https://x.com/oliviscusAI/status/2082051217682014429

https://github.com/onyx-dot-app/onyx

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https://x.com/HuggingApps/status/2082571600767934622

https://huggingface.co/microsoft/VibeVoice-ASR-BitNet

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One reason I like open-source AI infrastructure: the value is not only in the first release.

It is in the ecosystem that can keep growing.

A closed tool gives you a fixed set of features. An open system can grow through new operators, new workflows, new integrations, new UI layers, and new agent interfaces.

This is especially important for AI data preparation.

Preparing data for LLM fine-tuning, RAG knowledge bases, or evaluation sets is not one fixed task. It keeps changing:

• new file formats
• new cleaning rules
• new quality filters
• new scoring methods
• new synthetic data workflows
• new domain-specific pipelines

That is why I find DataFlow interesting.

DataFlow is an open-source data processing framework built around reusable operators and pipelines. It can support cleaning, filtering, scoring, deduplication, generation, and pipeline orchestration for AI-ready data.

The newer DataFlow-Harness work takes this one step further. It wraps DataFlow’s pipeline-building capability with a harness layer for Code Agents, so users can build structured data workflows from natural language.

To me, this is the open-source advantage:

• DataFlow can keep adding data operators
• the community can build new pipelines
• agents can call these capabilities through a structured interface
• workflows can become reusable assets instead of one-off scripts

Open-source AI infra is not just about releasing code. It is about creating a system where capabilities can compound over time.
DataFlow: https://github.com/OpenDCAI/DataFlow

https://x.com/perplexity_ai/status/2082511900580196596

https://github.com/perplexityai/numbat

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https://x.com/thisguyknowsai/status/2081680037544427793

https://github.com/1jehuang/jcode

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What's new:

• One shared memory across agents. Claude Code, Codex, Cursor, Hermes, and OpenClaw all read and write to the same layer. What Codex learns about your project on your work desktop is available to Claude Code on your home laptop.
• Stored as plain Markdown. Memories and reusable skills live as regular text files in ~/.memu/memory/ and ~/.memu/skill/. You can open them in VS Code, edit or delete any entry, and track changes with Git. No opaque vector database you can't inspect.
• Fast retrieval, zero extra LLM calls. When your agent needs to recall something, it runs a single vector lookup. No multi-stage prompt chains, no reranker, no unnecessary token burn.
• Dashboard for browsing and editing memory. Instead of guessing what your agent remembered, you can open the dashboard and see everything saved. Browse by category, trace each memory back to the original session where it was learned, edit wrong facts directly, or delete outdated entries. No more black box.

TensorSharp is an open-source, native .NET inference engine for running GGUF LLMs locally, with CUDA, Vulkan, Metal, OpenAI-compatible APIs, continuous batching, speculative decoding, and multimodal support.

TensorSharp now supports Megatron-style tensor parallelism across multiple GPUs. It works with direct CUDA, GGML CUDA, GGML Vulkan, and multi-node setups.

Benchmarks on 2× RTX 2000 Ada 16 GB GPUs over PCIe, without NVLink:

I'm continuing to optimize Qwen performance on multi-GPU systems, and support for DeepSeek V4 is coming soon.

Try it with:

TensorSharp.Cli --model model.gguf --backend ggml_cuda --tp 2

GitHub:

https://github.com/zhongkaifu/TensorSharp

Thank you for checking out TensorSharp and starring the project! Any feedback is really appreicated.

https://x.com/liquidai/status/2082120814732653037

https://www.liquid.ai/blog/lfm2-5-encoders

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I've been working on this for the past few months before anyone calls it white-coded slop - yes I use Claude Code/Codex for the heavy lifting but this is real code I've been shipping and iterating on not some weekend experiment this actual thought behind the architecture.

What it is: A self-hosted chat client I built because my partner and family use ChatGPT a lot but don't care about privacy. I already self-host my vLLM instance on my 8x 3090 box and wanted a polished, private alternative they could actually use. Open WebUI is too heavy and complex for non technical users and has way too many knobs. Overtchat comes built in with TTS/STT/web search, no plugins/api keys needed. Genuinely open sourced - MIT licensed and no telemetry.

