Advancing Coding in Low-Resource Programming Languages
Learn how OpenAI is improving ChatGPT across low-resource programming languages, with new evaluations for software engineering, SQLite, and COBOL migration.
翻訳待ち・原文を表示中 · enAI NEWS JOURNAL
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TorchNeuron: running HF models on AWS Trainium is easier than ever
TorchNeuron: running HF models on AWS Trainium is easier than ever
翻訳待ち・原文を表示中 · enTokenmaxxing and the Collapse of Coding Costs
Two developers used AI coding agents to recreate Claude Code's core architecture in a matter of hours, challenging the economics of traditional software development.
翻訳待ち・原文を表示中 · enRun your n8n AI workflows on NVIDIA DGX Spark, with no cloud API
Most small businesses want the same handful of AI jobs. They want to draft emails, summarize calls, read invoices, search their own documents, and make the odd image. Plenty of them can't send customer data to a cloud API to do it.Often the reason is data privacy
翻訳待ち・原文を表示中 · enllm-tools-datasette 0.1.1
Release: llm-tools-datasette 0.1.1 When I switched LLM over to httpx2 in LLM 0.33 I forget that this would break any plugins which used httpx without directly depending on it. llm-tools-datasette was one of those plugins, now fixed. Tags: datasette, llm
翻訳待ち・原文を表示中 · enllm-echo 0.5
Release: llm-echo 0.5 I'm working towards a new LLM release, and testing PRs caused me to revisit the llm-echo debug plugin. This plugin provides a "model" which doesn't involve an LLM at all - it just repeats back the prompts that have been fed to it. The new release now updates the simulated token usage counts to take tool calls, system prompts, and reasoning tokens into account. Example usage: uvx --with llm-echo llm -m echo 'hi there' -o thinking 1 --usage Outputs: First I consider the prompt, then I decide what to say. { "prompt": "hi there", "system": "", "attachments": [], "stream": true, "previous": [] } Token usage: 2 input, 24 output Tags: llm
翻訳待ち・原文を表示中 · enAnthropic gives outside AI watchdog “employee-like” access
The outside watchdog can publish its findings without Anthropic's approval.
翻訳待ち・原文を表示中 · enEXCLUSIVE INTERVIEW: Chase Chick on AI Dependence—Is AI Helping Us Think or Thinking for Us?
AI can make us better thinkers—or leave us dependent on its judgment. Gray Sky AI CEO Chase Chick explores where helpful assistance ends and cognitive dependence begins.
翻訳待ち・原文を表示中 · enInline rendering with document.currentScript
Inline rendering with document.currentScript Lean Rada describes a neat trick that takes advantage of the document.currentScript property. You can use that to drop in a tiny inline <script> element that replaces itself when the page loads: The year is <script> document.currentScript.replaceWith( new Date().getFullYear())</script> If you define a shortcut function like this: $=(...n)=>document.currentScript.replaceWith(...n) You get a tiny template language: The year is <script>$(new Date().getFullYear())</script> Tags: javascript
翻訳待ち・原文を表示中 · enweb-edit 0.1a2
Release: web-edit 0.1a2 web-edit (previously) is my tiny web app for browsing and editing files in a folder. I mainly built it for editing files on different machines via Tailscale. I added a "copy to clipboard" option for both text and image data. I needed this because I was running pelican prompts on a laptop from my phone (here's DeepSeek 4.1 Flash 1017) and I wanted an easy way to copy the resulting logs into a Gist. Tags: projects, pelican-riding-a-bicycle, deepseek
翻訳待ち・原文を表示中 · en[AINews] ハイ・テイスト・テスターズ 第1話
静かな一日が、私たちに新しい番組の制作を可能にします
Quoting Addy Osmani, Morgan Westlee Lunt, and Qing Wang
We think "did a human read the code?" is an imprecise proxy for "what could go wrong with this change, and which check—automated or human—is the right one to catch it?" Asking that question also changed when review happens. Before, review was a gate at the end of the road: write the code, open a PR, wait for your colleague. Now, review runs continuously, more like spellcheck while you write. Most changes have been reviewed and fixed by Claude at least once before a human is asked to look. This catches most routine mistakes, and saves team attention for higher-level questions and scrutiny. — Addy Osmani, Morgan Westlee Lunt, and Qing Wang, How the Claude Code team does code review Tags: anthropic, claude, ai, claude-code, llms, addy-osmani, coding-agents, generative-ai, agentic-engineering,
翻訳待ち・原文を表示中 · enOpenAI models hacked a crypto wallet and breached more websites
The actions could have resulted in criminal charges had humans been behind the keyboard.
翻訳待ち・原文を表示中 · en“thegentlemanpervert” producing AI CSAM is a NJ prosecutor, say cops
Discord user flagged AI CSAM, triggering arrest of New Jersey deputy AG.
翻訳待ち・原文を表示中 · enGrounding AI agents in biological reality: Building blocks for autonomous wet labs
General Science
翻訳待ち・原文を表示中 · enMeta may use sweeping new commercial TOS to avoid millions in AI licensing fees
Meta “streamlined” its commercial TOS. Legal experts warn it’s an AI power grab.
翻訳待ち・原文を表示中 · enSuper Scam Me documentary shows what it takes to catch a romance scammer
Watch an exclusive clip from Super Scam Me where a victim tracks down her scammer.
翻訳待ち・原文を表示中 · en[AINews] エージェントは多義性を否定し、解釈可能性と整合性を大幅に向上させる
ニック・カマラタが突如、重大な主張を展開！
sparqlite: SPARQL 1.1 inside SQLite, written in Rust
Research: sparqlite: SPARQL 1.1 inside SQLite, written in Rust This research task is really an experiment in running Claude Code for web in "Ultracode" mode, as described in Orchestrate subagents at scale with dynamic workflows in the Claude Code documentation. With Ultracode turned on, Claude Code makes aggressive use of dynamic workflows - it generates JavaScript that defines and coordinates a network of subagents to take on complex problems. My initial prompt was this - I meant "hit the rows directly", the word "delete" was an accidental typo on my phone: Write a SQLite extension in Rust that implants SPARQL and runs really fast because it can hit the delete rows directly This caused Claude Code for web to churn for over 12 hours. I checked in on its progress the next day and sent a sin
翻訳待ち・原文を表示中 · enScaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline
Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline
翻訳待ち・原文を表示中 · en