token billionaire

Joined June 2009
Codex measures the remaining Grok measures the used Same quota, opposite math. Why are we like this 😅
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Here at @OpenAI in NYC, let’s walk through some of the great things they are working on. 🧵🪡
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@gdb gave a wonderful background on where we are heading
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It’s not a PSA 10, but it’s nice 👍
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Ultra fast is said to be 7x faster… and you can see 7million tokens spent in ~3 mins
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Gpt 6 (astra) sol and Luna will be coming out soon,
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Kept it original for this one
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Pretty cool, Which name do I pick?!
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Tried out GPT Live, Here are my thoughts: I see no difference they current voice models today, if anything I found it a little slower and sluggish. I only did try the trivia function. I’ll go back in a few and try out the conversation function
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OpenCode seems to have badly underestimated my Astra Pro usage. OpenRouter ended up reporting roughly 3x the cost OpenCode showed me. Has anyone else run into this? Is OpenCode miscalculating token usage/cost, or is there another reason for the gap?
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My Life has become cycling through multiple codex instances.
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The jump from getting a plaque for 100M tokens to treating 1B/week as baseline happened fast.
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Enterprises should invest in tokens than in knowledge workers
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It's mid-2026. We can drop the vibe and call it code again.
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Zero-shot outfit selection
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Windows 95 anyone?
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No rate limits, no quotas, just always on
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One DGX Spark on your desk is powerful. Four is a local AI factory. NVIDIA DGX Spark now scales up to four clustered nodes, enabling inference on models up to 700B parameters — with near-linear performance scaling for fine-tuning and reinforcement learning workloads. Memory scales from 128GB on a single node all the way up for the full four-node configuration, supporting large context windows, high concurrency, and multi-agent workflows. For APAC enterprise teams building autonomous AI: each topology is purpose-built for your workload — from rapid local iteration to production-scale inference. And when you're ready to move to the cloud, Tile IR and cuTile Python carry your kernels across with minimal code changes. No rack. No rearchitecting. Just scale. 🔗nvda.ws/4w0RBPy
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because burning tokens is easier than fixing your retrieval pipeline
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