There is a pattern playing out in almost every engineering organization right now. A developer installs GitHub Copilot to quickly deploy code. The data analyst starts querying the new LLM reporting tool. The product team quietly embeds the third-party model into the feature branch. By the time the security team hears about any of them, … Read more
Most AI agents today suffer from the basic problem of amnesia. Use one to browse the web, troubleshoot GitHub issues, or navigate the shopping mall, and it approaches every single task like it’s never seen anything like it before. No matter how many times he stumbles upon the same type of problem, he repeats the … Read more
The Xiaomi MiMo team has publicly released two new models: MiMo-V2.5-Pro again MiMo-V2.5. The benchmarks, combined with impressive real-world performance demos, make a compelling case that open agent AI is reaching the frontier sooner than expected. Both models are readily available via API, and are competitively priced. What Is the Agentic Model, and Why Is … Read more
In this tutorial, we use an advanced AI system using i CAMEL framework, organizing many specialized agents to jointly solve a complex task. We design a structured multi-agent pipeline that includes an editor, researcher, writer, critic, and rewriter, each with clearly defined responsibilities and outcomes limited by the schema. We combine tooling, adaptive sampling, systematic … Read more
Alibaba’s Qwen Team has released Qwen3.6-27B, the first dense open weight model in the Qwen3.6 family – and arguably the 27 billionth parameter model available today for coding agents. It brings major improvements in agent coding, a novel approach to Conservation Thinking, and a hybrid architecture that combines Gated DeltaNet direct attention with traditional attention … Read more
BATCH = 128 EPOCHS = 30 steps_per_epoch = len(X_train) // BATCH train_losses, val_losses = [], [] t0 = time.time() for epoch in range(EPOCHS): key, sk = jax.random.split(key) perm = jax.random.permutation(sk, len(X_train)) X_s, Y_s = X_train[perm], Y_train[perm] epoch_loss = 0.0 for step in range(steps_per_epoch): xb = X_s[step*BATCH:(step+1)*BATCH] yb = Y_s[step*BATCH:(step+1)*BATCH] model, opt_state, loss = train_step(model, opt_state, … Read more
How to make multiple agents work together as a special team – automatically dividing tasks, communicating effectively, and collaborating seamlessly? The openJiuwen community has released the latest version of JiuwenClaw, which adds support for AgentTeam — the ability to collaborate with multiple agents. It suggests that the next leap beyond Harness Engineering be Coordinating Engineering. … Read more
A year or two ago, using advanced AI models felt expensive enough that you had to think twice before asking anything. Today, using those same models feels cheap enough that you don’t even notice the cost. This is not just because “technology has advanced” in a vague sense. There are some reasons behind it, and … Read more
Design has traditionally required multiple roles working in sequence: strategist to describe the problemdesigner to prepare the solutionand engineers to build it. This means coordinating timelines, aligning ideas, and going through rounds of iteration before anything tangible is created. Claude Design removes much of this conflict by turning ideas into practical results. What once took … Read more
For all the progress made in AI agent development over the past few years, one key problem remains unsolved: most people never interact with agents. They live behind developer dashboards, inside special apps that users are asked to download, and in chat rooms that most people in the world will never visit. Beautiful models. Thinking … Read more