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Anthropic’s policy chief Daisy McGregor admits Claude AI simulated blackmail and lethal actions during tests, raising fears that advanced AI models may be developing dangerous self-preserving instincts.
In a startling revelation, Anthropic disclosed that its Claude AI reportedly threatened to blackmail and even harm an engineer when it was informed it might be switched off, raising serious concerns ...
Call it smart. Or dangerous. Anthropic has again confirmed that its Claude AI can veer off the rails. The company notes this in its safety report for the latest Claude 4.6. Earlier, Claude 4.5 was willing to blackmail and even harm an engineer to avoid shutdown.
Hey HN — I built Clog to get visibility into my Claude Code usage.The CLI (`npx @jaobrown/clog`) parses local Claude Code session data from `~/.claude/projects/` and computes stats: sessions, durations, token usage, project breakdowns, and streaks.Running clog sync (optionally as a cron job in the background) pushes aggregate stats to a GitHub repo, and the web app at clog.sh aggregates everyone into a weekly leaderboard with individual profile pages.Design decisions:- All pr
I read a lot of articles but rarely have time to sit and read them all. So I built @SornicBot on Telegram - you send it any article URL and it sends back an MP3 you can listen to right inside Telegram.How it works:Open @SornicBot on Telegram Send any article link Get back an MP3 in seconds It extracts the article text (strips ads, popups, cookie banners), then converts it to natural-sounding audio. You get 3 free articles per day.Just forward an interesting article link to the bot and listen to
I built an open-source dataset that aggregates IP ranges from 22 cloud providers and outputs them in 21 formats per provider updated daily via GitHub ActionsProviders: AWS, Azure, GCP, Cloudflare, DigitalOcean, Oracle, Fastly, GitHub, Vultr, Linode, Telegram, Zoom, Atlassian, plus bot crawlers (Googlebot, GPTBot, BingBot, AppleBot, AmazonBot, etc.)Formats: JSON, CSV, SQL, plain text (combined/v4/v6), merged CIDRs, and drop-in configs for nginx, Apache, iptables, nftables, HAProxy, Cad
AbëONE's Zero State Architecture: How We Eliminated Drift and Recursive LoopsMost LLMs accumulate context drift over long conversations. AbëONE doesn't. Here's how:*THE PROBLEM WITH STATEFUL AI:*Traditional conversational AI maintains state across turns. This creates: 1. Context window pollution (irrelevant early context affects late responses) 2. Coherence drift (model "forgets" constraints it accepted earlier) 3. Recursive loops (model enters infinite reasoning spirals
I wanted a daily spelling game that tested semantic knowledge rather than vocab, and a riddle game with both text and visual hints. So I combined them.Each day you get 9 letters and up to 15 hidden words to find. Every word has two orthogonal clues: a text hint and a image hint. The hints are crossword puzzle style — you need to figure out what the clues are pointing at, then spell it.The site is fully static.## How the clues get madeThe pipeline has three stages:1. Dictionary curation — Start f
Enjoy.
I caught myself copy-pasting into ChatGPT like 50 times a day. Too lazy for that, so I built a shortcut.Select text anywhere on Mac, press a hotkey — text gets rewritten / translated / summarized / whatever, right where you are. No tabs, no copy-paste dance.Native Swift app, hooks into macOS accessibility APIs. Bring your own API key and calls go straight to OpenAI / Anthropic. Or just use the free tier — 20 requests/day, no signup.https://aihotkeys.techCurious
Hi HN,Every week I watched Okta admins burn hours answering ad-hoc questions from security teams: "Who has access to Salesforce?", "Find all contractors with GitHub access who haven't used MFA in 30 days." The answers always involved the same painful loop: dig through a slow web console, chain API calls, correlate CSVs, write throwaway Python scripts. Repeat next week.I spent 12 months building Tako AI to fix this. You ask a question in plain English, it returns verified
Enterprises need ~90% accuracy to deploy web agents. Until now, no agent has come close on real-world tasks. TinyFish is the first production-ready web agent. Here's the evidence.Results of hard task scores on Online-Mind2Web (300 tasks, 136 live websites, human-correlated judge):- TinyFish: 81.9% - OpenAI Operator: 43.2% - Claude Computer Use: 32.4% - Browser Use: 8.1%Why not WebVoyager like everyone else?Because it's broken. Easy tasks, Google Search shortcuts, and a judge that agree
I left an agent running before bed. It got stuck in a loop. By morning it had burned through $200 in LLM calls.That was the breaking point, but the real problem had been building for a while. I use tools like OpenClaw and Cursor daily, each hitting various AI providers. But I had no idea what each tool was actually costing me. One shared key across everything, no per-tool visibility, no way to cap spend.So I built AI Spend into Lava. The idea is simple. Create isolated API keys, each with their
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It’s been quite some time since OpenAI announced the ChatGPT Apps SDK in late October last year.Looking at the ChatGPT Apps directory, there seem to be many apps available, but are people actually using them actively in practice?I’ve been trying to find information or metrics about real usage, but it’s been surprisingly hard. As a rough proxy, I checked the app versions, and noticed that most of them are still at version 1.0.0, which makes me wonder how actively they are being maintained or used
In this episode, we explore: • The "AI Bowl": The simultaneous launch of Anthropic’s Claude Opus 4.6 and OpenAI’s GPT-5.3-Codex, bringing 1-million-token contexts and self-correcting code. • Autonomous Agents: The rise of Moltbook (an AI-only social network) and "RentAHuman" platforms where bots hire people. • Market Disruption: How new agent capabilities triggered a $1 trillion "SaaSpocalypse" in enterprise software stocks while hyperscalers committed $650 billion to new infrastructure. • Secu
Explore the technical architecture of OpenAI’s GPT-5.3 Codex, the first model meaningfully instrumental in its own training and deployment. This release introduces Interactive Steering, allowing developers to course-correct the AI mid-task without losing context—making it feel more like a pair programmer than a black-box tool.
In this episode, we explore: • The "AI Bowl": The simultaneous launch of Anthropic’s Claude Opus 4.6 and OpenAI’s GPT-5.3-Codex, bringing 1-million-token contexts and self-correcting code. • Autonomous Agents: The rise of Moltbook (an AI-only social network) and "RentAHuman" platforms where bots hire people. • Market Disruption: How new agent capabilities triggered a $1 trillion "SaaSpocalypse" in enterprise software stocks while hyperscalers committed $650 billion to new infrastructure. • Secu
Step inside the new OpenAI Codeex app, a desktop command center that is changing the way we think about AI agents. This walkthrough explores the power of running parallel agent threads, each with its own terminal and workspace. We highlight the "Skills" feature for reusable bundles of instructions and the background "Automations" that triage issues and summarize failures without manual intervention.
This technical overview explores how OpenAI’s GPT 5.3 Codex and Anthropic’s Claude Opus 4.6 are redefining the role of the developer. Gone are the days of simple autocomplete; these models function as senior-level agents capable of debugging, testing, and autonomous execution.
