OpenClaw Memory Masterclass: The complete guide to agent memory that survives • VelvetShark - interesting compilation of the memory system and the pitfalls with compaction in Openclaw. The agent is meant to run for a long time, but there is always the risk that compaction will strike right in the middle of complex situations. And openclaw runs autonomously, so you want to be sure that it continues continuously.
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unum-cloud/USearch - that's what it says. So a library that offers an index for vectors that can come from embeddings, for example, and can find semantically similar texts. Not text-similar, but semantically, i.e., content. Interesting topic, the models required for this are related to LLMs, but not large, but small - they don't need to fully understand and generate because they only create vectors that can then be compared against each other and the higher the similarity, the higher the similarity of the texts in the topic. Cool little feature for bDS.
waybarrios/vllm-mlx: OpenAI and Anthropic compatible server for Apple Silicon. I use this to run mlx-community/gemma-3-12b-it-4bit on my MacBook Air. It works very well, a small shell script to start the server and then I am autonomous. Not as comfortable as Ollama, but it perfectly supports Apple's MLX and thus makes good use of Silicon.
mlx-community/gemma-3-12b-it-4bit · Hugging Face is currently the best model for local operation, allowing me to implement image captioning and even local chat. It's not the fastest, as it's quite large, but it's absolutely suitable for offline operation if I come up with a few mechanisms for batch processing of images, etc. This could be super exciting for vacation times. An image description might take a minute, but hey, no dependencies.
Models.dev — An open-source database of AI models is a very practical site that provides framework parameters for all kinds of providers and all kinds of LLMs, including even API prices. And technical parameters such as input/output tokens.
Ollama - a runtime environment for LLMs that allows models to be run locally. My favorite model at the moment: qwen2.5vl:7b-q4_K_M. With only 6.6 GB in size, this runs smoothly on a MacBook Air M4 and still has enough memory and capacity to run programs alongside it. The model is surprisingly usable in chat and above all has excellent vision capabilities. Ideal for providing titles, alt text, or summaries for images without having to pay big providers for it. And an important building block to bring bDS back to full-offline.
mistralai/mistral-vibe: Minimal CLI coding agent by Mistral - accompanying the AI Studio - Mistral AI there is also the Vibe coding interface to Devstral as open source. Very nice, because it makes a good pair. Will definitely try it out, even if I will probably rather reach for the powerhouses (Opus 4.6) for larger projects.
AI Studio - Mistral AI - as the situation in the USA becomes a bit more tense again, and simply because one should always check what is happening outside the USA, here is a link to a European alternative to the major US operators. Mistral offers a coding model with Mistral 2 that is not only open weights (i.e., freely available and operable if you have the necessary hardware), but also quite affordable when using Mistral itself. And the performance is slightly above Claude Haiku 4.5, and below Sonnet 4.5, but not by much. So quite usable and my first experiments were not bad. Unfortunately, no vision capability, so not very suitable for experiments with images (and therefore not ideal for my bDS), but still interesting enough to keep an eye on.
If you, like me, want an overview of UI integration for LLMs and are wondering how A2UI and MCP Apps compare and what they offer: Agent UI Standards Multiply: MCP Apps and Google’s A2UI - Richard MacManus helps. I have implemented A2UI in bDS so that the LLM can also use visual aspects in the internal chat, and I really like that. But the idea of incorporating parts of my UI into external agents is also fascinating. Even if I find that "local HTML/JS in an IFrame" somehow sounds like a hack at first, but much in the LLM environment gives me the feeling right now, simply because everything is pushed through a normal text stream and you hope that the LLMs adhere to the formats (even A2UI works like this).