Sandboxed Python wrappers bottlenecked parsing. Claude Code migrated the 50k-line library to Go in 20 hours, doubling production speed.
A cloud security platform with over 2,000 employees whose entire codebase is written in Go, with a team responsible for scanning customer files across every cloud environment to identify exposed credentials and sensitive data.
Parsing PDF files required running a sandboxed Python library inside their Go codebase, a resource-intensive workaround that hurt performance and...
“If you go to Reddit or any other blog, you will see tons of questions like 'how do I solve this in Go?' and they will tell you, you can't.”
Cloud security platform for unified visibility and risk prioritization.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Wiz's Code migration is part of this use case:
Related implementations across industries and use cases
Migration delays and hour-long drafts bottlenecked teams. Claude now accelerates coding and cuts client follow-up time to minutes.
Compliance kept AI in pockets; security reviews backed up three weeks. After a weekend rollout, two-week sprints became three-hour sessions.
Novus's agent drew on analytics or code—never both—invisible to the team. Traces caught it; 60% of problems now caught before customers.
Migration delays and hour-long drafts bottlenecked teams. Claude now accelerates coding and cuts client follow-up time to minutes.
Compliance kept AI in pockets; security reviews backed up three weeks. After a weekend rollout, two-week sprints became three-hour sessions.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Sandboxed Python wrappers bottlenecked parsing. Claude Code migrated the 50k-line library to Go in 20 hours, doubling production speed.
A cloud security platform with over 2,000 employees whose entire codebase is written in Go, with a team responsible for scanning customer files across every cloud environment to identify exposed credentials and sensitive data.
Parsing PDF files required running a sandboxed Python library inside their Go codebase, a resource-intensive workaround that hurt performance and...
“If you go to Reddit or any other blog, you will see tons of questions like 'how do I solve this in Go?' and they will tell you, you can't.”
Cloud security platform for unified visibility and risk prioritization.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Wiz's Code migration is part of this use case:
Related implementations across industries and use cases
Migration delays and hour-long drafts bottlenecked teams. Claude now accelerates coding and cuts client follow-up time to minutes.
Compliance kept AI in pockets; security reviews backed up three weeks. After a weekend rollout, two-week sprints became three-hour sessions.
Novus's agent drew on analytics or code—never both—invisible to the team. Traces caught it; 60% of problems now caught before customers.
Migration delays and hour-long drafts bottlenecked teams. Claude now accelerates coding and cuts client follow-up time to minutes.
Compliance kept AI in pockets; security reviews backed up three weeks. After a weekend rollout, two-week sprints became three-hour sessions.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.