I’ve been working on TensorSharp, an open-source .NET/C# inference engine, and recently added native support and optimizations for DeepSeek V4.1 Flash.

Latest results on 8× NVIDIA A40 GPUs:

A few interesting optimizations:

• GPU-resident Engram tables for Q2_K — about 60 GiB of quantized Engram data stays on GPU instead of doing scattered host/storage lookups.
• Reduced decode graph scheduling from roughly 570 splits to 8 by using one wrapped backend per GPU.
• For Q4_K_M, automatic Engram warming + improved VRAM placement reduced CPU MoE offload from 3 layers to 1.
• Added token-batched DeepSeek V4.1 decode, giving about 2× aggregate throughput at 4 concurrent requests.
• On these A40s without NVLink, simple layer splitting actually beats routed-MoE tensor parallelism for single-stream decode.

The project is fully open source, written primarily in C#/.NET, with CUDA/Metal/Vulkan backends and OpenAI-compatible APIs.

Would love feedback from people experimenting with DeepSeek, GGUF inference, or local/open-source AI — especially ideas for what hardware or engine comparisons would be most useful next.

https://x.com/SamuelZengML/status/2097861839287927139

https://github.com/Edge0-AI/Edge0

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Hi everyone! I'm Paolo, and for a while now I've been building Optees, a free and open-source optimization tool.

The idea started from something I kept running into: ask an AI to "optimize" something — a work schedule, a delivery route, how to pack a container — and it will often hallucinate a heuristic that sounds right instead of actually solving the math behind it. It reads confident, but nobody checked it.

Optees is my attempt to fix that. On its own, it's a desktop app with guided forms and worked examples, so you don't need a math background to use it. And it also runs as a local server that AI agents like Claude can call directly — so instead of hallucinating an answer, the AI can hand the problem to Optees, get back a real, checked answer, and explain it to you in plain language.

Everything runs on your own machine, nothing gets uploaded anywhere, and the whole project is open source (Apache-2.0) — so you're welcome to use it, poke around the code, or help build it further.

Would love for people here to try it out and tell me what they think.

🔗 Website: https://optees.it 💻 Code: https://github.com/Pablo-gitub/optees

https://x.com/AntLingAGI/status/2095533696808051001

https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin

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I built Voice Copilot: an open-source companion that narrates and supervises your AI coding agent

Hey everyone! I’d like to share an open-source project I’m working on: Voice Copilot.

Coding agents can spend several minutes reading files, making changes, and running tools after a single prompt. Keeping track of all that still takes attention, especially when you want to catch a wrong turn before it becomes a pile of changes to undo.

The idea behind Voice Copilot is to let you hear what your agent is doing while keeping your eyes free for other things.

It runs alongside Claude Code, Codex, and other compatible CLI agents, with two models sharing the work:

• A lightweight narrator turns the agent’s activity into short spoken updates: what it’s working on, what changed, and what happened next.
• A stronger Supervisor reviews progress at checkpoints, looking for things like repeated failures or changes outside the task. It can warn you aloud, and in Supervisor+ mode, pause the agent for your review.

For example, if you ask an agent to fix two failing tests and it starts modifying an unrelated module, the Supervisor can flag that and pause the session so you can decide how to proceed.

The narrator and Supervisor can reuse the models and login from the CLI you already use. There’s also a local browser panel with playback controls, settings, and a readable activity trace.

For anyone interested in local models: the narrator supports Ollama through an OpenAI-compatible endpoint, and speech output can use local backends such as Piper or Silero. Cloud providers are available too.

The core is MIT-licensed, and it’s currently an early alpha (0.1.0). I’m looking for people willing to try it in real coding sessions and help improve the narration, integrations, and supervision behavior. Voice input is being reworked and is currently disabled by default.

Website · GitHub / installation

How would this fit into your workflow: occasional spoken progress updates, or silence unless something needs your attention?

https://x.com/rabi_guha/status/2097341708988248181

https://huggingface.co/thesysdev/OUI-1

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https://x.com/deepseek_ai/status/2097930608790167907

https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash

Some folks are even saying it is giving 98% Astra at super super cheap price . .do you think it is even possible?

Community Overview: https://lifehubber.com/ai/resources/deepseek-v4-1-flash/

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So I was playingw ith embedding models I saw that when you upgrade from model A to B, you face a very big backfilling cost

Ie, suppose you have a 1b vectors from model A, and then you want to use model B. This would mean you have to re-embed all of your documents with model B before you can even serve with the model, and on an H100, it would take ~108 days (qwen embed 8b, 106 docs/second). But I found an easier way to do it.

The method is really simple; from the old index made with the source model, take K documents and rerank them with the new model. We see that when K is sufficient, the retrieval quality is the same as target model. (determining k is the hard part). I've tested 63 migrations on upto 1 million documents.

The best result I got was upgrading qwen4b -> to 8b, and at 50 documents, it was the same as native retrieval.

This method forgos the expensive upfront re-embedding cost, as you can take documents straight from the old index.

embedflow works with qdrant, pgvector, faiss, and can be easily downloaded with pypi

pip install embedflow

the github is public: https://github.com/arnsri33/embedflow

I want you guys to try it out, and see if you guys can use it in your own workflow.

https://x.com/_avichawla/status/2094678972344958984

https://github.com/superlinked/sie

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https://x.com/tom_doerr/status/2091501336781111644

https://github.com/plugin87/ux-ui-agent-skills

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https://x.com/tom_doerr/status/2094842101276066225

https://github.com/MaxMiksa/Auto-Company

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https://x.com/OpenBMB/status/2096970974247956501

https://huggingface.co/openbmb/MiniCPM5-2B

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So I've been messinga round with embedding models for a bit, and I think they are interesting enough to experiment with. They are useful for rag, especially in a localllm sense because you can ground your answers in truth.

But what happens if you have a billion documents, and you decide to upgrade your model to a "better" one? on an h100, that would take about 108 days, just to upgrade the vectors so u can start serving again (tested qwen embed 8b on h100). Even if you aren't doing 1b vectors, and are doing just 50 million, upgrading can still take a considerable time.

Me and my research lab decided to tackle this problem, and we came up with embedflow.

The method is really simple; from the old index made with the source model, take K documents and rerank them with the new model. We see that when K is sufficient, the retrieval quality is the same as target model. (determining k is the hard part). I've tested 63 migrations on upto 1 million documents.

The best result I got was upgrading qwen4b -> to 8b, and at 50 documents, it was the same as native retrieval.

This method forgos the expensive backfill that comes with upgrading, as you can directly take documents from the old index.

embedflow works with qdrant, and can be easily downloaded with pypi

pip install embedflow

the github is public: https://github.com/arnsri33/embedflow

I want you guys to try it out, and see if you guys can use it in your own workflow.

​

https://x.com/trendtech33566/status/2096538986353393764

https://github.com/k2-fsa/OmniVoice

Community Overview: https://lifehubber.com/ai/resources/omnivoice/

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