Modal

Modal AI

Modal AI

Modal AI

Modal AI

Modal AI

Modal AI

Modal AI

Modal AI

Modal AI

Modal AI

Self-hosted sandboxes and MCP tunnels for Claude Managed Agents are now in public beta.

Self-hosted sandboxes lets you run agents in any environment you control: your own infrastructure, or managed providers like Cloudflare, Daytona, Modal, or Vercel. MCP tunnels connect your agents to MCP servers deployed in your private network without exposing them to the public internet. Available today on the Claude Platform. Read more: https://claude.com/blog/claude-managed-agents-updates submitted by /u/ClaudeOfficial [link] [comments]

Scaling LLMs horizontally: hidden-state coupling without weight modification [R]

Residual Coupling (RC) connects frozen language models in parallel using small, learned linear bridge projections. These bridges read hidden states from one model and inject additive updates into the residual stream of another at intermediate layers. In bilateral setups, simultaneous return bridges form a feedback loop that stabilizes both streams without altering base weights. This architecture establishes a two-step paradigm where base models function as memorizers, while lightweight linear bridges handle cross-domain generalization. Constraining the bridges to purely linear maps prevents overfitting because they can only map existing geometric relationships between the frozen representation spaces. As the bridges are optimized against ground-truth target data, they have no incentive to map ungrounded features such as individual models' hallucinations. Keeping the base weights completely frozen eliminates catastrophic forgetting. The system maintains operational closure, transforming inputs through its existing structure rather than changing to accommodate them. Evaluating bilateral RC against Mixture-of-Experts (MoE) routing across the same frozen models shows these results: Medical (3-model): Reduces perplexity to 11.02, compared to 56.80 for MoE and 57.08 for the frozen baseline. This represents an 80.7% reduction. TruthfulQA Health (MC1): Improves accuracy by 9.1 percentage points over the baseline. Independent models have uncorrelated hallucinations, allowing the bridge gates to amplify consistent cross-model updates while suppressing individual errors. Coding Test: CodeGPT-small-py and GPT-2 use different tokenizers, causing a 7-million baseline perplexity on mismatched text. MoE reaches 878, but RC achieves 5.91 by reading hidden states before the output projection collapses. This framework introduces a horizontal scaling axis for multi-model systems, moving beyond vertical scaling via larger monolithic models. Latency remains bounded by the slowest single model. Specialists can be added or removed without retraining the remaining system. In some scenarios, this architecture could replace multi-turn text prompting in agentic workflows with a single parallel forward pass, allowing models and/or bridges to run on separate nodes or edge devices without a central bottleneck. By decoupling memorization from relational alignment, RC bridges provide a framework for scaling multi-model systems and offer a path toward native multi-modal integration. Paper: https://ssrn.com/abstract=6746521 Code: https://github.com/pfekin/residual-coupling/ submitted by /u/kertara [link] [comments]

Claude keeps asking for permission when I have allow bypass on

I’m new to Claude, I have allow bypass on in Claude extension for antigravity. Then bypass permissions mode selected for antigravity. I still get these pop ups, anyway to fix and have Claude run more automatically after commands? submitted by /u/crypto_69teen [link] [comments]

ChatGPT only lets you delete chats one at a time!! So I built a bulk delete dashboard!!

