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GitHub热门项目 | The programming language for agents | Stars: 8,544 | 6 stars today | 语言: Rust

The 2024 indictment, now unsealed, accuses three Russians and two web hosts of aiding hackers and profiting from cybercrime.

GitHub热门项目 | A lightweight desktop app to manage, sync, and organize AI agent skills across 15+ coding tools — Cursor, Claude Code, Codex, Copilot, and more. | Stars: 3,051 | 51 stars today | 语言: Rust

GitHub热门项目 | The Rust package registry | Stars: 3,644 | 4 stars today | 语言: Rust

An old Oscar Mayer factory in Wisconsin will become America's latest fusion power research and development hub.

The guy behind TCP/IP is working on a standard for identifying AI agents in the wild.

Official case counts likely capture only a fraction of US cyclosporiasis infections, and the outbreak is likely to get worse before it gets better.

OpenAI employees have donated more than $215,000 to a political effort opposing Leading the Future, a group backed by the company’s president, Greg Brockman.

i've been that person. standing in front of leadership with an 18-month architecture diagram, explaining why we need six months of infrastructure before a user touches a single feature. and it made sense. for 25 years it made sense. writing boilerplate was expensive. every feature came with a tax — database migrations, routing config, auth wiring. build a shared platform first, pay that tax once. the roadmap justified the investment. then i saw a stat that wouldn't leave me alone. roughly 60% of features on a six-month roadmap are obsolete by launch. not slightly off. obsolete. the customer's problem shifted. the market moved. you spent six months building a precise answer to a question nobody asks anymore. the longer you invest before showing something real, the more expensive it is to admit you were wrong. so you don't. you ship the wrong thing and call it "on schedule." i've done it. i've watched it happen. AI didn't create this problem. but agents are making it impossible to ignore. the 82-point gap mckinsey's 2025 survey: 88% of organizations use AI. only 6% see real bottom-line impact. that 82-point gap isn't about tools. everyone has the same tools. but something shifted in their may 2026 report. they describe agents working overnight — enriching requirements, generating code, packaging outputs for morning review. they call it the "24-hour sprint." leading organizations see 3-5x productivity with 60% smaller teams. a product owner logs in at 9am and finds a feature went from requirements to tested code overnight. nobody worked late. agents did. that's not autocomplete. that's a different delivery model. and here's what most teams miss: it only works when the work is small, bounded, and complete. agents need to know where a task starts and ends. horizontal platform architectures don't give them that. the codebase is the prompt jeremy d. miller built wolverine for .NET. in june 2026 he wrote: "the structure of your codebase is now, effectively, part of the prom

i've been the incident commander who forgot to write down the first 20 minutes of the timeline because i was too busy reading logs. more than once. the war room is chaos — five engineers pasting logs, someone asking if the deploy from 30 minutes ago is related, nobody documenting anything. you start logging events in a doc while reading error logs while drafting a stakeholder update while deciding whether to rollback. you're the bottleneck. not because you're bad at your job — because you're doing four jobs at once. i got tired of watching smart people spend their incident energy on documentation instead of decisions. so i built ai-incident-commander — a CLI tool that handles the mechanical parts. timeline, updates, remediation research, postmortem draft. you make the calls. it does the paperwork. runs on your laptop with a local LLM. no API keys, no cloud, no docker. github.com/deghosal-2026/ai-incident-commander — MIT licensed. what it does one command: pip install git+https://github.com/deghosal-2026/ai-incident-commander.git incident-commander simulate --scenario db-connection-pool --auto-approve 8 pre-built scenarios ship with it. database connection pool, bad deploy, memory leak, cert expiry — the usual suspects. no real data needed to try it. for actual incidents, you point it at a directory with your alert, logs, messages, and github PRs. it outputs 10 markdown files: timeline, stakeholder updates, comms blocks you can paste straight into slack, remediation suggestions, a blameless postmortem, and a cost report. the safety part was the real engineering. three points in the pipeline where the graph pauses and waits for you to say yes — stakeholder update, remediation, postmortem. the AI never ships anything without approval. every remediation comes with a citation. suggestions below 0.7 confidence get suppressed. the postmortem prompt enforces blameless language. all AI content gets labeled [AI-GENERATED — review carefully] . and it never executes anything. i

