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AI agents are getting very good at writing final reports. The problem is not only that they make mistakes. The problem is that sometimes they make mistakes with excellent presentation. Proofline is a 5-skill Markdown pack that catches fake-ready output before it turns into a release, handoff, public post, or "yeah, looks done". What Proofline does It is not trying to be another giant agent. It works as a review route after the agent produces a result: Reference Gap Ready Gate Reality QA Lean Pass Repair Report Compiler Each step asks an annoying but useful question: what is missing from the references, what was not checked, where did the agent pretend everything was fine, and what actually needs to be fixed? Who it is for Builders working with Codex-style agent chats, AI coding workflows, Markdown handoffs, and any process where "done" needs to mean more than a confident paragraph. Release: https://github.com/aisflows/proofline/releases/tag/v0.2.0-rc5

General Intuition is in talks to raise around $300 million at a roughly $2 billion valuation from backers including Jeff Bezos. The startup trains AI agents on spatial-temporal reasoning.

Gas optimization is satisfying. You shave a few thousand gas off a function and feel clever. But some optimizations trade away safety in ways that are not obvious, and I have seen "optimized" contracts that introduced vulnerabilities. Here are the gas wins that are genuinely free, the ones that cost you safety, and how to tell the difference. Where gas actually goes Before optimizing, know what is expensive. Storage operations dominate. Writing a fresh storage slot ( SSTORE from zero to non-zero) costs a lot; reading storage ( SLOAD ) is cheaper but still meaningful; computation in memory is cheap by comparison. So the highest-leverage optimizations are about touching storage less. Free win 1: cache storage reads in memory If you read the same storage variable multiple times in a function, each read is an SLOAD . Read it once into a local variable instead: // WASTEFUL: reads storage `total` three times function distribute() external { require(total > 0, "empty"); uint256 share = total / count; emit Distributed(total); } // OPTIMIZED: one SLOAD, two memory reads function distribute() external { uint256 _total = total; // single storage read require(_total > 0, "empty"); uint256 share = _total / count; emit Distributed(_total); } This is free in the sense that it changes nothing about correctness. The value is identical; you just read it once. Pure win. Free win 2: calldata instead of memory for read-only arrays For external function arguments you only read (never modify), calldata is cheaper than memory because it skips the copy: // memory copies the whole array into memory function process(uint256[] memory ids) external { ... } // calldata reads directly from the transaction data, no copy function process(uint256[] calldata ids) external { ... } Again, free. If you do not mutate the array, calldata is strictly better. Free win 3: storage packing Solidity packs multiple variables into one 32-byte slot if they fit and are adjacent. Order your storage variables so smal

Disclosure up front: I'm affiliated with diagram.now — I'm connected to the product. I'm posting this to get developer feedback on diagram import/export interoperability, not to pitch an install. Most teams I've worked with don't have one source of truth for their diagrams. They have: a few Mermaid blocks living in READMEs and Markdown docs, an old Visio ( .vsdx ) or Lucidchart file someone made two reorgs ago, a SQL schema that is secretly the "real" ERD, and a pile of screenshots pasted into docs and tickets. The diagram is rarely the hard part. The hard part is that the same diagram lives in five formats and none of them stay in sync with the docs they're supposed to explain. I've been working on diagram.now , a browser-based editor for technical diagrams — flowcharts, UML, ERD, BPMN, cloud/network architecture, mind maps, wireframes. It's a free browser editor with no signup to start. There's an optional Confluence app for teams that want diagrams editable inside Confluence pages, but that's intentionally not what I want to talk about here. I want feedback on the editor itself, and specifically on the interoperability story. What it does today Import/insert from Mermaid and SQL — paste a Mermaid graph or a CREATE TABLE block to start an editable diagram instead of a static render. Import Lucidchart and Visio .vsdx files — this is migration-oriented, and honestly the part I most want real-world files to stress-test. Export to PNG, SVG, PDF, or a URL. Templates/shapes for the diagram categories above. I'm deliberately keeping the Confluence side secondary. The thing I actually want to learn is whether the browser editor plus import/export is useful on its own. Where I'd love feedback Imports: Which format matters most to you — Mermaid, SQL→ERD, .vsdx , Lucidchart, or something else (PlantUML, draw.io XML, Graphviz)? If you've ever tried to migrate diagrams between tools, where did it break? URL export: Is a shareable diagram URL genuinely useful in your workflow (

