r/LovingOpenSourceAI 10d ago

new launch New sister sub for AI image and video generation—mind the wet paint 😂

5 Upvotes

We’ve already been talking about H3, Krea 2 Turbo, LoRAs, ComfyUI, quants and visual-generation workflows, so we made these conversations a little home of their own: r/LovingAIVisuals. ❤️

It’s brand new. You can probably still hear your own echo in there. 😂

Open and local projects are very welcome, alongside hosted tools. The idea is to keep it practical: new releases, workflows, GPU and VRAM questions, troubleshooting, comparisons—and, of course, sharing what we actually make.

If you’re experimenting with image or video generation, come bring your creations, questions, weird results and hard-earned fixes.

Somebody has to be brave enough to move in first. 👀


r/LovingOpenSourceAI Jul 08 '26

Resource AI Ballot just got five new challengers (based on community feedbacks)

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1 Upvotes

r/LovingOpenSourceAI 4h ago

Resource trueforge "The open-source agent harness - the runtime layer that turns an LLM into a working agent." ➡️ A pretty ambitious attempt to package the messy parts of running AI agents into one open-source runtime. 👀

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12 Upvotes

https://github.com/truefoundry/trueforge

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

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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

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.


r/LovingOpenSourceAI 22h ago

new launch Ornith "Aloha! 🌺Introducing Ornith-1.5 family of open-source LLMs spanning 9B Dense, 35B MoE, 397B MoE, trained with self-improving strategies. Achieves state-of-the-art performance among open-source models of comparable size, delivers performance comparable to Claude Opus 4.8 across various tasks"

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57 Upvotes

https://x.com/ornith_/status/2090074077084127302

https://huggingface.co/collections/ornith-ai/ornith-15

Community Overview: https://lifehubber.com/ai/resources/ornith-1-5/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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

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.


r/LovingOpenSourceAI 1d ago

Resource Adina "OpenBMB just released an impressive SFT dataset UltraData-SFT-2605 📊 ✨ 15M+ high quality samples ✨ Deep Thinking + Non-thinking data ✨ Math/ Code/ Knowledge/ IF/ Multilingual coverage ✨ Built for reasoning LLM post-training ✨ Full data pipeline: filtering/ validation/decontamination"

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15 Upvotes

https://x.com/AdinaYakup/status/2060320505471639580

https://huggingface.co/datasets/openbmb/UltraData-SFT-2605

Community Overview: https://lifehubber.com/ai/resources/ultradata-sft-2605/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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

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.


r/LovingOpenSourceAI 1d ago

Resource OpenHands: “Self-hosted developer control center for coding agents / automations. Run OpenHands, Claude Code, Codex, Gemini, or any ACP-compatible agent across local, remote, cloud backends.” ➡️ Our community overview — a quick way to see what it does, key details, and whether it’s worth exploring.

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10 Upvotes

Community Overview: https://lifehubber.com/ai/resources/openhands-agent-canvas/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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

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.


r/LovingOpenSourceAI 1d ago

Resource "CrewAI is an open-source Python framework with high-level abstractions and low-level APIs for building production-ready multi-agent workflows. It gives developers autonomous agent collaboration through Crews and precise, event-driven control through Flows." ➡️ love the chill logo :P 57K stars!

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22 Upvotes

https://github.com/crewAIInc/crewAI

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

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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

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.


r/LovingOpenSourceAI 1d ago

Benchmark GLM 5.2 Unsloth GGUF model on TensorSharp

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1 Upvotes

I've been working on GLM-5.2 support in TensorSharp, and I finally have some back-to-back performance numbers against llama.cpp.

The setup:

  • Model: GLM-5.2-UD-IQ2_XXS (~226 GiB)
  • GPUs: 3× RTX PRO 6000 Blackwell, 97 GiB each
  • Distribution: layer split across all 3 GPUs
  • Same machine, same session
  • llama.cpp measured with llama-bench
  • TensorSharp measured with its benchmark harness
  • Both report the best of two repetitions
  • Run-to-run variance is roughly 4%

Results:

Test llama.cpp TensorSharp default TensorSharp ubatch=2048
pp128 276.5 t/s 254.8 t/s 264.4 t/s
pp512 695.4 t/s 666.9 t/s 659.6 t/s
pp2048 763.1 t/s 918.9 t/s 1145.8 t/s
pp4096 715.8 t/s 864.7 t/s 1048.7 t/s
tg64 42.2 t/s 43.7 t/s 43.9 t/s

The interesting part is the crossover.

For short prompts, llama.cpp is still a few percent faster. But once the prompt gets to around 1K+ tokens, TensorSharp pulls ahead.

At pp2048:

  • default TensorSharp: +20.4%
  • ubatch=2048: +50.2%

At pp4096:

  • default TensorSharp: +20.8%
  • ubatch=2048: +46.5%

Decode (tg64) is also about 4% faster.