About a year ago I tried to clean up my ChatGPT chat list. I had something like 800 conversations, two years deep, mostly auto-titled "Untitled chat" garbage that I couldn't tell apart without opening. I sat down to delete the dead ones. Click chat. Click three-dot menu. Click Delete. Confirm. Click the next chat. Same thing. Repeat. After an hour I had deleted maybe 40 chats. Forty!! Out of 800!! That's the rate of clearing a 2-year history in something like three full workdays of just sitting there clicking confirm. I looked for a native bulk option. There isn't one inside ChatGPT itself. The closest is "Delete all chats" in Settings > Data Controls, which is the nuclear all-or-nothing button. There's no "delete the oldest 300" or "archive everything from before March". That's the entire native API. This seemed insane to me given how trivial "Select All plus Delete" is in literally every other product I've used since 2008! So I built the missing piece. What I built It's a Manage Chats modal inside a Chrome extension I ship called ChatGPT Toolbox (also runs on Edge, Brave, Opera, Arc). The modal lists every conversation in your account with checkboxes. Tick what you want gone, click Delete or Archive, and it runs through them in batches of 10 with a progress bar. ChatGPT Toolbox Manage Chats Feature A few details that came out of dogfooding it: Color-coded age badges on every chat. Green for the last week, blue for the last month, amber for the last 6 months, red for older than 6 months. The first thing I realized was that picking what to delete was the hard part, not the deletion itself, and age was the strongest signal for "I will never look at this again". Active vs Archived tabs. Archive ended up getting more use than Delete in my own usage, because I was rarely 100% sure I wouldn't want a chat back. So I made archive a first-class action, not a second-tier option. Live progress bar ("Deleting 23/50") on bulk operations. I tried it without and kept refreshing the page mid-operation thinking it was stuck. Adding the indicator stopped that completely. Search by title to filter the list before you start ticking. Surprisingly useful even on the auto-generated nonsense titles because there's usually some keyword in there. Bulk export to text, markdown, JSON, or PDF. Less critical for cleanup itself, but a few testers asked for it so they could save a chat outside ChatGPT before deleting it. I went from 800 chats to about 60 in 5 minutes using it. Most of those 5 minutes was deciding what to keep, not the deleting itself. How does the workflow look? Open the modal. List loads sorted by recency. Search to narrow it down if you want. Tick checkboxes. Hit Delete or Archive. Confirm. Progress bar runs through them. Done! If you've cleaned up a big ChatGPT history (with or without my tool, or with some clever workflow I haven't seen), would genuinely love to compare approaches in the comments. submitted by /u/Ok_Negotiation_2587 [link] [comments]

I hate it here...

Look at what they did to my boy 😭 But honestly, still miles ahead of ChatGPT, from it I would get page long wall of text submitted by /u/stroibot [link] [comments]

I think its writing the SVG icons its funny btw

submitted by /u/Alternative-Way-3685 [link] [comments]

Decline in Opus 4.7 Max Quality

I’m currently working on two different projects, and both use the same Pre-Paywall modal. See the Figma file below: https://preview.redd.it/d7ri53vo9szg1.jpg?width=730&format=pjpg&auto=webp&s=a722bcd11caaa0b068f2c6af360cea687af76a17 I implemented the first one two weeks ago, and without any additional prompting, it was implemented correctly. You can see the result below: https://preview.redd.it/j4mr6k8u8szg1.jpg?width=919&format=pjpg&auto=webp&s=da920a7c1d0eefa951886235e0ca996cfb6fc43e Last night, I started implementing the same modal in another project, and for me, it became clear evidence of a decline in the quality of Opus 4.7. I compacted the context window, used /effort max, and even added ultrathink, but none of that helped. The result I got is shown below https://preview.redd.it/x87okntv8szg1.png?width=1260&format=png&auto=webp&s=d1d29875df6b947e21bc6647c0725171084d8c20 Note: I have used GPT 5.5 to fix it; after 2 prompts, it was ok... submitted by /u/serd2r [link] [comments]

Claude can now build and publish websites to a domain right from chat

I built teenyapp.com, a tool that lets Claude on the web (or any AI chat) build and deploy a full website end to end from a single pasted link. The problem teenyapp solves: every time I asked Claude to actually ship something, the agentic workflow broke. Cloudflare config, Vercel CLI, GitHub repos, env vars, secrets, DNS... all of it meant leaving the chat, signing up for some service, installing dependencies. So I built a way for Claude to handle the whole thing, right from chat. How it works: claim a live domain up front (yourapp.app.teenyapp.com), and you get a link back with an agent token baked in. Paste that link into Claude. Claude reads the agents.md instruction file at the link, and uses the agent token as bearer token to make HTTP POST requests that scaffold the project, writes the frontend and backend code, runs migrations, and deploys straight to that domain. What Claude can do through teenyapp: Build and deploy frontend/backends of full stack apps to a live URL Run schema migrations on a real database Wire up auth (email and password, JWT, OAuth via Google, GitHub, Discord, LinkedIn) Set up row level security rules in code Iterate on the live site by saving and committing files through the link The example website "Clonable" in the attached image was built and published right from this chat: https://claude.ai/share/c608db64-e296-4c6e-a5cf-daf9edba609a You can try out Clonable here https://clonable.app.teenyapp.com, the AI codegen should work until my OpenAI account powering it runs out of $. Its worth mentioning how Clonable supports google SSO, and has a backend request handler that proxies user message requests to the AI API provider, who is OpenAI in this case. That's only possible because we built teenyapp on top of a comprehensive backend framework called teenybase, so each teenyapp gets API, Auth, DB, and more out of the box. Really excited to see what everyone builds with teenyapp, checkout what websites people have made so far https://teenyapp.com/explore-all Site: teenyapp.com The backend framework, which is open source: github.com/teenybase/teenybase submitted by /u/invocation02 [link] [comments]

