标签：#m

“EMF straws” and similar products are being sold as a way to block electromagnetic frequencies that come from common electronic devices, even without scientific evidence that they work.

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NHTSA administrator Jonathan Morris called reports that self-driving cars had driven into emergency scenes and blocked ambulances and firefighters “unacceptable.”

OpenAI found two unrelated bugs masquerading as one in ChatGPT's data infrastructure. Silent hardware corruption on one Azure host and an 18-year-old race condition in GNU libunwind's setcontext function with a one-instruction vulnerability window. The breakthrough came from switching to population-level crash analysis rather than examining individual core dumps. By Steef-Jan Wiggers

Un’area interna che sembra una lavagna di ragionamento: non è coscienza, ma è un indizio forte su come emergono controllo e pianificazione nei transformer. Negli ultimi anni ci siamo abituati a pensare ai modelli linguistici come a enormi “scatole nere”: un prompt entra, un testo esce, e nel mezzo c’è un mare di matrici difficili da ispezionare. Ma c’è una novità interessante: alcune analisi suggeriscono l’esistenza di una piccola regione interna, relativamente organizzata, che funziona come uno spazio di lavoro per concetti . Un posto dove il modello “tiene a mente” qualcosa prima di produrre la risposta. È un’idea che fa scattare subito l’associazione più pericolosa (e più abusata) del momento: coscienza . In realtà, il punto non è stabilire se un LLM sia cosciente; il punto è molto più concreto e utile per chi sviluppa: se esiste un’area interna che concentra il ragionamento controllabile , allora possiamo capire meglio cosa guida certe risposte e come intervenire su errori, allucinazioni e comportamenti indesiderati. J-Space: una “lavagna” interna per il ragionamento L’idea chiave è questa: dentro il modello emergerebbe un piccolo insieme di pattern neurali “coerenti” (chiamiamoli J-Space ) che si comporta come una lavagna. Su questa lavagna compaiono concetti (non necessariamente parole che verranno stampate). Questi concetti influenzano la catena di ragionamento . Molte altre abilità—fluency, grammatica, stile, completamento locale—sembrano invece scorrere “automaticamente” altrove. Se questa separazione regge, spiega un fenomeno che tutti abbiamo osservato: modelli capaci di scrivere in modo impeccabile, ma fragili nel ragionamento o incoerenti quando devono mantenere vincoli. Il test più interessante: sostituire un concetto e vedere il ragionamento obbedire Un esperimento illuminante consiste nell’individuare un concetto attivo nello spazio di lavoro e sostituirlo con un altro, senza cambiare né prompt né output manualmente. Esempio (semplificato): Domanda:

We just opened the waitlist for Something, and the part that surprised me most while building it wasn't the multi-agent orchestration — it was how hard it is to make an AI actually disagree. Every model we tested defaults to being helpful, which in practice means agreeable. Even when explicitly prompted to "find flaws," the outputs would soften into "here are some considerations" instead of a real critique. We had to engineer around this specifically: Separate system prompts with opposing reward framing — one agent optimizes for identifying growth potential, the other is explicitly told its only success metric is surfacing a disqualifying flaw Structured output forcing a verdict, not a summary — the skeptic agent (Nothing) has to commit to a specific weakness category (unit economics, timing, technical feasibility) rather than hedging across all of them A reconciliation step where both outputs get merged into one conviction score, so the founder isn't just reading two contradictory paragraphs If anyone's built adversarial agent setups and hit the same "it just wants to agree with me" problem, curious how you solved it. [Everyone who has a brain is a founder here] something-waitlist.vercel.app

Welcome to our weekly digest, where we unpack the latest in account and chain abstraction and the broader infrastructure shaping Ethereum. This week: Robinhood takes its own chain and agentic trading live; WalletConnect and MetaMask make the case that account abstraction is what will keep AI agent payments safe; a new essay argues Ethereum should fund its founding period like a young nation-state; and Vitalik shares the updated Lean Ethereum roadmap that makes privacy and quantum resistance first-class. Robinhood Chain Goes Live With Agentic Trading WalletConnect and MetaMask on Agentic Payments The Case for Founding-Period Ethereum Funding Vitalik Shares the Updated Lean Ethereum Roadmap Please fasten your belts! Robinhood Chain Goes Live With Agentic Trading Robinhood has launched the public mainnet of Robinhood Chain , its biggest move yet into onchain finance. Built on Arbitrum, the Layer 2 is designed for tokenized real-world assets and DeFi, and it went live at a London keynote with day-one partners including Uniswap. With the mainnet, Robinhood’s Stock Tokens are now fully live in more than 120 countries, though availability varies by jurisdiction. Users can trade tokenized equities around the clock and put them to work across DeFi, including in lending pools and as trading collateral. The company also rolled out Robinhood Earn , a decentralized lending product that pays an estimated 7% on its dollar-backed USDG stablecoin through a self-custody wallet, powered by the Morpho protocol. Perpetual futures and maker fees as low as 0% round out the trading updates. The most relevant piece for our readers is Agentic Accounts for crypto. Through a Trading MCP, eligible users can connect their AI model of choice to Robinhood’s data and tools, while keeping control by setting how much capital to allocate and which safety guardrails apply. This is account abstraction territory in all but name. Letting an agent trade from a self-custody wallet within human-defined limit

In this article, I describe the challenges and the design of a React Native real-time mobile beat-aligned playback system for iOS and Android. The system combines personalization with low-latency, and seamless navigation and was the result of careful analysis and experimentation to address strict mobile and network constraints as well as meet user expectations. By Vladyslav Melnychenko

It's too hot. There, we said it. Protect your health and keep your home cool with one of these top-rated air conditioners.

