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OnePlus has confirmed what industry observers have long expected: it's quitting the US and European markets, and will no longer launch new products in either region. Parent company Oppo promises that it will honor existing support and warranty agreements, with devices transitioning to its ColorOS for future updates. "Software updates and after-sale support will be […]

When I started learning programming, I believed my future as a developer depended on one thing: How...

When the enemy occupies favorable terrain, don't attack head-on. Use a decoy to lure the tiger down the mountain. Then take the mountain. — The 36 Stratagems, Lure the Tiger Down the Mountain Previously on this series: #2: Derek Shaw Walked Into Another AI Promise. The Pipeline Had a Better Plan. — Derek lost the Finova contract at QualiGuard. In the parking lot, Lena turned back before getting in her car: "Next time you put together a proposal — make sure your boss knows what you're doing out there." That line followed him. At MediSys he nearly made the same mistake. Fixed the ETL pipeline instead of the model, beat OmniDx with their own white paper. But VP Morgan still saw through him: he still hadn't told his boss. #8: Alex Watched an AI Dashboard Take Over. He Kept the Keys Under the Table. — MedTech signed a seven-figure AI operations monitoring system. Alex was assigned as training lead. Under everyone's noses, he built a second monitoring panel labeled "training environment." Three weeks later the vendor dashboard went down. Alex's hidden panel was the only one still running. The first time Alex and Derek really talked, and it wasn't in a working group. At the FHIR standards committee quarterly meeting, they sat in the same row, three seats apart, and voted against the same proposal. They knew each other's names. That was it. Two months later, Derek was fixing a partition config in the staging environment. MediSys had just signed a major hospital; the AI diagnostic validation platform was in integration testing. He found an unrotated log directory in /etc/logrotate.d/ with a prefix that didn't match MediSys's naming convention. He traced it upstream. One server. Labeled "temporary data exchange node." MedTech's supply chain order stream and MediSys's diagnostic validation records were sitting in the same directory. Write permissions hadn't been restricted. The hospital's integration spec had a line saying "both parties are recommended to complete data alignme

Introduction: The Question Beneath All Questions I think a lot of people are searching for the same thing but using different words. Some people call it: success happiness freedom purpose wealth enlightenment achievement legacy But underneath all of it is a deeper question: "What is worth dedicating my life to?" That question is harder than finding a job. It is harder than making money. It is harder than becoming successful. Because even if I get everything I think I want, I still have to answer: "Why did I want it?" Chapter 1: The Strange Reality of Modern Life We live in one of the wealthiest civilizations in human history. We have: technology beyond anything past generations could imagine medicine that would look like magic centuries ago instant communication across the planet access to unlimited information Yet so many people feel like they are barely surviving. Why? Because humans were built for survival. Our brains evolved to constantly ask: What could go wrong? How do I stay safe? How do I belong? Am I enough? Am I falling behind? The threats changed, but the machinery stayed the same. Ancient humans worried about predators. Modern humans worry about: money careers status loneliness comparison identity the future The battlefield moved from the physical world into the mind. Chapter 2: Money Is Not Evil — Money Is Power There is a tendency to pretend money does not matter. That is false. Money changes what is possible. Money can buy: freedom of time experiences travel education tools comfort opportunities the ability to help others A person with resources has access to a larger range of possible lives. Wanting money does not automatically mean being greedy. Sometimes wanting money is really wanting: freedom adventure security creativity proof that you overcame something The question is not: "Do I want money?" The better question is: "What will I use money for?" Money is a tool. A hammer can build a house or destroy something. The tool is not the meaning. The pu

This continues from Part ③ . We have the system set to refresh data daily, but unexplained errors can sometimes occur. Symptoms The segments were not properly configured as expected. Checking System → Schedule showed that the automatic refresh schedule itself was fine. The data source refresh also appeared to have no issues. Opening the Unify screen, everything looks like it succeeded at first glance — but checking the Performance screen reveals that something is failing during unification. Drilling in further, the failure is happening at the Customer Profiles step. Resolution Steps Attempt to manually re-run just the "Customer Profiles" task → Error persists Redo everything manually from the data source refresh step onwards This resolved the issue successfully. Takeaway Since this is not a misconfiguration issue, patient repetition of the refresh process is enough to recover. Hopefully this saves someone some troubleshooting time.