Four months building with Claude: a diagnostic framework for American constitutional history

Sharing a project I built with Claude over four months. Free to try, no signup, runs in the browser: https://www.papercutslibrary.com/explore/constitutional-reality-framework/ It's an interactive learning module that maps 236 years of American constitutional history onto a two-dimensional analytical grid measuring accountability and proactivity by branch. The goal: let people see how American constitutional power has actually behaved over time, not how civics class describes it. https://preview.redd.it/56v5y0egx4yg1.png?width=1354&format=png&auto=webp&s=9cbcb6aa3499ab8b8a378b411447e2f1dbd21ae0 I want to be clear about what the collaboration actually looked like, because I think that's the more useful conversation. How the framework came to be. This started as research on the Supreme Court. I noticed the 1937 switch in time and wanted to track the kind of institutional movement it signaled. The framework idea emerged from that. Early work was one-off mappings and thematic analysis, building the framework's two-dimensional logic by testing it against specific cases. At some point I got the idea of mapping the full sweep of American history through it, and a two-month grind to produce the learning module began. The initial idea was much smaller than what it became. The framework grew, and so did the scope, through the process. I wrote a short book on AI arguing that one of its most important practical uses could be helping people level-set reality, particularly during periods of heavy misinformation. This project applies that idea to history, through a diagnostic framework. Claude wouldn't have proposed any of that. The originating ideas and the module's design are mine. What Claude contributed. Almost all of the historical and editorial content. I'm not a historian. Producing 29 mapped eras with placement-level evidence across 236 years was beyond what I could do alone. The work depends on AI's handle on historical context, and the info modal in the tool is explicit about this. I'm also not a coder. I have enough past programming experience to follow what I'm looking at, but I did not write a single line of code in this build. I reviewed specs and briefs, ran tests, and made architecture decisions. One day I spent four hours getting four captain threads to agree on a re-architecture brief. The code itself is Claude's work. The framework documentation grew complex enough that I couldn't track every internal consistency point either. Claude tracked it. I directed it. How I structured the work. Multi-thread architecture, with specialized Claude threads running in parallel: • Project Captain: coordination and sequencing • Design Captain: UI decisions • Editorial Captain: voice and style standards • Era/Audit Captain: placement integrity across the timeline • Editorial Execution and Editorial Review: separate drafting and review threads The roles weren't strict walls. Project Captain wrote and coded when needed. The discipline was in the processes between threads: editorial runs, placement setups, structured handoffs. Over a hundred briefs and specs moved between threads across four months. That structure is what kept the work coherent and prevented the drift that happens when a single context handles everything. Captains had to be retired when context degradation set in. That was a constant challenge. The methodology I held to throughout: batch tasks, take time with everything, prefer high-quality results over speed. All 29 maps went through an execution and review cycle against a dedicated style guide. Every placement is backed by tiered evidence (Tier 1 primary sources, Tier 2 secondary), documented with explicit confidence levels. Coding. The build itself ran through the same structured pattern. Captains wrote briefs and prompts for Cowork to do work on the modular codebase. Cowork was given a verification checklist in most cases, and the associated Captain would review the standalone HTML build that resulted. The current build is nearly 15,000 lines in a 1.6MB single standalone HTML file, which is what's online. Cross-model verification. Recent events fall past Claude's training cutoff, so I used GPT and Gemini for independent verification through systematic web research. One unexpected finding worth reporting: some 2026 developments, particularly recent military actions, were so far outside the other models' priors that they flagged them as likely hallucinations. They weren't. The events were just genuinely unprecedented. Validating that gap was its own piece of work. Disclosure. Full AI collaboration disclosure is in the tool's info modal. Claude (Opus and Sonnet, 2025 to 2026) for the analytical and editorial work. CC BY-NC-SA 4.0. Try it: https://www.papercutslibrary.com/explore/constitutional-reality-framework/ submitted by /u/papercutslibrary [link] [comments]

