Alternatives
Products that do what Cypher does
No sugarcoating. No lies. Just the unvarnished truth.
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NEO is an autonomous machine learning engineering AI agent capable of implementing complex ML tasks - From data cleaning, preprocessing, handling structural issues to model exploration, training, optimization with its own reasoning and code execution capabilities. We are soon releasing our early beta access. Join our waitlist. Would highly appreciate your feedback and use-cases you could potentially use NEO for.
2025 · heyneo.so
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Been working on data sovereignty recently and started this list. Hope you can contribute too.
2025 · github.com
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Frustrated by AI assistants that can only suggest actions but never take them, I built Merlio (https://merlio.app) - an AI that generates charts, searches the web, creates images, and analyzes YouTube videos directly within conversations. Built with React, it functions as an AI hub providing access to multiple models (Claude, GPT, Gemini) through a unified interface. The custom LLM orchestration layer enables the AI to execute tools with proper parameters and process results while maintaining conversation flow. Users are visualizing data, generating designs, and summarizing…
2025 · merlio.app
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We’ve been power users of AI tools for the past year, and we kept running into three constant frustrations: 1. Too many subscriptions – Paying separately for OpenAI, Anthropic, Perplexity, and others quickly adds up. 2. Losing memory & context – Switching between models or platforms means you start over each time. 3. Privacy concerns – With most closed-source models, your data may be stored or used for training. That’s not acceptable for sensitive or professional use cases. So we built AgentSea: a private and safer chat interface where you can access the latest models, agents, and tools in…
2025 · agentsea.com
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Hello Hacker News! I am Bertrand from Pruna AI. With my associates, John, Rayan, and Stephan, we are fellow researchers in AI efficiency and reliability coming from TUM. We are building an optimization engine that combines compression methods (e.g. quantization, pruning, compilation, batching…) in the aim of saving compute power when running AI models. This optimization engine take one base model as input and returns a compressed model as output. It aims to help for two things: - Make various AI models faster and/or smaller for various hardware (because they can require significant…
2024
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Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
Jan 2026 · github.com
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Hey HN, I wanted to share something I’ve been working on: *RAG-Guard*, a document AI that’s all about privacy. It’s an experiment in combining Retrieval-Augmented Generation (RAG) with AI-powered question answering, but with a twist — your data stays yours. Here’s the idea: you can upload contracts, research papers, personal notes, or any other documents, and RAG-Guard processes everything locally in your browser. Nothing leaves your device unless you explicitly approve it. ### How It Works - * Zero-Trust by Design*: Every step happens in your browser until you say otherwise. - * Local…
2025 · github.com
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Hey HN, My workflow for any complex queries is to ask it in multiple AI chats (Gemini, Claude, o3,..) in parallel and then continue the conversation with the chat response that I found the most useful. I built a simple open source app that queries 10+ AI models at once and summarizes their answers with a selected combiner AI model. There's a GIF in the github repo that shows it in action. You can try it on your local machine: https://github.com/Nexarithm/multi_model_chat If you are interested, I also made a detailed blog post on technical details, feature of the personal…
2025 · github.com
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I didn't want to buy a standalone computer or repurpose a laptop to run constantly so I could maintain a system to sync my LLMs, so I built this. It's a simple overview of my system, laid out in a way easy to unpack and replicate for yourself. The project is meant to be configured individually, and uniquely, since one solution might not be what's best for another. If anything, maybe it gives you some ideas on how to implement things for your own project. Best wishes, Ryan.
27d ago · pacslate.com
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You might know Cube as an open-source semantic layer (https://github.com/cube-js/cube). Started in 2018, now 19K+ stars, 1000+ releases. We kept hitting the same wall: everyone wants AI analytics, but AI without business context hallucinates. The fix is a semantic layer — a model that defines what "revenue" or "churn" actually means. But building one by hand takes weeks. So we built an AI agent that writes the semantic layer itself, then uses it to answer questions and build dashboards with no hallucinations. Connect your data → agent builds the model in seconds → ask…
Feb 2026 · youtube.com
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Hi HN, We’re @sumants and @rmehtany, working on Pontus. Pontus makes it easy to use AI with privacy embedded. We were concerned about the volume of personal data that goes to large LLM models without protection. We tried find an easy solution where didn’t change the simple apis given by LLM providers. However, most required you to invest significant engineering effort. We wanted privacy and LLMs to be easy, so we built Pontus. Through a declarative YAML, we orchestrate a microservice with the most common element of the LLM stack. - Anonymize Prompts before it hits LLMs, yet keeps context on…
2023 · github.com
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hello everyone, my first post! AA here, founder of ⌘ Langbase.com — we are a developer platform for building and scaling serverless AI memory agents. I know surveys can be boring, but this one’s different—it’s interactive! That's very much intentional. My team and I have been up for the last 21 hours putting together this report. This was a looot of work, so I hope y'all like it. Introducing … State of AI Agents 2024 report On Langbase, we processed 184 billion tokens and handled 786 million AI agent runs from 36K developers. From all that data plus insights from 3.4K builders who filled out…
2024 · langbase.com
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Founder here. I built NEO, an AI agent designed specifically for AI and ML engineering workflows, after repeatedly hitting the same wall with existing tools: they work for short, linear tasks, but fall apart once workflows become long-running, stateful, and feedback-driven. In real ML work, you don’t just generate code and move on. You explore data, train models, evaluate results, adjust assumptions, rerun experiments, compare metrics, generate artifacts, and iterate; often over hours or days. Most modern coding agents already go beyond single prompts. They can plan steps, write files, run…
Jan 2026 · marketplace.visualstudio.com
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I’ve posted here before about Julie, an open-source desktop AI assistant I’ve been building in public. The OSS version is local-first and powerful, but it assumes you’re comfortable bringing your own models or API keys. A lot of people told me the same thing: “I just want to install it and have it work.” So I built Julie Zero. Julie Zero is the premium tier that works straight out of the box. No API keys, no setup. Install it and start using it immediately. What Julie Zero does: Sees your screen and understands what you’re looking at in real time Helps across apps by clicking, typing,…
Jan 2026 · github.com
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