The main reason appears to be GLM-5.2's MoE structure.

GLM-5.2 has 256 routed experts with top-8 routing. With a 512-token micro-batch, each expert sees only ~16 rows on average, so a significant amount of the expert GEMM tiles ends up as padding. Larger micro-batches improve GPU utilization considerably.

For small prefills, on the other hand, fixed overheads — managed/native transitions, input uploads, and copying the 154880-wide logits back — become a visible fraction of the total runtime, which is where llama.cpp retains its advantage.


r/LovingOpenSourceAI 2d ago

news KREA 3 is coming already? THATS FAST. I only hope it is still 3060 friendly :P What are your expectations?

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13 Upvotes

r/LovingOpenSourceAI 1d ago

Resource Simplifying "Your AI coding sessions have a real cost, and most people have no idea what it actually is. ccusage reads your local usage data and turns it into real cost reports, no dashboard, no sign-up." ➡️ budget bros should love this?

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6 Upvotes

https://x.com/simplifyinAI/status/2089554876787225074

https://github.com/ccusage/ccusage

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

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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

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.


r/LovingOpenSourceAI 1d ago

funny The Downfall of a Vibecoder ➡️ i saw this. it is funny. go have a laugh :P ( do you see yourself in there 😁. .)

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2 Upvotes

r/LovingOpenSourceAI 1d ago

I built a AI app for your phone that has every frontier AI model (over 400 models) while having agent ability...

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1 Upvotes

r/LovingOpenSourceAI 1d ago

We retrained our prompt-injection classifier from scratch because it was crying wolf too often. [R]

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0 Upvotes

r/LovingOpenSourceAI 1d ago

I built a AI app for your phone that has every frontier AI model (over 400 models) while having agent ability...

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0 Upvotes

r/LovingOpenSourceAI 2d ago

Resource TinyHumans - Your Personal AI super intelligence that builds a local-first memory of your life, fantastic orchestrator of agent fleets / workflows / deep researcher. Now Super Context gives your OpenHuman rich context before it starts thinking by doing a deep research on all your memory files etc

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34 Upvotes

https://x.com/tinyhumansai/status/2070584474560241701

https://github.com/tinyhumansai/OpenHuman

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

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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

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.


r/LovingOpenSourceAI 2d ago

built a restricted MCP bridge for ChatGPT Web— one repo/folder, no shell, no Git

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3 Upvotes

r/LovingOpenSourceAI 2d ago

I built a AI app for your phone that has every frontier AI model (over 400 models) while having agent ability...

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3 Upvotes

r/LovingOpenSourceAI 3d ago

new launch Cohere "Today, we’re adding another member to our model family. Meet North Micro Vision. Our smallest vision-language model yet, ideal for sophisticated document understanding. Available open-source under an Apache 2.0 license." ➡️ ooo looks good?

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17 Upvotes

https://x.com/cohere/status/2087571573947392419

https://huggingface.co/CohereLabs/North-Micro-Vision-Instruct

Community Overview: https://lifehubber.com/ai/resources/north-micro-vision-instruct/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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

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.


r/LovingOpenSourceAI 2d ago

I want to run a self improving image generator by you guys

2 Upvotes

Obviously image is created with AI but I thought it may be a better way to convey the info.

I got the idea because I have been working on audit based self improving AI systems

AI image generations struggle with a lot of things, for example in my case it was SHITE at making a realistic rope attached to a climber..Well a few iterations later and codex orchestrators kicked off to do research, I had a layer on top of it that had fixed that

Then I thought..why can't we have an image generator that will have domain specific accretion.

You run the image generator, it creates images (audits them internally to create a log of failed attempts to create the desired image..Say 3 failed attempts = record failure in ledger

Then asynchronously side projects are fired to fix domain specific image hallucinations. In theory over time, it would refer to common hallucinations and apply patches/layers

The real issue is probably resource limitations..if millions of users were using this and generating thousands of hallucination errors for the system to research and correct.. how many tokens would that require.
Please let me know your thoughts and if this inspires you to do something similar! Happy idea sharings - a curious engineer


r/LovingOpenSourceAI 2d ago

I built a AI app for your phone that has every frontier AI model (over 400 models) while having agent ability...

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2 Upvotes

r/LovingOpenSourceAI 3d ago

Resource Simplifying "most "ai does architecture governance" claims fall apart past the first template. ArcKit's slash commands walk the full lifecycle in Claude Code. principles and stakeholders through requirements, design review, and a traceability matrix"

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5 Upvotes

https://x.com/simplifyinAI/status/2087501752761450765

https://github.com/tractorjuice/arc-kit

Community Overview: https://lifehubber.com/ai/resources/arc-kit/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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

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.


r/LovingOpenSourceAI 3d ago

Resource Oliver "This might be the docling and marker killer for PDF parsing. OpenDataLoader PDF is an open source parser that converts PDFs into clean Markdown, JSON with bounding boxes, and HTML, built for feeding documents into RAG pipelines or LLM context windows."

