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Satellites show burn scars and fires at AWS data centers and Saudi oil refinery.

A federal judge said the Trump administration has not presented enough evidence to justify labeling Anthropic a supply-chain risk, casting doubt on the government's ban on its AI technology.

The Galaxy Watch 9 launches on August 7th, and not only does Costco have the best preorder incentives we’ve seen so far, the watch is also discounted. The deal includes the 40mm Watch 9 in cream or graphite, two wireless fast chargers for the watch, and a $50 Costco gift card, all for $339.99 — […]

Hey DEV Community! 👋 I am currently building MOKSHA, an HTML5 Canvas game deeply rooted in Vedic philosophy. The game involves managing your Karma, avoiding Maya (Illusions), and achieving spiritual liberation. Ironically, while building a game about waking up from cosmic illusions, I fell into a technical illusion myself yesterday. Let me tell you a chaotic detective story about how my game froze on a fresh repository clone, and how I found the silent assassin hiding in plain sight. 🤡 🚫 The Disaster: Works on Itch.io, Freezes on GitHub So, there I was, ready to release a fresh update. I generated my build packages locally, zipped them up, and proudly uploaded them to Itch.io. I hit Publish, tested the live link, and everything worked flawlessly. High scores, smooth frames, total spiritual awakening. Then, I casually walked over to my terminal, ran git add . followed by git push, and went to bed thinking I was an absolute pro. The next morning, I wanted to double-check my clean repository, so I cloned it fresh into a new folder. I booted up the local server, and... the entire game was completely unclickable. Dead clicks. Frozen canvas. Total illusion (Maya). 💀 Opening up the browser console revealed a fierce wall of red text: style.min.css:1 Failed to load resource: the server responded with a status of 404 (Not Found) main.min.js:1 Failed to load resource: the server responded with a status of 404 (Not Found) 🕵️‍♂️ The Realization: It Wasn't Me, It Was My .gitignore! Initially, I blamed my sleep-deprived brain, thinking I forgot the chronological order of pushing and building. But when I opened my root directory to inspect the crime scene, I found the real culprit staring right back at me on lines 46 and 50 of my .gitignore file: dist/ *.zip index.min.html The Ultimate Trap Exposed 🪤 Because dist/ was explicitly blacklisted in my .gitignore, Git was literally doing its job perfectly by completely ignoring my production builds during staging! Here is exactly how the

Whether you are using off-the-shelf AI coding tools like Claude Code and Cursor or building custom autonomous AI agents with TypeScript and LLM APIs, you hit the exact same fundamental wall: AI agent amnesia . As an agent user , you spend forty-five minutes explaining your architecture, deployment quirks, and database rules. The agent writes brilliant code. You close the CLI or tab, open a new session the next morning, and the agent suggests the exact legacy library you rejected yesterday. As an agent builder , you struggle to keep your custom agentic loops focused. As multi-step agent trajectories expand, LLM token limits force context compaction, wiping out subtle rules and past decisions while escalating API costs. The intelligence is real. The amnesia is structural. Context Windows Are Working Memory, Not Long-Term Memory The AI industry’s standard reflex to agent amnesia has been pushing context windows to 1M+ tokens. But a context window is working memory (RAM), not long-term storage (disk). Relying on massive context windows introduces three critical engineering bottlenecks for both users and builders: Context Compaction Destroys Rationale : When a session reaches token limits, agents automatically compact their context history. Compaction summarizes conversations into short summaries, quietly wiping out subtle architectural constraints, edge cases, and past decisions. Context Drift & Attention Loss : LLMs struggle with needle-in-a-haystack attention degradation when context windows are stuffed with 100k+ lines of raw conversation history. Escalating API Costs & Latency : Re-sending full project transcripts on every prompt burns tokens rapidly and adds seconds of input processing delay for users while skyrocketing LLM bills for agent builders. Agents do not need larger transcripts. They need a durable, inspectable, versioned memory layer . Why Vector Databases Fall Short for Local & Workspace Agent Workflows When developers and AI engineers realize raw contex

If you're an African developer, SaaS founder, freelancer, or online business, one of the biggest challenges isn't finding customers. It's getting paid by them. Most articles about African payment APIs focus on moving money out of Africa. But what if your customers are the ones sending money to you? Whether you're billing international clients, collecting subscription payments, or accepting payments from marketplace users, you need a collection method that's easy for customers and simple to reconcile on your end. The Afriex Business API offers three different ways to collect payments, each designed for a different use case. Depending on who your customers are and how they prefer to pay, you can collect funds through dedicated virtual accounts, shared pool accounts, or stablecoin wallets. In this guide, you'll learn how each collection method works, when to use it, and how to integrate it into your application. The Three Collection Methods Although all three collection methods ultimately deposit funds into your Afriex Business wallet, they differ in how customers send money and how you identify who made each payment. Method Best for How the payer sends Dedicated virtual account Known customers, repeat payments Bank transfer to a unique account number Pool account Quick collection, one-off payments Bank transfer with a reference Crypto wallet Customers holding USDT or USDC Crypto transfer to a wallet address Choosing the right method depends on your product and your payment flow. If you already know your customers and expect them to pay repeatedly, dedicated virtual accounts provide the smoothest experience. If you want to launch quickly without creating individual accounts for every customer, pool accounts are a great fit. And if your users prefer paying with stablecoins, crypto wallets make that process straightforward. Method 1: Dedicated Virtual Accounts Dedicated virtual accounts are the easiest way to reconcile bank transfers from repeat customers. Instead of ask

Florida wants to use federal EV charger funds to build an air taxi network connecting golf courses, luxury apartment buildings, and airports.

