... official text, detected changes, AI summaries, scores and final conclusions are often collapsed into a single output. That makes the result difficult to inspect and even harder to challenge or correct. So we rebuilt the platform around a canonical evidence graph: Entity Document Version Change Provision The operating engine now follows six controlled stages: Signal Discover Capture Qualify Publish Correct What changed in version 4: A Site Atlas mapping 23 product surfaces and 44 relationships. A Feature Atlas documenting ... ... capability records across 15 domains. An adaptive public Evidence

... Rigorous Orthodox theology, Scripture, and source verification in ChatGPT Short description: A specialized ChatGPT plugin for Orthodox theology, Scripture, patristics, Councils, Liturgy, Church history, and primary-source research built to distinguish evidence, authority, reception, interpretation, and verified quotation. I built Orthodox Theology because asking a general AI to answer from an Orthodox perspective is not enough. The difficult part is not just retrieving relevant information. It is determining what a source actually establishes. For example: - A Father teaching something does ... ... Councils, and Liturgy are interpreted together within Holy Tradition rather than treated as interchangeable sources. Doctrinal-status analysis It distinguishes between dogmatically defined teaching, broadly received teaching, admissible nonbinding opinions, genuinely unresolved questions, historical attestation, and cases where the evidence

... Dear PH community, Everyday, Ceptize scans hundreds of newly launched products and emerging customer problems, then investigates the most promising features and workflows to identify software opportunities with evidence of existing willingness to pay. We discover opportunities in order to reject them, not to publish them. We only deliver survived ones. Ceptize delivers to "Pro" members each year at least 2 software opportunities which passed all rejection tests (Categorization, Uniqueness, Pain, Frequency, Existing Spending, WTP, Competition, Evidence

... context. MCP accesses structured resources through a standardized tool interface. The Agent pipeline plans and executes multiple steps using available tools. The Security-aware pipeline adds scoped retrieval, security references, code scanning, and guardrails. TraceLogicAI captures the execution evidence Instead of evaluating only the final response, the platform exposes what occurred during execution: Retrieved content Source citations Tool calls Agent steps Token consumption Latency Estimated cost Groundedness Safety signals The results are compared side by side This reveals whether added ... ... wins. Its purpose is to help teams identify the simplest architecture that satisfies their requirements for quality, speed, cost, traceability, and security. As the solo founder and builder of TraceLogicAI, I believe architecture decisions should be supported by observable evidence

... Most analytics products are designed to always return a number. For AI-search visibility, I think a trustworthy product needs the opposite safeguard: the ability to withhold a score when the underlying evidence is not decision-grade. At Corank, I am testing five publication gates: 1. Traceability every chart point must lead back to the exact prompt, raw model answer, citations, timestamp, collection status, and methodology version. 2. Denominator integrity failed and excluded runs must remain visible. A timeout cannot ... ... silently become brand absent, and an ineligible observation cannot quietly disappear. 3. Replayability the stored answer should reproduce the original classification. A fresh model response is useful for comparison, but it cannot replace historical evidence

... changes faster than the test suite. Agentic UI testing gives us a different shape. Instead of encoding every assertion as rigid script logic, we can let an agent read a test case, drive the browser, inspect the page, collect evidence, and judge whether the user-facing expectation is satisfied. The script becomes the harness: it controls safety, browser access, reporting, credentials, screenshots, setup, and repeatability. The agent handles the parts that require interpretation. This article describes a practical design ... ... worker. For UI testing, a remote headed browser is not just a convenience. It changes the debugging loop. Instead of asking "what did the script think happened?", you can ask "what did the user actually see?" ### Harness Code vs

... encryption at the keyboard layer, before plaintext reaches the chat app. The difficult problem is not adding an Encrypt button. It is trust. A keyboard can see sensitive input, so enabling a third-party keyboard should require unusually strong evidence. If you were evaluating this idea, which signal would matter first? A. A two-device demo: type encrypt send open offline B. A clear permission screen showing that the APK has no INTERNET permission C. A public cryptographic format with ... ... test vectors D. Reproducible-build evidence

... issuance, so it's not just "we said it was reviewed." It's a cryptographic statement tied to the capture records that existed at the time. What I'm genuinely curious about: what does your team produce today as evidence when AI code ships? Is there anything machine-verifiable in that process? And where do you think the enforcement gate should actually live pre-merge block, or post-merge audit evidence

... thought we knew We assumed enterprise customers wanted the same things as our small business users, just more of it. More security. More control. More features. We never talked to them. We read blog posts. We looked at competitor pricing pages. We guessed. Here's what we missed: Procurement cycles. Enterprise deals take 6-12 months. We had 30-day sales cycles. We weren't built for that. Security reviews. We needed SOC2. We didn't have it. Customers asked ... ... pays us first. Not asks. Pays. What I'm curious about Have you ever built something for a customer you didn't have? How much did it cost you? What did you learn? Imed Radhouani Founder & CTO Rankfender Evidence

... Concipe earlier this month to help teams turn scattered feedback into buildable specs you can use with CLI Agents and AI Code editors such as Claude and Cursor. Since then, one question kept coming up: How strong is the evidence behind an opportunity? So we shipped a few updates to make that clearer. Every opportunity now shows an Evidence

... Problem. The EU AI Act is creating real demand, fast. But most 'leads' are noisy: vague press, copied lists, or orgs with no concrete trigger. Cold outreach falls flat without evidence. What it is. AI Act Tracker is a data-as-a-service that surfaces EU companies with clear obligation triggers , and backs each one with public evidence