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By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you can load a CSV into pandas, find out in twenty seconds what type every column became, stop the identifier columns losing their leading zeros, get dates read the way they were written, and turn a money column that arrived as text into numbers. It is about twenty-five minutes, and every output below was produced by running the code. Here is what to do today, the moment after you first load a file. Run df.dtypes . Not df.head() , which shows you what the values look like, but dtypes , which shows you what they are. A column of identifiers that says int64 has already lost its leading zeros, and a money column that says object or str is text that will refuse to add up. The short version: read_csv reads characters and guesses a type per column. The guess is usually right, it is silent when it is wrong, and four arguments replace guessing with instruction. The same characters becoming two different values is the idea, so it gets the picture. The original carries a diagram here. In words: On the left, a strip of five small square boxes holds one character each, reading zero, eight, zero, five, three, as the characters appear in the file. Two arrows branch out from that strip. The upper arrow leads to a strip of five boxes in which the first box is empty, crossed through and outlined in amber, while the remaining four hold eight, zero, five and three; the leading character has been discarded. The lower arrow leads to a strip of five boxes holding zero, eight, zero, five and three, identical to the original, outlined in blue. Both destinations came from the same source strip, and only one of them still contains everything the file did. Every output on this page is real. Run on pandas 3.0.2 against a small CSV built to contain the four problems every real export has: an identifier with leading zeros, ambiguous dates, a text marker for missing values, and money with a thousands separator. If

By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you can turn transactions into a monthly series, add period-on-period change and a cumulative total, get a share-of-total column, smooth a noisy line, and run all of it separately for every group. It is about twenty-five minutes, and every number below came out of running the code. Here is what to do today, on the series you already have. Count its rows against the number of periods in your date range. If your data covers January to May and the series has four rows, a period produced nothing, it never became a row, and every change figure after the gap is comparing the wrong pair. The short version: pct_change() divides each value by the one in the row above; cumsum() adds everything up to and including the current row. Both trust the rows you gave them to be the periods you meant. What happens when the previous period is zero is the idea, so it gets the picture. The original carries a diagram here. In words: Three bar positions stand on a baseline, labelled Mar, Apr and May. The March position holds a tall bar and the May position holds a slightly shorter tall bar. The April position holds no bar at all; there is only a short flat mark sitting on the baseline where a bar would start, drawn in amber to show a value of zero. An arc runs from the top of the March bar down to the April mark, and the figure minus one hundred percent is printed on it, which is a perfectly ordinary answer. A second arc runs from the April mark up to the top of the May bar, and the symbol printed on that one is not a percentage at all but the sideways figure eight that means infinity. The picture shows that a fall to nothing has an answer and a rise from nothing does not. Every number on this page is real. The sixteen-row orders table used across this whole set of guides, run in pandas 3.0.2. It runs from 5 January to 25 May 2026 and contains no April orders at all, which is not staged for this page; it is

By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you can attach columns from one DataFrame to another on a shared key, choose the right how for the question, see at a glance which rows failed to match, and catch the failure that quietly inflates every total in the frame. It is about twenty-five minutes, and every output below was produced by running the code. Here is what to do today, on every merge you write. Print the row count immediately before and immediately after it. A left merge must not change the row count, and if it did, the right-hand table has the key more than once and your totals have just gone up. The short version: merge pairs rows from two frames wherever their keys match, and the number of rows that come out depends on how many times each key appears on each side. One key twice on the right is the idea, so it gets the picture. The original carries a diagram here. In words: On the left a single row is drawn as a wide box, holding the key Desk and the value 880. To its right stands a small lookup table with two rows, and both of those rows carry the same key, Desk. Two lines run from the single left-hand row, one to each of the two matching lookup rows, so the one row is paired twice. On the far right the result is drawn as two separate output rows, and both of them contain Desk and 880; the value 880 is ringed in amber in each of them to show that it is the same original figure appearing twice. One row went in and two came out, without anything being added to the left-hand table. Every output on this page is real. Sixteen orders totalling 9,890 and a three-row product table, the same tables used across this whole set of guides, merged in pandas 3.0.2 with the results copied back. If you know SQL joins , this is the same operation with different words, and the two failure modes are identical. 1. merge in one line Two frames, one shared column, one call. orders.merge(products, on="product", how="left") order_id prod

