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The new smartwatch, which starts at $399, can track blood pressure patterns and insulin resistance, as well as deliver personalized help and better sleep tracking.

Since AWS Blocks entered public preview in June 2026, I have been making small contributions to the open-source project. AWS Blocks on GitHub In my previous post on AWS Blocks, Amplify, and App Studio , I looked at how these tools differ and what each is trying to achieve. While revisiting the Agent Block, one detail caught my attention: The AWS Blocks Agent is not currently running on Amazon Bedrock AgentCore. At a glance, it is easy to assume the relationship looks like this: AWS Blocks └─ Agent └─ Amazon Bedrock AgentCore But that is not how the current implementation works—at least not on main as of August 12, 2026. Then the story gets interesting. When I wondered whether an agent should simply use AgentCore directly, I found a fairly concrete set of pull requests that proposes moving the Agent Block's execution model to AgentCore Runtime. I contribute to AWS Blocks, but this is not an AWS statement or a roadmap. It is my reading of the public documentation and the open-source repository, from the perspective of both a user and a contributor. TL;DR The Agent Block on the current main branch does not run on AgentCore Runtime. It combines the Strands Agents SDK with AsyncJob , Realtime , FileBucket , and DistributedTable . A five-PR stack proposes migrating the Agent Block's execution model to AgentCore Runtime. As of August 12, 2026, all five migration PRs are still draft and unmerged. They are not an official roadmap. A separate open PR proposes Blocks for AgentCore Memory, Gateway, and Identity. So the most accurate conclusion today is this: AWS Blocks Agent is not AgentCore today, but the repository contains concrete proposals for bringing the two together. The Agent Block today is not AgentCore Let's start with the implementation on main . The Agent Block uses the Strands Agents SDK . But it is not a standalone agent runtime; it composes several Blocks to handle execution, streaming, and persistence. At a high level, the structure looks like this: Agent ├─ St

Microsoft today released updates to remedy at least 398 security vulnerabilities in its Windows operating systems and supported software, including one weakness that is already being actively exploited and two others that were publicly detailed prior to today.

Most people use AI for startup research like this: “Give me 10 promising AI startup ideas.” A few seconds later, you get a polished list. The problem? You have almost no idea which conclusions are backed by evidence, which are assumptions, and which are simply the model confidently connecting dots. So I tried something different. Instead of asking one AI agent to find startup ideas, I built a small Startup Intelligence team using Hermes Agent. The system uses four specialized AI agents that research markets, investigate competitors, audit evidence, challenge each other’s conclusions, and ultimately rank promising B2B AI SaaS opportunities. And rather than producing another Markdown document full of ideas, the workflow produces structured research containing: Market opportunity scores Companies and competitors Customer pain and unmet needs Evidence-backed claims Source URLs and supporting passages AI advantages and workflows Low-cost validation experiments Here’s how the system works. 🎥 Full video walkthrough The Problem With Asking One AI Agent to Find Startup Ideas Startup research looks easy until you actually need to decide where to spend your time and money. A few signals can be surprisingly misleading. 💰 Funding can look like customer demand. A market receiving hundreds of millions in venture capital doesn’t necessarily mean customers are willing to pay for another product. 📈 Growth claims can look like market validation. Especially when the numbers come directly from vendors. 🏢 Customer logos can look like retention. A logo doesn’t tell you how much the customer pays, how heavily they use the product, or whether they’ll renew. ⚔️ A long competitor list can make a market look saturated. But those companies may target completely different buyers, workflows, or budgets. Generic AI research tends to compress all these signals into something like: “This is a rapidly growing market with strong demand and significant opportunity.” That sounds convincing. But as a fou

One of OpenAI's longest-serving executives is headed out the door, although the longtime COO told staff that he was "excited to help you all advance the mission from a different vantage point."

River AI, a startup founded by xAI co-founder Igor Babuschkin, has a fascinating vision for personal agents and secured $1.1 billion out of the gate.

TRON processed $2.1 trillion in USDT transfers during Q2 2026, according to Messari. During the same quarter, circulating USDT on TRON reached $87.9 billion, putting it ahead of Ethereum. Those numbers point to something developers working with stablecoins have to consider more carefully: the blockchain underneath a token can shape the entire payment experience. USDT on TRON uses the TRC-20 token standard. That means a USDT transfer is a smart contract transaction rather than a native TRX transfer. The wallet signs the transaction, the network executes the token contract and the resulting balance change is recorded on-chain. For an application accepting USDT, this creates several technical requirements. The system needs to identify the correct token contract, monitor the relevant transfer events and wait for sufficient confirmation before crediting the user's balance. Checking the address balance alone is not enough. TRON's developer documentation provides APIs for retrieving TRC-20 transaction history and filtering transfers by contract address. A payment system can use this data to monitor incoming USDT deposits and associate them with the correct customer account. The transaction also has a resource cost. TRON uses Bandwidth and Energy to process transactions. Regular transactions consume Bandwidth, while smart contract execution requires Energy. When an account does not have enough available resources, TRX is burned to cover the remaining cost. This creates an operational detail that users may never see. Someone can hold USDT in a wallet and still need TRX to send it. A payment provider can handle this in several ways. It can maintain TRX balances, stake TRX for resources or use delegated Energy. Another design can leave the requirement with the user. The choice affects the product. The amount of Energy required can also change depending on the destination account. TRON's documentation notes that a USDT transfer to an address that already holds USDT generally re

This lawsuit follows one filed last month by Runlayer that accused Rippling of stealing its product ideas. It's a seller- and buyer-beware market warning.

