550B parameters. If that made you flinch at the bill, you're reading it wrong. Nemotron 3 Ultra only activates 55B per token.
Here is why that changes your cost-per-agentic-task 👇
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And on CLōD, energy-aware routing puts it up to 60% below official pricing. Model efficiency on top of infra efficiency. Full breakdown: buff.ly/YAn7w3t
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🥇 #1 open-weights model on Artificial Analysis (Intelligence Index 51)
→ 62.1 on SWE-bench Pro, ahead of GPT 5.5
→ #1 on Design Arena, above Fable 5
→ MIT licensed, 1M-token context, built for agentic coding
It's an Anthropic-compatible endpoint, so it drops into Claude Code, Cline, or OpenClaw with a base URL swap, and it runs at roughly 1/6 the cost of GPT 5.5.
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We just made GLM 5.2 available on CLōD. One API, no lock-in, no borders.
👉 Set it up and try GLM 5.2 today app.clod.io
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After the PocketOS incident — Cursor agent wiped a prod database + all backups in 9 seconds, then wrote a confession naming the exact rule it chose to ignore — the question is obvious: how do you stop this?
Project Witsmith (Cursor Hackathon Vancouver 2026 Feature Project) puts a contract outside the agent with that insight, powered by CLōD.
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It's a local safety, evidence, and memory layer that sits between your coding agent and the shell. Every command passes through a repo-level AGENT_WIT.yaml contract before execution: allow, ask, or deny — deterministically, outside the model's reasoning loop. When something gets blocked, CLōD (50+ models, one API) explains the risk and proposes a contract amendment. That amendment commits to the repo. Next session, the pattern is blocked without a model call.
The loop: contract → action → evidence → amendment → safer next run.
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Demo: blackbox-agents.pages.dev
Code: buff.ly/v3ldq5Q
Powered by @clod — 50+ models, one API
Built by Yao Du, Juan Aristizabal, Tallal Mohar, Nour Habib at #CursorHackathon Vancouver 2026.
Free your Smith. 🛡️
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Your agentic pipeline makes dozens of model calls per task.
In 2023, the decision was simple: GPT-4 for everything.
In 2026, you're choosing from a large catalog of models across free, mid-range, and high-end tiers from different providers: coding specialists, reasoning models, fast completers, long-context workhorses.
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🤔 The teams building efficiently aren't picking "the best model." They're matching capability and routing the workload appropriately to step:
- Classification, routing, summarization → free tier (Meta Llama 3.3 70B, GPT OSS 120B)
- Code generation → Qwen 3 Coder 480B or DeepSeek V4 Pro
- Reasoning-heavy steps → DeepSeek R1 or Qwen 3 235B Thinking
- Fast simple completions → Trinity Mini at $0.05/M
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The teams paying the most and running over budget are routing premium models at every step because they never mapped model capability to task requirement. And this needs to be changed with #Inference strategy.
What's your current per-step model selection logic? Check out our framework on how you can also route your AI workloads intelligently: buff.ly/PCytgJI
#DeveloperTools #AIEngineering #LLM #InferenceCosts #AgenticAI #CLōD #CTO #TokenCost #AIAgent
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Uber burned through its entire 2026 AI tools budget in four months.
Not over the year. Four months in, it was gone. Their COO went public questioning whether the spend is worth it.
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The teams that stayed on budget treated inference as an infrastructure decision before deployment, not after. Per-task cost visibility, routing that responds to model price changes automatically, no surprise tier jumps mid-build.
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Budget discipline in AI doesn't come from cutting capability. It comes from not paying premium pricing for tasks that don't need it.
🔍️ Find out what the teams that stayed on budget did differently in our latest blog: buff.ly/ksj6Bsd
#AIEngineering #Inference #AgenticAI #DeveloperTools #CLōD #AIInfrastructure #TokenCost #AI
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