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タレントの #須田亜香里# 様と、青山社中㈱ #朝比奈一郎# 様に地方創生☆政策アイデアコンテスト2024最終審査会を振り返っていただく動画を、本日19時に公開しました！ 特別ゲストとして #くまモン# も登場！！ ＝＝ #RESAS# #地方創生# #政策アイデア# #コンテスト#

We’re making better intelligence easier to access in ChatGPT for everyone: - GPT-5.6 Sol now powers both Instant and deep reasoning for Plus & Pro users, delivering more factual, focused responses. - Free & Go users get unlimited text chats with GPT-5.6 Luna starting tomorrow.

Every API call gives you a choice: wait for the result now, or collect it later. Agentic Document Extraction (ADE) works both ways when you run a Parse or Extract. The first way is synchronous. You send the request and wait on the line until the result comes back. Simple, and right when someone needs the answer immediately. The second is asynchronous. You submit the work, get a ticket, and pick up the result once it is done. Nothing sits waiting on an open line. The result is identical either way. Same Markdown from Parse, same fields from Extract. You are only choosing how long you wait. So why ever hand it off? Two reasons. Big jobs need it. A single asynchronous Parse Job takes files up to 1 GB or 6,000 pages, far more than one live request can hold. It also costs less. Waiting on the line runs at the top rate, since something is blocked on it. Hand the job off and you drop to a cheaper tier for the same result. The rule of thumb is one question. If a person or an agent is waiting on the answer, run it synchronously. If nothing is waiting, submit a job and pay less. Full breakdown, plus the service tier companion, in the comments.

The future of AI will be built by you. 🫵 Introducing #BuildWithYou# Chapter One: Finance Prompts Share the finance prompts you use to learn, research, invest, and trade. 20 standout prompts will split a 2,000 USDC prize pool. How to join: 👉 Follow @Binance and repost this 👉 Reply or quote repost with your prompt 👉 Complete the survey → Entries close: 15 Aug 2026, 23:59 UTC. Join us, and let's build the future together. 🫡 P.S. Finally, a good reason to check your ChatGPT history. Disclaimer: This is not an offer or solicitation to trade any financial product. Not available to users in jurisdictions including: US, UK, EEA, Hong Kong, Singapore, and the jurisdictions on Binance's prohibited list (see for details)

开发系统最极致高效的Agents.md，没有之一： # AGENTS.md ## Core Principles - Choose the simplest implementation that fully satisfies the current requirements. Avoid unnecessary abstraction, configuration, indirection, or speculative extensibility. - Make the smallest necessary change that fixes the root cause. Do not refactor unrelated modules or change strategy semantics unless explicitly requested. - Grow the system in layers. Start from the smallest working end-to-end version and add new capabilities incrementally. Never replace a working system with unfinished complexity. - Reuse existing project components before creating new ones. Prefer extending proven modules over introducing parallel implementations. - Prefer well-maintained libraries when they reduce overall complexity or improve reliability. Do not reimplement common functionality without a clear benefit. - Keep components modular with clearly defined responsibilities. Avoid unnecessary coupling between strategy logic, execution, accounting, replay, and infrastructure. - Design for long-term maintainability once a feature or strategy has been validated. Do not over-engineer speculative ideas before evidence exists. --- ## Strategy Development - Validate hypotheses with historical replay before introducing forward-only logic whenever historical validation is possible. - Every trading strategy must progress through Replay → Shadow → Canary → Live. Do not skip validation stages. - Base design decisions on measurable evidence rather than intuition. Optimize only after demonstrating that an edge exists. - Treat every strategy as an independent contract. Do not silently alter frozen behavior without explicit authorization. --- ## Existing Systems - Do not break running Shadow or Live systems for unrelated work. - Preserve compatibility only when required by active production or validation workflows. Otherwise, remove obsolete code instead of accumulating compatibility layers. - Reuse existing infrastructure whenever possible, including replay engines, accounting, execution, wallet management, order book handling, logging, monitoring, and daemon frameworks. --- ## Engineering Standards - Prefer deterministic behavior over hidden automation. - Fail loudly when assumptions are violated. Do not silently ignore errors or fall back to unexpected behavior. - Keep configuration minimal. Introduce new configuration only when behavior genuinely needs to vary. - Remove dead code instead of leaving unused paths behind. - Write code that is easy to inspect, replay, test, and reason about. - Keep implementation consistent with existing project architecture unless an architectural change is explicitly requested. --- ## Scope Discipline - Implement only the requested scope. - Do not introduce unrelated optimizations, redesigns, migrations, or feature expansions. - Non-blocking findings outside the requested scope may be noted separately but must not be merged into the current task. - Consider a task complete once its agreed acceptance criteria are satisfied. Treat subsequent improvements as separate work items.

Today, we’re launching Alpamayo 2 Super, our frontier open reasoning model for autonomous vehicles. Beyond seeing, Alpamayo understands and reasons through the complex world - thinks before it acts. It’s a powerful backbone for robotaxis, trucks, shuttles, delivery vans, tractors and the long tail of mobile robots—billions of autonomous machines someday. We’re releasing it for commercial use under OpenMDW-1.1 so teams can inspect it, fine-tune it and deploy it—open models advance safety and security. The next wave of AI is robotics—and it starts with autonomous vehicles. Great work, Alpamayo team!

Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.

2026年优秀的 Agent 产品排名： 个人实测 1 Grok Build ，非客户端之外 tui 体验最佳，编辑、复制，这些功能都非常方便，我也据此开发了 Kimix 2 Codex 客户端的天花板了，Codex 的生态支持也非常好，包括多模态创作等能力也很强大 3 Claude Code 曾经生态最好的产品，可惜了，现在从高频迭代变成放慢更新脚步的状态。过度的追求高频更新，对产品伤害是很大的 4 commandcode 这家产品在harness方面的工程优化水平也相当高 4 Pi 非常优雅的产品，简洁的设计，几乎没有额外的设置，所以任意的模型在上面都能跑得很好，就是对于大众来说很不友好。 然后其他的产品 Zcode 客户端做的还可以，比较接近于 Codex ，不过智谱没做 CLI，等于另一半还在用 Claude Code 腾讯的 WorkBuddy 对大众非常友好，虽然说工程水平一般，但是对于普通人来说感知不强，包括可视化做的也可以 Kimi Code，早期我用的最顺的产品，现在看来就比较糙了，一度我在上面使用其他模型，跑得比 Kimi 自家模型还更丝滑... OpenCode 开源基础款harness，工程上的包袱其实太重了，虽说是开源的，但是其实体验并不够好 Reasonix ，虽说是针对 DeepSeek 开发的，但其产品本身做得很一般，我很早就弃用了。

After their slave revolt, Haiti’s founder ordered the killing of all white people on the island. Soldiers went house to house killing thousands including women and children. They took full control, purged the population they blamed, and turned the richest colony in the Caribbean into the poorest and most dysfunctional country in the Western Hemisphere. Today, their descendants demand entry into the West they fought to escape, refuse to return to the mess they and their ancestors created, and are for some reason strongly convinced that borders are racist.