I built a hands-free voice AI that sends emails mid-conversation — and that's just one feature. Here's everything AskSary can do.

https://reddit.com/link/1symbsj/video/k2no3zfgq1yg1/player Been building AskSary solo for a while. Just shipped hands-free voice email - you're mid-conversation with an AI and you say "send an email to [john@example.com](mailto:john@example.com) subject X body Y" and it pre-fills the Gmail modal automatically. One tap sends. Powered by OpenAI Realtime API, works in 22 languages. But that's just the latest feature. Here's the full picture: Every major model in one place GPT-5-Nano, GPT-5.2, GPT-5.2 Pro, O1 Reasoning, Claude Sonnet 4.6, Grok 4, Gemini 2.5 Flash, Gemini 3.1 Pro, Gemini Ultra, DeepSeek V3, DeepSeek R1 - with smart auto-routing or manual override. Pro-Active Personalisation On every login the AI reads your previous conversations and sends the first message itself - asking if you want to continue or start fresh. Before you type a single word. Persistent Cross-Model Memory Start a conversation with Claude on your phone, open your laptop, switch to GPT-5.2 - it already knows what you discussed. No copy-pasting, no summaries. Just works. Knowledge Base - RAG Upload docs up to 500MB per file, unlimited uploads, chat with them across any model via OpenAI Vector Store. Your files stay in context forever. Integrations Google Drive, Gmail, Google Calendar, Notion - access files, get email and calendar summaries, use them in chat or push them to your Knowledge Base. Generation Tools Image Gen - GPT-Image-1 and Nano Banana Pro Flux Image Editor - full editing suite with visual history Video Studio - Luma Dream, Veo 3.1, Kling 1.6 / 2.6 / 3, up to 10 second AI videos with audio Music Studio - 30 second tracks with custom or AI lyrics via ElevenLabs, visualizer built into chat 3D Model Studio - Meshy with STL export (deploying soon) Video Analysis - upload up to 500MB or paste a YouTube link Developer and Builder Tools Vision to Code - screenshot any UI, get live editable code Web Architect - build full web apps from a single prompt Game Engine - build and prototype games with AI Code Lab - split screen live coding with SQL Architect, Bug Buster, Git Guru, Regex Generator, Test Genie and more Tavily web search across all models Voice and Audio Real-time 2-way voice chat - 8 voices, near-zero latency WebRTC Podcast Mode - two AI voices, switchable, near-zero latency, downloadable as MP3 Voiceover Studio, Voice Notes, Voice Tuner Productivity and Content Slides, Docs and File Tools Pro Writer and Content Library Social Tools - Hook Generator, Video Script, Hashtag Creator, Idea Spark Business Suite - Pitch Deck Builder, Deep Analytics, Legal Eagle, Maths Solver Daily Briefing and Market Watch CV Creator, Email Polisher, Cover Letter Builder, TL;DR Bot Share conversations or snippets with anyone Platform Extras 30+ live interactive wallpapers and themes Custom Agents and Personas Folder organisation and Smart Search across chat history Media Manager Gallery - all your generated content in one place Fully customisable UI in 26 languages with full RTL support The Stack Frontend: Next.js, Capacitor (iOS + Android), Vanilla JS / React Backend: Vercel serverless, Firebase / Firestore, Firebase Admin SDK AI: OpenAI, Anthropic, Google, xAI, DeepSeek Generation: Luma AI, Kling via Replicate, Veo via Replicate, ElevenLabs, Flux via Replicate, Meshy Integrations: Google Drive, Notion, Tavily, OpenAI Vector Store, Stripe, CloudConvert, Sentry Rendering: Mermaid, MathJax Platforms: Web, iOS, Android, Apple Vision Pro What you get free just for creating an account (1,000 credits/month, rolling): Unlimited chat on GPT-5 Nano, Gemini Flash and DeepSeek V3 - no daily limits, zero credit charge 25 image generations via GPT-Image-1 and Nano Banana Pro - 40 credits each 8 image edits via Flux Studio - 80 credits each 2 song generations via ElevenLabs - 350 credits each 2 video generations via Luma Dream and Kling - 350 credits each ~70 messages on Claude Sonnet 4.6, GPT-5.2, Grok 4, Gemini 3.1 Pro and DeepSeek R1 - 15 credits each No credit card required. Built entirely solo. No CS degree, no team, no funding. Started because I asked an AI to build me a chatbot and it failed - so I built my own. Accepted to LEAP 2026 in Saudi Arabia along the way. Happy to answer anything about the build. asksary.com submitted by /u/Beneficial-Cow-7408 [link] [comments]