Разберёмся, к чему нас приведут нейросети и что будет дальше. Это хайп, пузырь или новая реальность? Взгляд разработчика и дорожная карта для входа в AI. Хочу провести небольшой анализ и понять, какие сценарии развития нейросетей могут быть и к чему мы можем подготовиться. Я разработчик, и последние пару лет моя лента — это бесконечный хайп вокруг AI. Но если отключить эмоции и включить холодный анализ, возникает ощущение дежавю. Давайте ненадолго погрузимся в историю. Прошлое. Что мы уже пережили Мы, поколение миллениалов и зумеров, стали свидетелями уникального явления: технологии начали сменять друг друга с огромной скоростью. Каждые два-три года появлялось что-то. Вспомним главные тренды: • Социальные сети (2007–2012) — пугали, что мы перестанем общаться вживую, а приватность умрёт навсегда. Стали рекламным рынком, появились SMM-щики и таргетологи. Кто не пошёл в digital — остался на обочине. • Big Data (2010–2015) — кричали «Большой брат следит за тобой», аналитиков заменят алгоритмы. Сегодня это стандартный слой систем, дата-инженеры — обычная роль. • Облака (2010–2018) — боялись, что данные украдут, а сисадмины вымрут как класс. Облака стали коммунальной услугой. DevOps и SRE — must-have, сисадмины просто переквалифицировались. • IoT (2014–2018) — пугали тем, что хакеры взломают ваш чайник, а вещи станут умнее людей. Технология ушла в промышленность, быт не перевернула. • 3D-печать (2012–2015) — паника «заводы закроются, каждый напечатает пистолет». Прижилась в прототипировании и стоматологии, пистолеты печатают только в новостях. • VR (2016) — боялись, что люди уйдут в виртуал и перестанут различать реальность. Стало игрушкой для геймеров и тренажёром для пилотов. • Метавселенные (2021–2023) — говорили, что жизнь окончательно переедет в цифру, а без аватара на работу не выйдешь. Хайп прошел. • Блокчейн (2015–2018) — страх, что банки исчезнут, а юристы и нотариусы станут не нужны. Web3-революция не случилась, но разработчики были на вес золота. • Крипта (2017

Laravel is one of my favorite frameworks because it allows you to move from idea to production incredibly fast. But after spending months building CelebrateMe a platform that helps people celebrate life's special moments through virtual gifts, wishlists, messages, and verified vendors—I realized something. I wasn't just using Laravel. I was relying heavily on the incredible ecosystem around it. Some packages solved problems that would have taken days (or weeks) to build myself. Others helped me monitor, debug, and secure the application as it grew. Here are the Laravel packages I now consider essential for almost every project. 1. Laravel Sanctum Use it for: API Authentication CelebrateMe has a Laravel API with a React frontend, so authentication needed to be secure without adding unnecessary complexity. Laravel Sanctum was the perfect choice. It provides: Personal access tokens SPA authentication Mobile API authentication Lightweight implementation For most APIs, Sanctum is more than enough. 2. Laravel Horizon Use it for: Queue Monitoring As CelebrateMe grew, background jobs became increasingly important. Things like: Sending emails Processing uploads Notifications Payment-related jobs Instead of wondering whether jobs were running correctly, Horizon gave me a beautiful dashboard to monitor everything in real time. If you're using queues and not using Horizon, you're missing out. 3. Laravel Telescope Use it for: Debugging Telescope quickly became one of my favorite development tools. Instead of scattering dd() statements throughout my code, I could inspect: Requests SQL queries Jobs Exceptions Cache operations Notifications It made debugging significantly easier. 4. Spatie Laravel Permission Use it for: Roles & Permissions CelebrateMe has multiple user types, each requiring different permissions. Managing authorization manually would have become difficult very quickly. Spatie's Permission package made it straightforward to assign roles and permissions while integra

Bridging the Gap: Navigating the Chasm Between Academic Coding and Real-World Software Development The transition from academic coding to professional software development is fraught with challenges, particularly when it comes to navigating and debugging large, unfamiliar codebases. This gap, often overlooked in educational curricula, leaves new developers ill-prepared for the complexities of real-world projects. Below, we dissect the technical mechanisms involved in codebase navigation and debugging, their constraints, and the resulting instabilities, while reflecting on the disconnect between academic training and industry expectations. Mechanisms of Codebase Navigation and Debugging The process of understanding and working within a large codebase involves several interconnected mechanisms. Each plays a critical role in a developer's ability to efficiently and accurately contribute to a project. Code Navigation : Involves traversing a codebase using tools like "go to definition" to map code structure and dependencies. This mechanism relies on the developer's ability to interpret relationships between files and functions. Impact : Efficient navigation reduces time spent understanding the codebase. Internal Process : Iterative exploration of code paths. Observable Effect : Reduced time to locate relevant code segments. Code Comprehension : Analyzing existing code to infer purpose, logic, and side effects. Requires pattern recognition and logical deduction. Impact : Accurate comprehension minimizes unintended modifications. Internal Process : Mental modeling of code behavior. Observable Effect : Correct identification of code functionality. Debugging : Identifying and resolving bugs while minimizing collateral damage. Relies on isolating root causes and understanding dependencies. Impact : Effective debugging prevents regressions. Internal Process : Hypothesis testing and validation. Observable Effect : Bug resolution without introducing new issues. Documentation Ana