Take the opportunity to pilfer a goat. — The 36 Stratagems, Take the Opportunity to Pilfer a Goat Previously on this series: #5: Leo Walked Into a Burning House. He Walked Out With a Client. — At 1 AM, Leo received an anonymous message and drove across town to fix a competitor's outage. A second message followed — a screenshot with a name: Automated Compliance Lab. He didn't remember the acronym. He didn't delete the screenshot. #10: Lena Watched a Team Adopt Her AI Template. Leo Didn't Know the Knife Was in the Contract. — Lena joined CoreStack as a consultant and built Leo a reporting template. Leo thought she was there to help. Five weeks later the template went live. Six months later the data baseline was locked. He only then realized he'd been inside her palm the whole time. Taken down by a smile. This was a few months later. The Archive Cleanup SOC 2 Type II renewal had just passed. The auditors were gone. CoreStack's compliance team was doing the post-audit archive — classifying every record produced during the audit and tagging them with retention periods. Leo got the cleanup part. The training pipeline's cache directory. The cleanup cron job hadn't run for a week — nobody noticed. When he looked inside, the output folder had a few records with train_ prefixes mixed in among inference outputs. One of them had a model_version that wasn't CoreStack's own. model_version : " acl-train-2026q2-v3" Leo copied that line out. Didn't delete it. Didn't report it. Dropped it into a folder called _misc/ .Set a quiet keyword alert for "acl-train" before closing the terminal. He noticed the naming convention wasn't FinOptima's — FinOptima used fin-model- plus timestamps. acl- — he'd seen that prefix somewhere before. Couldn't place it. He didn't let himself try. He filed it away. Went back to archiving. The Trace Not every CTO digs through cache write logs during archive cleanup. He did. He spent two hours cross-referencing FinOptima's API call records against CoreStack's

SpaceXAI's Grok Build AI coding tool was spotted uploading users' entire codebases to Google Cloud before it was reported, and the company turned it off. The Register reports that Cereblab published findings on Monday showing how the Grok Build CLI was packaging and uploading entire code repositories, "including files it was told not to open […]

Spotify is experimenting with a new AI feature that allows Premium subscribers to play and explore music, audiobooks, and podcasts by having conversations with a chatbot. The "Talk to Spotify" feature appears across the Home and Now Playing view on Spotify's mobile app. You can interact with the chatbot by typing your request in the […]

This isn't a wish for the internet to stop — just a moment to imagine what it'd mean to breathe without it. Not everyone, but a huge percentage of the world now relies heavily on the internet. What if it were unavoidably shut down for just 24 hours? How long would those hours actually feel — and how much would they reshape our daily routines? I see the irony everywhere already. The moment a page hangs, I instinctively dial a USSD code to check my data balance. I know someone who pings google.com just to see if he's still connected — using the internet to check whether the internet is still there. The first hour would probably be spent staring at the network icon, refreshing pages, waiting for life to resume. That's when we'd notice how much of the day quietly depends on the cloud: deliveries stall, payments freeze, navigation disappears, businesses pause. Millions would discover just how many invisible gears keep everyday life moving. Then the smaller shifts. Looking at the sky to guess the weather instead of opening an app. Realizing the only people who "exist" are the ones actually in front of you. Sitting in a room where the loudest sound is the silence of the feed. Maybe one day, staying offline will be a skill of its own. Have we gotten so used to consulting the network before taking a step that we've stopped trusting our own judgment? Perhaps 24 hours of silence wouldn't just be an outage. It would be a reminder — that before the cloud, there was memory. Before search engines, there was curiosity. Before notifications, there was presence. And before constant connection, we still knew how to walk on our own. If you asked me, What cloud or internet service would you miss most for a day? For me, I don't remember the last time I went 48 hours without Gemini.