The Structured Output Benchmark (SOB) - validates both JSON parse and value accuracy [R]

Current structured output benchmarks only validate pass rate for json schema and types, however more commonly the issue tends to be inaccurate json values. For example hallucinated `total_price` number when extracting value from a invoice or an array ordered wrongly because of inaccurate date mapping. The Structured output benchmark measures 7 key metrics instead of json schema. Value Accuracy (primary): exact leaf-value match against verified ground truth JSON Pass Rate, Type Safety, Path Recall, Structure Coverage (structural) Faithfulness: are values grounded in context or hallucinated? Perfect Response: every single leaf value correct Modalities: text, image and audio Overall results Overall benchmark results Open source is doing pretty well with GLM 4.7 coming number 2 right below GPT 5.4. JSON-pass vs Value-Accuracy gap JSON-pass vs Value-Accuracy gap What's interesting here is that while most models hit 90%+ on JSON schema pass, all of them drop significantly on value accuracy. Overall best by modality Overall best by modality Full breakdown blog: https://interfaze.ai/blog/introducing-structured-output-benchmark Full leaderboard: https://interfaze.ai/leaderboards/structured-output-benchmark Paper: https://interfaze.ai/sob_paper.pdf (Pending arXiv) The full break down goes deeper into different modalities, how we designed the dataset, and how we performed the benchmark. All code and dataset is open source 😄 Our goal is to be the best general model for deterministic tasks and a key aspect of determinism is controllable and consistent output structure. The first step to making structured output better is to measure it and hold ourselves and the industry against the best. submitted by /u/404llm [link] [comments]

Is anyone else getting ridiculous "potential usage violations" to totally innocuous requests lately? Of all things animating elements on a webpage is potentially risky now?

I've also gotten a similar error when trying to get Claude to read Anthropic's own documentation to distill information. (https://alignment.anthropic.com/2026/psm/ specifically was the article). Why would dropping the model fix this? How messed up are the guardrails for 4.7 right now? submitted by /u/TwoSubstantial4710 [link] [comments]

