Alternatives
Products that do what Comet Connect does
Switch LLMs, Not Context. Migrate Without Limits.
- 1CO
I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…
2025 · github.com
- 2YA
Built this for my LLM workflows - needed searchable, persistent memory that wouldn't blow up storage costs. I also wanted to use it locally for my research. It's a content-addressed storage system with block-level deduplication (saves 30-40% on typical codebases). I have integrated the CLI tool into most of my workflows in Zed, Claude Code, and Cursor, and I provide the prompt I'm currently using in the repo. The project is in C++ and the build system is rough around the edges but is tested on macOS and Ubuntu 24.04.
2025 · github.com
- 3
- 4
Switch to Gemini without losing your AI memories
Mar 2026 · blog.google
- 5
- 6
- 7
- 8AL
Dec 2025 · github.com
- 9LI
Hey HN! We built Lunon to make LLM development way less of a headache. Ever wanted to see how different models handle the same prompt without all the setup hassle? That's what we fixed. Our API lets you compare Claude, GPT, Mistral and others in real-time with just a few lines of code. No more complex infrastructure or managing multiple API connections - we handle all that boring stuff behind the scenes. Plus, you can cut costs by intelligently routing requests to the right model for each task. Use the powerful (expensive) models only when you really need them. If you're building with LLMs…
2025 · lunon.com
- 10
- 11CI
One of the most frequent questions one faces while running LLMs locally is: I have xx RAM and yy GPU, Can I run zz LLM model ? I have vibe coded a simple application to help you with just that. Update: A lot of great feedback for me to improve the app. Thank you all.
2025 · can-i-run-this-llm-blue.vercel.app
- 12
- 13AO
I've built an airgapped Retrieval-Augmented Generation (RAG) system for question-answering on documents, running entirely offline with local inference. Using Llama 3, Mistral, and Gemini, this setup allows secure, private NLP on your own machine. Perfect for researchers, data scientists, and developers who need to process sensitive data without cloud dependencies. Built with Llama C++, LangChain, and Streamlit, it supports quantized models and provides a sleek UI for document processing. Check it out, contribute, or suggest new features!
2024 · github.com
- 14
- 15
- 16
- 17LO
Hi HN! I built LLM OneStop (https://www.llmonestop.com), a unified interface for accessing multiple AI language models in one place. The main problem I wanted to solve: constantly switching between different AI platforms, managing multiple subscriptions, and losing conversation context when comparing outputs across models. Key features: Switch between GPT-4, Claude, Gemini, Llama, and other models mid-conversation Compare responses side-by-side Single interface instead of juggling multiple tabs/subscriptions Free tier available to try it out (no credit card needed) "Connect"…
Nov 2025 · llmonestop.com
- 18IB
I got tired of the overhead required to run even a simple data analysis - cloud setup, ETL pipelines, orchestration, cost monitoring - so I built a fully local data-stack/IDE where I can write SQL/Py, run it, see results, and iterate quickly and interactively. You get data lake like catalog, zero-ETL, lineage, versioning, and analytics running entirely on your machine. You can import from a database, webpage, CSV, etc. and query in natural language or do your own work in SQL/Pyspark. Connect to local models like Gemma or cloud LLMs like Claude for querying and analysis. You…
Apr 2026 · stream-sock-3f5.notion.site
- 19
- 20LS
Hi HN, I built llm.sql, an LLM inference framework that reimagines the LLM execution pipeline as a series of structured SQL queries atop SQLite. The motivation: Edge LLMs are getting better, but hardware remains a bottleneck, especially RAM (size and bandwidth). When available memory is less than the model size and KV cache, the OS incurs page faults and swaps pages using LRU-like strategies, resulting in throughput degradation that's hard to notice and even harder to debug. In fact, the memory access pattern during LLM inference is deterministic - we know exactly which weights are needed…
Apr 2026
- 21
- 22EC
Hi! I've found myself repeatedly writing little scripts to do bulk calls to LLMs for various tasks. For example, run some analysis on a large list of records. There are a few "gotchas" to doing this. For example, some service providers have rate limits, and some models will not reliably return JSON (if you're asking for it). So, I've written a command for this. What I've tried to do here is let the user break up prompts and configuration as they see fit. For example, you can have a prompt file which includes the API key, rate limit, settings, etc. all together, or break these up into…
2025 · github.com
- 23DL
2024 · github.com
- 24
Secure, private and easy chat and context migration tool
Apr 2026 · chatgpt2claude.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →