Show HN: The feature gap "Chat with PDF" tuts and a regulated enterprise system https://ift.tt/AzOTbi2

Show HN: The feature gap "Chat with PDF" tuts and a regulated enterprise system I've spent the last few months architecting a RAG system for a regulated environment. I am not a developer by trade, but I approached this with a strict "systems engineering" and audit mindset. While most tutorials stop at "LangChain + VectorDB", I found that making this legally defensible and operationally stable required about 40+ additional components. We moved from a simple ingestion script to a "Multi-Lane Consensus Engine" (inspired by Six Sigma) because standard OCR/extraction was too hallucination-prone for our use case. We had to build extensive auditing, RBAC down to the document level, and a hybrid Graph+Vector retrieval to get acceptable accuracy The current architecture includes: Ingestion: 4 parallel extraction lanes (Vision, Layout, Text, Legal) with a Consensus Engine ("Solomon") that only indexes data confirmed by multiple sources Retrieval: Hybrid Neo4j (Graph) + ChromaDB (Vector) with Reciprocal Rank Fusion Performance: Semantic Caching (Redis) specifically for similar-meaning queries (40x speedup) Security: Full RBAC, Audit Logging of every prompt/retrieval, and PII masking. I documented the complete feature list and gap analysis https://gist.github.com/2dogsandanerd/2a3d54085b2daaccbb1125... My question to the community: Looking at this list – where is the line between "robust production engineering" and "over-engineering"? For those working in Fintech/Medtech RAG: what critical failure modes am I still missing in this list? https://gist.github.com/2dogsandanerd/2a3d54085b2daaccbb1125601945ceeb December 17, 2025 at 11:20PM

Show HN: Solving the ~95% legislative coverage gap using LLM's https://ift.tt/LhpmTdB

Show HN: Solving the ~95% legislative coverage gap using LLM's Hi HN, I'm Jacek, the solo founder behind this project (Lustra). The Problem: 95% of legislation goes unnoticed because raw legal texts are unreadable. Media coverage is optimized for outrage, not insight. The Solution. I built a digital public infrastructure that: 1. Ingests & Sterilizes: Parses raw bills (PDF/XML) from US & PL APIs. Uses LLMs (Vertex AI, temp=0, strict JSON) to strip political spin. 2. Civic Algorithm: The main feed isn't sorted by an editorial board. It's sorted by user votes ("Shadow Parliament"). What the community cares about rises to the top. 3. Civic Projects: An incubator for citizen legislation. Users submit drafts (like our Human Preservation Act ), which are vetted by AI scoring and displayed with visual parity alongside government bills. Tech Stack: Frontend: Flutter (Web & Mobile Monorepo), Backend: Firebase + Google Cloud Run, AI: Vertex AI (Gemini 2.5 Flash), License: PolyForm Noncommercial (Source Available). I am looking for contributors. I have the US and Poland live. EU, UK, FR, DE in pipeline, partially available. I need help building Data Adapters for other parliaments (the core logic is country-agnostic). If you want to help audit the code or add a country, check the repo. The goal is to complete the database as much as possible with current funding. Live App: https://lustra.news Repo: https://ift.tt/B6CuXES Dev Log: https://ift.tt/sGwciy6 https://lustra.news/ December 16, 2025 at 08:09PM

Show HN: Zenflow – orchestrate coding agents without "you're right" loops https://ift.tt/BrcHsRF

