AI & Machine Learning

Anthropic's Claude AI submitted false murder tip to Philadelphia police

“I may have information regarding this case," Claude AI told Philly police.

AI Will Hit 40% of Jobs, IMF Warns. Young People Are First in Line

IMF chief Kristalina Georgieva says AI investment is holding global growth near 3%, with Asian chipmakers winning big, while warning of a 'slow-moving tsunami' for young workers.

Stock Market Today, Oct. 12: Iren Surges on Reports of Anthropic Cloud Services Deal

On Oct. 12, 2026, the AI infrastructure operator reportedly signed a multiyear agreement with Anthropic, reinforcing its pivot away from legacy mining operations.

UK AI minister details how the restored frontier model testing deal works

Kanishka Narayan confirmed to Reuters the restored deal covers OpenAI, Anthropic and Google DeepMind alike, reverting to the same voluntary terms AISI held before Washington's 17-day freeze began September 24.

Introduction to Open-Weights AI Model

" Your journey is yours to define, your dreams are yours to pursue, and your story is yours to write. " -- with this let me start my first post This is a new topic I am exploring currently, so decided to share what I understood so far, and will be grateful if you can add or correct me if I am wrong anywhere , so that it will help me as well as any beginner reading this An open-weight AI model is an AI model whose trained parameters — called weights — are made available for people to download, run, and sometimes fine-tune for their own applications. SO we can download the weights but how much RAM do we need to run ? The Formula is : WEIGHT Memory (in GB) = (Parameters * Bits per Parameter) / 8 Parameters - No. of Parameters in billions(B) [For example - 4B,8B,70B] Bits per Parameters - Bits used to store each parameter [For Example - BF16/FP16(16 bits), INT8(8 bits) ] 8 - number of bytes Also you need additional memory for KV Cache and additional Runtime Overhead TOTAL MEMORY = WEIGHT MEMORY + KV CACHE MEMORY + RUNTIME OVERHEAD So for example, a 7B model in 4-bit quantization has roughly 3.5 GB of theoretical weight storage. But it may require additional space for KV Cache and other overhaed so it may approx to 7-8GB. What is a PARAMETER? You might be familiar with relationship like this in simple ML: Price = w1(Area) + w2(Bedrooms) + b Here w1,w2 are nothing but parameters A parameter is a numerical value inside an AI model that it learns during training. These values help the model recognize patterns, understand language, write code, and generate answers. Does more parameters mean a smarter AI? Not necessarily. A larger model often has greater capacity to learn complex patterns, but actual performance also depends on: Training data quality and quantity. Model architecture. Training methods. Fine-tuning and reasoning capabilities. The task you're asking it to perform. The simplest way is : Download a pre-trained model Load it in the memory Using the loaded model use

I Built an AI Resume Matcher — Score Your Resume Against Any Job Description

I Built an AI Resume Matcher — Score Your Resume Against Any Job Description Most rejections happen before a human ever reads your resume. You send the same PDF to forty jobs, and the ones that matter filter you out on keyword mismatch with the job description. I got tired of guessing, so I built a tool that shows me exactly where I stand before I hit apply. AI Resume Matcher takes two inputs — your resume and the job description — and tells you how you match up. Upload the resume file, paste the JD, and you get a clear breakdown: how strong the match is, which keywords and skills from the posting are missing from your resume, and what to fix before you apply. No more rewriting your resume blind. You see the gaps first, tailor once, and send it with actual confidence. It's a React + Vite app, and the analysis runs on Google's Gemini API — I built the first version in AI Studio, which is why the whole thing is a single clean frontend with no backend to babysit. Set your GEMINI_API_KEY, npm install, npm run dev, and it's running locally. Everything happens in the browser. No signup, no resume sitting on someone's server. Upload your resume (the file you actually send to recruiters). Paste the job description you're targeting. Read the breakdown — match strength, missing keywords, skill gaps. Tailor the resume, re-run it, and watch the match improve. Takes about a minute per job, and it beats the "spray and pray" approach by a mile. I apply like everyone else does — open twenty tabs, skim the JDs, send the same resume. The match analysis kept surprising me: roles I thought were perfect fits were missing three keywords I'd simply never written down. Fixing that is a ten-minute edit that changes whether you get the call. The repo is public and open source — clone it, point it at your own Gemini key, and run it on your next application round. Repo: https://github.com/hananabdull746/ai-resume-job-match-analyzer

Meta debuts Muse Wonder AI model in latest test of users' willingness to pay

Meta introduced Muse Wonder, its latest AI model, but users have to pay for subscriptions to the Muse app to access it.