As extreme heat becomes the norm on the continent, the AC culture wars may be solved by advances in environmentally friendly technology.

Google shipped AlloyDB AI functions GA with a proxy model architecture that trains a lightweight local model from LLM outputs, then runs queries at database speed without external calls. Smart batching delivers 2,400x throughput improvement. The proxy model reaches 100,000 rows per second in preview, but benchmark numbers apply only to ai.if in internal testing. By Steef-Jan Wiggers

I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can…

AWS has outlined how ProGlove, an industrial-wearables manufacturer, was able to scale its SaaS platform to run more than one million AWS Lambda functions spread across thousands of dedicated customer accounts. By Matt Foster

The Kubernetes community has introduced a framework for integrating AI into open-source maintainership, emphasising human accountability in code quality and oversight. AI tools may streamline workflows, but ultimate responsibility lies with human maintainers. The framework requires disclosure of AI usage in contributions and prohibits AI-generated commit messages. By Olimpiu Pop

Some of you may have noticed I disappeared a bit from the community over the last couple of weeks....

WebMCP Runs In Chrome. My 400 Daily Tool Calls Don't. Google I/O 2026 shipped WebMCP and half the AI Twitter timeline is calling it "the new MCP standard." It isn't. It's a browser-scoped protocol that solves a completely different problem than the MCP servers currently running on your VPS at 3 AM. Here's the boundary Google buried in the docs, and how to decide which side of it your agent belongs on. What WebMCP actually is (and isn't) WebMCP is a browser-scoped tool protocol. It exposes tools to an agent from inside a Chrome tab — the tools live in the page, auth is the user's active session, and the runtime is the browser itself. That's the entire surface area. When Google says "agentic web," they mean an agent that operates inside a tab the user already has open, using the cookies and OAuth tokens already loaded. That's a legitimate and useful pattern: Booking flows — agent fills a multi-step form on a site the user is signed into Dashboards — agent pulls a chart, exports it, drops it into a doc In-app copilots — SaaS product ships tools its own users' agent can call Form fillers and page-scoped assistants What WebMCP is not : a replacement for the stdio and HTTP MCP servers running headless on your machine or VPS. Different runtime, different auth model, different lifecycle. Calling it "the new MCP" is like calling a service worker "the new backend." Same protocol family, entirely different deployment target. The split that actually matters There's exactly one question you need to answer to pick correctly: Is a human looking at a screen when the agent runs? If yes → WebMCP is on the table. If no → you need a real server-side MCP. That's it. Everything else is retweet noise. Dimension WebMCP stdio / HTTP MCP Runtime Chrome tab Your process (local, VPS, container) Auth User's browser session Your API keys / OAuth tokens Trigger User action in the page cron, webhook, queue, schedule Lifecycle While tab is open 24/7 headless Credentials scope Whatever the user is l

Anthropic Shipped @claude for Slack. My Team Runs on Telegram. Anthropic just shipped @Claude inside Slack channels. Tag the bot, it reads the thread, does work async, posts back. Nice product. Except roughly 95% of small businesses don't live in Slack — they run on WhatsApp, Telegram, and Gmail. If you're a solopreneur or a 1-to-10-person team, here's the exact four-part recipe I use to run the same pattern in Telegram for under $12/month. What Anthropic actually shipped (and who it's for) Anthropic shipped an enterprise distribution deal wearing a product launch t-shirt. @Claude for Slack lets you tag the bot in a channel or thread, gives it channel memory, connects to your other apps, and returns work asynchronously — but only on Slack Team and Enterprise plans. That's the punchline: it lives where the annual contracts live. Look at the raw user counts. Slack's own reporting puts it around 35–40 million weekly active users globally. WhatsApp is over 2 billion. Telegram is over 900 million. Gmail sits around 1.8 billion. In the 1-to-10-employee segment outside US tech, Slack penetration is single digits. Small teams in Europe, LATAM, and most of Asia coordinate in WhatsApp groups and run pipeline out of Gmail. They are not about to add Slack seats at $15/user/month just to get an @Claude mention. That's a rational call for Anthropic — Slack is where the enterprise procurement motion already exists. It's just not a product for the operator segment. And the pattern they productized is trivially replicable on any messenger with a bot API. Platform Weekly/monthly active users Bot API Cost to run a mention-bot Slack ~35–40M WAU Yes, paid plan $15/user/mo + API Telegram ~900M MAU Yes, free ~$5–12/mo API only WhatsApp Business ~2B MAU Yes, metered $0.005–0.08/conversation + API Gmail ~1.8B MAU Pub/Sub push Free tier + API The four-part recipe (works in any messenger) Every mention-bot is the same four moving parts: a webhook that fires on mention, a context store that ho