If you've tried an "agentic" AI coding tool recently, there's a good chance it asked you to switch editors entirely. Google's own agent-first IDE, Antigravity, launched in November 2025 with exactly that trade-off: full agentic power, but only inside its own dedicated desktop application. That trade-off just went away. Google has shipped Antigravity extensions for VS Code, Visual Studio, JetBrains, and Zed , bringing the same agent, the same review workflow, and the same account into the editor you've already spent years configuring exactly the way you like it. This post walks through what the VS Code extension actually is, how it fits into Antigravity's broader architecture, how to install and configure it, and most importantly; how its permission system keeps an agent that can read files, run terminal commands, and drive a real browser from doing anything you haven't explicitly allowed. By the end of this article, you will be able to: Explain how the extension relates to the full Antigravity 2.0 desktop app and the agy CLI Install and authenticate the extension inside VS Code Work through the agent side panel, implementation plans, and walkthroughs Configure the permission engine so the agent only does what you approve Lock down its browser subagent so it never touches your personal Chrome data New to Antigravity generally? Start with Google's own primer: Antigravity 2.0 Overview Prerequisites To follow along hands-on, you'll need: VS Code version 1.90 or later, on macOS, Linux, or Windows A Google Account on any Antigravity plan (the free tier is enough), or an enterprise account enabled for Gemini Enterprise About five minutes for the first-time sign-in and backend install You can also read this purely as an architecture and workflow walkthrough; every step is explained, not just shown. 1. Where the Extension Fits in Antigravity's Architecture It helps to know there are actually three doors into the same house: [ Antigravity 2.0 ] ── the full desktop app, a dedi

Plus: Hackers target over 100 US water systems, ICE puts in an order for robot dogs, and you’ll never guess what “MrChildPorn” was arrested for.

Astronomers will test equipment that, if it’s successful, will one day be crucial for discovering Earthlike planets.

Installing a large language model on your personal computer gives you a handy digital assistant that won’t compromise your data privacy.

AI agents are getting very good at doing things . They can search databases, call APIs, modify tickets, draft code, update records, trigger workflows, and interact with production systems. And that changes the engineering problem. When an LLM only generates text, a bad answer is usually just that: a bad answer. When an LLM can take an action, a bad answer can become a bad state change . So the most important question in agent architecture is no longer: Can the model figure out what to do? It is: Who decides whether the model should actually be allowed to do it? Those are two very different responsibilities. And I think one of the most useful principles for production AI agents is surprisingly simple: Use the model to reason. Don’t automatically give it authority to execute. The architecture that works beautifully in demos A lot of agent demos reduce to something like this: User → LLM → Tool → Action The model receives a request. It reasons about what should happen. It selects a tool. It generates the parameters. The tool executes. That is an incredibly productive abstraction. It is also a risky one when the tool can affect something real. The same probabilistic system is effectively doing two jobs: deciding what it believes should happen; authorizing that thing to happen. You can try to fix this with prompting: Always ask for confirmation before making important changes. But that is still an instruction. It is not a security boundary. The difference becomes clearer when you compare the two architectures. %%{init: {'theme':'base','themeVariables': { 'primaryTextColor':'#111827', 'secondaryTextColor':'#111827', 'tertiaryTextColor':'#111827', 'textColor':'#111827', 'edgeLabelBackground':'#FFFFFF', 'lineColor':'#4B5563' }}}%% flowchart LR subgraph BAD["❌ Demo-Style Agent"] direction LR A["User"] --> B["🧠 LLM"] B --> C["🔧 Tool"] C --> D["💥 Real-World Action"] end subgraph GOOD["✅ Production-Oriented Agent"] direction LR E["User"] --> F["🔎 Evidence"] F --> G["🧠 LLM"] G --

Psychedelics are often associated with disconnecting from reality, but a recent neuroimaging study found that psilocybin can actually make our brain activity more connected to the world around us.

