... real documents as part of their work. Invoices, delivery slips, receipts, statements, forms, anything where you extract fields and then do something with the result afterwards. One real workflow per team, roughly 20 to 100 documents through space ocr. Redacted is completely fine. I set the fields up with you, and we compare the output against your real values together. The number I actually want. When the extraction is wrong, does space ocr notice? Every value it returns is checked ...

... precheck is exactly where that ambiguity gets rebuilt one level down. Comment from Aadil Ghani(@aadilghani): @alex_amancio_candoa_   Both of those are the same move at different layers, and it generalises well enough to be worth naming. Redaction takes the model out of the trust path for perception, the floor takes it out for adjudication, and in both cases its contribution is one directional. That is what makes an unreliable judgment safe ... ... cannot see from the inside. The part I think is undersold: filtering before the model sees it also filters before it can enter a snapshot, a transcript, or anyone's audit row. Everything downstream is a copy, and redacting

... delta, instead of overloading empty fragments to mean something they don't one explicit signal beats three guards you'd maintain forever. On the Anthropic side it's the arrival of the reasoning block that counts, not its contents: redacted blocks are empty by design, so presence is the signal. The SSE test harness counts reasoning reports now, so a spurious one can't hide inside the streamed text. 172 tests , green. Verified live against Ollama (cold and warm start ...

... that, it can only pull stuff from its knowledge base, stuff that was created by a human mind. But I'm interested in your perspective, which benefits do you see ? Do you have concrete example of letting AI redact threads and produce marketing materials and it felt right, interesting, constructive & worked very well ? NB: English is not my mother tongue and this was redacted

... generated code, tool outputs, repository context, approvals, and policy decisions all tied together. At that point retention is no longer just how long do we keep logs? it becomes a lifecycle governance problem around what can be stored, exported, redacted, deleted, or legally preserved across the entire AI workflow. Comment from Keesan(@keesan12): Capture is the easy part. If the record can't explain why a blocked change was blocked and whether the next retry is actually new, the dashboard ... ... around it hits really close to what we've seen working with enterprise AI deployments. When policy and provenance are decoupled, audit trails start depending on everything staying perfectly aligned and they rarely do in practice. On the SSO vs

... latest beta room previews the future shopify_oauth_states table, including: state fingerprint payload hash signature hash session fingerprint tenant binding workspace binding Shopify store binding operator user binding expiry window consumed-at marker replay tracking callback receipt reference redacted metadata only The goal is to make Shopify OAuth state one-time-use, auditable, replay-resistant, tenant-bound, and safe before enabling any real exchange path. What is still left before first test users: Create the actual OAuth state vault ... ... after state exchange is proven. Token storage is its own separate gate. The system should not persist live Shopify access tokens until: OAuth exchange is working safely state replay protection is working token vault insert path is tested token redaction

... latest beta room previews the future shopify_oauth_states table, including: state fingerprint payload hash signature hash session fingerprint tenant binding workspace binding Shopify store binding operator user binding expiry window consumed-at marker replay tracking callback receipt reference redacted metadata only The goal is to make Shopify OAuth state one-time-use, auditable, replay-resistant, tenant-bound, and safe before enabling any real exchange path. What is still left before first test users: Create the actual OAuth state vault ... ... after state exchange is proven. Token storage is its own separate gate. The system should not persist live Shopify access tokens until: OAuth exchange is working safely state replay protection is working token vault insert path is tested token redaction

... everyone , I've been building Prompt Armour a browser extension that detects and redacts sensitive data in real-time inside ChatGPT, Gemini, and Claude before you hit send. The problem: every time you paste code, emails, documents, or customer data into an AI chatbot, that data potentially gets stored, logged, or used for training. Most people don't think twice about it. How it works: - Scans your input in real-time as you type or paste - Detects names, emails, phone ... ... paint highlights without touching the DOM. Built with Plasmo, TypeScript, React. Available on Chrome Web Store now, Firefox version pending review. I'm trying to figure out what to build next. A few things on my radar: - Standalone redaction

... composing. * Scoped & read-only: Chips are TTL (time-to-live) bound, no auto write-back; your current project s memory stays clean. * Inline provenance: Replies show which chip informed which part. * Admin guardrails: Allow/deny lists, approvals, label-based redaction (PII/secrets), and audit logs. Why it helps * Cut repeat explanations across repos/teams. * Reuse proven auth/logging/CI/CD patterns instead of reinventing. * Faster onboarding by bridging Architecture + Conventions. * Safer incident response: pull the right runbook just for this thread. MVP flow 1. Compose ... ... project/label/item. 3. Attach 1 3 chips (e.g., Auth , Runbook ). 4. Send. Chips are used for this reply only and auto-expire. Privacy & compliance * Default-deny cross-project recall; everything is explicit & logged. * Sensitive labels can be auto-redacted

... confusing? I'm not sure what "team management" means in the context of an "operations platform" that also handles "financials" and "investor relations", but I like the screenshots! (I do see it later explained.) And I really like the alternating split view and the black and red color contrast. What s missing? Where does all the data come from? What s great but buried? Nothing? The page flows well to me. Would you actually sign up or bounce? Probably bounce ... ... sure what "device" your video is supposed to be on, I would just remove that personally honestly i think this whole thing is really cool and I found it quite interesting! the report with some data redacted