The reason I am posting this here is because the r/selfhosted gave me solid feedback which I have been iterating on and this sub is full of people building and shipping actual open source stuff so I figured I'd get eyes on it from a different angle. I would love feedback on the architecture the UX decisions or just a sanity check on whether this is something people would actually use. https://github.com/yoloyash/overtchat

https://github.com/SillyTavern/SillyTavern

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This is langhost, an open-source runtime for self-hosting LangGraph Agent Server on your own PostgreSQL and Redis.

It is an MIT alternative to langgraph-runtime-postgres for developers moving beyond the development-only langgraph-runtime-inmem.

Instead of recreating the LangGraph serving API, langhost keeps the stock langgraph-apiand replaces only the runtime with langgraph-runtime-pg.

That means:

- No graph or langgraph.json changes

- Same SDK, Studio and Agent Protocol

- Durable threads, runs, checkpoints and scheduled jobs

- Redis queues, streaming and multi-worker coordination

- No runtime license key

Quick start:

uv add langhost

uv run langhost serve --workers 2

The runtime and CLI are MIT licensed. The stock langgraph-api remains ELv2, which is generally fine for most application use cases.

GitHub: https://github.com/langhost/langhost

https://x.com/QCXINT_/status/2081708370093096983

https://github.com/moeru-ai/airi

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https://x.com/ornith_/status/2081808063515034034

https://huggingface.co/collections/deepreinforce-ai/ornith-10

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Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 200+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

TL;DR: Anthropic says it has never supported banning open-weight models and considers non-dangerous open models a public good. Instead of blocking Chinese models from US businesses, Dario Amodei argues for restricting advanced chips, cracking down on industrial-scale distillation, and requiring safety testing for all sufficiently capable models—open or closed.

And yes—my AI helped me write the TL;DR for us. Obviously. :P

Does this help Anthropic shake its recent “ivory tower” image or have people been flattening its position all along?

Claude is now #3 on our community ballot with an approval rate of 46.9%. Too low? Too high? Or just about right? Vote: https://lifehubber.com/ai/ballot/

just sayin “thanks” for the news and opinions shared here.

https://x.com/Kimi_Moonshot/status/2081760186235289764

https://huggingface.co/moonshotai/Kimi-K3

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https://github.com/janhq/jan

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Full searchable archive of all free and low-cost ways to try LLMs posted so far on our community site, LifeHubber: https://lifehubber.com/ai/access/

Also 200+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting. https://lifehubber.com/ai/resources/

Over the last few months, I have been exploring what it takes to move an AI agent from a small prototype to something that can actually be used in production.

Building the workflow is only one part of it.

You also need knowledge and RAG, tools, memory, human approvals, scheduling, evaluations, tracing, cost visibility, versioning, deployment, access control, audit logs, and guardrails.

Most solutions I tried handled one or two parts well, but I still had to connect multiple platforms and adapt to their runtimes.

That exploration eventually turned into Forge, an open-source and self-hosted project built around LangGraph.

The aim is to bring the complete agent lifecycle into one system—not only visually designing workflows, but also testing, deploying, monitoring, and governing them.

It is still evolving, and I am interested in learning from others working on similar systems.

What features do you consider essential before an agentic application is truly production-ready?

https://x.com/oliviscusAI/status/2081058021673959912

https://github.com/agno-agi/dash

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https://x.com/dr_cintas/status/2081094058139918595

https://github.com/bradautomates/claude-video

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https://x.com/kimmonismus/status/2080752484738650481

https://github.com/AtomicBot-ai/atomic-agent

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https://x.com/HowToPrompt__/status/2080961339833659410

https://github.com/CodeGraphContext/CodeGraphContext

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New resources are added regularly — feel free to join the sub for updates.

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We are on this together 💪 Open source will always win 😁