Spotify is rolling out a new AI-powered conversational feature that lets Premium subscribers chat with the app to discover music, podcasts, audiobooks, and more.

GitHub热门项目 | WeChat local data CLI with daemon architecture | Stars: 4,006 | 85 stars today | 语言: Rust

TL;DR: If you only collect metrics, Prometheus Agent mode is lightweight, familiar, and difficult to beat. If you collect metrics, logs, or traces together, or expect to in the future, Grafana Alloy's unified pipeline is usually worth the additional complexity. Once you've decided to move from pull-based scraping to a push architecture , the next question is which agent should actually run on each host. In 2026, the two strongest choices are Prometheus Agent mode and Grafana Alloy. I run Alloy across my production fleet, but that doesn't automatically make it the right answer for everyone. The Shift in the Monitoring Landscape Over the last couple of years, Grafana has consolidated both metrics and log collection into Grafana Alloy. Grafana Agent reached end of life on November 1, 2025, and Promtail followed on March 2, 2026. Neither receives security fixes anymore. The practical choice moving forward: Feature Prometheus Agent Grafana Alloy Metrics ✅ ✅ Logs ❌ ✅ Traces ❌ ✅ Config Prometheus YAML Alloy components Footprint Smaller Larger Learning curve Low Moderate Future direction Metrics agent Unified telemetry The table gives the short answer. The rest of this article explains where those differences actually matter in practice. Prometheus Agent mode. Run the Prometheus binary with the --agent flag and it stops acting as a full Prometheus server. It no longer stores local TSDB blocks, evaluates alerting rules, or serves queries. Instead, it scrapes targets, buffers samples in a write-ahead log, and forwards them upstream via remote_write . It is Prometheus with the storage and query layers removed. Grafana Alloy. A single agent that collects metrics, logs, and traces, processes them in a component pipeline, and pushes each signal to its backend. It embeds many exporters directly, so a line like prometheus.exporter.unix "node_exporter" {} gives you full node_exporter functionality without installing a separate binary. The Case for Prometheus Agent If you only need m

Leave a carton of milk on the counter and it spoils in a day. Put the same carton in a refrigerator and it lasts a week or more. Nothing about the milk has changed — the same bacteria, the same enzymes, the same chemistry. What changed is temperature, and temperature does not nudge reaction rates gently. It controls them with an exponential lever. A swing of just a few degrees can stretch shelf life from hours to days. This article explains the equation behind that lever — the Arrhenius equation — what each term means physically, how to use it to compare rates at two temperatures, and the mistakes that quietly corrupt activation-energy estimates. Why this calculation matters Almost any process that involves chemistry running over time depends on the temperature-rate relationship. Food spoilage, drug degradation, battery aging, polymer curing, corrosion, and the cracking reactions in a refinery all speed up or slow down with temperature in the same exponential way. Engineers who design accelerated life tests rely on it directly: they run a product hot for weeks to predict how it behaves cold for years. The reason a quantitative model is essential is that intuition fails here. A linear guess — "twice as hot, twice as fast" — is badly wrong. Reaction rate climbs far faster than temperature does, and how much faster depends on the activation energy of the specific reaction. Without the Arrhenius equation you cannot convert an oven-shelf test into a real-world prediction, and you cannot tell whether a 5 C process drift matters or not. The core formula Svante Arrhenius proposed the relationship in 1889, building on earlier work by van 't Hoff. It states that the rate constant k of a reaction depends on temperature as: k = A * exp( -Ea / (R * T) ) Here A is the frequency factor (sometimes called the pre-exponential factor), Ea is the activation energy in J/mol, R is the universal gas constant 8.314 J/mol K, and T is the absolute temperature in kelvin. The physical picture

The man is said to be doing well in a Frankfurt hospital.

OnePlus and its parent company, Oppo, plan to announce in the coming days that OnePlus brand will be leaving the US and European markets, according to a machine translation of a WinFuture report. Should the exit actually happen, it will mark a conclusion to months of rumors about the future of OnePlus. Android Headlines said […]