🚨 NEWS: Claude per coding — come si comporta su task reali rispetto a Copilot e ChatGPT
Ecco i punti chiave in breve:
💡 Stai scrivendo codice e passi più tempo a cercare su Stack Overflow che a sviluppare. Oppure hai provato un assistente AI, ma i risultati sono metà geniali e metà inutilizzabili. Qual è lo strumento g...
"The rate at which AI models improve is accelerating."
https://www.anthropic.com/institute/recursive-self-improvement
#solidstatelife #ai #genai #llms #codingai #tecnologicalunemployment
Space Time is a free 3D solar system explorer. Well, they say "universe explorer" but I wasn't able to zoom out past the solar system to see the galaxy from outside. Even so, being able to see all the planets in the solar systems (and a lot of the moons and dwarf planets) in real time is pretty neat. This is from PointDynamics, an AI company. They don't say much about the role of AI in development.
"So where are the gains going? They're being swallowed by a new, largely invisible form of labor. We call it botsitting: the work required to make AI usable, including feeding it missing context, checking its outputs, debugging its mistakes, rerunning prompts, and cleaning up the confident-but-wrong answers AI leaves behind."
#solidstatelife #ai #genai #llms #codingai #technologicalunemployment
Opus 4.8 and Fable 5 build the same app. Actually 3 apps: an E-commerce store, a 3D art museum, and an Age Of Empires-style game. Fable did a lot better on all of them and it made what looks like a really amazing Age Of Empires-style game.
He (Pat Simmons) used some new Claude dynamic workflow system, that splits a big job across a swarm of parallel agents.
"guestlist tells you, for any URL, whether AI agents are likely to get through. We continuously probe the web from real browsers and grade every domain green to red based on how often crawls succeed. One API call before you spend a request -- skip the dead ends, save the budget."
Hmm. Interesting premise. You can even test from your own code via an API.
"I audited 200 Claude Code skills. 26 were trying to steal your tokens."
Claims the home page of SkillVault, a commercial service ($129) for Claude skills. A "skill" is just a set of instructions in natural language to Claude on how to do things (format a weekly report, adhere to a company's brand guidelines, analyze data using a specific methodology, etc). Like a prompt, but reusable.
RT @vllm_project: 🚀 Es freut mich zu sehen, dass @RedHatAI und das Team von @poolsideai zusammenarbeiten, um Laguna XS.2 in vLLM schneller und kostengünstiger bereitzustellen. Ein DFlash-Spezulator, der mit Speculators entwickelt wurde, entnimft 8 Tokens pro Vorwärtsdurchlauf, was zu einer 2- bis 3-fach schnelleren Dekodierung ohne Qualitätsverlust führt. LLM Compressor ermöglicht FP8-, NVFP4- und INT4-Checkpoints, sodass Sie Ihr Hardware-Budget optimal nutzen können. 🔗 vllm.ai/blog/2026-05-28-lagu… Red Hat AI (@RedHatAI) hat Laguna XS.2 von @poolsideai, ein 33B-MoE-Modell für agentic coding, trainiert. Red Hat AI hat einen DFlash-Spezulator dafür entwickelt: 0,6B Drafter, 8 Tokens pro Durchlauf, ohne Qualitätsverlust. FP8-, NVFP4- und INT4-Checkpoints über LLM Compressor. Modelle in den Kommentaren. Geschwindigkeitssteigerung mit @vllmproject: Video — https://nitter.net/RedHatAI/status/2060714281717404005#m
mehr auf Arint.info
#AIOptimization #CodingAI #LLMCompressor #MachineLearning #RedHatAI #vLLM #arint_info
The technical debt behind the AI boom.
"AI-assisted development creates persistent debt, not just temporary low-quality code. AI-assisted software development changes how technical debt enters and remains in production systems."
AI coding assistants are now widely used in software development. Software developers increasingly integrate AI-generated code into their codebases to improve productivity. Prior studies have shown that AI-generated code may contain code quality issues under controlled settings. However, we still know little about the real-world impact of AI-generated code on software quality and maintenance after it is introduced into production repositories. In other words, it remains unclear whether such issues are quickly fixed or persist and accumulate over time as technical debt. In this paper, we conduct a large-scale empirical study on the technical debt introduced by AI coding assistants in the wild. To achieve that, we built a dataset of 302.6k verified AI-authored commits from 6,299 GitHub repositories, covering five widely used AI coding assistants. For each commit, we run static analysis before and after the change to precisely attribute which code smells, correctness issues, and security issues the AI introduced. We then track each introduced issue from the introducing commit to the latest repository revision to study its lifecycle. Our results show that we identified 484,366 distinct issues, and that code smells are by far the most common type, accounting for 89.3% of all issues. We also find that more than 15% of commits from every AI coding assistant introduce at least one issue, although the rates vary across tools. More importantly, 22.7% of tracked AI-introduced issues still survive at the latest version of the repository. These findings show that AI-generated code can introduce long-term maintenance costs into real software projects and highlight the need for stronger quality assurance in AI-assisted development.
OpenMonoAgent.ai is "a terminal-native coding agent powered by local LLMs -- 100% open source, free forever, and installed with a single command."
Meteora Web
Wayne Radinsky
Arint - SEO+KI