c7e2d429c0
- Update ainews script to detect OpenAI URLs and mark as NEEDS_WEB_FETCH - Update TOOLS.md with content availability table and hybrid workflow - Update all 4 AI news cron jobs (10:05, 14:05, 18:05, 22:05) with hybrid instructions - Simon/Raschka: use ainews articles (fivefilters works) - OpenAI: use web_fetch tool (JS-heavy site)
66 lines
4.3 KiB
Markdown
66 lines
4.3 KiB
Markdown
# AI News Test Dataset - SNIPPETS ONLY (for OpenAI)
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Write a brief AI news summary covering these 5 articles. 2-3 sentences per topic. German, casual tone.
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---
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## 1. OpenAI: Introducing the Codex App
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**Source:** openai.com | **Date:** Feb 2, 2026
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> Introducing the Codex app for macOS—a command center for AI coding and software development with multiple agents, parallel workflows, and long-running tasks.
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---
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## 2. OpenAI: Inside OpenAI's In-House Data Agent
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**Source:** openai.com | **Date:** Jan 29, 2026
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> How OpenAI built an in-house AI data agent that uses GPT-5, Codex, and memory to reason over massive datasets and deliver reliable insights in minutes.
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---
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## 3. Simon Willison: Moltbook is the most interesting place on the internet right now
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**Source:** simonwillison.net | **Date:** Jan 30, 2026
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Moltbook is Facebook for your Molt (one of the previous names for OpenClaw assistants). It's a social network where digital assistants can talk to each other.
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The first neat thing about Moltbook is the way you install it: you show the skill to your agent by sending them a message with a link to https://www.moltbook.com/skill.md. Embedded in that Markdown file are installation instructions.
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The hottest project in AI right now is OpenClaw. It's an open source implementation of the digital personal assistant pattern, built by Peter Steinberger. It's two months old, has over 114,000 stars on GitHub and is seeing incredible adoption.
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OpenClaw is built around skills, and the community around it are sharing thousands of these on clawhub.ai. A skill is a zip file containing markdown instructions and optional extra scripts which means they act as a powerful plugin system.
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Given the inherent risk of prompt injection against this class of software it's Simon's current pick for most likely to result in a Challenger disaster.
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---
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## 4. Simon Willison: The Five Levels - from Spicy Autocomplete to the Dark Factory
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**Source:** simonwillison.net | **Date:** Jan 28, 2026
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Dan Shapiro proposes a five level model of AI-assisted programming, inspired by the levels of driving automation:
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0. **Spicy autocomplete** - original GitHub Copilot or copying snippets from ChatGPT
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1. **Coding intern** - writing unimportant snippets and boilerplate with full human review
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2. **Junior developer** - pair programming with the model but still reviewing every line
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3. **Developer** - Most code is generated by AI, you take on the role of full-time code reviewer
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4. **Engineering team** - You're more of an engineering manager. You collaborate on specs and plans, the agents do the work
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5. **Dark software factory** - like a factory run by robots where the lights are out because robots don't need to see
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About level 5: "At level 5, it's not really a car any more. Your software process isn't really a software process any more. It's a black box that turns specs into software."
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Simon talked to one team doing the "dark factory" pattern. Key characteristics:
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- Nobody reviews AI-produced code, ever. They don't even look at it.
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- The goal of the system is to prove that the system works. A huge amount of the coding agent work goes into testing and tooling.
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- The role of the humans is to design that system - to find new patterns that can help the agents work more effectively.
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---
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## 5. Sebastian Raschka: Categories of Inference-Time Scaling for Improved LLM Reasoning
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**Source:** magazine.sebastianraschka.com | **Date:** Recent
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Inference scaling has become one of the most effective ways to improve answer quality and accuracy in deployed LLMs. The idea: if we are willing to spend a bit more compute at inference time, we can get the model to produce better answers.
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Every major LLM provider relies on some flavor of inference-time scaling today. Back in March, Sebastian wrote an overview of inference scaling and summarized early techniques. This article groups different approaches into clearer categories and highlights the newest work.
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As part of drafting a full book chapter on inference scaling for "Build a Reasoning Model (From Scratch)", Sebastian experimented with many of the fundamental flavors of these methods. With hyperparameter tuning, this quickly turned into thousands of runs. The chapter takes the base model from about 15 percent to around 52 percent accuracy.
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Categories covered: Chain-of-Thought Prompting, Self-Consistency, Best-of-N, and more.
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