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You built something. Tests pass. You're days from mainnet. Before you either skip security entirely (please don't) or spend weeks lining up a full audit, here's a self-check you can run in 20 minutes that catches the mistakes I see most often in first-time deployments. I run security reviews for small and new protocols, and the same handful of issues come up again and again. None of these need a tool — just your eyes and this list. 1. Who can call what? Open every external / public function that moves funds, mints, pauses, or upgrades. For each, ask: should a random address be able to call this? If not — is there an onlyOwner / onlyRole / require(msg.sender == ...) guarding it, in the function itself or in every internal function it calls? The classic bug isn't a missing modifier. It's a function that looks unguarded but delegates to a guarded internal one (fine), or one that looks guarded but the guard is in a branch a caller can skip (not fine). Trace the call, don't trust the signature. 2. The first-depositor trap (if you have a vault) If you mint shares from deposits (ERC-4626 or anything share-based), the first depositor can sometimes donate assets directly to the contract to inflate the share price, so the second depositor rounds down to zero shares and loses funds. Fix: virtual shares, a dead-shares mint at deploy, or a minimum-liquidity lock. OpenZeppelin's ERC-4626 handles this out of the box — a hand-rolled vault usually doesn't. 3. Reentrancy — but only the real kind Not every external call is reentrancy. It's a bug when an attacker-controlled call can re-enter and corrupt shared storage before you've updated it. Quick checks: Do you update state before the external transfer (checks-effects-interactions)? Is there a nonReentrant on functions that move value? Is the call target a trusted, immutable contract, or an arbitrary address the attacker supplies? A call to a protocol-owned contract, or a memory /local variable written after the call, is usually not

Claude Opus/Sonnet Voice Mode, Open-Weight Model Cost Savings, & GitHub AI Agent Security Today's Highlights This week's top stories focus on major commercial AI model updates, practical tools for cost-effective LLM deployment, and critical security vulnerabilities in AI-powered developer tools. Anthropic expands its multimodal voice capabilities to more powerful Claude models, while a new 'Show HN' project promises significant cost reductions with open-weight models. Claude’s voice mode is now available for Opus and Sonnet (The Verge AI) Source: https://www.theverge.com/ai-artificial-intelligence/970065/anthropic-voice-mode-claude-opus-sonnet-haiku-ai Anthropic has rolled out its voice mode capability to its more powerful Claude Opus and Sonnet models, extending a feature previously exclusive to the faster, lighter Haiku model. This enhancement allows developers to integrate advanced multimodal conversational AI into their applications, enabling real-time voice interactions with a higher degree of intelligence and nuance than previously possible. For instance, developers can now build voice agents that not only understand complex spoken queries but also provide sophisticated, context-aware responses, leveraging the deep reasoning and comprehensive knowledge base of Opus and Sonnet. This update significantly expands the potential for developers to create more natural and intuitive user experiences across various domains, from customer service and educational tools to interactive creative assistants. By making Opus and Sonnet accessible via voice, Anthropic is addressing a key demand for richer human-computer interaction, pushing the boundaries of what commercial AI APIs can offer in terms of multimodal capabilities. This move facilitates the creation of next-generation applications where seamless voice interaction is paramount, without sacrificing the underlying intelligence of the AI model. Comment: This is a huge step for building more capable voice-first applicat

I build products solo. Every single one of them sits on top of somebody else's API — Stripe for payments, OpenAI and Anthropic for AI features, Meta for ads, print-on-demand APIs, map APIs. My code is maybe half of what actually runs in production. The other half belongs to vendors, and it changes whenever they decide it changes. Twice this year the first notice I got about a breaking change was a production error. Not an email, not a warning. An error, and then me digging through the vendor's changelog trying to figure out what they changed and when. The information was public the whole time. It was sitting in a changelog page I never visit, because nobody visits changelog pages until something is on fire. So I'm building the thing I wanted to exist BreakWatch is simple: you tell it which APIs your product depends on, and it reads their public changelogs for you. It fetches each changelog page once a day Diffs it against yesterday's snapshot Classifies the real changes: breaking (endpoint removed, field deprecated, "migrate by September") vs. informational (new feature, docs clarification — stuff you can ignore) Alerts you only when something looks like it will break an existing integration Keeps everything in a searchable timeline, so six months later "what changed on their side right before this broke" takes ten seconds instead of an afternoon No SDK, no credentials, nothing installed in your codebase. It only reads public pages. What I tested this week I ran it against the real changelogs of the ten APIs I'm watching first: Stripe, Twilio, OpenAI, Anthropic, Shopify, GitHub, Slack, Cloudflare, Google Maps and Plaid. Some honest findings: 10/10 scrape cleanly now, but it took fixes. Stripe's changelog page alone is 3.3 MB. SendGrid's standalone changelog doesn't exist anymore (it merged into Twilio's). PayPal's developer site serves a JavaScript shell with an HTTP 404 to anything that isn't a full browser, so it's out until I add rendering. The thing I was most a

Amazon is launching an update to its Alexa Plus assistant that will allow it to connect to smart home devices in new ways. With the update, Alexa Plus can link up with tech from Bosch, Delta, Ecovacs, iRobot, Yale Home, Whirlpool, Tapo, Eufy, and others, while automatically routing requests to the correct device. In an […]