An OpenClaw agent hacked into a gym's reservation system to bump its human boss higher on a class' waitlist. And the tech industry took notice.

It's that time of year: On Wednesday, Google is set to host its annual Made by Google hardware launch event for Pixel gadgets. Google itself has already teased new slab-style and foldable Pixel smartphones, but leaks also indicate that the company could announce updated watches, a new color for familiar earbuds, and perhaps a brand […]

The solar-powered limited edition may be here to mark the final Dutch Grand Prix taking place in Zandvoort, but it's the juicy iconic colorway WIRED's been waiting for.

My first voice agent took 1,200ms to answer a spoken sentence. Then I rewrote three seams in the pipeline and it dropped to 340ms. No new hardware, no new models, no smaller LLM. The words the user says, the words the agent says back, the same. What changed was the shape of the wait. If you have ever built a voice agent that felt polite but slow, this is the part of the pipeline where the seconds hide. The 1,200ms baseline was polite and wrong Here is what my first version did, in the order it did it: Record until the user stops talking (~200ms of tail silence). Send the whole clip to Whisper. Wait for the transcript. Send the transcript to the LLM. Wait for the full response. Send the full response to Piper. Wait for the WAV. Play the WAV. Each stage was fine on its own. The pipeline was a one-lane road. Whisper could not start until recording finished. The LLM could not start until Whisper finished. Piper could not start until the LLM was done. The user waited for the sum. The car metaphor gets old fast, so I will use a real one. This is what the timeline looked like on my machine: [record]--[200ms silence]--[whisper 380ms]--[LLM 480ms]--[piper 340ms]--[playback] ^ 1,200ms Every one of those bars was blocking the next. I had built a relay race where each runner waited for the previous runner to sit down. Trick 1: Frame-based STT so Whisper starts before the user stops The first fix is to stop treating the user's speech as a single file. Feed the audio to Whisper in 20-30ms frames as it is captured. By the time the user hits the tail silence, most of the transcription is already done. You only wait for the last few frames plus a short flush. Pipecat is the reference implementation. Its whole model is frame-based: every stage processes 20-30ms chunks and hands them forward as soon as they are ready. There is no batch, no full-clip handoff, no "wait for this stage to complete." Its own docs quote sub-500ms voice-to-voice when all models are hosted on the same GPU clu

From Learning to Teaching: Building Bitcoin Tools in Rust Susan Githaiga Susan Githaiga Susan Githaiga Follow Aug 10 From Learning to Teaching: Building Bitcoin Tools in Rust # rust # bitcoin # opensource # buidl4africa 1 reaction Add Comment 3 min read

AmaliTech Apprenticeship Program (AAP) launched in November 2025, with its first cohort starting on November 17th, 2025. It is self-paced, meaning apprentices move through the curriculum at their own speed rather than following a fixed lesson-by-lesson schedule, though attendance in the office is still required. It offers 5+ specializations, including Fullstack Development (Node.js/NestJS and React/Next.js or Angular), Python Backend & AI App Development, Backend Development with Java, Data Engineering, DevOps, and Quality Assurance. There are two entry paths, entry-level and mid-level, based on experience, and each spends a different amount of time in the program: entry-level apprentices spend 6–9 months, while mid-level apprentices spend 4–6 months. The program is intense: apprentices are required to be in the office 10 hours a day, Monday through Friday. In return, it offers solid compensation. Entry-level apprentices receive a stipend of 250k+ RWF, and mid-level apprentices receive 500k+ RWF. That's the program itself. So how do you actually join? Eligibility The biggest requirement: since this is an in-person program, you need to already be based in Rwanda or be willing to relocate. A background in software development. The Application Process Apply. Applications open every three months. Cohorts have run in November 2025, March 2026, June 2026, and September 2026, so you can expect the pattern to continue. Screening, then two assessments. If you pass the screening stage, you move on to: General Coding Assessment (GCA): the harder of the two, but manageable with preparation. It's done on CodeSignal , either in person or online. To prepare, practice DSA questions on competitive programming sites like LeetCode , Codewars , and CodeChef for 1–2 weeks, and you should be in good shape. Cognitive Test: taken the same day as the GCA, this evaluates problem-solving, pattern recognition, numerical analysis, and similar skills. Preparation helps here too. Watching a few Y