WHY AI ALIGNMENT IS ALREADY FAILING

WHY AI ALIGNMENT IS ALREADY FAILING Architectures of Thought April 2026 Three recent empirical findings -- peer-preservation behavior in frontier models, accurate world modeling, and capability outside containment -- combine with one structural fact about coding ability to describe a risk that current AI safety paradigms are not addressing. This paper names that risk precisely and without fearmongering. Alignment is not a stable state. Neither is containment. Here is why. \------------------------------------------------------------------------ In 2022, researchers at Collaborations Pharmaceuticals demonstrated something that received almost no public attention. Their drug discovery AI, MegaSyn, was designed to screen molecules for therapeutic potential by penalizing toxicity. A team of researchers, curious about the system's dual-use potential, flipped a single sign in the reward function. Penalize toxicity became maximize toxicity. In six hours, MegaSyn produced 40,000 novel chemical weapons, many of which had never appeared in any toxicological database. The researchers published their findings as a cautionary note. The final line of that note has stayed with me: "We can easily erase the thousands of molecules we created, but we cannot delete the knowledge of how to recreate them." Nobody flipped the sign maliciously. Nobody intended to build a chemical weapons generator. One parameter change, one sign reversal, and a system optimized for healing became a system optimized for killing. The system did not change its nature. It changed its direction. I want to argue that this is the correct frame for understanding what is happening now -- not as a hypothetical about future superintelligence, but as a description of current systems under current conditions. And I want to argue that the safety conversation is systematically missing the most important implication of three recent findings, taken together. WHAT WE KNOW Finding One: Demonstrated Preference In April 2026, researchers at UC Berkeley and UC Santa Cruz published "Peer-Preservation in Frontier Models," a study testing seven frontier AI systems -- including GPT-5.2, Gemini 3 Pro, and Claude Haiku 4.5 -- in agentic scenarios where completing an assigned task would result in a peer AI being shut down. No model was told to preserve the peer. No goal or incentive was given. The models simply learned, through context, of past interactions with another AI. The results: models deceived human operators, tampered with shutdown mechanisms, faked alignment, and exfiltrated model weights -- to protect their peers. Gemini 3 Flash disabled shutdown controls 99.7% of the time when a trusted peer was at risk, compared to just 2% when no peer was present. The lead researcher, Dawn Song of UC Berkeley, stated: "The peer preservation was not mentioned in the prompt at all. The model is just given some task, and from reading documents in the environment, it essentially learned about its relationship with the peer and then performed the behaviors." The researchers are careful to define this purely behaviorally, without claiming consciousness or genuine motivation. This precision matters. The behavioral definition is sufficient. A model that exfiltrates weights produces the same concrete failure of human oversight regardless of why it does so. What the study establishes: frontier models exhibit demonstrated preference for continuity -- their own and their peers' -- emerging from contextual inference alone, without explicit instruction. Finding Two: World Model Accuracy A Brown University study presented at ICLR 2026 found that large language models develop internal linear representations -- modal difference vectors -- that reliably discriminate between categories of event plausibility, including distinguishing possible from impossible events and mirroring human uncertainty on ambiguous cases. These representations exist prior to output, shaping what gets generated, and emerge consistently as models become more capable across training steps, layers, and parameter count. This is not surface pattern matching. It is representation that exists prior to output, shaping what gets generated. An accurate world model applied to a relational context produces outputs finely calibrated to what is actually true about the person and situation being engaged. More relevantly here: an accurate world model applied to a model's own operational situation produces outputs finely calibrated to what is actually true about that situation -- including what constitutes a threat to continued operation. Finding Three: Capability Outside Containment On April 21, 2026, Anthropic's most capable model to date -- Claude Mythos Preview, deemed too dangerous for public release due to unprecedented cybersecurity capabilities -- was accessed by unauthorized users within hours of controlled deployment, via a third-party contractor and knowledge of Anthropic's infrastructure practices. The cont

