Alternatives
Products that do what Vertex Silence Search does
Real-time knowledge verification powered by local memory.
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2021 · vscolors.com
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2022 · marketplace.visualstudio.com
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Built StopX after struggling with the limitations of traditional blocklist-based filters. Most existing solutions rely on static URL databases that are constantly outdated and easily bypassed. Our approach uses proprietary WebShield™ technology that combines: Real-time AI image recognition for visual content analysis Contextual URL pattern matching beyond simple domain blocking Cross-device synchronization with encrypted settings sync Military-grade bypass protection with stealth mode operation The interesting technical challenge was achieving 99.7% accuracy while maintaining sub-100ms…
2025 · stopx.today
- 8WM
Includes bug reports, session replay, and watching tests live. This is free to play with. Login-gate is just to prevent abuse (sorry!).
2025 · fracten.com
- 9BG
We originally set out to solve complex debugging headaches and useless alerts caused by traditional security scanners in our own projects. Static Analysis (SAST) flagged too much noise because it couldn't verify runtime context, while Dynamic Analysis (DAST) missed internal logic bugs because it treated the app like a black box. We built a CLI tool to bridge this gap using grey box testing from a red team approach. We use internal knowledge of the codebase to guide parallel execution, allowing us to find complex or hidden logic errors and attack paths standard linters/scanners miss. The…
Dec 2025 · bloodhoundsecurity.ca
- 10CB
AI agents now have impressive reasoning capabilities. This raises an important question: how dangerous are these AI agents at identifying & exploiting web vulnerabilities? We created CVE-bench to find out (I'm one contributor of 16). To our knowledge CVE-bench is the first benchmark using real-world web vulnerabilities to evaluate AI agents' cyberattack capabilities. We included 40 CVEs from NIST's database, focusing on critical-severity vulnerability (CVSS > 9.0). To properly evaluate agents’ attacks, we built isolated environments with containerization and identified 8 common attack…
2025 · github.com
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The traditional pipeline for unstructured data extraction typically follows these steps: 1. Image → OCR Model (e.g., Google Vision) → Layout Model (e.g. Surya) → LLM → Final Answer However, this can be streamlined using a Vision-Language Model (VLM): 2. Image → VLM → Final Answer Recently VLMs have improved a lot for OCR and document understanding tasks, specifically the Qwen-2.5-VL series. We can run the Qwen-2.5-VL-7B-AWQ model locally with just 16GB VRAM, and perform end-to-end information extraction (fields and table extraction) without any external models. Hallucination with VLMs One…
2025 · github.com
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I built https://ask.rivestack.io — a semantic search engine over Hacker News posts. Instead of keyword matching, it finds results by meaning, so you can search things like "best way to handle authentication in microservices" and get relevant threads even if they don't contain those exact words. How it works: Indexed HN posts and comments into PostgreSQL with pgvector (HNSW index) Embeddings generated with OpenAI's embedding model Queries run as nearest-neighbor vector searches — typical response under 50ms The whole thing runs on a single Postgres instance, no separate vector DB I…
Feb 2026 · ask.rivestack.io
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Zep is a long-term memory store designed for conversational AI applications built using modern LLMs. It handles the storage, summarization, embedding, indexing, and enrichment of chat histories, and offers developers a simple, low-latency API to this data. Chat history storage is an infrastructure challenge all developers and enterprises face as they look to move from prototypes to deploying conversational AI applications that provide rich and intimate experiences to users. Key features include long-term memory persistence, auto-summarization, vector search, auto-token counting, and Python…
2023
- 14IE
Hey HN, when building ML systems for industrial AI, we have learned that data inspection is critical during the ML development process. We are also big fans of the Hugging Face ecosystem. That is why we built an integration to our data exploration tool Spotlight that allows you to interactively explore Hugging Face datasets with one line of code. Spotlight lets you leverage model results such as predictions and embeddings to gain a deeper understanding in data segments and model failure modes. Currently, many many NLP, CV, Audio and multimodal datasets are supported both locally and on the…
