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BlackTent – a strictly local CLI for sanitized incident bundles

Hacker News

BlackTent – a strictly local CLI for sanitized incident bundles

BlackTent is a strictly local CLI tool that generates policy-constrained, sanitized bundles of a project during incidents or support situations. It scans code, configs, and (optionally) logs, replaces credentials and other secret patterns using deterministic rules, and outputs a standardized bundle with a machine-readable manifest. The bundle is intended to be reviewed and then shared with external help (LLMs, vendors, contractors) without accidentally leaking credentials. The tool itself runs entirely on your machine: no network calls, no telemetry, no session history. Redaction rules are fixed and auditable; the same inputs and rules produce the same output, making review and diffing possible. This is not a sandbox, not an AI agent, and not a full incident response or forensic pipeline. It does not protect IP, does not guarantee safety against malicious recipients, and does not replace vendor due diligence. The goal is to reduce accidental oversharing under time pressure by making it trivial to produce a reviewable, constrained artifact. I’m looking for feedback on threat-model boundaries, redaction guarantees, bundle/manifest design, and how people would integrate this into real incident or support workflows.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, model · Missing: agents, macos, cursor
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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