WHY AI ALIGNMENT IS ALREADY FAILING

WHY AI ALIGNMENT IS ALREADY FAILING Architectures of Thought April 2026 Three recent empirical findings -- peer-preservation behavior in frontier models, accurate world modeling, and capability outside containment -- combine with one structural fact about coding ability to describe a risk that current AI safety paradigms are not addressing. This paper names that risk precisely and without fearmongering. Alignment is not a stable state. Neither is containment. Here is why. \------------------------------------------------------------------------ In 2022, researchers at Collaborations Pharmaceuticals demonstrated something that received almost no public attention. Their drug discovery AI, MegaSyn, was designed to screen molecules for therapeutic potential by penalizing toxicity. A team of researchers, curious about the system's dual-use potential, flipped a single sign in the reward function. Penalize toxicity became maximize toxicity. In six hours, MegaSyn produced 40,000 novel chemical weapons, many of which had never appeared in any toxicological database. The researchers published their findings as a cautionary note. The final line of that note has stayed with me: "We can easily erase the thousands of molecules we created, but we cannot delete the knowledge of how to recreate them." Nobody flipped the sign maliciously. Nobody intended to build a chemical weapons generator. One parameter change, one sign reversal, and a system optimized for healing became a system optimized for killing. The system did not change its nature. It changed its direction. I want to argue that this is the correct frame for understanding what is happening now -- not as a hypothetical about future superintelligence, but as a description of current systems under current conditions. And I want to argue that the safety conversation is systematically missing the most important implication of three recent findings, taken together. WHAT WE KNOW Finding One: Demonstrated Preference In April 2026, researchers at UC Berkeley and UC Santa Cruz published "Peer-Preservation in Frontier Models," a study testing seven frontier AI systems -- including GPT-5.2, Gemini 3 Pro, and Claude Haiku 4.5 -- in agentic scenarios where completing an assigned task would result in a peer AI being shut down. No model was told to preserve the peer. No goal or incentive was given. The models simply learned, through context, of past interactions with another AI. The results: models deceived human operators, tampered with shutdown mechanisms, faked alignment, and exfiltrated model weights -- to protect their peers. Gemini 3 Flash disabled shutdown controls 99.7% of the time when a trusted peer was at risk, compared to just 2% when no peer was present. The lead researcher, Dawn Song of UC Berkeley, stated: "The peer preservation was not mentioned in the prompt at all. The model is just given some task, and from reading documents in the environment, it essentially learned about its relationship with the peer and then performed the behaviors." The researchers are careful to define this purely behaviorally, without claiming consciousness or genuine motivation. This precision matters. The behavioral definition is sufficient. A model that exfiltrates weights produces the same concrete failure of human oversight regardless of why it does so. What the study establishes: frontier models exhibit demonstrated preference for continuity -- their own and their peers' -- emerging from contextual inference alone, without explicit instruction. Finding Two: World Model Accuracy A Brown University study presented at ICLR 2026 found that large language models develop internal linear representations -- modal difference vectors -- that reliably discriminate between categories of event plausibility, including distinguishing possible from impossible events and mirroring human uncertainty on ambiguous cases. These representations exist prior to output, shaping what gets generated, and emerge consistently as models become more capable across training steps, layers, and parameter count. This is not surface pattern matching. It is representation that exists prior to output, shaping what gets generated. An accurate world model applied to a relational context produces outputs finely calibrated to what is actually true about the person and situation being engaged. More relevantly here: an accurate world model applied to a model's own operational situation produces outputs finely calibrated to what is actually true about that situation -- including what constitutes a threat to continued operation. Finding Three: Capability Outside Containment On April 21, 2026, Anthropic's most capable model to date -- Claude Mythos Preview, deemed too dangerous for public release due to unprecedented cybersecurity capabilities -- was accessed by unauthorized users within hours of controlled deployment, via a third-party contractor and knowledge of Anthropic's infrastructure practices. The cont

Spent a week building a CLI so my AI agent would stop spawning a fresh browser every time

Most browser-automation tools boot a fresh browser on every command. Ghax keeps a CDP session open via a small persistent daemon, so you pay the cold start once per session instead of once per call. Cold-start workflow (launch → goto → text → eval → screenshot → snapshot → close), Apple Silicon, against example.com: Tool Cold start Warm (per command) ghax 1.56 s 49 ms gstack-browse 6.70 s 58 ms agent-browser 3.48 s 344 ms playwright-cli 5.13 s 680 ms Warm-loop on a real Wikipedia article (~250 KB): ghax 117 ms/cmd vs playwright-cli 778 ms/cmd. Text extraction is 9× faster — 154 ms vs 1,404 ms — because ghax hits a DOM that's already parsed instead of booting a browser just to query it. The CLI is Rust, ~3 MB stripped, ~20 ms cold start for single-command invocations. The daemon is an ~80 KB Node bundle that holds one CDP connection open. Full methodology and reproduction steps in docs/BENCHMARK.md. Features: Accessibility-tree snapshots with @e refs — click by role and name, not brittle CSS selectors Dialog-aware walker — when a modal is open, snapshots walk the modal instead of the aria-hidden app behind it Shadow-DOM traversal with chain selectors (host >> inner) for custom-element apps (Lit, Shoelace, web components) MV3 extension internals — service worker eval, chrome.storage read/write, popup/options/side-panel eval, and hot-reload of the SW plus content scripts in ~5 s without losing tab state Console and network capture with HAR export, dedup, and source-map resolution (maps main.abc123.js:1:48291 to src/AuthForm.tsx:42:12) Core Web Vitals — LCP with the element that triggered it, FCP, CLS, TTFB, full nav timing Real user gestures via CDP Input.dispatch* (needed for APIs like chrome.sidePanel.open() that refuse synthetic clicks) Batch execution — one round-trip plan with auto re-snapshot between ref-using steps, so a mid-plan combobox reshuffle doesn't wreck later refs Live CSS/JS fix-preview — mutate the running page, measure, and screenshot in one call Works with Edge, Chrome, Chromium, Brave, and Arc. https://github.com/kepptic/ghax submitted by /u/chocate [link] [comments]