The Federal Communications Commission will vote next month on whether a single company can own broadcast stations that reach more than 39 percent of US TV households. In a Breitbart op-ed on Wednesday, Republican Chair Brendan Carr announced an August 6th vote to end the national ownership cap rule, which was intended to prevent one […]

Disinformation researchers praise ruling blocking Trump visa denials and deportations.

The Future of Rust: Why This Memory-Safe Language is Dominating Systems Programming in 2026 In its early years, Rust was often viewed as a "rising star"—a promising language with significant potential but a steep learning curve. As we navigate through 2026, that narrative has fundamentally shifted. Rust has transitioned from a niche interest to a cornerstone of modern, memory-safe infrastructure. The language is no longer just proving its worth; it is defining the gold standards for systems programming, cloud-native backend services, and kernel development. This deep dive explores the key pillars driving Rust's evolution, its massive ecosystem growth, and the roadmap for the years ahead. Seamless Ownership: The New Era of Rust Ergonomics One of the most significant shifts in recent Rust development has been the relentless focus on language ergonomics. Historically, developers occasionally felt that custom smart pointers were "second-class citizens" compared to built-in references. The "Beyond the & " initiative has successfully bridged this gap. Through advancements in Smart Pointer Parity , developers can now use custom pointers—such as Rc , Arc , or specialized interop pointers—with the same intuitive syntax and capabilities as standard references. Furthermore, the introduction of sophisticated field projection mechanisms and &own references allows for unprecedented precision in managing ownership and borrowing. This makes complex data structures much easier to implement and reason about, drastically reducing the friction typically associated with high-level abstractions. The Async Revolution: Making Asynchronous Code Natural Rust has achieved a milestone often referred to as "Async Parity," aiming to make asynchronous programming feel as natural and seamless as synchronous code. Several key developments have fueled this revolution: Async-in-Traits: The stabilization of async fn in traits has removed the need for external crates like async-trait , greatly simplify

#NebiusServerlessChallenge Live dashboard: Myth-Busting Quantitative Terminal · Live endpoint: /predict on Nebius Serverless · Code: github.com/KoralZakai/stocksPredictionAfterTweet Everyone on a trading desk knows the story. He tweeted about Intel, and the stock ran for months. He posts about Iran, oil spikes. The anecdotes are vivid, specific, and everybody has one. While the prevailing myth suggests that Trump's tweets drive market movements, my research — built on an analysis of 78,130 posts, with Llama-3.3-70B reading every market-relevant tweet — reveals a more nuanced reality. The findings demonstrate that the market tends to price in significant events long before the tweet is even posted. This indicates a case of reverse causality, where the market dictates Trump's narrative, rather than the other way around. He isn't moving the market; he is simply riding the wave of established trends. Forward — the direction you could trade — the signal is a coin flip, and I can show that with pre-registered tests rather than vibes: 0 of 63 cells survive correction. Getting to that answer honestly was the hard part. Seven times this pipeline produced a beautiful, publishable, completely false positive — each one looking exactly like the discovery the anecdotes promise. Those seven are the engineering content of this post. The setup Data. 78,130 public posts; 8,317 in the study window (2025-01-01 → 2026-07-06). Daily OHLCV bars for 62 tickers, committed to the repo. Public data only — a stranger can re-run every number without a single private key. The model. meta-llama/Llama-3.3-70B-Instruct on Nebius AI Studio , zero-shot, reading only the tweet text : what is this post about, which instruments does it touch, and which way does each one go. 476 tweets, 1,296 instrument calls. No fine-tuning — the question is whether the text carries signal, and a fine-tune would smuggle the outcome into the answer. The architecture. Nebius Serverless AI Jobs run the batch research pipel

