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What agent are you running more recently?

@koltregaskes Feels like OpenClaw is in a much better place than a few months ago. That said, stability still seems tied to how you manage updates/configs. Hermes Agent does look promising, though.

Is it time to try OpenClaw again? I ditched it a while back as it was too much work to keep it stable and was very error prone. The security was a huge concern too. Has it improved recently? Also, how are you guys finding Agent Hermes? It seems to be the current top agent with OpenClaw?

@rileybrown Honestly, both are solid. It just depends on what you're trying to do.

Is Hermes better than OpenClaw or is it yet another psyop on the timeline?

@TTrimoreau 2026: build with agents

2024: build a product. 2025: build with AI. 2026: ? What's the move now? 👇

@Rasmic How long can GLM-5.1 actually run autonomously in OpenClaw on a complex task?

GLM 5.1 has been my favourite model for OpenClaw. It's capable and cheap... been enjoying it

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@melvynx What about others? Like Kimi K2.5 and GLM-5.1.

Minimax for now is the only good open source model for OpenClaw

@KaiXCreator Totally agree. It's actually powerful once you start using it. The real challenge now is planning and optimizing your token usage.

The more I use OpenClaw, the more I think it's not just hype. The only thing limiting me now is the API costs.

@konnydev 8 hours autonomous run is impressive. Feel like we're moving to agent capability competition now.

GLM-5.1 out now🤯 At this rate it feels impossible to keep up with launches. - #1 in open source - #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo - Runs autonomously for 8 hours 😅 How is this even possible???

@ArtificialAnlys GLM-5.1 is impressive! Leading open-weights on the intelligence Index with big gains in agentic tasks.

GLM-5.1 takes the open weights lead on the Artificial Analysis Intelligence Index with a modest gain over GLM-5, with most of the improvement driven by gains on agentic real-world use cases (GDPval-AA) GLM-5.1 is now the leading open weights model in GDPval-AA, ahead of MiniMax-M2.7, and behind GPT-5.4 (xhigh), Claude Opus 4.6 (max) and Claude Sonnet 4.6 (max). @Zai_org has now released GLM-5.1’s weights. The model has been available for a few days, but only to subscribers of Zai's Coding Plan. There is no architecture change from GLM-5: GLM-5.1 retains the 744B total / 40B active parameter Mixture-of-Experts design with DeepSeek Sparse Attention, a 200K context window, and BF16 native precision. Since GLM-5, Zai has also released two proprietary models: GLM-5-Turbo, a text-only model that Zai describes as "deeply optimized for the OpenClaw scenario", scoring 47 on the Intelligence Index, and GLM-5V-Turbo (Reasoning), a natively multimodal variant scoring 43 on the Intelligence Index. Both sit below the open weights GLM-5 (Reasoning, 50) and GLM-5.1 (Reasoning, 51) on the Intelligence Index. Key takeaways from benchmarking GLM-5.1 (Reasoning): ➤ GLM-5.1 (Reasoning) scores 51 on the Intelligence Index, a 1 point gain over GLM-5 (Reasoning, 50), and takes the leading open weights position. GLM-5.1 sits ahead of all other open weights models, including MiniMax-M2.7 (50) and Kimi K2.5 (Reasoning, 47), and behind frontier proprietary models including Gemini 3.1 Pro Preview (57), GPT-5.4 (xhigh, 57), and Claude Opus 4.6 (Adaptive Reasoning, max effort, 53) ➤ GDPval-AA is the standout result, with GLM-5.1 reaching an Elo of 1535. This is a +128 Elo gain over GLM-5 (1407) and places GLM-5.1 #4 overall on GDPval-AA, behind only GPT-5.4 (xhigh), Claude Sonnet 4.6 (Adaptive Reasoning, max effort), and Claude Opus 4.6 (Adaptive Reasoning, max effort). GDPval-AA measures performance on real-world knowledge work tasks across 44 occupations and 9 major industries ➤ Underlying eval movement is broadly positive, with gains in graduate-level reasoning (GPQA Diamond), instruction following (IFBench), and research-level physics (CritPt). Versus GLM-5 (Reasoning), we observed gains in GPQA Diamond (+4.8 points), IFBench (+4.0 points), CritPt (+2.6 points), and HLE (+0.8 points), with a small regression in SciCode (-2.4 points). TerminalBench Hard, τ²-Bench Telecom, AA-LCR, and AA-Omniscience remain equivalent to GLM-5 ➤ GLM-5.1 is slightly less token efficient than GLM-5, using ~120M output tokens to run the Intelligence Index versus ~109M for GLM-5. Among the open weights peers at the top of the Intelligence Index, GLM-5.1 uses more output tokens than both MiniMax-M2.7 (87M) and Kimi K2.5 (Reasoning, 89M) Key model details: ➤ Context window: 200K tokens, equivalent to GLM-5 ➤ Multimodality: Text input and output only ➤ Size: 744B total parameters, 40B active parameters, requiring ~1,490GB of memory to store the weights in native BF16 precision ➤ License: MIT ➤ Availability: GLM-5.1 is available via Zai's first-party API and several third-party providers including @DeepInfra, @friendliai, @novita_labs, @gmi_cloud, @parasail_io, @FireworksAI_HQ and @SiliconFlowAI. We will be releasing provider coverage soon as we expect more providers to serve this model

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@TheZachMueller Same here, but honestly... really hope it drops soon.

I will not fall for the DeepSeek leaks I will not fall for the DeepSeek leaks I will not fall for the DeepSeek leaks I will not fall for the DeepSeek leaks I will not fall for the DeepSeek leaks

@victormustar Super excited! Been checking Hugging Face every day.

MiniMax-2.7 open weights confirmed and coming super soon - this is going to be a very big one 🔥