Recently, I have open-sourced an AI emotional companion product based on openclaw, called opensoul.
On this platform, you can create a "soulmate" that matches your personality, and configure it with the skills, tools you want it to have, as well as the platforms it can integrate with (such as Telegram, Discord, etc.). You can even create group chats, invite multiple agents and your friends to chat about recent events, discuss projects together, and so on.
On the one hand, I hope it can better accompany you in daily life by virtue of its unique memory mechanism, self-feedback and iteration mechanism, and the modeling of users' emotions. On the other hand, I also hope it can help you better handle your work with its unique skills, tools and ability to deal with complex task scenarios.
Although the entire product has taken shape, I think there are still many areas that need adjustment and optimization. I also hope to rely on the strength of the community to do a good job in AI emotional companionship.
Poll: Will 2026 be the year of subquadratic attention?
The transformer architecture is cursed by its computational complexity. It is why you run out of tokens and have to compact. But some would argue that this is a feature not a bug and that this is also why these models are so good. We've been doing a lot of research on trying to make equally good models that are computationally cheaper, But so far, none of the approaches have stood the test of time. Or so it seems.
Please vote, don't be shy. Remember that the Dunning-Kruger effect is very real, so the person who knows less about transformers than you is going to vote. We want everyone's opinion, no matter confidence.
π if you think at least one frontier model* will have no O(n^2) attention by the end of 2026 π₯ If you disagree
* Frontier models - models that match / outperform the flagship claude, gemini or chatgpt at the time on multiple popular benchmarks
I just pushed Claude Code Agent Swarm with 20 coding agents on my desktop GPU workstation.
With local AI, I donβt have /fast CC switch, but I have /absurdlyfast: - 100β499 tokens/second read, yeah 100k, not a typo | 811 tok/sec generation - KV cache: 707β200 tokens - Hardware: 5+ year old GPUs 4xA6K gen1; Itβs not the car. Itβs the driver.
Qwen3 Coder Next AWQ with cache at BF16. Scores 82.1% in C# on 29-years-in-dev codebase vs Opus 4.5 at only 57.5%. When your codebase predates Stack Overflow, you don't need the biggest model; you need the one that actually remembers Windows 95.
My current bottleneck is my 27" monitor. Can't fit all 20 Theos on screen without squinting.
I am going to showcase some other people's tuning work, that I have put into a GATED Distill MOE (Qwen3) ; 256 K context. Special thanks to all the tuners (listed in the model tree and repo page with special shoutout to "TeichAI" - using Unsloth for a lot of the Distills in this model):
Savant Commander is a specialized MOE model that allows you to control which expert(s) (of 12) are assigned to your use case(s) / prompt(s) ... directly (by name(s)), as opposed to having the "choices" made for you.
The model is composed of 12 DISTILLS (compressed 12x4B MOE) of top closed (GPT5.1, OpenAI 120 GPT Oss, Gemini (3), Claude (2) ) and open source models (Kimi, GLM, Deepseek, Command-A, JanV1 ) all in one.
256k Context, 2 experts activated.
PS: There is also a "heretic" / "decensored" version too ; listed on this model page.