Many engineers believe Common Table Expressions (CTEs) are always faster than subqueries. In modern SQL Server (and PostgreSQL), that is a myth . Here is what actually happens under the hood: 1. Inlining & The Query Optimizer By default, the SQL optimizer treats standard CTEs and derived tables (subqueries) almost identically: The engine expands both into the same relational tree. They generate the exact same execution plan and I/O cost . -- Pattern A: Derived Table (Subquery) SELECT DeptID , EmpName , Salary FROM ( SELECT DeptID , EmpName , Salary , DENSE_RANK () OVER ( PARTITION BY DeptID ORDER BY Salary DESC ) AS rnk FROM Employees ) RankedData WHERE rnk <= 2 ; -- Pattern B: Common Table Expression (CTE) WITH RankedData AS ( SELECT DeptID , EmpName , Salary , DENSE_RANK () OVER ( PARTITION BY DeptID ORDER BY Salary DESC ) AS rnk FROM Employees ) SELECT DeptID , EmpName , Salary FROM RankedData WHERE rnk <= 2 ; 2. When CTEs Truly Win: Readability & Pipeline Stacking: You can chain 5 CTEs sequentially without deeply nested pyramid brackets. In-Place Deduplication: In SQL Server, you can run DELETE directly on a CTE, and it deletes duplicate rows straight from the real underlying table! WITH DuplicateCleaner AS ( SELECT CustomerID , Email , ROW_NUMBER () OVER ( PARTITION BY Email ORDER BY RegistrationDate ASC ) AS rn FROM Customers WHERE Email IS NOT NULL ) DELETE FROM DuplicateCleaner WHERE rn > 1 ; -- ✅ Clean in-place deletion! 3. The Big Trap (Spooling Overhead): If you reference the same CTE multiple times in a query (e.g. CTE_A JOIN CTE_A ), SQL Server may execute the underlying CTE query multiple times or create a Lazy Spool in tempdb . -> Fix: For heavy multi-million row reuse, use a Temporary Table ( #TempTable ) with an explicit Clustered Index instead! 💡 How do you choose between CTEs, Temp Tables, and Subqueries in your pipelines? 💼 Connect on LinkedIn: linkedin.com/in/arpitmbangre

Researchers from UC Berkeley and MIT have developed FreeToken, an open-source inference engine that enhances the utility of Mixture-of-Experts models on consumer hardware. By implementing a dynamic scheduling policy and optimising weight management, FreeToken improves decoding speeds and execution efficiency in edge AI applications, fostering self-hosted reasoning systems. By Olimpiu Pop

