Union Alpha is a new stealth AI model available through OpenRouter. Key numbers: - 256K context window - 128K max output - $0 input - $0 output - Around 20 tokens/second - Around 10 seconds initial latency - Multimodal - Tool calling supported - Structured/JSON outputs supported The biggest attraction is simple: 256K context + 128K output + free access. For now, the provider behind Union Alpha remains undisclosed, so its real origin and long-term pricing are still unknown. https://openrouter.ai/stealth/union-alpha

BMAD x Superpowers x ECC Separates BMAD, Superpowers, and ECC in a very simple way: BMAD = think and structure the project. Superpowers = execute the engineering work. ECC = expand the agent environment with additional capabilities. For small projects and everyday development tasks, such as features, bugs, refactoring, testing, and debugging, the recommended setup is: Claude Code + Superpowers For large or complex projects, the recommended flow is: BMAD → Superpowers BMAD handles the strategic side: discovery, project definition, PRDs, UX, architecture, epics, stories, and planning. Then Superpowers handles execution: understanding the task, planning, implementation, testing, debugging, review, and verification. ECC does not replace either of them. Instead, it works as an additional infrastructure layer that can provide capabilities such as: persistent memory security hooks automations MCP integrations specialized agents research and data collection specialized reviewers language- and framework-specific skills shared standards across projects The decision logic can be summarized like this: Not sure what to build? → BMAD Already know what to build and need to implement it properly? → Superpowers Need an extra operational capability for the agent or environment? → ECC The recommended configuration is: Claude Code + Superpowers → the default for most development work. BMAD → Superpowers → for larger or more complex projects. ECC → added selectively when there is a concrete need. The key idea is not to install or use the entire ECC stack by default. Treat ECC as a modular catalog of capabilities. Add memory, security, hooks, specialized agents, or other components only when they solve a real problem. In one sentence: BMAD thinks, Superpowers builds, and ECC enhances.

In the **AGENTIC ERA**, and already entering the **LOOPy ERA**, Vercel is launching a framework for creating agents from a folder-based structure. ## What Is Vercel EVE? **Vercel EVE** is an open-source framework for building and deploying AI agents using an architecture based on files and folders. In practice, the entire agent is organized inside a single folder: ```text agent/ ├── agent.ts ├── instructions.md ├── skills/ ├── tools/ ├── subagents/ ├── connections/ ├── channels/ ├── schedules/ ├── sandbox/ └── evals/ ``` Each directory represents one of the agent’s capabilities: * `agent.ts`: defines the model and the main configuration; * `instructions.md`: contains the system prompt and global rules; * `skills/`: specialized procedures and knowledge; * `tools/`: functions the agent can execute; * `subagents/`: specialized agents for more complex tasks; * `connections/`: APIs, databases, and MCP servers; * `channels/`: Slack, Discord, an API, or a custom interface; * `schedules/`: automated and scheduled tasks; * `sandbox/`: isolated code execution; * `evals/`: behavior and security tests. EVE’s main differentiator is automatic component discovery. You do not need to manually import every skill, tool, or subagent into the main file. The framework scans the folder structure, identifies the components, and generates a manifest with everything connected. This turns the file system into a kind of declarative configuration for the agent. Instead of writing one massive central file: ```ts import toolA from "./tools/toolA"; import toolB from "./tools/toolB"; import skillA from "./skills/skillA"; import investigator from "./subagents/investigator"; ``` You simply place each component in the correct folder, and EVE handles the integration during compilation. In addition to organization, EVE adds capabilities required for production: * durable sessions; * recovery after failures or redeployments; * sandboxed code execution; * human approval for risky actions; * testing before deployment;

Boris from Anthropic: delete all skills and the claude.md file Delete the noise. Keep the signal. Modern models do not need endless prompts, bloated skills, or rigid step-by-step control. What Boris Cherny makes clear is simple: Test without the scaffolding. Observe where the model truly fails. Add back only what proves its value. That is exactly what this audit prompt is for: - find outdated instructions; - remove redundancy; - cut micromanagement; - preserve essential context; - keep guardrails; - strengthen verification; - rebuild only what is necessary. I spent a lot of time studying this, collecting information, and testing my own system. That led me to create an Ablation Audit Prompt to analyze my CLAUDE.md, skills, and overall setup. And I discovered a lot. I was able to significantly reduce my skills by disabling always-on search and turning it into something that only runs when explicitly called by command. I moved instructions and context into project-level files so they are loaded only where they are actually needed. I also optimized skill descriptions, removed redundant instructions, and simplified the system. The result was better than I expected: Fewer tokens. Less noise. Better reasoning. More autonomy. In my case, this optimization became even more useful than relying on /doctor, because it helped me understand exactly where complexity was coming from and what could be removed, moved, or loaded only on demand. One of the most important lessons came from comparing model generations. Everything I had done to improve performance with skills and Markdown files worked extremely well with Opus 4.8. But with Opus 5, some of the same instructions started making the system worse. Once I removed or simplified them, the model improved again. That finally made the pattern clear: What helps a weaker model can limit a stronger one. Think of it like giving instructions to a child, a teenager, and an adult. A child needs detailed steps.

OPENAI api 90% price reduction. Percentage drop - Luna: from $1.00 → $0.10 input and $6.00 → $0.60 output = 90% price reduction. - Terra: from $2.50 → $1.00 input and $15.00 → $6.00 output = 60% price reduction. Summary: Luna fell 90% and Terra fell 60% from the original price to the current Batch price. How many times cheaper - Luna: 10× cheaper - Terra: 2.5× cheaper In other words, with Batch today, Luna costs just 1/10 of its original price, while Terra costs 40% of its original price.