Disclosure: this is a write-up about my own side project — a combat analytics tool for AION 2. No affiliation with the game's publisher. Links at the end. Architecture A Windows desktop app: Rust backend + Tauri v2 webview UI . It passively captures the game's TCP traffic (npcap), reassembles streams, parses the game's undocumented binary protocol, feeds a combat model (damage, heals, buffs, deaths, boss detection), and pushes aggregates to a small JS frontend. Nothing touches the game client — no injection, no memory reading. If the packet didn't say it, we don't know it. Pain #1: the protocol is a moving target Nobody hands you a spec. The protocol is varint-heavy, partially compressed, and changes with game patches. You end up doing packet archaeology: capture a fight, stare at hex dumps, correlate "I pressed this skill at 19:32:04" with byte patterns, build a parser, and then — the fun part — keep it alive after every patch , usually reverse-engineering the diff within hours because your users' raids are tonight, not next week. One hard-won lesson: log everything behind toggleable trace categories. Our tracing setup keeps hot-path log callsites at literally zero cost when disabled (Rust tracing with Interest::never() + atomic per-category flags), so a user can flip a "Trace: Packets" checkbox, reproduce a bug, and send a log that actually contains the bytes we need. Pain #2: entity identity is a lie The single hardest correctness problem wasn't parsing — it was identity . A player is not one ID: your character has a stable "owner" entity that carries your buff bar, your damage lands under a transient combat entity whose ID changes between pulls, leave the dungeon and the game re-binds you to a brand-new ID, names arrive from different packets than damage, sometimes seconds later, sometimes never (mid-fight app start). Get any of this wrong and a healer's healing lands on a ghost row, or a player's buffs vanish from the saved fight because their ID was recycled 7

The safety board confirmed Tesla's account of the crash, which the company shared days after it happened last month.

pinto lets teams manage Scrum backlogs as plain text. Every backlog change can be inspected with git diff , carried across a branch, merged, and reviewed in a pull request—just like source code. The backlog follows the same lifecycle as the product it describes. pinto is local-first: no server, account, or hosted database is required. It works with AI agents, but never depends on them. And unlike a generic CLI to-do list, its core model is Scrum: Product Backlog Items (PBIs), Sprints, Kanban, and the metrics teams use to inspect and adapt. Why pinto exists Jira, Asana, and Notion are capable products. But as more features accumulate, the process can start serving the tool instead of the other way around: Creating one ticket means navigating required fields and workflow settings. Running a Sprint begins with configuring permissions, automations, or dashboards. Even a small status change feels expensive when the tool is slow to open. Scrum is meant to be a lightweight framework for rapid inspection and adaptation . When maintaining the board becomes work in its own right, the tooling has lost sight of that goal. The name pinto comes from the Japanese word ピント “focus.” That is also the design intent: keep the team's attention on the Product Backlog, the Sprint, and the work flowing across the board. pinto deliberately stays small. It starts quickly, keeps dependencies and vocabulary limited, stores durable data as readable files, and excludes project-suite features such as Gantt charts and CRM. Your backlog belongs in the repository Running pinto init creates a .pinto/ directory beside your code. Each PBI is a Markdown file with TOML frontmatter. Here is a real item from pinto's own backlog: +++ id = "P-2" title = "Investigate and fix Windows CI while retaining Windows support" status = "done" rank = "j" labels = ["ci", "windows"] created = "2026-07-15T06:24:58.731847Z" updated = "2026-07-15T09:34:50.653786Z" +++ # Summary Investigate, verify, and fix the current Windo

Babies are tremendous learning machines, and key advances for AI may soon be found in the architecture of their little brains.

Apple has published the policies governing its upcoming Maps advertising business, revealing a strategy that differs from Google’s. The new rules prohibit home services businesses like plumbers, electricians, locksmiths, and roofers from advertising on Apple Maps, along with several other sensitive categories, suggesting Apple is taking a more curated approach to these ads.

The stock has steadily fallen from the euphoric post-IPO high, showing that markets may be sobering up to the promises CEO Elon Musk made before and after SpaceX went public.

The stock has steadily fallen from the euphoric post-IPO high, showing that markets may be sobering up to the promises CEO Elon Musk made before and after SpaceX went public.

Inkling, a 975-billion-parameter open source model, was trained to understand video and audio. It could help Thinking Machines establish itself among competitors like Anthropic and OpenAI.

OnePlus could also wind down its operations in India by 2027.

Suno data obtained in a hacking incident has exposed that the AI music generator was trained by scraping millions of songs and lyrics from online audio platforms, including YouTube Music, Deezer, and Genius, 404 Media reports. Given that Suno has avoided revealing what's in its training datasets and how they were acquired, this a rare […]

Lucid Motors found itself in a tough bind this week, fending off bankruptcy rumors and watching its stock price plunge as a result. The company quickly denied the report, calling it "completely false" and pointing to its available free cash flow as evidence that it has enough runway to operate into next year. But despite […]