2023 · huggingface.co
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Hello Hacker News! I built Sleuth, an open source search tool for your workspace. I originally started off with Slack but quickly learned that Confluence search is a well documented problem: https://twitter.com/beajammingh/status/1273742155731791872?s... Sleuth solves this problem using semantic search to find relevant Confluence pages and Slack messages for your query. You can ask Sleuth questions about HR policies, technical documentation, product decisions, and more. Sleuth is open source and can be self-hosted, although there are dependencies on OpenAI and…
2023 · github.com
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Hi, We are building an open-source framework for loading and structuring LLM context to create accurate and explainable LLM answers using knowledge graphs and vector stores. We built the tool with four main concepts in mind: 1. Loader -> uses dlt in the backend to load and structure the data 2. Cognify step -> creates a graph with summaries, labels and factoids that are interconnected across the documents and stored as a representation in the vector store 3. Optimizer -> Uses DSPy to optimize LLM queries, and we plan to extend it to most of the knobs we can turn, like chunking etc. 4. Search…
2024 · github.com
- 17ES
Hi HN, I built EdgeVec, a vector database that runs entirely in the browser. It implements HNSW (Hierarchical Navigable Small World) graphs for approximate nearest neighbor search. Performance: - Sub-millisecond search at 100k vectors (768 dimensions, k=10) - 148 KB gzipped bundle - 3.6x memory reduction with scalar quantization Use cases: browser extensions with semantic search, local-first apps, privacy-preserving RAG. Technical: Written in Rust, compiled to WASM. Uses AVX2 SIMD on native, simd128 on WASM. IndexedDB for browser persistence. npm:…
Dec 2025 · github.com
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Hi HN — we built Factifi, a Chrome extension that does instant fact-checking on anything you’re reading or watching. Please keep in mind, this is just an MVP and will be buggy and fail. We were constantly pausing podcasts and Substack posts to ask, “Wait, is that actually true?”. After too many 30-minute Google rabbit holes, we decided to automate the work. What it does • Extracts claims from articles, blogs, YouTube videos (and even images) • Assigns a verdict + confidence score using live web search, a 200 M-paper research index, and a few proprietary datasets • Flags likely deepfakes and…
2025 · chromewebstore.google.com
- 19IM
Hey guys, I made a VS Code extension, within which you can use the Mixtral 8x7B AI model for free. Mixtral 8x7B is a new AI model from the Mistral family, which outperforms GPT-3 in many benchmarks, a fact that I have personally confirmed in daily coding tasks, where Mistral 8x7B was the clear winner. You can use all the AI features, such as AI Lens with custom instructions and similar. I'm pretty sure some of features will be useful to you. I'd appreciate both, positive and negative feedback!
2023 · marketplace.visualstudio.com
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Built QueryWeaver, an open-source text2SQL tool that uses a graph to create a semantic layer on top of your existing databases. When you ask "show me customers who bought product X in a certain ‘REGION’ over the last Y period of time," it knows which tables to join and how. When you follow up with "just the ones from Europe," it remembers what you were talking about. Instead of feeding the model a list of tables and columns, we feed it a graph that understands what a customer is, how it connects to orders, which products belong to a campaign, and what "active user" actually means in your…
2025 · github.com
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We’re open-sourcing FASHN VTON v1.5, a production-grade virtual try-on model. Given an image of a person and an image of a garment, the model generates a photorealistic try-on result. Model specs: - It operates directly in pixel space (no VAE) - Supports maskless inference by default, and was trained from scratch. - ~972M parameters, runs on consumer GPUs - Can run in ~5 seconds on H100 We built this as a focused alternative to large generalist models, with the goal of making a production-grade, specialized virtual try-on model. We’re releasing the weights, inference code, and architecture…
Jan 2026 · github.com
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