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Opengrep quickstart

Since I could not find a quickstart to run opengrep with the full set of rules from their fork I thought I'd document what I found out. Setup Download the opengrep binary from github and make it executable with chmod +x . Clone the rules repo: git clone git@github.com:opengrep/opengrep-rules.git and clean it up to make it usable to opengrep: cd opengrep-rules rm -rf ".git",".github",".pre-commit-config.yaml", "elixir", "apex" find . -type f -not -iname "*.yaml" -delete rm -rf .github rm -rf .pre-commit-config.yaml Ensure opengrep can load the rules with: opengrep_manylinux_x86 validate . The same can be done for custom rules maintained in a separate repository. AFAIU Multiple repositories can be specified by repeating -f option as needed, see below. We are now ready to scan a repo, from the repo root directory run: opengrep_manylinux_x86 scan \   -f <path_to>/opengrep-rules \   --error \   --exclude-rule=VAL some ti...
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Mirth: recover space when mirthdb grows out of control

I was recently asked to recover a mirth instance whose embedded database had grown to fill all available space so this is just a note-to-self kind of post. Btw: the recovery, depending on db size and disk speed, is going to take long. The problem A 1.8 Mirth Connect instance was started, then forgotten (well neglected, actually). The user also forgot to setup pruning so the messages filled the embedded Derby database until it grew to fill all the available space on the disk. The SO is linux. The solution First of all: free some disk space so that the database can be started in embedded mode from the cli. You can also copy the whole mirth install to another server if you cannot free space. Depending on db size you will need a corresponding amount of space: in my case a 5GB db required around 2GB to start, process logs and then store the temp files during shrinking. Then open a shell as the user that mirth runs as (you're not running it as root, are you?) and cd in...
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AI in engineering: useful or not, and what's different?

The narrative around AI keeps bouncing from it's slop and it kills software engineering to it has allowed to clear 2 years of backlog in two weeks . I think both are true, and ultimately the difference is made by how AI is deployed by Leadership. So what's different? Durable Teams and Domain Context:  while AI makes individual implementation much cheaper and faster, high-judgment individuals and agents still hit limits when they lack deep domain context. Durable, high-ownership teams remain the fundamental building block of engineering. AI can accelerate the writing of code, but teams must still possess the proprietary context to know what should be built—and what production metrics or instrumentation are actually required. Structural Improvements Over Haphazard AI Adoption:  clearing a multi-year backlog isn't just a matter of telling engineers to use AI; it requires leadership to focus on structural improvements and clear architectural patterns. When teams use AI to migr...
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