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This article covers a technique my team and I use to work on versioned datasets across organizations (team A works on features, team B) works on other features. It's been invaluable for us since we don't have a shared infrastructure, so we can always verify the latest version by storing the latest hash on-chain. It could be useful in other ways where integrity or immutability is paramount, like versions of models, model weights, etc. No need to share access to a central key management server and everyone can validate it simply. submitted by /u/Nice-Dragonfly-4823 [link] [留言]

I've been admitted into both UPS and TUD for Applied Mathematics, and I wanted to hear some advice on which one would be better. For context, I'd like to work in some form of AI research, most likely within industry. At the moment, I'm most interested in privacy preserving machine learning or mechanistic interpretability. Which one do you think would leave me with better career opportunities after completion, alongside the best chances of getting admitted into competitive PhD positions? Thanks! submitted by /u/Far_Investigator6900 [link] [留言]

Hi r/MachineLearning , Wanted to share something I'm excited about. I’ve been fascinated by AlphaEvolve and its results for more than a year now, but using open source frameworks seems overwhelming because of the high costs. I can’t really afford hundreds of Claude Opus calls every time I want to run it. I want to be able to try it out many times and all sorts of unique domains. What if it was possible for AlphaEvolve to be much more affordable while getting a better performance? Over the last six months or so, I’ve been working on LEVI, an open source AlphaEvolve-like system that can outperform existing open source frameworks at a fraction of the cost (upto 35x cheaper!). It can also run on Claude Code or Codex, making it even more accessible (I've mostly been using it with a QWEN-30B). LEVI comes in two flavors where I felt it’ll make the most difference: Code Optimization, and Prompt Optimization (sorry math, you got a less direct path; workable through the code route). The core thesis behind LEVI is that with the right search architecture, smaller models can substitute for or outperform larger ones. This means it’s much more economical to rely on smaller models for most of the work. That’s the entire takeaway. Making this work in practice is a different problem, but if you forget everything else from this post this is the only message I think I’m really trying to convey here. LEVI does it in three ways: 1) Invest in solution diversity from the start and ensure its maintained. We don’t want to converge to the same solution, especially with smaller models in the mix, and rely on large models to pull us out of the basin. 2) Use smarter routing across larger and smaller models (i.e. most mutations don’t require a Claude Opus X) 3) For prompt optimization not every rollout is as important. Build a proxy subset to approximate. I’ve tried LEVI on systems problems (like MoE scheduling or database transaction scheduling) and found that LEVI outperforms existing framework

Google is making Gemini 3.5 the default model in NotebookLM

OpenRouter is a unified API gateway to hundreds of language models from providers such as OpenAI, Anthropic, Google, and Meta. You use one API key and one billing surface, and swap models by changing a provider/model slug. OpenRouter exposes a Chat Completions -compatible HTTP API. This post shows three Node.js integration paths: the official @openrouter/sdk , the openai package with baseURL , and the Vercel AI SDK with @openrouter/ai-sdk-provider . For deeper patterns on each stack, see the Chat Completions API , OpenAI Responses API (OpenAI direct only), and Vercel AI SDK posts. Prerequisites OpenRouter account API key Credits or billing enabled as needed Node.js version 26 Install packages for the path you use: @openrouter/sdk ( npm i @openrouter/sdk ) openai ( npm i openai ) ai and @openrouter/ai-sdk-provider ( npm i ai @openrouter/ai-sdk-provider ) Configuration Read credentials from the environment in production. Variable Purpose OPENROUTER_API_KEY Bearer token from OpenRouter settings OPENROUTER_MODEL Default model slug, for example openai/gpt-5.5 OPENROUTER_SITE_URL Optional site URL sent as HTTP-Referer for rankings on openrouter.ai OPENROUTER_SITE_TITLE Optional app name sent as X-OpenRouter-Title Model IDs use the provider/model format, for example openai/gpt-5.5 , anthropic/claude-opus-4.8 , or google/gemini-3.1-flash-lite . Browse the full catalog at openrouter.ai/models . The examples below use openai/gpt-5.5 , matching the model in the other LLM posts in this series. Override it with OPENROUTER_MODEL when you want a different model. @openrouter/sdk OpenRouter's official TypeScript SDK is type-safe and generated from the OpenAPI spec. Client setup import { OpenRouter } from ' @openrouter/sdk ' ; const client = new OpenRouter ({ apiKey : process . env . OPENROUTER_API_KEY , httpReferer : process . env . OPENROUTER_SITE_URL , appTitle : process . env . OPENROUTER_SITE_TITLE , }); Basic integration const response = await client . chat . send ({ chatReques

A new feature in the Amazon Shopping app allows users to generate designs with Alexa, then print them on products like T-shirts, hoodies, and tumblers.

The messaging giant announced that it disrupted a phishing campaign targeting its users with NSO’s spyware.

