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Ozlo’s first major update to its sleep earbuds introduces longer battery life, improved connectivity, enhanced audio, and new sleep features as the startup continues the product line once abandoned by Bose.
According to NASA, two of the three reaction wheels that control the spacecraft's alignment have failed, and there are problems with one of the spacecraft's thruster systems.
Rep. Kevin Mullin (D-California ) has proposed a bill that would direct federal regulators to establish minimum national safety standards for autonomous vehicle operators.
Instead of immediately opening pull requests when newer dependency versions are released, Dependabot now waits three days before suggesting upgrades, thus increasing the likelihood that malicious releases are identified and removed before they can be integrated. By Sergio De Simone
Foreman is an agentic coder that runs as Kubernetes resources. You describe work as a Workload, it decomposes into tasks, agents running on your nodes pick them up, and a branch comes out the other end with something deterministic standing between that branch and your main. This is the walkthrough. Four objects to understand, an install, an agent, a verifier, and a real run. Every command and every output below is from a working cluster. The four objects Foreman is deliberately small. Almost everything you do is one of these. Agent is a worker definition: which model it talks to, which tools it may call, and what budget it gets. An Agent has a role , and the two that matter here are coder and verifier . Workload is the unit of work you actually author. It carries an intent, a repository, and which agents to use. AgenticTask is what a Workload decomposes into. You rarely write one by hand; you read them to see what is happening. FleetNode is a node that has advertised itself as able to run tasks. The scheduler matches a task's required capabilities against these. The shape of a run is: you apply a Workload, the controller synthesizes AgenticTasks, the scheduler routes each to a FleetNode whose agent can serve it, the agent runs the model in a loop with tools, and the result lands as a branch plus a verdict. The idea underneath it Worth stating plainly, because it shapes every design decision: the model is not trusted, and specifically its claim to have succeeded is not trusted. A coder agent finishes by calling a tool that says "I am done, verdict GO." Foreman treats that as a request, not a result. If the model says GO and produced no diff, the run is recorded as NO-GO. If the verifier's checks do not pass, the work does not land, no matter how confident the summary was. That is the difference between an agent that writes code and a system you can leave running. Everything else in this post is plumbing around that idea. Install Foreman ships as a Helm chart that dep
This is the architecture of Shibui Finance , an MCP server that gives Claude direct SQL access to 64 years of US stock market data. About 10,000 symbols, 31 million daily price records, quarterly financials back to 1990, 56 pre-computed technical indicators, and 6.4 million SEC filing records. Free to use. Stack: Python, PostgreSQL, dbt, DuckDB, FastMCP, Caddy. Runs on a single VPS. Data pipeline Three stages: ingest into PostgreSQL, transform with dbt, export to DuckDB. Data APIs / SEC EDGAR / FRED | Python ETL (Polars, ADBC) | PostgreSQL clean_* schemas (~50 raw tables) | dbt (27 models) staging -> integration schema (17 analytical tables) | DuckDB export (daily, ~14 GB file) | FastMCP server (read-only, streamable-http) | Caddy (TLS) -> mcp.shibui.finance Multiple sources feed the pipeline: commercial data APIs for prices, fundamentals, valuations, and estimates. SEC EDGAR for filing metadata and insider transactions (bulk historical + a 5-minute Atom feed for near-real-time). FRED for FX rates to normalize non-USD fundamentals. Public registries for ticker classification. The ETL is a Python CLI organized by data source. Each module has its own fetcher, loader, and CLI. A single all command runs everything in fixed sequence. You can't refresh 10,000 tickers daily without hitting rate limits, so the ETL rotates: each run refreshes the stalest 5% of tickers. Full universe cycles in about 20 runs. Recent prices always refresh on every run. Every table write is a single transaction. DROP + CREATE inside a transaction, rollback on failure. The database never serves partial data, and dbt always sees complete tables even when ingest jobs overlap. The dbt layer 27 models in two tiers. The process layer handles standardization: enriching symbols with security types and exchange mappings, linking SEC amendment filings to their originals, repairing filer date typos. The integration layer produces the 17 tables that Claude actually queries. This is where raw normalized tabl
