Is it too late to try to create a Claude Max plan account to OpenClaw?

Peter Steinberg — the solo developer who built OpenClaw (fastest-growing open source AI agent project) — is joining OpenAI. But here's the twist: OpenClaw isn't being sold. It's becoming an independent foundation, staying open source, and Steinberg is keeping his "non-negotiable" commitment to openness. Why this matters: This is the biggest story in AI agents right now. A one-person project that hit 60,000 GitHub stars in months just got the backing of the world's most prominent AI lab — while keeping its open source soul intact. The Timeline (Because Context Matters) Nov 2025 •⁠ ⁠What Happened: Steinberg starts OpenClaw as a personal project Dec 2025 •⁠ ⁠What Happened: Goes viral. Crosses 60,000 GitHub stars Jan 9, 2026 •⁠ ⁠What Happened: Anthropic blocks OAuth token access for third-party projects (including OpenClaw). Backlash ensues Jan 2026 •⁠ ⁠What Happened: Anthropic forces name change from "Claudebot" → "Moldbot" → "OpenClaw" (three names in 72 hours) Jan 2026 •⁠ ⁠What Happened: Scammers hijack old accounts in under 5 seconds. Security nightmare Feb 2026 •⁠ ⁠What Happened: Steinberg announces talks with Meta and OpenAI on Lex Fridman's podcast Feb 15, 2026 •⁠ ⁠What Happened: Official announcement: Steinberg joins OpenAI; OpenClaw becomes independent foundation What Sam Altman Actually Said "Peter Steinberg is joining OpenAI to drive the next generation of personal agents. He is a genius with a lot of amazing ideas about the future of very smart agents interacting with each other to do very useful things for people. We expect this will quickly become core to our product offerings." Translation: OpenAI sees agents as their next major platform play. They're betting on Steinberg to lead it. What Steinberg Actually Committed To His Non-Negotiables (From His Own Blog Post): 1.⁠ ⁠OpenClaw stays open source — Not negotiable. Period. 2.⁠ ⁠Foundation structure — Moving to a proper foundation, not OpenAI ownership 3.⁠ ⁠Independence — Foundation remains independent (though OpenAI is sponsoring it)

The "Personal AI Operating System" era is officially here. Tools like OpenClaw (formerly Moltbot) are letting AI handle our emails, calendars, and digital lives. But there’s a massive problem: If you’re using closed-source APIs, you don't actually own your OS. 🧠 Most people are piping their most private data into "black box" providers. You lose control, you risk data logging, and you’re locked into one company’s roadmap. I just read a great breakdown from Fireworks AI on how to fix this. Here is the TL;DR on why you should switch your agent's brain to Fireworks: 1. Privacy First 🔒 Fireworks is US-based and has a Zero Data Retention default. They don’t use your inference data to train their models. If your AI has access to your messages and files, this isn't a "nice-to-have"—it's a requirement. 2. Up to 10x Cheaper 💸 Running an agent 24/7 to monitor notifications adds up. Fireworks lets you use efficient models for routine tasks and swap to heavy-hitters only when needed. It makes a personal AI economically sustainable. 3. Access to SOTA Models (Like Kimi K2.5) ⚡ You aren't stuck with one "flavor" of intelligence. You get Day-0 access to the best open-source models like Llama, DeepSeek, and Kimi K2.5. 🛠 How to make the switch (The 30-Second Version): 1. Get your Key: Sign up at Fireworks AI to get your API key. 2. Configure: Run this in your terminal to set up the Fireworks config: curl -fsSL https://storage.googleapis.com/fireworks-public/openclaw/setup-fireworks.sh | bash -s -- YOUR_API_KEY 3. Onboard: Run openclaw onboard --install-daemon and choose "Keep Current" for the model to use the Fireworks setup. The Question: Are you guys still using closed-source APIs for your agents, or have you started moving toward "owning" your AI with open models? Let’s discuss in the comments! 👇

Hey folks/quick question for anyone running Clawdbot. what do you consider a healthy “always-on” context size per session from a token + cost perspective? I’m currently tightening (or at least trying to/lol) things to: - ~15K tokens for active context - Last 3–5 messages only - Aggressive summarization + compaction beyond that - Using a cheaper model for non-thinking tasks (summaries, formatting, validation) Curious: - Where do you cap context in practice? - Do you rely on auto-compaction (maybe the gateway helps to compact but the bot adds all contexts which always pushes the size of contexts) or manual summaries? - Any gotchas you’ve hit with session memory blowing up costs? Would love to hear real-world numbers vs theory.