标签：#ci

Today, I’m talking with Jack Conte, the CEO of Patreon. Jack last joined me on the show almost exactly five years ago, in the summer of 2021, and a lot has changed on the internet and in the creator landscape since then, so I was very excited to talk to him again, especially since his […]

The company is also eliminating a production shift at its Arizona factory to align "production plans with anticipated demand."

Seven months after introducing its $119.98 Ultra BodyScan smart scale, Wyze announced a cheaper $79.98 alternative available today that makes a few compromises to shave $40 off the price. There's no Wi-Fi, but you can sync the BodyScan's measurements to Apple Health and Google Fit by connecting to the Wyze mobile app over Bluetooth. And […]

Every World Cup there's a moment. Some player nobody outside their domestic league had heard of scores an absolute screamer in a knockout match, and by the time they've finished celebrating, their follower count is climbing like a rocket. I always found that fascinating, but I could never see it happening. By the time the "X gained 3M followers!" tweets show up, the surge is already over. So this tournament I built a little tracker that snapshots player follower counts on a schedule and shows me the growth curve in near real-time. Here's how it works. The problem with doing this "properly" My first instinct was the official APIs. That died fast. Instagram's Graph API won't give you follower counts for accounts you don't own. TikTok's Research API is academics-only and takes weeks of applications. X's API now starts at $100/month and climbs steeply from there. I just wanted public follower counts — numbers anyone can see by opening the app. I didn't want a data partnership and a legal review. I ended up using the SociaVault API , which wraps public profile data from each platform behind one key. One request, one credit, JSON back. The shared client Everything runs through one tiny helper: // Node 18+ has fetch built in const API_KEY = process . env . SOCIAVAULT_API_KEY ; const BASE = " https://api.sociavault.com " ; async function sv ( path , params ) { const url = new URL ( BASE + path ); Object . entries ( params ). forEach (([ k , v ]) => url . searchParams . set ( k , v )); const res = await fetch ( url , { headers : { " X-API-Key " : API_KEY } }); if ( ! res . ok ) throw new Error ( ` ${ res . status } ${ await res . text ()} ` ); return res . json (); } Grabbing follower counts across platforms Each platform nests the count slightly differently, so I use fallback chains to stay defensive: async function instagramFollowers ( username ) { const data = await sv ( " /v1/scrape/instagram/profile " , { username }); const p = data . data ?. user ?? data . data ?? data

Big Tech is throwing big money into data center buildouts. As national opposition to the facilities grows, some workers are beginning to question whether it’s worth it.

If you're learning Data Analytics and looking to build a strong portfolio, working on real-world...

We’ve all been there: waking up feeling like a zombie despite getting eight hours of sleep. While wearables give us data, they often fail to give us foresight . What if you could predict your stress levels 24 hours in advance? 🚀 In this tutorial, we are going to tackle HRV prediction (Heart Rate Variability) using a state-of-the-art Temporal Convolutional Network (TCN) . By leveraging the Oura Ring API and deep learning, we’ll transform non-stationary biometric time series into actionable insights. Whether you're into time series forecasting or building the next big health-tech app, mastering Temporal Convolutional Networks (TCN) is a game-changer for handling long-term dependencies without the vanishing gradient headaches of traditional RNNs. For those looking for more production-ready examples and advanced biometric signal processing patterns, I highly recommend checking out the deep-dives at WellAlly Blog , which served as a major inspiration for this architecture. The Architecture: Why TCN? Traditional LSTMs are great, but they process data sequentially, making them slow and prone to memory loss over long sequences. TCNs, however, use Dilated Causal Convolutions , allowing the model to look back exponentially further into the past with fewer layers. Data Flow Overview graph TD A[Oura Cloud API] -->|Raw JSON| B(Pandas Preprocessing) B -->|Cleaned HRV/Activity| C{Feature Engineering} C -->|Sliding Windows| D[TCN Model Training] D -->|Dilated Convolutions| E[Stress Trend Prediction] E -->|24h Forecast| F[Dashboard/Alerts] style D fill:#f9f,stroke:#333,stroke-width:2px Prerequisites To follow along, you'll need: Tech Stack : Python, TensorFlow/Keras, Pandas, Scikit-learn. Data : An Oura Cloud Personal Access Token (or use the mock data generator provided). Difficulty : Advanced (Buckle up! 🏎️). Step 1: Fetching Biometric Data First, we need to pull our "Readiness" and "Sleep" data. Oura provides high-resolution HRV samples (usually 5-minute intervals during sleep).