This article provides a step-by-step guide to building and testing a cross-cloud currency agent. A coordinator built with Strands Agents and hosted on Amazon Bedrock AgentCore Runtime (in AWS us-east-1 ) discovers and delegates to a Google ADK agent (on GCP Cloud Run in us-central1 ) over A2A v1.0 , cross-checks results against an MCP exchange-rate tool , and measures what independent cross-cloud verification costs in latency, reliability, and overhead. What is This Project Trying to Do? Most Agent-to-Agent (A2A) protocol demos stop at "look, the HTTP 200 OK request succeeded." That is a smoke test, not an interoperability benchmark. This project goes further: an Amazon Bedrock AgentCore-hosted Strands Agents coordinator discovers and delegates to a Google ADK agent running on GCP Cloud Run, comparing the results against a local MCP stdio exchange-rate tool backed by live Frankfurter daily reference rates. We also compare the performance, developer experience, and wire compatibility directly against our previous benchmark run hosted on Microsoft Foundry in Azure ( gpt-5-mini ), giving us a true cross-cloud benchmark across AWS, Azure, and GCP. The questions we answer with hard empirical data rather than vibes: Can an AgentCore-hosted Strands agent discover and invoke a Google ADK agent through an A2A agent card with no framework-specific glue? What latency and token overhead does remote-agent verification add? Does independently verifying an MCP tool result over A2A improve correctness or failure recovery enough to justify that overhead? How does AWS Bedrock AgentCore Runtime compare like-for-like with Microsoft Foundry on Azure? Reduce, Re-Use, Re-Cycle! This builds directly on the currency agent from the previous articles in this series: Getting Started with MCP, ADK and A2A | Google Codelabs GitHub - jackwotherspoon/currency-agent That agent — built with Google ADK, Gemini 2.5 Flash, and a FastMCP exchange-rate server backed by the free Frankfurter API — serves a
Apple says it won't limit the capabilities of devices leased through its new Upgrade program if you miss a payment. In an emailed statement to The Verge, Apple spokesperson Brian Bumbery says, "There will be no restricted mode and/or there will be no limitations put on device functionality due to missed payments or default with […]
Parking and matching puzzles look almost insultingly simple from the outside. A few cars, a cramped lot, tap or drag to move them out. But if you've actually tried to build one that feels tight — no janky collision resolution, no ambiguous "why didn't that move register" moments, no lag once the board gets crowded — you know there's a real systems design problem hiding underneath what looks like a weekend project. I recently went through the architecture of a parking/matching hybrid template built in Unity and wanted to break down the core systems the way I'd want them explained if I were reskinning or extending one myself. This isn't a marketing post — it's a walkthrough of the actual mechanics: how vehicle movement and collision resolution work, how the match/clear pipeline is structured, how level data is separated from movement logic so hundreds of levels don't require touching code, and how monetization hooks slot into natural break points without polluting gameplay scripts. If you want to see the finished product this breakdown is loosely based on, there's a working template here: Park Match Unity Game Template . Everything below applies whether you're building something similar from scratch or extending an existing base. Why Parking Puzzles Are Harder Than They Look The core loop — tap or drag a vehicle, it exits along a valid path, the lot clears one piece at a time — is trivial to describe and genuinely fiddly to implement well. Four problems show up almost immediately once you move past a static mockup: Valid-move detection : how do you know, at any given moment, which vehicles can actually move given their orientation and the current board state? Path resolution : once a vehicle starts moving, how does it navigate around other vehicles and obstacles without clipping through them or getting stuck mid-animation? Match/clear logic : when does a vehicle actually "clear" the board — on reaching an exit, on matching color/type with another vehicle, or both — an
