Spotlight by Backplanes

Spotlight replaced a bespoke string of skills I would have to run by hand. Super helpful!

Interesting history for how you stumbled on the product. Excited to try.

So cool. Will this help me track what actions Claude is actually taking on my behalf? Is there a way that I can use this report to help convince my boss that it's ok for me to use coding agents?

I can definitely see that value here. It reminds me that I constantly have this nagging fear as I'm building with agents, with that inside voice constantly thinking "what are you really doing under the hood". The great thing about humans doing the development is we're slow! That acts as a natural fishing net to catch inadvertent security disasters. But as we transition to agentic engineering, speed will overwhelm us unless we have tools like this.As the other conversations have mentioned here... the real secret sauce here is how you "discover" these issues. Its going to be tricky across different models and platforms, as each will probably have their own screw-up signatures that will need tuning.

This hits a real blind spot with coding agents. They can move fast, but knowing what they quietly touched, broke, or exposed afterward feels just as important as the code they shipped.

This is rad. kind of terrifying to see how much some of my sessions cost!

Congrats on the launch and best of luck today. Looking forward to seeing how developers use Spotlight to build better feedback loops around their AI-assisted development process.

It is not built for windows right? because i am only seeing for macOS, Linux, and WSL 2.

Really like how Spotlight turns hidden agent behavior into clear reports, what’s been the most eye-opening pattern you’ve seen teams discover so far?

The interpretation layer on top of raw transcripts is the real product here. Distinguishing a retry storm from deliberate re-verification, or flagging a credential class without holding the value, it's genuine signal extraction. We've wrestled with agent filesystem boundary decisions. How do you handle cross-session pattern detection when the same agent operates across different repos or machines?

The Neil story is the real pitch here, not productivity, but security. Most devs assume they're reviewing what the agent does, but at scale (multiple sessions, multiple team members), drift is invisible. The framing as "session reports" makes it feel like a dev tool, but this is really an audit trail. Smart. Curious whether you'll add diff-level visibility (which files were read, not just that 47 were).

300$ for 50min coding. what kind of models are you running? 😅 How does it get recursivly better for each session i dont get it? reminds off entire.io

This is a useful direction. For coding agents, the hard part is usually not generating more code, it is making the session reviewable afterward.The report I’d want is pretty boring: changed files, risky assumptions, tests/checks run, failed attempts, and a short “what a human should look at first” section.

The OS-level instrumentation approach is smart. It captures what agents actually do rather than what they report back. We've run into exactly this problem: an agent silently inlining an API key when it couldn't find the env var, and that key landing in git history. How do you distinguish intentional credential usage in test fixtures from actual leakage?

The "what your agents actually did" angle is great, that read-47-files scare is too real. When you're running several harnesses at once, does Spotlight give you one combined report or one per session?