Field journal of Dr. E. Rempel, Department of Minority Neurological Studies, University of New Carthage (A work of fiction. "Allism" is a real term used by some autistic people to describe the neurological profile of the non-autistic majority.) March 3, 2089 I have now spent three months embedded with an allistic community in the outer provinces. Allism, for those unfamiliar, is a rare neurological variant affecting approximately 1% of our population. My colleagues at the University have long debated its origins and persistence. After direct observation, I am no more certain of the answers, but I have accumulated a remarkable set of field notes. The allistic subjects I have observed appear, on the surface, entirely functional. They hold jobs, maintain relationships, raise children. And yet their neurological profile diverges from the norm in ways that are at once fascinating and bewildering. March 11, 2089 The most immediately striking feature of the allistic profile is their relationship with information. Where a typical individual experiences the sharing of useful knowledge as a basic social reflex, the allistic subject appears to require an elaborate ritual before any information exchange can occur. Approach an allistic subject directly with a piece of useful data and observe what happens. Rather than receiving it, they freeze. A threat-assessment process appears to engage, entirely pre-consciously, before the content of the communication can be evaluated at all. One subject described it to me as feeling "strange" when a stranger approached with unsolicited information, though she could not articulate why. I have learned to preface all information exchanges with what my translator calls "the preamble ritual" — a sequence of social signals that appears to deactivate the threat response and allow communication to proceed. The exact form varies, but typically involves eye contact, a softening of posture, and verbal acknowledgment that one is about to speak. Only the

In multi-turn agent loops, the full context re-sends on every API call. A tool result added at turn 3 gets billed again at turns 4, 5, 6, 7... forever. Most of it is never read again. Standard observability tools tell you the total token count. They never tell you what's in there or how much of it is waste . That's what ContextLens fixes. What it does ContextLens is a diagnostic profiler for LLM agent context windows. It: Decomposes the context window into regions: system prompt, tool schemas, tool results, retrieved chunks, user messages, assistant messages Tracks which blocks get re-billed across turns using SHA-256 content hashing Runs 5 waste detectors and ranks findings by dollar cost Prints a concrete one-line fix for each finding Renders an interactive D3 treemap report as a self-contained HTML file No API key required. Works offline on saved traces. The five detectors Detector What it finds Duplicate Same block re-sent verbatim across multiple turns Near-Duplicate >85% Jaccard similarity between distinct blocks Stale Tool Result Tool output never referenced by a later assistant message Unused Tool Schema Tool defined every turn but never called Redundant Retrieval Retrieved chunk with <15% overlap with model output ---Run the built-in demo (simulates a 30-turn agent loop, no API key needed): python -c "import contextlens; contextlens.demo()" python examples/demo.py Live capture — Anthropic import anthropic import contextlens as cl client = anthropic.Anthropic() with cl.capture_anthropic(client, model="claude-3-5-sonnet-20241022") as collector: for turn in range(20): client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1024, system="You are a helpful assistant.", messages=build_messages(turn), ) report = cl.analyze_trace(collector.build_trace()) print(f"Recoverable waste: {report.recoverable_tokens:,} tokens (${report.recoverable_cost_usd:.4f})") Live capture — OpenAI import openai import contextlens as cl client = openai.OpenAI() with cl.ca

Why This Deserves More Attention Than It Gets Credential leaks are one of the most common and preventable security incidents in software. Bots actively scan GitHub for newly pushed API keys, database URLs, and private credentials — and they find them within minutes of a commit going public. Rotating compromised credentials is painful, and in some cases the damage is done before you even realize what happened. This isn't just an enterprise problem. It happens on solo side projects, open-source repos, and internal tools at startups. And the root cause is almost always the same: someone treated secrets like regular configuration and didn't have a clear strategy for keeping them out of version control, logs, and error messages. The patterns in this guide aren't bureaucratic overhead. They're the minimum viable approach for any app that talks to a real database or a real API. What Environment Variables Actually Are An environment variable is a key-value pair that lives in a process's environment — a set of values the operating system makes available to any running program. Every process inherits the environment of the process that spawned it. In Node.js, you access them through process.env : const port = process . env . PORT ; const dbUrl = process . env . DATABASE_URL ; In Python: import os port = os . environ . get ( ' PORT ' ) db_url = os . environ . get ( ' DATABASE_URL ' ) The core idea — and the reason env vars are the standard approach — is that they decouple what the app does from where it runs . The same application code can point at a local development database or a production cluster. The code doesn't change; only the environment does. This is the heart of the 12-Factor App principle: store config in the environment, not in the code. The .env File: What It Is and What It Isn't In local development, you don't set environment variables by hand before every terminal session. Instead, you use a .env file — a plain text file at the root of your project with one key

The FTX co-founder is serving a 25-year sentence, doled out in 2024.

Virtua Fighter Crossroads isn't an outright reboot of Sega's biggest fighting series.

The popular wildfire tracking app is adding flood monitoring to its platform. It’s the first new disaster alert on the service, with many more to come.

Pinterest introduced MIQPS, a URL normalization system that identifies which query parameters affect page identity using rendered content fingerprints. It reduces duplicate processing across millions of domains by replacing rule-based approaches with offline analysis, anomaly detection, and runtime parameter maps, improving ingestion efficiency and scalability in large-scale content pipelines. By Leela Kumili

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Hackers likely took over 20,225 Instagram accounts using Meta's AI support chatbot, the company confirmed in a notice filed with the state of Maine. In the notice, spotted earlier by Bleeping Computer, Meta blames a "bug" for the exploit that allowed attackers to hijack accounts without two-factor authentication simply by asking the chatbot for a […]

Here's where you can watch Christopher Nolan's 'The Odyssey' in jaw-dropping IMAX 70mm.

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OpenAI confirms a confidential S-1 submission to the SEC and has not yet determined timing for further action.