A short case study from my "building and testing MCP agents" series — it stands on its own, but the method behind it is laid out in https://dev.to/langensjonathan/the-parameters-that-actually-matter-when-youre-tuning-an-ai-agent-2agd . TL;DR: I benchmarked two agents that are identical except for one thing — how many MCP servers they're connected to — on the exact same question. Both got the right answer, both called the same single tool. The one with more MCP servers attached still cost 28% more per question , purely from the extra tool schemas the model has to be told about on every single call, whether it uses them or not. The setup MAVERIK is my open-source MCP test bench: define a suite of questions with pass criteria, run it against one or more agent configurations, and compare the results on hard numbers. This post is one deliberately tiny experiment with it: change exactly one thing about an agent, hold everything else fixed, and see what the numbers attribute to that one change. I have a small "GitHub summarizer" agent: one system prompt, one job — answer questions about my GitHub account by calling the GitHub MCP server . I duplicated its configuration (MAVERIK supports this directly — same model, same prompt, same everything) and changed one field on the copy: the set of attached MCP servers, adding deepwiki , microsoft-learn , and context7 . Neither agent needs any of those three for the question I was about to ask; they were attached because that's what the "kitchen sink" version of this agent had accumulated over a few sessions of general-purpose use. Then I wrote the simplest possible test suite — one question: "How many repositories do I have?" with a contains criterion checking the answer includes the correct count. No judge model, no subjectivity — it either says the right number or it doesn't. I ran both agents against it, 2 repetitions each, same model ( claude-haiku ) for both, and pulled up MAVERIK's Agent Comparison report. Agent A — github on

I built a small Flask example that turns voicemail into a routing workflow. Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/voicemail-smart-router-python The app accepts either: a voicemail transcript an uploaded voicemail audio file For audio, it transcribes the voicemail first. Then it uses Telnyx AI Inference to classify the message and decide where it should go. Categories The app classifies voicemails into: urgent billing support sales spam routine Each category maps to a route: urgent -> Slack alert billing -> email support -> ticket queue sales -> CRM lead spam -> blocklist + archive routine -> daily digest Run it git clone https://github.com/team-telnyx/telnyx-code-examples.git cd telnyx-code-examples/voicemail-smart-router-python cp .env.example .env pip install -r requirements.txt python app.py Configure .env : TELNYX_API_KEY=your_telnyx_api_key AI_MODEL=zai-org/GLM-5.2 FALLBACK_MODEL=meta-llama/Llama-3.3-70B-Instruct HOST=127.0.0.1 Optional Slack webhook for urgent messages: SLACK_WEBHOOK=https://hooks.slack.com/... Classify a transcript curl -X POST http://localhost:5000/voicemails/transcript \ -H "Content-Type: application/json" \ -d '{ "transcript": "This is an emergency. Our production system is down and we need help immediately.", "caller_number": "+17177247292" }' Example response: { "category" : "urgent" , "confidence" : 1.0 , "priority" : "high" , "reason" : "The caller reports a production system outage requiring immediate attention." , "suggested_action" : "Escalate immediately to the on-call engineering team." , "route" : "slack" , "routed_to" : "#oncall-alerts" , "routing_status" : "delivered" } Process voicemail audio curl -X POST http://localhost:5000/voicemails/process \ -F "file=@voicemail.wav" \ -F "caller_number=+17177247292" For audio, the app calls: POST /v2/ai/audio/transcriptions using: distil-whisper/distil-large-v2 Then it calls: POST /v2/ai/chat/completions to classify the transcript. Routes included POST /voic

Google's next foldable doesn't look all that different, save for the camera bar.

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Buying multiple lamps for different rooms can get expensive. Govee’s rechargeable Table Lamp Classic gives you one lamp you can use throughout your home instead of getting one for every room, and right now it’s down to $59 ($21 off) at Amazon. That’s just $4 shy of the all-time low set during Prime Day, and […]

This week on Uncanny Valley, we discuss the open- vs. closed-source debate in AI, key players in White House AI policy, and how to stop your chatbot logs from showing up in search-engine results.

Add another member to the fast-and-furious AI unicorn club: Simile

Here are the best back-to-school deals under $200 on comfy, ergonomic chairs for all-nighters and cram sessions

Spotify says the new feature is designed to let users add personal captions to their favorite songs, such as noting why a track was added to a playlist or when they first discovered it

Despite the slump, Apple and Samsung have both seen their market share go up.