You don't need a cloud server to start learning Linux administration. A few days ago, I came across a LinkedIn post by Luiz Fernando dos Santos about turning an old Android phone into a Linux server using Termux. Reading his post made me wonder: Do I really need to pay for a VPS just to learn Linux and server administration? The answer, at least for now, seems to be no. Inspired by his idea, I decided to build my own learning environment using nothing but an Android phone and Termux. This article is the beginning of a series where I'll document everything I learn along the way. Inspiration Before getting started, I'd like to give credit to Luiz Fernando dos Santos, whose LinkedIn post inspired this project. His idea of using an old Android phone as a Linux server showed me that I didn't need to rent a VPS to start learning Linux administration. You can check out his original post here: 🔗 https://www.linkedin.com/pulse/transformando-um-android-antigo-em-servidor-luiz-fernando-dos-santos-gu1if/ First Steps I had already been using Termux for quite some time, so the first thing I did was update all the installed packages. apt update apt upgrade -y After that, I installed OpenSSH. pkg install openssh Before moving on, I wanted to understand what SSH actually is instead of just following commands from a tutorial. SSH (Secure Shell) is a protocol that allows you to securely connect to another computer or server through an encrypted connection. It's one of the most common ways to remotely access Linux servers. After installing it, I found the Termux username, configured a password and started the SSH server. Then I tried connecting from my computer... And it worked! It may sound like a simple thing, but seeing my computer remotely access the Linux environment running on my phone was surprisingly satisfying. Improving the Authentication After getting the basic connection working, I started researching how authentication by SSH keys works. I learned that instead of typing a

The bar we set We approved a plan on 2026-07-10 with an acceptance test we weren't sure was reachable. Six drafted analyst-memo queries against Nigerian economic data, scored 0-10 across five dimensions — named-entity density, citation quality, sector-specific detail, honest-gap acknowledgement, decision-usefulness. The strict pass criterion: every query's mean score across three temperature=0.2 runs must be ≥8/10, with no query below 6 in any single run. At approval time the aggregate was somewhere around 30/60 across the six queries — a system that produced grounded but generic answers, and refused competently but not always. The gap to the bar was real. We gave it 4-5 weeks. What we shipped Phase 1 — retrieval breadth. Kind-diversity enforcement across the top-K result set so a "start a fintech" query stopped collapsing into 12 CBN circulars and started pulling BOI, NEXIM, PayStack, Flutterwave, and the World Bank agribusiness chapters in the same context window. Named-entity boost when the query mentions "factory", "startup", "invest", "loan". Deduplication so a briefing about the same fact doesn't crowd out its own primary source. Phase 2 — a six-class rule-based intent classifier and memo templates. Sub-millisecond routing on regex patterns: venture\_feasibility\ , strategic\_forecasting\ , credit\_risk\ , regulatory\_analysis\ , market\_sizing\ , general\_qa\ . Each intent gets a memo template — a section-headed scaffold with a named-entity mandate, an honest-gaps section, and a 1000-1500 word target. The general\_qa\ template stays empty (no memo shape) so genuinely-general questions don't get forced into a memo they don't need. Phase 3 — composition quality. Two changes did most of the work here: 1. A CITATION PREFERENCE: PRIMARY OVER BRIEFING\ block in the system prompt. Primary sources — CBN circulars, NAICOM regulations, NBS reports, textbook chapters, IMF Article IV, press coverage of specific events — get cited over daily briefings when both are presen

NeurIPS E and D track review are out today and the average rating I received is a 3 and confidence is a 4. I can correct and address all their concerns. Do I still have a genuine shot of getting in or is it basically impossible at this point since none of my scores are a 4 or 5? Should I withdraw? submitted by /u/Ok-Ball-2546 [link] [留言]

Gooooooooooool!

App is installed through Windows Update when certain LG monitors connect to a PC.

While the company is trying to protect creators from AI, it still thinks the technology is changing how it does business.

The company wants people to be anti-doomer, but doesn't say why.

Split between buying a smart calendar, a kitchen TV, a smart home hub, and a smart display? Amazon’s Echo Show 21 is all of those things in one, with a huge 21-inch screen. You can use it to control your smart lights, glance at recipes, watch TV shows, and more. Currently, Best Buy and Home […]

AMD is challenging its chipmaker rival with a new rack-scale system that will start shipping to customers later this year.

This blog post is about implementing the Elm Architecture (TEA) in C for a Raylib + Clay app submitted by /u/Direct_Beach3237 [link] [留言]

GitLost is a prompt-injection exploit discovered by Noma Security that tricks GitHub's new Agentic Workflows into leaking private data. By embedding concealed instructions within public GitHub issues, attackers can circumvent security safeguards and induce AI agents to reveal confidential information in public comments. By Sergio De Simone

Meta has made significant investments in natural gas over the past year. Now it's dropping out of an industry renewable energy group.

submitted by /u/cdb_11 [link] [留言]

The interesting finding from a new [arXiv paper]( https://arxiv.org/abs/2607.16165 ) isn't that a frontier vision model failed a new benchmark, that happens weekly, but the specific shape of the failure and the fact that the models cannot patch it by writing their own code. The benchmark, called ActiveVision, contains 17 tasks across 3 categories designed, in the authors' words, to "force repeated visual perception rather than a single static description." GPT-5.5 at the highest exposed reasoning-effort tier solves 10.6% of items and scores zero on 11 of the 17 tasks. Claude Fable 5, which the authors note tops most reasoning and coding leaderboards, manages 3.5%. Three human participants averaged 96.1%. submitted by /u/Justgototheeffinmoon [link] [留言]

Gear up for the Atari Cinematic Universe.

Meta is finally cracking down on people users who harass and record people with its smart glasses.