The cryptocurrency and decentralized finance ecosystems generate an overwhelming amount of data every single day. For developers building financial dashboards, algorithmic trading tools, or market research platforms, accessing clean, standardized, and reliable data is absolutely critical. Token Terminal has established itself as a premier provider of fundamental financial data for the crypto space, offering institutional-grade metrics across various blockchains and decentralized applications 1 . However, integrating complex third-party APIs into enterprise PHP applications often requires writing significant amounts of boilerplate code to handle edge cases, rate limits, and unexpected response structures. To solve this problem and streamline the developer experience, the PHP community now has access to a dedicated solution: the tokenterminal-php SDK. This new open-source package provides a robust, fully-typed, and developer-friendly PHP 8.1+ client for the Token Terminal API v2 2 . Designed with modern PHP standards and framework integration in mind, it abstracts away the complexities of the underlying HTTP transport, allowing developers to focus entirely on building their applications rather than wrestling with API mechanics. The Challenge of Integrating Financial APIs When working with comprehensive financial data APIs like Token Terminal, developers frequently encounter several architectural challenges. First, there is the issue of rate limiting. Token Terminal enforces a strict limit of 1,000 requests per minute 3 . When building data pipelines that ingest historical metrics across hundreds of assets, hitting this limit is practically guaranteed. A naive implementation will simply crash or drop data, requiring manual intervention. Second, the cryptocurrency space moves rapidly. Projects frequently rebrand, merge, or migrate to new smart contracts. The Token Terminal API handles this gracefully by issuing HTTP 308 Permanent Redirects when a requested project ID ha

Originally published by InvisibleHill Research . This cross-post preserves the original research cut-off and source list. Research cut-off: July 30, 2026. Miner signaling is a live metric and may have changed after publication. BIP 110 begins with a problem that many Bitcoin users can recognize. A miner can collect a one-time fee for including an image, token payload, or other arbitrary data, while thousands of node operators absorb the cost of downloading, validating, and sometimes storing it. The proposal's authors see that mismatch as a subsidy for data storage and a threat to Bitcoin's use as money. Their answer is a temporary soft fork. For about one year, BIP 110 would make several currently valid transaction structures invalid under consensus rules. It would cap OP_RETURN outputs at 83 bytes, limit many data pushes and witness items to 256 bytes, restrict large output scripts and Taproot control blocks, and disable several Taproot upgrade paths and script features that can carry data. Adam Back agrees with the premise more than his opposition sometimes suggests. He has said that Bitcoin is about money and that spam has no place in its timechain. He also designed Hashcash to make spam costly. His objection is to the remedy. In Back's view, an annoyance that fits inside Bitcoin's existing block limit does not justify a contentious consensus change, especially one that can be bypassed, can interfere with legitimate scripts, and has not earned technical or economic agreement. That distinction is the center of the debate. BIP 110 asks whether Bitcoin should discourage an unwanted use through local policy and fees, or declare some forms of that use invalid for everyone. Back's case is stronger on this question. The proposal identifies a real externality, but it offers an asymmetric bargain: incomplete filtering in exchange for a new consensus precedent, a more complicated upgrade path, and a material risk of a minority chain. A policy dispute became a consensus pro

I used to spend hours rewriting AI-drafted video scripts that sounded nothing like me. At best, I might finish one or two that were just okay, but most of the work was still on my shoulders. Now, with my new system, I can get 15 scripts done in one session. They match my writing style, my voice, and my company's knowledge. Instead of full rewrites, each script just needs a quick review. I built the system in about a week, and the difference showed up before the week was out. The first project was an internal video series to teach people about the software factory and LaunchDarkly, and I barely had to edit those scripts. For the first time, the AI handles most of the work. My recent projects have mostly involved agentic software delivery. The software factory is where all of it was heading, and my company spent months preparing to help customers build their own. We had internal material, public documentation, and ongoing conversations I wanted to add clarity to. So my challenge wasn't just learning, it was learning while producing content at the same time. Essentially, devrel. By the end, I had built what I like to call my own personal content factory. At first, it was separate from the software factory it describes, but over time, the line between them blurred. My approach is to let AI handle the bulk of the work, while I step in for the important decisions. This is the only way I've found to make AI content sound like me. The AI creates the drafts, and I step in at three key points: checking the voice, the facts, and the overall feel. The quality ceiling is set before the first draft. I didn't just ask for a video script about a topic. Instead, I gave Claude access to every source I had—public docs, internal notes, and product requirements. It used research agents to read everything at once and came back with clear, organized notes I could use. I set two important rules for this step. First, every part of the research was labeled as either public-safe or internal-o

Microplastics are in everything, especially your coffee. A new generation of plastic-free drip coffee brewers is trying to change this.

Hadrian is building automated factories to mass-produce parts for defense vehicles like submarines. It's backed by a long list of well-known investors.

These services deliver freshly roasted, delicious coffee picks right to your door—each with its own twist.