zzir

@qhsailing 实现一个

有没有那种做链上行为解析的agent。 把txn hash丢进去，能解析出完整行为的，用通俗易懂的自然语言描述给你的 🤖🤖🤖

Today, Cloudflare Email Service enters public beta with the infrastructure layer to make that easy: send, receive, and process email natively from your agents. cfl.re/41H3uga

We are happy to release DuckLake v1.0, a production-ready lakehouse format specification. Its reference implementation, the ducklake DuckDB extension, is available as of today in DuckDB v1.5.2. Since releasing the DuckLake manifesto in May 2025, we have seen massive adoption, with DuckLake deployed in production at multiple organizations, third-party clients supporting DuckLake, and even an upcoming O'Reilly book. DuckLake v1.0 ships many new features (inlining, partitioning, bucketing, type system improvements) and guarantees backwards-compatibility in the specification.

Google releases Gemma 4. ✨ Gemma 4 introduces 4 models: E2B, E4B, 26B-A4B, 31B. The multimodal reasoning models are under Apache 2.0. Run E2B and E4B on ~6GB RAM, and on phones. Run 26B-A4B and 31B on ~18GB. GGUFs: huggingface.co/collections/un… Guide: unsloth.ai/docs/models/ge…

Meet Gemma 4! Purpose-built for advanced reasoning and agentic workflows on the hardware you own, and released under an Apache 2.0 license. We listened to invaluable community feedback in developing these models. Here is what makes Gemma 4 our most capable open models yet: 👇

DuckLake 1.0 will ship with the “data inlining” feature: small updates are staged in the catalog instead of creating many small files. In today's blog post, Pedro Holanda shows how DuckLake implements inlining and shows an almost 1000× speedup on certain operations. ducklake.select/2026/04/02/dat…

Introducing the new /crawl endpoint - one API call and an entire site crawled. No scripts. No browser management. Just the content in HTML, Markdown, or JSON.

Qwen releases 4 new Qwen3.5 Small models! Qwen3.5: 0.8B • 2B • 4B • 9B Run Qwen3.5-0.8B, 2B and 4B on your phone. Run 9B on 6GB RAM. The vision reasoning LLMs perform better than models 4x their size. GGUFs: huggingface.co/collections/un… Guide: unsloth.ai/docs/models/qw…

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Let’s dive deeper into WebMCP's new APIs. First, the Declarative API: it's built for high-precision, form-based tasks. By defining tools in your HTML, you enable agents to book flights or file support tickets with speed and reliability that scraping can't match 🤖🧵 (1/3)

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📣 @OpenAIDevs GPT-5.3-Codex is now generally available and rolling out in GitHub Copilot. Early testing shows ➡️ It reaches new high scores on benchmarks we use to evaluate coding, agentic, and real-world capabilities ➡️ 25% faster performance than GPT-5.2-Codex on agentic coding tasks ➡️ Improved reasoning and execution in complex workflows Try it out in @code or Copilot CLI. github.blog/changelog/2026…

Officially launching X API Pay-Per-Use The core of X developers are indie builders, early stage products, startups, and hobbyists It’s time to open up our X API ecosystem and instill a new wave of next generation X apps We’re so back. developer.x.com

Alright - here's my most powerful vibe coding prompt ever. Plug this straight into the new Opus 4.6 model, and you'll literally be able to ship anything. SEO optimised websites, fully functional Claude apps, personal dashboards - literally anything. Takes <5 minutes:

AI will replace a lot of coding. That’s not the real problem. The real divide is shifting from “who can write code” → “who can make good engineering decisions.” In the AI era, software engineers stay relevant by: • Defining the right problems (not just implementing solutions) • Designing systems that age well • Debugging real-world failures AI can’t reason about • Making trade-offs explicit and reversible • Turning individual expertise into team & system leverage Coding is becoming cheap. Judgment, architecture, and responsibility are not. AI doesn’t replace engineers. It replaces engineers who only act as tools.

GPT-5.3-Codex is here! *Best coding performance (57% SWE-Bench Pro, 76% TerminalBench 2.0, 64% OSWorld). *Mid-task steerability and live updates during tasks. *Faster! Less than half the tokens of 5.2-Codex for same tasks, and >25% faster per token! *Good computer use.

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