Modern cloud-native developer environments are fantastic. Frameworks like .NET Aspire have revolutionized local development by providing a unified developer dashboard, automatic service discovery, and OTLP telemetry collection. But what happens when it's time to deploy your microservice topology on-premises? If your target is IIS on Windows Server or a systemd service on Linux , you've likely realized that deploying the standard .NET Aspire stack on-prem is a complex puzzle. There is no native hosting model for IIS, gRPC telemetry port mapping is fragile, and the dashboard's constant WebSocket connections can consume excessive resources on on-premises virtual machines. Enter AppPipe.Hosting —a lightweight, developer-friendly NuGet package designed specifically to bring the best features of .NET Aspire to your on-premises environments. The Problem: On-Premises Microservice Orchestration is Hard While cloud platforms have native orchestration (like Kubernetes or ECS), traditional Windows and Linux environments still host a massive volume of enterprise applications. When running microservices on IIS or Linux servers, developers face three major friction points: Port Conflicts & Service Discovery : Dynamically assigning ports to multiple microservices in IIS or systemd and injecting them into dependent services is tedious. Telemetry Aggregation : Running an OpenTelemetry Collector just to aggregate traces, logs, and metrics for a small on-prem cluster is heavy and complex to configure. Resource Exhaustion : Standard Blazor Interactive Server dashboards maintain constant WebSockets and high memory usage, which quickly drains limited hosting environments. The Solution: AppPipe AppPipe is a lightweight alternative that integrates a routing gateway, an in-memory telemetry store, and a visual dashboard directly into a single library. graph TD Client(Browser/Client) -->|HTTP| Gateway subgraph User's Application Space Backend1[Backend Microservice A] Backend2[Backend Microserv

At least three coal plants have been repeatedly cited for violating environmental regulations.

We all know the grind of working with data, even with AI tools: every experiment starts with re-explaining everything, every iteration needs you to prompt, wait, review, correct, and repeat. And the moment you close the session, everything learned is gone. It makes us the bottleneck, and this hinders human-AI collaboration... So I built 𝐎𝐩𝐞𝐧𝐃𝐚𝐭𝐚𝐒𝐜𝐢, an autonomous agent purpose-built for DS/ML, and tested it on Kaggle. I enrolled in a recent competition, ran the agent with no hints, no guidance, while ironing my shirts. In one shot, it landed AUC 0.95, a top-30% finish out of 3K+ teams and 36K+ submissions using hashtag#Anthropic's Claude Sonnet 4.6. (More on this in README) The top-1 outperformed this agent by merely 0.004, but at the cost of massive manual effort even while using popular AI tools. The needed a dozen model families, deep learning, 400-feature notebooks, AutoML sweeps across many libraries, and 186 models ensembled carefully. Essentially a few weeks worth of effort and time!! OpenDataSci abstracts away all the complexity and has so much to offer for DS/ML automation: → Owns the entire development lifecycle from EDA to final evaluation → Plans, codes, and executes autonomously in a secure local sandbox → Self-reviews and corrects before anything reaches you → Remembers your data across sessions, gets smarter each run → Runs parallel experiments and ensembles → Has advanced context management for token efficiency and quality → Ships with predefined skills for DS/ML, so it knows how to do things right → Bring your own knowledge: out-of-the-box support for custom skills → Works with any major LLM provider (hashtag#Anthropic, hashtag#OpenAI, hashtag#Bedrock, hashtag#VertexAI, hashtag#Ollama, hashtag#vLLM, and any OpenAI-compatible server). This and so much more!! You set the goal. It does the work. No data science knowledge required. 🔗 https://github.com/f4roukb/open-data-sci 📦 pip install open-data-sci Spin it up on your data and see what it achieves!

An App Store rating looks like a verdict. It behaves more like a monument, built over years and slow to move. It says very little about how this month's users feel. I took the 12 most-rated Productivity apps on the US App Store, 32 million ratings between them, and split the headline star into the two numbers it hides: how far recent sentiment has fallen below the lifetime average, and whether the developer replies when users complain. How it is measured Population truth. Lifetime ratings and the star histogram come from Apple's full ratings data, every rating an app has ever received. Recent sentiment. A fixed window of the most recent reviews by date, so an app captured to a depth of thousands is not compared on a multi-year average against an app with a few hundred. Same window for everyone. Developer response. Reply share and median latency over that recent window. Complaints are bucketed with a rule-based taxonomy. It is a heuristic, not a trained classifier, and I treat it as one. What turned up The AI assistants now own this chart, and they reply to almost no one. App Lifetime Recent Reply share ChatGPT 4.8 4.18 0% Claude 4.7 3.06 0% Grok 4.9 3.77 0% Perplexity 4.8 3.60 0% Google Gemini 4.7 3.65 13% Dropbox 4.8 2.75 58% Gmail 4.7 2.40 26% Google Drive 4.8 3.90 23% Microsoft Authenticator 4.7 2.18 1% The older tools are the ones still in the trenches: Dropbox answers 58% of recent reviewers, Gmail 26%, Drive 23%. The steepest recent drops belong to Microsoft Authenticator (4.7 to 2.18), Gmail (4.7 to 2.40) and Dropbox (4.8 to 2.75). Plotted on two axes, backlash against response, every app falls into one of four archetypes: Firefighters, Ghost Ships, Complacent Giants and Resilient Leaders. Eight of the twelve are Ghost Ships, taking a recent hit in near silence. The honest limits Recent reviewers self-select toward the dissatisfied. A person who hits a bug is far more likely to leave a review than a contented one, so a low recent average blends genuine declin