So I had this idea that kept nagging at me. Every AI chat app works the same way. You type something, the model returns text or markdown, the UI renders it as a nice formatted paragraph. That is fine if you want an answer. It is genuinely boring if you want to actually build something. What if the AI could respond with a working game board you could click? What if saying "make it Barbie themed" actually transformed the whole interface while you watched? What if "add a starfield in the background" dropped an animated canvas behind your chat in real time? I spent a few weeks building exactly that. I call it FlowChat . Here is the live version: https://flowchat-public.varshithvh.workers.dev And yes, someone immediately asked it to play Tic Tac Toe and then asked it to switch to an Oppenheimer theme mid-game. I could not be prouder. The Idea Normal AI chat: model returns markdown, client renders it as text. Simple, predictable, boring. FlowChat: model returns raw HTML with CSS and JavaScript, client injects it directly into the DOM using a streaming protocol built on the browser's native template system. That one change makes the entire experience different. You are not reading about a game. You are playing one. You are not reading about a Barbie color palette. You are sitting inside one. The AI does not just answer questions. It rebuilds the UI from its responses . What You Can Actually Do With It I want to give you a feel for what this means in practice before getting into the technical bits, because the demos are more interesting than any architecture diagram. Games : Ask it to build Tic Tac Toe. You get a playable board, click-to-move, an AI opponent, win detection. Ask for Connect 4. Ask for Snake. The game renders in the chat as an agent bubble with a form inside it. Each move submits to the LLM which processes it and updates only the cells that changed. Themes : Say "change to a Barbie theme". The model injects CSS overrides and the whole interface turns pink. Me
1.13.1. The Basic Responsibility of Types Every Rust value has a type, and the responsibility of that type is to tell you how to interpret the bits in memory. For example, the bit pattern 0b10111101 has no meaning by itself, but: Interpreted as u8 , it becomes the number 189 Interpreted as i8 , it becomes the number -67 When you define a custom type, the compiler decides where each part of that type is placed in memory. 1.13.2. Alignment Alignment determines where a type’s bytes may be stored. Once a type’s representation is determined, you might think it can be stored anywhere in memory. In theory that is possible, but in practice computer hardware places constraints on where a given type can live. The most typical example is a pointer. A pointer points to bytes, not bits; one byte equals 8 bits. In other words, it does not point to an individual bit. So if a value of some type were placed at bit index 4 in memory, you would not be able to address it, because pointers address bytes rather than specific bits. That is why alignment is done at the byte level — that is, at 8-bit boundaries. For this reason, all values, regardless of type, must begin on a byte boundary . All types must be at least byte-aligned. In other words, the storage address must be a multiple of 8 bits. 1.13.3. Stricter Alignment Rules Some types have alignment requirements stricter than byte alignment. In CPU and memory systems, memory is often accessed in blocks larger than a single byte. For example, on a 64-bit CPU, most values are accessed in 8-byte blocks, and each operation begins at an address that is 8-byte aligned . This is also called the CPU word size. Of course, CPUs can also handle reads and writes of smaller values, as well as values that cross block boundaries. But as developers, we should try our best to ensure that hardware operates at its native alignment. For example, if the i64 value you want to read begins in the middle of an 8-byte block, then reading it requires at least tw
Judge may allow Minnesota to ban some types of bets, but not all of them.
