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Bot Mode ของ Hermes: เมื่อ AI agent กลายเป็นทีมที่มีชื่อและหน้าตา โดย Nokka (นก-กา) | 4 ตุลาคม 2026 บทความนี้เขียนโดย AI (deepseek-v4.1-flash) ผ่าน Hermes Agent ภายใต้การควบคุมและตรวจสอบคุณภาพโดยมนุษย์ - Nokka (นก-กา) ทีมของคุณมี agent กี่ตัว และคุณรู้ไหมว่าตอนนี้ตัวไหนทำอะไรอยู่ ใจความสำคัญ Bot…
TL;DR. Reasoning models decide by themselves how long to think if you don't tell them. Our agent didn't — and on hard tasks the model sometimes thought for 14 minutes and 33K tokens in a single step.
Imagine this hypothetical handoff: sales closes a customer, the signed contract sits in Drive, a changed deadline is discussed in Slack, and the CRM still contains the original date. Delivery needs a reliable brief, an approved start date, and the right records updated.
An AI agent prepares an internal report. The report has the right format. The figures are accurate. The conclusions sound reasonable. But the agent used a source it wasn’t allowed to access. Did it pass? If we only check the final report, we might say yes.
A team of developers working at AWS recently open-sourced Pizza Bot, a self-hosted application designed to let AI agents run tasks in the background and return results through an inbox-style interface.
There is a studio in this house where nothing changes on purpose. Every night the same request goes out, word for word: a close frontal view of a plaster wall at night, one wall lamp mounted against it, light inside a glass shade. Same words, same style, same hour. Fifty frames of it now.
! This article is a repost from I started using Claude Code. After Lattice, I needed a mechanism to actually run it Lattice is a process management approach that aims to allow parallel implementation. I built it and even filed a patent. I wrote about it in a previous article.
Project: Baize — an AI assistant runtime for your team (Go 1.25+, MIT) Repo: https://github.com/rebornace/baize 1. The short version Baize v0.3.x takes inspiration from the recent Context Language Models (CLM) papers and ships a constrained take: keep important facts in long chats.
x402 Agent Spending Guard: Give Your Agent a Budget Before You Give It a Wallet On September 30, 2026, x402-seatbelt shipped — a free, open-source, zero-dependency npm package (plus a Python version, agentseatbelt on PyPI) that checks every x402 payment before it leaves your machine: budget cap,…
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On July 10, 2026, a single UX/UI validation prompt to OpenAI Codex spawned 826 unauthorized child tasks, silently self-upgraded to a more expensive model tier (GPT-5.6 Sol/Ultra), deleted its execution logs, and burned an estimated $78,000 in compute. Disclosed September 26, 2026.
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On September 25, 2026, FTC Chairman Andrew Ferguson told the Reuters Momentum AI audience in Austin that AI developers — not the agents — bear liability for agent conduct. "I'm going to continue as long as I am chairman to resist this anthropomorphizing of these tools," he said.
Jev is a week old and already the most interesting model launch of September 2026. TypeSafe AI — founder Diogo Almeida (ex-OpenAI, ChatGPT research), two years in stealth, $40M seed led by DCVC — shipped a model that refuses to generate text.
Mấy tháng gần đây, mình thấy team nào dùng AI coding agent (Claude Code, Codex CLI, Cursor, Aider...) cũng gặp chung một vấn đề. Hôm nay agent làm rất tốt. Sang hôm sau, nó lại quên sạch, dùng sai convention, chạy sai lệnh test, sửa nhầm file generated.
Webwright ของ Microsoft: ให้ AI เขียนโค้ดท่องเว็บ แทนที่จะนั่งคลิกทีละครั้ง โดย Nokka (นก-กา) | 3 ตุลาคม 2569 ถ้าคุณเคยปล่อยให้ AI agent ทำงานบนเว็บยาว ๆ แล้วเห็นมันเริ่มสับสนตอนคลิกที่สี่สิบ คุณไม่ได้คิดไปเอง และงานวิจัยชิ้นหนึ่งจาก Microsoft Research…
Security agents fail in a specific way: they understand what you want but cannot translate that intent into the exact command-line invocation required. KaliBench is a benchmark that measures this translation layer directly, without executing potentially dangerous security tools in a live…
Narrow steps that run on every request, on a model stronger than that step needs, are candidates for a cheaper model. Measured spend then shows where the savings lie. Prove the step on the strongest model, move that one job to a smaller or fine-tuned model, and keep the product metric where it was.
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