Building a transaction-heavy product? Today’s @BNBCHAIN workshop made it clear: for payments, trading, AI agent execution, prediction markets, and tokenized assets, chain selection is a product decision. You need: 🟡 Low fees 🟡 Liquidity 🟡 Active users 🟡 Composability 🟡 Distribution While on BNB Chain: • 3.6M daily trading wallets • ~$13.7B stablecoin market cap on BSC • $5.2B+ tokenized stock volume • Fees as low as ~$0.005 on BSC / ~$0.001 on opBNB Beyond high-performance infra, BNB Chain is also a warm, founder-friendly ecosystem. Come build here, and you’ll get support across technical, marketing, and ecosystem growth. If that’s what your product needs, build on @BNBCHAIN. 🫴

Chinese researchers did it again! OpenBMB just open-sourced MiniCPM5-2B, a dense 2B-parameter model built for reasoning, coding, and tool use on resource-constrained hardware. Artificial Analysis ranked it highest among models under 4B in its Agentic Index comparison. It scored 20, while Granite 4.2 8B scored 9. The model is particularly strong at coding and tool calling, so I tested both capabilities locally. I pulled it onto my machine, connected it to a constrained CI repair agent, and gave it one issue: > A customer reports that retrying checkout with the same idempotency key returns a larger total. The first request returns $109, while the retry returns $118. Find the root cause, fix it without changing the public API contract, and verify the complete test suite. The Python checkout service had 18 tests. Sixteen passed, while two failed on the retry path. The agent could list files, search code, read selected ranges, run approved tests, apply a patch, and inspect its diff. It reproduced the failure, then followed the checkout and idempotency paths through the repository. The model found that shipping was added to mutable order state before the cached result was checked. On retry, the same order already contained shipping, so the calculation added it again. It generated a narrow patch that moved the idempotency check ahead of the mutation without changing the public API. The agent ran the targeted tests and the complete suite. All 18 tests passed. The model was never told which file contained the issue or what change to make. Each test result, search result, and code inspection determined its next action. The video below shows the full trajectory, including the investigation, tool calls, generated patch, diff, and final verification. Everything ran 100% locally on my machine throughout the run. MiniCPM5-2B supports llama.cpp, Ollama, vLLM, SGLang, iOS, Android, and HarmonyOS for local deployment. The model weights, training recipes, reasoning datasets, and UltraX data-refinement system are open-source. GitHub Repo: A 2B model can now inspect a repository, reason across multiple files, modify code, and verify its patch while remaining small enough to target local hardware.

AI Agents are evolving from assistants into autonomous on-chain actors. On May 24, OpenBuild and Mantle will host the Mantle Turing Test Hackathon 2026 Meetup in Shanghai, bringing together AI builders, Web3 developers, and creators exploring the future of Agentic AI × Web3. @Mantle_Official @0xMantle @0xMantleCN 💰 $120,000 prize pool ⚡ AI Trading, RWA, DevTools, Agentic Wallets, and more 🤝 Team matching and project networking This is not just a hackathon. It is an experiment in autonomous AI on-chain. 🗓 May 24, 2026 📍 Shanghai 🔗 #Mantle# #AI# #Web3# #Hackathon# #OpenBuild# #Shanghai# #Layer2# #ZK# #DeFi# #RWA#

📰 A 1.56GB local model just took the #1# spot for open models under 4B on Artificial Analysis. Let's run it on ThumbLLM. OpenBMB released MiniCPM5-2B TODAY. Stats 👇 🧠 2.52B dense parameters 💾 Q4_K_M GGUF: just 1.56GB 📚 131K native context ⚖️ Apache 2.0 🦙 llama.cpp 🟢 Ollama 🖥️ LM Studio 🍎 MLX 🤖 Native tool calling + agent training Artificial Analysis Intelligence Index v4.2 ... 🥇 MiniCPM5-2B → 15 Qwen3.5-4B Reasoning → 14* Qwen3.5-9B Reasoning → 15* Granite 4.2 3B → 11 *Qwen scores are estimated by Artificial Analysis. So a 1.56GB Q4 GGUF is landing in the same AA Intelligence Index tier as Qwen3.5-9B. 🤖 GDPval-AA v2 Elo → 831 🏦 τ³-Banking → 21% ⚡ 19K output tokens/task vs 56K for Ling 3.0 Tiny OpenBMB even released the training data and an official DSpark speculative decoding model. No trustworthy local tok/s numbers yet. That is the benchmark I want next. 🔥 Let's run it on ThumbLLM! 🔗 HF /openbmb/MiniCPM5-2B

🤝 Day-0 support for MiniCPM5-2B on stable vLLM. ⚡ Dense 2.6B model with 131K native context 🧠 Think / No-Think from the same checkpoint 🔧 Tool Calling support via vLLM’s minicpm5 parser Congrats @OpenBMB on the release, and thanks for keeping it on stock LlamaForCausalLM and opening the training data alongside the weights! 🙌 🔗