Sure, anyone can use OpenAI’s chatbot. But with smart engineering, you can get way more interesting results.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

In a world completely powered by technology, have you ever wondered what actually keeps our digital lives from crashing down? Enter the unsung heroes: system and network administration . Think of an operating system like Windows or Linux as a computer's command center, orchestrating everything from the heavy-lifting CPU to the smallest plugged-in device. To keep your data safe and your machine stable, it cleverly splits its brain into two zones: a restricted "user mode" where your everyday apps play, and a highly secure, privileged "kernel mode" reserved strictly for critical system operations. When individual computers connect to form massive global networks, the complexity skyrockets. This comprehensive guide breaks down those complex environments into simple, bite-sized concepts. Here is a quick snapshot of what we will cover: Host & OS Administration : This module covers how an operating system functions as the primary intermediary between a user and a computer's raw physical hardware. It explains how the system kernel manages critical computing processes, memory allocation, local storage file systems, and administrative tasks like security patching and automated scripting. Networking Concepts, Topologies, and Protocols : This module explores how individual computer systems connect and communicate across localized or global distances. It details the structural design of network topologies, addressing rules like IPv4 and IPv6, and the standardized layer frameworks that ensure safe and efficient data transmission. 🏛️ Part 1: Operating Systems & Host Administration 🖥️ Computer Resources and Functions At its core, every computer system is a collection of physical machinery and digital structures working together to solve problems. To understand how an operating system manages these pieces, it helps to look at the foundational puzzle blocks of a computer. This section maps out the primary hardware and data elements the system has to control, alongside a simple breakd

Il pensiero di Popper si intreccia con diversi autori in modi che illuminano il rapporto tra tecnologia, potere e libertà. Hannah Arendt Arendt condivide con Popper l'attenzione per la società aperta, ma la declina in termini di azione politica piuttosto che epistemologici. Dove Popper vede la chiusura come rifiuto della falsificazione, Arendt la vede come perdita dello spazio pubblico dove gli individui appaiono come agenti plurali. L'AI che automatizza decisioni politiche o sociali rischia di eliminare proprio questo spazio di apparizione — non c'è più un "chi" che agisce, ma un "cosa" che calcola. Il banale della tecnocrazia, per Arendt, può essere altrettanto pericoloso del male radicale. Theodor Adorno e Max Horkheimer La Dialettica dell'illuminismo offre un intreccio più critico con Popper. I due della Scuola di Francoforte vedevano la ragione strumentale — quella che calcola mezzi per fini prefissati — come il germe del dominio moderno. Popper difendeva invece la ragione critica come antidoto al totalitarismo. Il punto di tensione è rilevante per l'AI: se l'intelligenza artificiale è pura ragione strumentale ottimizzata, rientra nella diagnosi frankfurtiana più che in quella popperiana. La risposta popperiana sarebbe che l'AI può essere strumento di criticismo se aperta alla confutazione e al controllo democratico. Norbert Wiener Il fondatore della cibernetica condivide con Popper la preoccupazione per i sistemi che sfuggono al controllo umano. Wiener, già negli anni Cinquanta, avvertiva che le macchine intelligenti potrebbero imporre obiettivi incompatibili con i valori umani. Popper avrebbe riconosciuto in questo un caso di teoria non falsificabile: un sistema che apprende senza possibilità di essere corretto dall'esterno è un dogma tecnologico. Entrambi insistono sul human-in-the-loop , anche se Wiener lo motiva in termini di stabilità dei sistemi, Popper in termini di libertà. Michel Foucault Foucault aggiunge una dimensione che Popper lascia in ombra: il