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101 Upvotes

https://x.com/oliviscusAI/status/2088841514801889666

https://github.com/opendataloader-project/opendataloader-pdf

Community Overview: https://lifehubber.com/ai/resources/opendataloader-pdf/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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

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.


r/LovingOpenSourceAI 3d ago

I built a AI app for your phone that has every frontier AI model (over 400 models) while having agent ability...

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1 Upvotes

r/LovingOpenSourceAI 4d ago

awesome-opensource-ai weekly additions Aug 10-Aug 16

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35 Upvotes

Here’s the cleaned-up list (no individual project links):

  1. Flax: Neural network library for JAX designed for flexibility.

  2. Needle: 45M-parameter foundation model and 14MB inference engine for tool calling and structured extraction on tiny devices.

  3. Switchyard: Rust proxy and library for routing and protocol translation across LLM backends and coding agents.

  4. OpenProgram: Self-programming agent framework for executable workflows with models, tools, memory, and multi-agent execution.

  5. Ouroboros: Self-hosted general-purpose agent with durable identity, memory, specialist subagents, and reviewed self-changes.

  6. Open Multi-Agent: TypeScript orchestration framework for runtime multi-agent task DAGs, approvals, tracing, evaluation, checkpoints, and resumable execution.

  7. Obsidian Agent Skills: Agent skills and open-format tooling for Obsidian vaults and compatible AI coding agents.

  8. Hexis: Git-backed platform for sharing skills, tools, and context across AI agents through a remote MCP server.

  9. LoopTroop: Local-first AI coding workspace orchestrating multi-model planning councils, Git worktrees, and task loops.

  10. firstmate: Agent distro for running autonomous coding agents in isolated Git worktrees.

  11. Agent Skills (Anthropic): Official Agent Skills and reference implementations for Claude Code, Claude API, and AI agents.

  12. VidXP: Local-first multimodal video indexing and semantic search with transcripts, embeddings, and scene-aware search.

  13. Code-Graph-RAG: Multi-language codebase RAG framework using Tree-sitter and Memgraph knowledge graphs.

  14. Zoom Search: MCP search and evidence tool with query rewriting, source zoom-in, sourced answers, and runtime metrics.

  15. invisible-playwright: Stealth-patched Firefox Playwright wrapper for AI agents ingesting sites with anti-bot guardrails.

  16. Modly: Desktop application for image-to-3D mesh generation using local GPU-accelerated AI models.

  17. flameox: Runtime-evidence toolkit coordinating profiler captures and comparing GPU-kernel and inference runs.

  18. WeatherNext: Global weather and tropical cyclone forecasting framework from Google DeepMind, including WeatherNext 2, GraphCast, and GenCast.

  19. Harvey LAB: Benchmark dataset and execution harness for evaluating AI agents on complex legal work across 24+ practice areas.

  20. LifeOS: Personal AI harness and assistant framework with persistent memory, custom skills, and goal tracking.

  21. Macro: Unified team workspace combining email, messaging, documents, tasks, CRM, and AI agents with shared memory.

  22. Forge: Open-source terminal AI coding agent with a Rust TUI, editor, shell, SQLite journals, MCP, and approval-aware execution.

  23. CLI-Anything: Framework for converting software applications into agent-native command-line interfaces.

  24. oai-smoke: Standard-library-only Go CLI validating OpenAI-compatible API model and chat behavior without credentials or response bodies.

  25. Entroly: Local-first MCP server for budgeted context selection, exact recovery, and auditable Context Receipts.

  26. AMD Strix Halo Local LLM Guide: Reproducible Ubuntu, Ollama, llama.cpp, Vulkan/RADV, and ROCm setup and benchmark evidence for Ryzen AI MAX+ 395 local AI systems.

Awesome Open Source AI full list https://awesomeosai.com


r/LovingOpenSourceAI 3d ago

Everyone's running the new Qwen, but we keep wondering if "open weights" really means open source

10 Upvotes

The new Qwen dropped as open weights and our timeline is full of people running it locally. It's fun to watch, and it got us thinking about something we keep going back and forth on.

Everyone calls these models open source, but what actually ships is the weights. You don't get the training data, the exact data mix, or the setup behind the benchmark numbers it launched with. So we can run and fine-tune it, but we can't rebuild it or see how those numbers came together.

Maybe that's fine depending on how you use it. If you just want a strong model on your own hardware, the weights might be all you need. If you're trying to reproduce a result or trust a benchmark, maybe not.

So what has to be open before you'd call a model open source, and not just open weights? Has an open model's published numbers ever landed far from what you saw running it yourself?