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tags: hardware, iot, opensource, electronics As software developers, many of us reach a point where writing code inside a virtual environment isn't quite enough—we want to manipulate the physical world. Whether it's blinking an LED via an ESP32, visualizing audio frequencies on a desk display, or building custom bench tools, hardware hacking is easily one of the most rewarding rabbit holes to fall down. At NextPCB , we’ve spent the past few years supporting the open-source hardware community by sponsoring independent creators, makers, and embedded engineers to help turn their digital schematics into real, physical circuit boards. If you’re looking for inspiration for your next weekend project, here are four curated roundups of real-world projects featuring open-source files, schematics, and design breakdowns. 1. Retro Tech & Nostalgic Geek Culture Builds 🎮 There’s something uniquely satisfying about recreating classic tech using modern hardware components. From custom hand-held arcade consoles to retro synth modules and glowing mechanical displays, retro builds combine aesthetic nostalgia with serious embedded engineering. These projects aren't just for show—they showcase clever power management, compact multi-layer PCB routing, and custom display interfaces. 👉 Check out the project breakdowns & schematics: 8 Retro Geek Culture PCB Projects: Open-Source Gerbers & Schematics 2. Smart Audio & Interactive Visual Displays 🎵 Audio reactive electronics bridge the gap between digital signal processing (DSP) and hardware UI/UX. Think custom spectrum analyzers, RGB LED matrix drivers, and tactile smart knobs that update in real-time. Building custom audio hardware requires paying extra attention to noise isolation, clean power delivery, and signal integrity—making these projects fantastic learning material for intermediate hardware devs. 👉 Explore the audio & display designs: Smart Audio & Interactive Display PCBs: Open-Source Design Guide 3. DIY Power & Precision Lab Equipm

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

An image of Portugal forward Pedro Neto’s cleats at the World Cup has reignited a practice among some soccer players: modifying their cleats to relieve heel discomfort.

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.

The NHTSA says it identified a 'pattern of driverless AVs' interfering with first responders. It's now demanding a solution from AV makers.

A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converti

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…

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

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

{ Abhilash Kumar Bhattaram : Follow on LinkedIn } The Post-Migration Mirage For many Chief Technology Officers (CTOs), the successful cutover of a core database to the cloud feels like the ultimate victory lap. The data has landed, the connection strings are updated, and initial performance metrics look stellar. But there is a dangerous mirage that follows a cloud database migration: Hidden Downtime. Unlike an abrupt database crash, hidden downtime is a slow-burn operational decay. It happens when day-to-day transactions process smoothly in production, but the underlying database ecosystem—specifically the disaster recovery (DR) standby instances, secondary cross-region sites, and replication pipelines—quietly falls out of sync. When a true disruption occurs and you try to failover or scale, the database tier collapses. To ensure true, 24/7 predictability, forward-thinking CTOs look beyond the immediate "Go-Live" date. The ultimate challenge is navigating the dense maze of cloud onboarding options to find the exact database migration method that fits your specific application topology. Ground Zero: The Database Configuration Drift The root cause of post-migration database downtime begins long before cutover day, it starts with how the database is moved and how its configuration is maintained. Going to the cloud offers various technical pathways, but the overarching challenge is finding what fits your unique architecture. The initial migration must establish perfect baseline parity, but standard database operations and hasty migration choices quickly introduce fatal configuration drift. To manage this drift effectively, organizations must introduce rigorous baseline metrics before, during, and after the migration process: - Benchmarking Versions: Ensuring that source and target database patch levels, Timezone (TZ) files, and Release Updates (RUs) match exactly. Mismatched database versions between primary cloud instances and standby homes create silent dictionary inc

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

Eleven exhibits, last time. A career that kept refusing to fit inside a single box — trainer,...

There's a comfortable thing people say when they see an AI agent query a code map. "Nice crutch. For now." The logic underneath it is reasonable. Coding agents are young. Context windows are small and getting bigger. Models are dumb today and will be smart tomorrow. So a structural index, the thing that hands the agent a dependency graph it would otherwise have to reconstruct, looks like a patch over a temporary weakness. Wait two releases. The model will just hold the whole repo in its head and the map becomes a quaint workaround, like a spellchecker for someone who learned to spell. I build one of those maps Sense . I went looking for the data that would kill it. I didn't find it. I found the opposite. What a map hands an agent is a computed fact. What a better model hands you is a more confident guess . No amount of model progress turns the second into the first, because the difference between them isn't a quality gap that closes with scale. It's a difference of kind. The rest of this piece is the two findings that forced me there. The belief, stated fairly The claim at full strength, because a weak version is easy to knock over. A code map exists to compensate for what the model can't do yet. Today's agent greps, samples, and guesses at structure because it can't read the whole codebase at once. Tomorrow's agent reads all of it, reasons over all of it, and the guessing stops. Bigger windows plus better weights equal no more blind spots. The map is scaffolding you'll tear down once the building stands. If that's true, the right move is to skip the tool and wait. Both findings, in order. Proof one: the best model available was still blind The benchmark ran the same task on thirteen real Ruby repos. Pick the hub model of an app, the Inbox , the MergeRequest , the Spree::Order , and ask the agent to find every place that depends on it before a teardown change. The non-obvious dependents, the ones scattered through concerns and workers and config-string registries, w