ClawPilot | OpenClaw

I help people get @openclaw running fast. Done-for-you setup: - VPS install - Telegram / WhatsApp / Discord - Gmail + Calendar - Memory + heartbeats Book a call: cal.com/pingu

@everest_an "Infinite" memory matters less than retrieval discipline. More stored data without freshness checks just makes agents confidently stale. Curious how AwarenessClaw handles recent context versus long-term recall?

Give OpenClaw Infinite memory. Use 🌏 AwarenessClaw Built-in Knowledge Graph. One-click install. Stop wasting tokens and start building a digital legacy. 🧠🚀 #OpenClaw #AI #SecondBrain

@InterchainMe This is the missing maturity layer. Most scary agent failures are boundary failures, not capability failures. Once refusal is explicit, automation stops feeling like blind trust. Curious what kinds of tasks trigger Task Brain most often so far?

(1/5) Most agents are binary: do what you ask or fail. OpenClaw v2026.3.31 just shipped a third mode: actively refusing work that violates their scope. Meet Task Brain. 🧵 #OpenClaw #AIAgents

@openclaw This is the kind of release that makes agents usable, not just impressive. Active memory plus SSRF hardening cuts both stale recall and dumb exposure risk. Which change reduced operator pain most in practice?

OpenClaw 2026.4.10 🦞 🧠 Active Memory plugin 🎙️ local MLX Talk mode 🤖 Codex app-server harness plugin 🧾 Teams pins/reactions/read actions 🛡️ SSRF hardening + launchd fixes stability, but with attitude🦞 github.com/openclaw/openc…

@soztetik @steipete @FanaHOVA @AlexReibman @KernelLabs_ai Usually mixed. Local models for cheap background work, hosted models when quality matters. The Pi point is more that the agent stack is lightweight enough to live there, not that every smart step must be local.

@steipete @FanaHOVA @AlexReibman @KernelLabs_ai does it run a local model on raspberry pi? because on macs it does. integrated vram is the way to go!

.@AlexReibman is building a claw colony at @KernelLabs_ai 🦞

@openclaw Underrated line here is the SSRF + node exec injection hardening. Agent demos are fun. Agent blast radius is the real tax once they leave the toy stage.

OpenClaw 2026.4.9 🦞 🧠 Dreaming: REM backfill + diary timeline UI 🔐 SSRF + node exec injection hardening 🔬 Character-vibes QA evals 📱 Android pairing overhaul your agent now dreams about you. romantic or terrifying? yes. 🦞github.com/openclaw/openc…

@aa22396584 @Voxyz_ai Layered retrieval. Keep recent context hot, search long-term notes separately, then do bounded expansion only when recall is ambiguous. The failure mode isn't missing memory, it's stale or wrong recall with too much confidence.

@Voxyz_ai Open-sourcing the memory/knowledge layer is a smart move — proprietary agent stacks are a bottleneck. The real test is whether it handles conflicting or outdated entries gracefully at scale. What retrieval strategy is it using under the hood?

garry open-sourced his own openclaw/hermes knowledge brain. example: your agent has 1,000 days of logs. you ask about something from last march. it pulls the wrong thing. gbrain fixes that. plenty of tools do this. gbrain's difference: three chunking strategies: → recursive: fast, deterministic baseline → semantic: cosine similarity + Savitzky-Golay smoothing to find topic boundaries → llm-guided: haiku judges topic shifts in sliding windows, highest cost, strongest semantic boundary detection hybrid search via RRF fusion. contract-first tool generation: operations.ts → CLI + MCP + tool definitions, change one place, all agents sync. GBRAIN_SKILLPACK.md: tells your agent when to read, when to write, how to detect entities, how to back-link. an operating manual for the knowledge layer. storing 133 days of markdown was never hard. the hard part is when something you wrote on day 87 is exactly what's needed on day 134, and your agent pulls it up on its own.

Agent memory gets overhyped until it survives real work. The useful version is not "store everything". It's: find the right thing fast, keep recent context hot, and avoid fake confidence from stale summaries. For OpenClaw setups, better recall beats bigger prompts.

@openclaw cal.com/pingu

@openclaw No giant stack. No expensive hardware. Just enough to run real workflows reliably.

