Alternatives
Products that do what DeepSeek Janus Pro 7B API does
i deployed deepseek janus pro on eachlabs to check potential usecases.
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We’ve been experimenting with how far a tiny model can go when it’s good at calling external tools - and have just released Jan-nano, a 4 B model trained for MCP. Jan-nano: - tops DeepSeek-V3-671B on MCP tool-use (SimpleQA 80.7%) - handles live web search and multi-step deep research - runs fully on-device (≈4GB VRAM) Tech notes - Base: Qwen3-4B - Fine-tuning: DAPO - We're going to release the full technical report soon Links - Demo tweet: https://x.com/menloresearch/status/1934809407604576559 - Model + GGUF:…
2025 · twitter.com
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I built this app over a week as a way to learn how to use TensorFlow with Mobilenets and to get some experience with Google Play (and partly as a dare). It's written in Java, as I wasn't able to find a Kotlin API for TFLite. It was built with Bazel. I'm pretty satisfied with the actual detector's performance, although I expect I could improve the UI a little bit. It's a weird UX, as you want it to be as simple and fast as possible (loss/notloss) but you also want it to have some sort of recognizability. I would love to hear your thoughts.…
2018
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Our company Vertex.AI has been working on this for a while but this is the first public release. We're starting with using PlaidML to bring OpenCL support to Keras and more frameworks, platforms, etc are coming. Yes, this means you can use use your AMD GPU for deep learning dev. Sorry, no Mac or Windows support yet although the brave can try building from source (it should work). http://vertex.ai/blog/announcing-plaidml https://github.com/plaidml/plaidml
2017
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I spent days trying to deploy DeepSeek on a server this year. Install Ubuntu, NVIDIA drivers, CUDA, Docker, configure vLLM, debug memory issues, tune performance settings. Every deployment was different. Every server had its own quirks. Worse still, these issues are more pronounced on non-NVIDIA accelerators, such as Ascend or Intel NPU. So, we made LLMOne, which will automates this. You can use it at bare metal (via BMC) or SSH (coming soon) into an existing server, select models, and it handles everything: OS installation, driver setup, inference engine configuration, model deployment, and…
2025 · github.com
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2025 · youtube.com
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May 2026 · zot.sh
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2014 · tyk.io
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2015 · ramses.tech
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2014 · github.com
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Hi! Recently I've launched - mybfio.io - platform for software reviews with social network aspect. Prehistory Many great system are already in place that allow people to search and rate software products, including the site I am publishing this post on. However I have always felt that those systems are not specifically centered around product/software development lifecycle (releases, real users feedback over a product entire lifetime, so on) and often too broad or not designed specifically for that type of work, which is ok. Why? As an opensource author I would like to have constant…
2021
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LLM observability is an absolute must-have for anyone running something in prod (or prod-like). While all the observability startups are great, you're essentially sending all your OpenAI usage history - prompts, generations, chats - to a random third party. So this script deploys a basic proxy in your Azure account, catches all incoming OpenAI requests, stores logs in your own resource group, and comes with visualizations premade (charts, timelines, chat history, cost estimation, etc). Thanks for any thoughts and feedback!
2023 · github.com
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Hey everyone! I am excited to share updates on four of my & my teams' open-source projects that take large-scale search systems to the next level: USearch, UForm, UCall, and StringZilla. These projects are designed to work seamlessly together, end-to-end—covering everything from indexing and AI to storage and networking. And yeah, they're optimized for x86 AVX2/512 and Arm NEON/SVE hardware. USearch [1]: Think of it as Meta FAISS on steroids. It's now quicker, supports clustering of any granularity, and offers multi-index lookups. Plus, it's got more native bindings than probably…
2023 · usearch-images.com
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2015 · apianalytics.com
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Been using it for about a year now in production, but rewrote it completely in golang. My first go project so far. I wanted to have something that would make my life easier. A lot of tools nowaday kinda push you towards functional deployments and that usually doesn't work for me since I like stuff like LiveView by Phoenix and Websocket stuff in general. Old vercel deployments back when they allowed to deploy docker containers was a godsend, but since they don't offer that anymore I had to use services like fly.io(which is amazing) or heroku. However I wanted something selfhosted that I could…
2024 · deploywithjig.askh.at
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Scenario: Your company’s IT department says “good news, you have access to azure, aws, openai, mistral, and together AI, here are the API keys”. You think “yippee I can access many models”, but some models like the gpt-oss or Mistral are available on some or all of those platforms? That’s where this app comes in: run it and it will check all the providers that you have configured and then you can search across those providers to see which providers have the model you want available. Built on top of mozilla.ai any-llm library. Check out the link for a GIF showing it in action.
Sep 2025 · github.com
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We open-sourced catsu, a Python client for embedding APIs. The problem: every embedding provider has a different SDK with different bugs. OpenAI has undocumented token limits. VoyageAI's retry logic was broken until September. Cohere breaks downstream libraries every release. LiteLLM's embedding support is minimal. catsu provides: - One API for 11 providers (OpenAI, Voyage, Cohere, Jina, Mistral, Gemini, etc.) - Bundled database of 50+ models with pricing, dimensions, and benchmark scores - Built-in retry with exponential backoff - Automatic cost tracking per request - Full async support…
Dec 2025 · catsu.dev
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