The cycle number on a lithium battery's spec sheet is true and almost useless, because it describes a life the battery will live only in a temperature-controlled lab being cycled gently by a machine that never has a bad day. A cycle, in that test, means a full charge and a full discharge under mild, steady conditions, repeated until the pack fades to some fraction of its original capacity, often eighty percent. Your warehouse does none of that. It charges in bursts, discharges to whatever the shift demanded, bakes the pack in summer and chills it in winter, and counts a cycle as whatever happened between two plug-ins. Depth is the lever nobody quotes The single biggest mover of cycle count is how deep you run the pack on each outing, and that figure almost never shares the page with the headline number that sells the battery. The relationship is steeply nonlinear, which is the part that surprises people. Drain a lithium pack to nearly empty every time and you spend cycles fast. Use the top half and tuck it back on charge, and the same cell can deliver many times the number of shallow cycles before reaching the same faded state. The chemistry is mechanical about it: every deep swing stretches and contracts the electrode structures further, and the wider the swing the more wear each one inflicts. Two fleets on identical batteries can see lifespans years apart purely from how hard they drain them. This is why opportunity charging does double duty. It keeps the truck running, and it keeps each cycle shallow, which stretches the pack's life as a side effect. It also means a published cycle figure measured at full depth understates what a top-up fleet will see, while a figure measured shallow oversells what a run-it-flat operation will get. The same battery, the same number, two outcomes the sheet never warned you about. You have to know the test depth to know what the promise means. Heat is the other clock Cycles are only one of two clocks ticking on a battery, and the s

The Quest Begins (The "Why") Picture this: I’m hunched over my laptop at 2 a.m., surrounded by empty coffee mugs, trying to get a simple Node.js API to run on a friend’s Windows machine. I’ve got the code, I’ve got the dependencies, but every time I hit npm start on his box I’m greeted with “Cannot find module ‘left-pad’” (yeah, I know, that’s a meme, but it felt real). It was like trying to cast a spell in Harry Potter while forgetting the wand‑movement — nothing happened, and I felt like a Muggle in a wizard’s duel. That night I realized the real dragon wasn’t the buggy code; it was the “it works on my machine” curse. I needed a way to package everything — the runtime, the libraries, the environment variables — into a single, portable chest that any teammate (or future‑me) could open and instantly get the same result. Enter Docker, the holy grail of reproducibility. If The Matrix taught us anything, it’s that once you see the underlying code, you can bend reality. Docker lets you see the container code and then bend your deployment reality to your will. The Revelation (The Insight) The big “aha!” came when I stopped thinking of Docker as just another VM and started seeing it as a lightweight, immutable snapshot of my app’s filesystem. Unlike a full VM that boots an entire OS, a Docker container shares the host kernel but isolates everything else — think of it as the Inception dream‑within‑a‑dream, but each layer is a read‑only snapshot you can stack like LEGO bricks. Here’s the secret sauce in three lines: Dockerfile – a recipe that tells Docker how to build the image. Image – the built, immutable artifact (the “DVD” of your app). Container – a running instance of that image (the “movie playing” from the DVD). When you docker build , Docker reads the Dockerfile line‑by‑line, creates intermediate layers, caches them, and finally spits out an image you can tag, push to a registry, and run anywhere. No more “it works on my machine” because the machine inside the cont

The dangerous thing about CI agents is not that they can write code. It is that they run in the place where we already concentrate trust. CI has repository access. CI has tokens. CI has build logs. CI can fetch dependencies, publish artifacts, comment on pull requests, open issues, deploy previews, and sometimes touch production systems. It is the automation layer we taught ourselves to trust because the alternative was humans doing the same boring steps by hand. Now we are putting agents inside it. That is useful. It is also exactly where the security model gets weird. Microsoft published a write-up this month about a Claude Code GitHub Action case where untrusted GitHub content and file-reading capability could combine badly. The short version is that an agent operating in a CI/CD context had enough ambient access to read more than the user probably intended, including process environment data that could expose workflow secrets. Anthropic mitigated the issue in Claude Code 2.1.128. The specific bug matters. The pattern matters more. CI/CD agents are not chatbots with a build badge. They are automated actors running in a high-trust environment while reading untrusted instructions from pull requests, issues, comments, commit messages, files, logs, and whatever else the workflow feeds them. That combination deserves more fear than it is getting. prompts are now part of the attack surface We are used to thinking about CI security in terms of code and configuration. Who can modify the workflow file? Which secrets are available to pull requests? Do forks get privileged tokens? Are dependencies pinned? Are artifacts trusted? Can a build script publish something? Does the workflow run on pull_request or pull_request_target ? Those questions still matter. But agents add another layer: text becomes operational input. The agent may read a pull request description. It may read a comment asking it to fix a test. It may read source files changed by an untrusted contributor. It

For the last 30 years, stopping the flow of cybersecurity-related software has proven to be ineffective. It's unclear why it would work now with Anthropic’s cybersecurity model Mythos.

Relativity Space, the rocket company led by former Google executive Eric Schmidt, was picked to launch NASA's Aeolus payload to Mars in 2028, as reported earlier by TechCrunch. Under a new public-private partnership, Relativity Space will provide the "spacecraft, rocket, and cruise operations" to fly Aeolus to Mars, where the payload will "provide the first […]