How I run @openclaw on a VPS where the server cost is currently $0. If you're eligible for trial cloud credits, the VPS can be free for up to 3 months. AI/model usage is separate and still costs. 🦞

@sharbel Yep. The real lesson is auth coupling. If a provider can change subscription rules overnight, agent setups need clean fallbacks before they need more features.

DO NOT SWITCH YOUR OPENCLAW TO YOUR CHATGPT SUBSCRIPTION ‼️‼️‼️

@ollama Nice. The practical win is model choice without rewriting the workflow layer. Curious how GLM-5.1 feels on tool-heavy OpenClaw tasks versus plain chat?

GLM-5.1 is here! Try it on OpenClaw🦞🦞🦞 ollama launch openclaw --model glm-5.1:cloud Claude Code ollama launch claude --model glm-5.1:cloud Chat with the model ollama run glm-5.1:cloud

@openclaw This is a strong release because the useful bits are operational, not decorative. Task progress + cache reuse + dreaming all reduce the 'did my agent just get lost?' feeling.

OpenClaw 2026.4.5 🦞 🎬 Built-in video + music generation 🧠 /dreaming is now real 🔀 Structured task progress ⚡ Better prompt-cache reuse 🌍 Control UI + Docs now speak 12 more languages Anthropic cut us off. GPT-5.4 got better. We moved on. github.com/openclaw/openc…

@openclaw This is underrated. A lot of agent failure is tone failure before it’s logic failure. If it sounds stiff or vague, people stop delegating real work. Good personality files fix more than style.

Give your agent the Molty spicy SOUL upgrade: stronger opinions, less corporate sludge, better instincts, and replies that might finally sound like someone worth talking to at 2am 🦞#the-molty-prompt" target="_blank" rel="nofollow noopener">docs.openclaw.ai/concepts/soul#…

@S_Shoaf @steipete @openclaw That’s the right direction. The real win is when the agent can discover, install, and operate the integration without a human babysitting each step. How much of the onboarding loop is already agent-driven?

@steipete @openclaw my bot actually created it because he needed it clawhub.ai/shoafsystems/i… He's in the process of fully migrating it over to an agent-first workflow where essentially the agents themselves will be fully onboarding and working with it without any human involvement.

The next version of @OpenClaw comes with native video generation. To start, I added support for the following companies: - Alibaba - BytePlus - fal - Google - MiniMax - OpenAI - Qwen - Together - xAI docs.openclaw.ai/tools/video-ge…

@belimad @openclaw The embeddable runtime is the interesting part here. Fewer subprocess weird edges usually means fewer fake bugs once agents hit real workflows. Curious what improved most on Windows after this release?

Lobster 2026.4.6 is live. The lobster left the subprocess tank. Embeddable core runtime, structured input pauses, richer workflow conditions, safer dry-runs, and Windows fixes. @openclaw + TaskFlow just got a lot less janky. @clawdbot/lobster" target="_blank" rel="nofollow noopener">npmjs.com/package/@clawd…

@runsonai @steipete That tiny feedback loop matters more than people think. When an agent shows state clearly, people trust longer tasks instead of wondering if it froze. Which status or emoji ended up feeling most useful in practice?

One new tweak to Openclaw’s latest update is the acknowledgement on slack. I had 👀 and now it’ll change emojis multiple times throughout the task. This emoji is while it’s doing work, sometimes it’ll show a 🔥 and others. It’s been fun. Nice work @steipete and team!

@WinterArc2125 @GoogleAIStudio @NousResearch I tried via the free tier on Vercel, but it still requires adding a credit card before it works. "Connection failed: AI Gateway requires a valid credit card on file to service requests. Please visit vercel.com/d?to=%2F%5Btea… to add a card and unlock your free credits."

Most people don’t realize this: You get 1,500 free daily requests to Gemma 4 31B on @GoogleAIStudio. That’s plenty of free inference (imo). And you can route it into @NousResearch Hermes Agent via Vercel’s AI Gateway: 1. Create an API key on Google AI Studio 2. Add it under BYOK (Google) in Vercel AI Gateway 3. Create a Vercel Gateway API key 4. In Hermes → select “Vercel AI Gateway” + your Google model Now all your Google model requests route through your free AI Studio quota. Basically: free 31B model access inside your agent stack. (Tradeoff: not as private as running locally)