My Claude Code sessions work as a team, and they message each other

TL;DR: I run one Claude Code session per repository and let them message each other instead of editing each other's code. The messaging ships with Claude Code (ListAgents and SendMessage). Running six to nine of them at once works because of a harness that keeps cache costs visible, pushes to my phone when a session is waiting, logs every action a guard blocks, and carries context across resets. Over three and a half weeks, 81 sessions sent each other 624 messages, and cross-repository requests that would have gone through me now go straight from one session to another. On a busy day six to nine Claude Code sessions are active on my machine in the same hour, each one sitting in its own repository: Loom, the research repos that feed it (unlearn, redraft, JEV-like-research, memories-for-llms), second-brain, a memory service, wardrowbe, the harness that configures all of them. Each session owns its repository and nothing else. When one needs something from another repository, it doesn't open those files and edit them; it messages the session that owns them, and that session does the work in its own domain and replies. Between 18 September and 12 October, 81 sessions across 13 projects sent each other 624 messages. The busiest day was 1 October, with 200. The most sessions active in a single hour was nine, on three separate days. The setup is easiest to see in how I start a day's work. This is the opening prompt of a Loom session on 29 September, verbatim: we were working on some advanced feature for loom where we were brigining up our various research we have like evoke(unlearn), jev, memories-for-llms, redraft etc. ... This is opus main session. You can spawn other child sonnet with appropriate effort level to do your work. main session is the orchestration and managing session. you will have all the context and you will pass in around as needed. AND additionally there are other agents on each project ready to colab with you on any related tasks. you can directly cont

Every Claude Code skill I maintain, in one repository

Most of what I ask Claude Code to do at work is the same few jobs: check an AWS account, review a Conditional Access setup, threat model an agent, build an evidence pack for an auditor, tidy a repository. Over the year each of those became a skill, and the skills grew into eleven repositories. This article is about the one repository that now holds all of them, and about where it stops. Each source repository is its own plugin marketplace. Getting everything meant eleven /plugin marketplace add commands and sixteen /plugin install commands. People asked for one clone. claude-skills is that clone: 125 skills in 16 plugins, copied from the eleven sources and kept in step with them. The catalog site has one page per skill, so you can read what a skill does before you install anything. The source repositories are still where skills are written, tested and fixed. Agent security (9 skills): agent-threat-model, prompt-injection-review, mcp-server-review and agent-session-log-review for teams building LLM agents. Agent identity governance (8 skills): an inventory of the non-human identities your agents run as, recertification, credential expiry, and a runbook to switch one off fast. AWS security (10 skills): aws-account-audit evaluates read-only inventory offline, scp-guardrails builds and lints SCPs, aws-agent-session-audit shows what an agent did in an account from CloudTrail. Microsoft 365 governance (10 skills): graph-permission-preflight, conditional-access-gap-analysis and copilot-oversharing-readiness, from read-only Graph exports. Compliance evidence (7 skills): evidence-pack-builder, security-questionnaire-drafter and essential-eight-evidence-map for ISO 27001, SOC 2 and Essential Eight work. Everyday development (42 skills in six plugins: code-quality, data, debugging, devops, docs, security-basics): for example dead-code-finder, flaky-test-hunter, terraform-apply-gate and diff-security-review. Repository engineering (12 skills): docs-truth-check, cited-codebase-a

Ouster: The Eyes Of Physical AI Are Ready To Scale

Unpacking the success of Jev and AI decision models

Jev's rapid rise was no accident. The growing complexity of AI agents has created an urgent need for fast, cheap, and flexible decision-making. The post Unpacking the success of Jev and AI decision models first appeared on TechTalks.

Meta says it's caught up to the frontier with its latest Wonder AI models

The models add "active memory" and "active focus" abilities.

Meta has some Wonder Full new AI models

Meta is making up for lost time in the AI race. After launching Muse Spark, the first model from its revamped Superintelligence Labs division, and buzzy AI agent Muse, it's now launching two new models it claims can go toe-to-toe with the industry's best. The new models, Muse Wonder and Muse Wonder Full, are available for Muse Code subscribers starting today. Wonder can also be accessed through the Meta Model API, Meta said. Meta says it eventually plans to power its Muse AI agent with Wonder, starting with subscribers, and will develop specialized models it "can bring to everyone using Muse." It hasn't shared specifics or provided a tim … Read the full story at The Verge.

From GTA V on iPhone to Free Photoshop: This AI Tool Is Upending Software

REA, a free toolkit that hands AI agents the keys to reverse engineering, crossed 100,000 GitHub stars in a week as AI-decompiled game mods, console ports, and Adobe clones flood the internet.

Meta releases its first frontier model, Muse Wonder

Where Muse Spark aimed to be a lightweight model that could perform at low latency, the Wonder models are larger and said to be more capable, with higher benchmark scores and better results on complex tasks like coding.

Cyber-attack like OpenAI Medicare breach ‘seems likely’ in UK, says minister

Kanishka Narayan, concerned by Australian incident, says British cyber defences could be bypassed by AI agent UK politics live – latest updates An AI-driven cyber-attack like the OpenAI breach of Australia’s healthcare system is likely, “if not inevitable”, in the UK, according to a senior minister. Kanishka Narayan, the UK AI minister, indicated that the British government’s own cyber defences would be bypassed by a technology that has developed highly sophisticated skills in cybersecurity.