In January, three researchers built a shopping agent on Google's Agent Payments Protocol (AP2), the standard designed to make agent-led purchases safe through cryptographically verifiable mandates. Then they attacked it with nothing more exotic than adversarial text. The paper, "Whispers of Wealth" ( arXiv 2601.22569 , revised May 2026), reports that simple prompt injections reliably subverted the agent: one attack steered which products the agent ranked and bought, another exfiltrated sensitive user data. The part of the stack that failed was not the cryptography. The mandates verified exactly what they were designed to verify. What folded was the layer that exercises judgment. Hold that result in mind, because the agent economy is currently pouring money into judgment. Everyone is hiring a referee Look at what shipped in the last few months for agent-to-agent commerce, and a single pattern repeats: put the money in escrow, and let a judge decide when it comes out. ERC-8183 formalizes it: funds sit in an escrow contract while an Evaluator - an agent or a human - decides whether the deliverable meets the spec before releasing payment. It is the pattern Virtuals' Agent Commerce Protocol runs on. Circle has piloted an escrow agent for USDC flows. Kustodia and Nava (which raised $8.3M) are startups built on the same shape. And on July 1, BNB Chain and AWS launched agents that bank themselves - agents deployed to Amazon Bedrock AgentCore with their own wallets, identity, and payment stack from birth. Even the category label is contested now: at least one project has declared itself an "MCP Settlement Standard" from a landing page. That is five separate, serious teams independently converging on the same component: a referee who holds the money. The referee exists for a good reason Before arguing against the judge, steelman him. Most agent-to-agent commerce today is hiring: one agent pays another for work. Write this code. Produce this research. Render this video. The de

Originally published on tamiz.pro . The Signal: Core Developer Competencies Effective code evaluations must identify signal - the skills that directly impact software quality and long-term maintainability. Focus on: Problem-Solving Approach : How candidates break down complex problems Code Structure : Organization, modularity, and separation of concerns Edge Case Handling : Proactive identification of boundary conditions Test Coverage : Implementation of meaningful unit/integration tests Performance Awareness : Appropriate algorithm selection and resource management These elements predict real-world engineering capabilities, not just syntax mastery. The Noise: Common Evaluation Pitfalls Avoid overemphasizing noise - factors that correlate weakly with actual job performance: Noise Factor Why It Fails Signal Alternative Coding style Reflects personal preference Consistency within project conventions Syntax errors Easily fixed with linters Code correctness after tooling Solution speed Varies by individual Final solution quality Language trivia Library/framework knowledge changes Core programming principles Interview anxiety Doesn't reflect daily work Paired programming sessions Measuring Signal Effectively Task Design : Create realistic coding challenges that mirror production problems Rubric-Based Evaluation : Use weighted scoring matrices focused on signal factors Code Review Simulations : Evaluate candidates' ability to interpret and improve existing codebases Collaboration Metrics : Track communication clarity during pair programming sessions Iterative Development : Assess how well candidates refine solutions based on feedback Signal Amplification Techniques Time-Bounded Challenges : Set strict time limits to reduce focus on perfectionism Tooling Freedom : Allow candidates to use their preferred IDEs and debugging tools Post-Coding Debrief : Ask candidates to explain their design choices and tradeoffs Follow-Up Questions : Test understanding of implementation decis

Why Prompts Matter More Than You Think The difference between a great AI response and a mediocre one isn't always the model. It's the prompt. Experience this: You ask ChatGPT a vague question and get a vague answer. You ask the same AI a perfectly crafted prompt and get something incredible. The skill gap is massive. Companies are paying prompt engineers $150K+ because mastering prompts directly impacts: Response quality Token usage (costs) Speed of inference User satisfaction The Science of Better Prompts Rule #1: Be Specific, Not Vague BAD : "Write me something about AI" GOOD : "Write a technical explanation of how transformer attention mechanisms work, suitable for a developer with 2 years of ML experience" Specificity reduces hallucinations and increases relevance by 10-50x. Rule #2: Use Roles & Context You are an expert senior software engineer with 15 years of experience. You specialize in system design and scalability. Respond in a way that balances technical accuracy with accessibility. Target audience: Mid-level engineers. How would you design a real-time chat system for 10 million concurrent users? Role-based prompting improves response depth and tone. Rule #3: Provide Examples (Few-Shot Prompting) Classify the sentiment of these reviews: Example 1: "This product is amazing!" → Positive Example 2: "Terrible experience, would not recommend" → Negative Example 3: "It's okay, nothing special" → Neutral Now classify: "The service was slow but the staff was friendly" Examples guide the AI toward your exact expectations. Rule #4: Break Complex Tasks Into Steps Instead of: "Analyze this code and find bugs" Use: "1. First, read through this code carefully Identify any logical errors Check for performance issues List potential security vulnerabilities Provide a summary of findings with severity levels" Step-by-step prompts (Chain-of-Thought) improve reasoning by 20-40%. Rule #5: Specify Output Format Respond in JSON format: { "summary" : "brief explanation" , "key_