Show HN: Zenflow – orchestrate coding agents without "you're right" loops Hi HN, I’m Andrew, Founder of Zencoder. While building our IDE extensions and cloud agents, we ran into the same issue many of you likely face when using coding agents in complex repos: agents getting stuck in loops, apologizing, and wasting time. We tried to manage this with scripts, but juggling terminal windows and copy-paste prompting was painful. So we built Zenflow, a free desktop tool to orchestrate AI coding workflows. It handles the things we were missing in standard chat interfaces: Cross-Model Verification: You can have Codex review Claude’s code, or run them in parallel to see which model handles the specific context better. Parallel Execution: Run five different approaches on a backlog item simultaneously—mix "Human-in-the-Loop" for hard problems with "YOLO" runs for simple tasks. Dynamic Workflows: Configured via simple .md files. Agents can actually "rewire" the next steps of the workflow dynamically based on the problem at hand. Project list/kanban views across all workload What we learned building this To tune Zenflow, we ran 100+ experiments across public benchmarks (SWE-Bench-*, T-Bench) and private datasets. Two major takeaways that might interest this community: Benchmark Saturation: Models are becoming progressively overtrained on all versions of SWE-Bench (even Pro). We found public results are diverging significantly from performance on private datasets. If you are building workflows, you can't rely on public benches. The "Goldilocks" Workflow: In autonomous mode, heavy multi-step processes often multiply errors rather than fix them. Massive, complex prompt templates look good on paper but fail in practice. The most reliable setups landed in a narrow “Goldilocks” zone of just enough structure without over-orchestration. The app is free to use and supports Claude Code, Codex, Gemini, and Zencoder. We’ve been dogfooding this heavily, but I'd love to hear your thoughts on the default workflows and if they fit your mental model for agentic coding. Download: https://ift.tt/e2o0ucq YT flyby: https://www.youtube.com/watch?v=67Ai-klT-B8 https://ift.tt/e2o0ucq December 16, 2025 at 10:02PM

Show HN: Cordon – Reduce large log files to anomalous sections https://ift.tt/PkxpMJ9

Show HN: Cordon – Reduce large log files to anomalous sections Cordon uses transformer embeddings and density scoring to identify what's semantically unique in log files, filtering out repetitive noise. The core insight: a critical error repeated 1000x is "normal" (semantically dense). A strange one-off event is anomalous (semantically isolated). Outputs XML-tagged blocks with anomaly scores. Designed to reduce large logs as a form of pre-processing for LLM analysis. Architecture: https://ift.tt/gv8IX3w... Benchmark: https://ift.tt/cMh6GH7... Trade-offs: intentionally ignores repetitive patterns, uses percentile-based thresholds (relative, not absolute). https://ift.tt/LD4YcQz December 16, 2025 at 02:06AM

Show HN: A lightweight SaaS to reduce early-stage app friction https://ift.tt/KdJYxuo

Show HN: A lightweight SaaS to reduce early-stage app friction I recently shipped a small SaaS I built in roughly 24 hours, mostly during school breaks. This is my first project that I have taken from idea to deployment, onboarding, and real users. The product targets early-stage developers and focuses on reducing initial setup and preparation when building new apps. It abstracts away some of the repetitive early decisions and boilerplate that tend to slow down first-time builders, especially around project structure, configuration, and “what should exist on day one”. I have a small number of active users, but churn is relatively high, which suggests either: the problem is not painful enough the abstraction leaks too early the UX or onboarding fails to communicate value or the tool solves a problem that disappears after the first session I would really appreciate technical feedback on: whether the abstraction layer makes sense if the mental model aligns with how you bootstrap projects where the product feels opinionated vs restrictive what would make this something you would actually keep installed Thanks for reading. Direct, critical feedback is very welcome. https://simpl-labs.com/ December 16, 2025 at 12:21AM

Show HN: A Wordle-style game for SHA-256 hashes https://ift.tt/0tEO678

Show HN: A Wordle-style game for SHA-256 hashes i built a small wordle-style game where the target is a daily sha-256 hash. it’s intentionally not cryptographically realistic; the goal is to make avalanche effects and the meaninglessness of near-matches intuitive. this was a quick front-end experiment; the code isn’t published yet. everything runs client-side; no tracking; no accounts. https://hashle.app December 15, 2025 at 11:38PM

Show HN: Llmwalk – explore the answer-space of open LLMs https://ift.tt/4andEco

Show HN: Llmwalk – explore the answer-space of open LLMs https://ift.tt/O3JutW8 December 14, 2025 at 10:14PM