Over the last several months, XRP Ledger (XRPL) has fundamentally shifted in how amendments move from concept to mainnet. Historically, amendment development was largely focused on functional correctness, performance testing, traditional security audits, bug bounties and independent validator testing as the last line of defense to catch security vulnerabilities. As XRPL continues to grow in complexity and the value secured by the network increases, we recognized that the previous model was no longer sufficient. Advances in AI are also rapidly reducing the cost of vulnerability discovery, making it increasingly important to identify issues as early as possible in the development lifecycle. With that in mind, we set out to establish a stronger, repeatable, defense-in-depth model that makes it increasingly difficult for critical vulnerabilities, consensus risks, and feature interaction bugs to reach mainnet. The result is a significantly higher bar for amendment activation that combines specification rigor, adversarial testing, multiple independent audits, attackathons with expert security researchers, AI-assisted security reviews and phased deployments. The Lending Protocol ( XLS-66 ) and Single Asset Vault (SAV) - XLS-65 are among the first major amendments to undergo this full review process, making them some of the most rigorously tested amendments in XRPL's history. They also represent some of the most significant new financial capabilities added to the XRP Ledger since 2012, introducing native primitives for lending and borrowing built around Single Asset Vaults. Together, the Lending Protocol and Single Asset Vault bring lending and borrowing capabilities directly into the core XRPL protocol, advancing XRPL's capabilities for Institutional DeFi. Lending Protocol Security and Quality Gates This report provides transparency into the development and security process behind one of the most financially complex features XRPL has ever shipped. As context, the Lending P

Guardrails positions itself as a populist political movement that runs on small donations from people in the trenches of the AI boom.

We can't blame the Neolithic Transition for the plague anymore.

Your daily memo that turns notes into a plan Discussion | Link

The rich are getting richer. Here’s what wealth advisers are telling their tech clients right now.

Netflix has detailed a cloud-based system for scaling camera file processing across global film and TV workflows. The pipeline handles ingest, validation, metadata extraction, and media transformation at scale using FilmLight API and distributed compute. It standardizes workflows across editorial, VFX, and color pipelines, improving consistency and reducing manual handling across productions. By Leela Kumili

Two expeditions, two spacewalks, 322 days in space.

Running out of color options for events in Google Calendar shouldn't be an issue going forward. The previous limit of 11 predefined colors has now been expanded to give users access to up to 200 custom colors for individual events across the native Calendar web and mobile apps, and the Calendar API. This started rolling […]

One messy database is threatening to disenfranchise thousands or even millions of registered voters, while leaving even more at risk of intimidation or data breaches, in the name of solving a problem that barely exists. As the 2026 midterm elections approach, election and privacy experts are sounding alarms about the Department of Homeland Security's Systematic […]

Amazon Prime Day 2026 lifts off on June 23rd and will hopefully deliver the best deals of the summer. We’ve been covering the most notable pre-Prime Day discounts happening, and come next week, we’ll be bringing you many more deals — ones we can’t tell you about just yet. As usual, expect to see price […]

We are thrilled to announce the winners of the Hermes Agent Challenge! Over the past few weeks, the...

If you’ve been wanting to buy a robot vacuum but have been put off by how much it can cost to get a good one, now is not a bad time to start looking. Prime Day is next week, but many retailers are offering early deals on several models we’ve tested from brands like Roborock, […]

On today’s episode of Decoder, my guest is Hayden Field, senior AI reporter for The Verge. Often when Hayden comes on the show, it’s because something has gone wrong in the world of AI. Last weekend, that something was a pretty intense mix of Anthropic, the Trump administration, and Anthropic’s new AI model, Fable 5. […]

Spotify is launching "Reserved," a new system that will hold two concert tickets for an artist's superfans before they're on sale to the public.

Now bring back the Microsoft Zune, I double dare you.

Vinay Chella and Akshat Goel discuss the challenges of running traditional CDC across heterogeneous databases during peak order traffic. They explain how Debezium hit limits under high load and share how they built Write-Ahead Intent Log (WAIL) - a custom architecture that utilizes a dumb producer proxy and a smart consumer pattern to cleanly separate the intent from the state payload. By Vinay Chella, Akshat Goel