ running on top of an enterprise LLM gateway that connects to the model providers you choose (OpenAI, Anthropic, Google, and others). Platform and security teams manage everything centrally: who can use which models, what each team spends, and where data lives โ inside your own infrastructure.
Because SecondStack is self-hosted, prompts, files, and knowledge bases never leave your environment. For teams that prefer not to operate it themselves, a managed deployment run by the SecondStack team is also available.
Pricing is not per-seat, so rolling AI out to the whole company does not multiply the bill. Deployment and operations are backed by an ISO 27001-certified implementation partner.
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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 / about 10 hours 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 / about 16 hours 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 / 10 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 / 12 days ago
Fallback chains โ og:title โ twitter:title โ- SSRF protection โ if you fetch user-supplied URLs, you MUST block
localhost, RFC-1918 ranges, and internal hostnames, or your preview endpoint is a proxy into your own infrastructure- Caching โ you do not want to re-fetch a URL on every render
- Rate limiting โ a public...
- Source: dev.to / 14 days ago
GitLab - Create, review and deploy code together with GitLab open source git repo management software | GitLab
liteLLM - One library to standardize all LLM APIs
BitBucket - Bitbucket is a free code hosting site for Mercurial and Git. Manage your development with a hosted wiki, issue tracker and source code.
Portkey - Build production-grade & reliable AI apps with Portkey
VS Code - Build and debug modern web and cloud applications, by Microsoft
Merlin Unified API - One Super API for all AI models (with 90% less error rates)