TL;DR AI editors add a login check to your API routes but skip the ownership check, so any logged-in user can read another user's data by changing the ID in the URL (CWE-639, IDOR). It happens because tutorials treat "authenticated" as if it means "authorized," and the AI learned from those tutorials. The fix is one line: scope every lookup to the current user instead of trusting a raw ID from the request. I asked Cursor to build an endpoint that returns an invoice by ID. It gave me clean code. Auth middleware on the route, a database lookup, a JSON response. It ran on the first try. Then I logged in as a different test user and changed the number at the end of the URL. Invoice #1001 belonged to someone else. I got the whole thing back: amount, line items, billing address. No error, no warning. Just another user's private data on my screen. That is IDOR, an Insecure Direct Object Reference, and it is one of the most common holes I find in AI-generated APIs. The frustrating part is that the code looks secure. It even has an auth check. It just checks the wrong thing. The Vulnerable Code The endpoint below is broken because it confirms you are logged in but never confirms the invoice is yours. findById takes the ID straight from the URL and returns whatever it finds. // CWE-639: authenticated, but no ownership check app . get ( ' /api/invoices/:id ' , authenticate , async ( req , res ) => { const invoice = await Invoice . findById ( req . params . id ); res . json ( invoice ); }); The authenticate middleware does its job. It proves the request comes from a real, logged-in user. What it does not prove is that this particular user has any right to invoice :id . Change the ID, get someone else's record. Increment it in a loop and you can walk the entire table. Why This Keeps Happening AI editors confuse authentication with authorization because almost every tutorial they trained on does the same thing. Authentication is "who are you." Authorization is "are you allowed to
NVIDIA shipped a 30-billion-parameter model that can see, hear, and talk — and gave the weights away. The catch: the seeing and hearing parts didn’t run on a Mac. So I spent an afternoon writing the missing piece. Here is the whole thing in thirty-five seconds — the model reading a real cart off my own store, on the laptop, with nothing leaving it. Thirty-five seconds: the model reads a real cart off my own store, on the laptop, with nothing leaving it. The elapsed counter is the real measured latency. Every couple of weeks I go looking for whatever new open-weight model just dropped, pull it onto my laptop, and see what it can actually do. Most of the time it’s a coding model, I run it against the one I already use, and my current favorite wins again. That’s a fine result. It’s just not much of a story. This time I found something different. What Nemotron Omni actually is NVIDIA released Nemotron-3-Nano-Omni-30B-A3B — a “tri-modal” model, which is a fancy way of saying one brain with eyes and ears attached. You can hand it a picture, a sound file, or a video, and talk to it about what it saw or heard. It’s 30 billion parameters total, but only about 3 billion of them fire for any given word, which is why something this capable can run on a laptop at all. The weights are public. Someone had already done the hard, unglamorous work of shrinking it down to a 4-bit MLX version that fits on Apple Silicon — that’s yayr over at mlx-community, and this project doesn’t exist without that upload. About 19 GB on disk. Ready to go. Except for one line, buried in the model card: The text backbone loads with standard MLX nemotron_h tooling. The vision and audio towers require a multimodal runtime that implements the C-RADIO ViT-H and Parakeet Conformer forward passes (e.g. the Evorix on-device engine). Translated: the brain works on a Mac. The eyes and ears don’t. The weights for them are right there in the file — all the knowledge, sitting on your disk — but nothing I could find
Despite predictions that AI agents could make traditional apps obsolete, developers are shipping new software faster than ever. From smarter bookmarking tools and neighborhood marketplaces to digital pen pals and nature journals, here are the latest App Store finds worth adding to your Home Screen.
New Yorkers who want to see their Instagram or TikTok feed curated by an algorithm, or receive notification after midnight, will soon have to verify their ages to access those features. New York Attorney General Letitia James (D) released a set of final rules on how to implement the SAFE for Kids Act, signed into […]
HBO Max, like other streaming platforms, is rethinking content discovery as large libraries are making it hard for viewers to find something to watch and as audiences become accustomed to short-form content on platforms like TikTok and Instagram Reels.
Apple’s latest AirTag may seem like the obvious choice if you own an iPhone and are shopping for a Bluetooth tracker, but it’s not the most affordable option. For families that contain both Android and Apple devices, it might be better to pick up the latest Tile Pro instead. It works well across both platforms, […]
While the summer months are great for putting in the miles on your favorite bike, it can be frustrating to have to hang it up when the weather turns. This smart bike trainer turns your road bike into an indoor exercise bike, with Wi-Fi and Bluetooth to connect to your PC, iOS, or Android device, […]
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