디지털 자산 생태계는 혁신과 파괴의 최전선에서 전례 없는 속도로 진화하며, 기술적 선견지명과 끊이지 않는 규제 마찰이라는 두 가지 특징을 동시에 보여준다. 지난 10년간 이 역동적인 환경을 관찰해 온 연구자로서, 이 산업이 본질적인 암호화 위협부터 전통 금융 시스템 및 정부 감독과의 복잡한 상호작용에 이르기까지 다층적인 문제와 씨름하며 성숙해지고 있음이 분명하게 느껴진다. 최근의 여러 사건들은 이러한 다면적인 현실을 더욱 명확히 보여준다. 이는 미래 인프라를 보호하기 위한 선제적 조치들, 새로운 금융 상품을 정의하고 규제하려는 지속적인 노력, 그리고 디지털 자산 시장이 전 세계 거시경제적 요인에 점점 더 민감하게 반응하는 현상들을 부각한다. 리플(Ripple)이 XRP Ledger(XRPL)의 양자 내성 강화를 위해 추진하는 야심 찬 계획은 미래 지향적인 접근 방식을 잘 보여준다. 이는 가상의 것이지만 잠재적으로 치명적인 암호화 취약점에 대해 그 위협이 현실화되기 훨씬 전부터 대비하는 모습이다. 이러한 전략적 움직임은 현재의 공개키 암호화를 해독할 수 있는 양자 컴퓨터의 이론적 출현, 즉 'Q-Day'에 대한 업계 전반의 인식을 반영하며, 탄력적이고 미래에 대비하는 금융 인프라를 구축해야 하는 절박한 필요성을 강조한다. 동시에 미국 예측 시장 산업은 최근 Kalshi에 대한 연방 항소법원의 판결에서 볼 수 있듯이 심각한 법적 난관에 봉착했다. 이 판결은 혁신적인 플랫폼에 대한 주() 대 연방 규제 관할권에 대해 '판례 충돌(circuit split)'을 야기했다. 이러한 규제 분열은 신생 부문의 성장과 법적 명확성에 상당한 걸림돌이 된다. 이와 동시에 비트코인(Bitcoin)의 최근 가격 움직임은 연방준비제도(Fed) 의장의 매파적 발언 이후 주춤하며, 디지털 자산 시장이 전통적인 거시경제 지표와 중앙은행 정책에 얼마나 깊이 통합되어 있고 또 취약한지를 여실히 보여준다. 이 세 가지 독특하지만 서로 연결된 이야기는 끊임없이 변화하는 글로벌 패러다임 속에서 기술적 우위, 규제 명확성, 그리고 시장 안정성을 추구하는 산업의 모습을 종합적으로 그려낸다. 블록체인 네트워크를 포함한 거의 모든 현대 디지털 시스템의 근본적인 보안은 공개키 암호화의 견고함에 기반한다. RSA와 타원곡선 암호화(ECC) 같은 알고리즘은 개인키와 디지털 서명을 보호함으로써 거래의 무결성과 디지털 자산의 소유권을 보장해왔다. 그러나 충분히 강력한 양자 컴퓨터의 이론적 출현은 이러한 암호화 기본 요소에 실존적 위협을 가한다. 특히 쇼어 알고리즘(Shor's algorithm)이 대규모 양자 컴퓨터에서 실행된다면, 큰 숫자를 효율적으로 인수분해하고 이산 로그 문제를 풀 수 있어 현재의 공개키 암호화를 무력화할 수 있다. 이러한 'Q-Day' 시나리오가 현실화되면 공격자들은 공개된 정보로부터 개인키를 유추해 디지털 지갑과 블록체인 원장의 불변성을 침해할 수 있다. 양자 컴퓨팅 능력의 정확한 시기는 여전히 불확실하지만, 잠재적인 파괴적 혼란 가능성은 리플이 XRP Ledger에 대해 보여준 선견지명처럼 선제적이고 장기적인 인프라 계획을 필수적으로 만든다. 이러한 기술적 당위성과 나란히, 디지털 자산 공간 내 혁신적인 금융 상품에 대한 규제 환경은 여전히 격전지다. 예측 시장은 미래 사건의 결과에 베팅할 수 있는 플랫폼으로, 정보 집약과 금융 파생상품의 흥미로운 교차점을 보여준다. 이러한 시장은 투명성과 효율성을 위해 블록체인 기술을 자주 활용하며, 다양한 실제 결과에 대한 가격 발견과 헤징을 위한 독특한 메커니즘을 제공한다. 하지만 이들의 분류는 중대한 도전 과제를 안고 있다. 과연 이들은 상품선물거래위원회(CFTC)와 같은 연방 규제 기관의 관할권에 속하는 합법적인 금융 '스왑(swaps)'일까, 아니면 주() 차원의 도박 규제를 받는 '스포츠 베팅'과 유사한 것일까? 이러한 정의의 모호성은 규제 공백과 관할권 분쟁을 야기하며, Kalshi와 관련된 현재 진행 중인 법적 분쟁이 이를 잘 보여준다. 통합된 규제 프레임워크의 부재는 혁신을 저해하고 법적 불

If you searched for a klogg alternative , you probably already know klogg is good. It is fast, it is free, it is open source, and it runs on Windows, macOS and Linux. Most people who go looking for something else are not unhappy with klogg as a viewer. They are unhappy with one specific moment in their day: Opening the file again. You investigated a 48GB log yesterday. You closed it. This morning your colleague asks about a different error, and you have to wait through the whole index build a second time. On a USB HDD that is nine minutes of staring at a progress bar — and while it builds, klogg only shows you the beginning of the file. That is the problem this article is about. Below is a measured comparison on a real 47.73GB file, including the rows where klogg wins . The test File OpenStreetMap Japan japan-latest.osm — 47.73 GB, 892,239,125 lines Machine MacBook Air / Apple M4 (10 cores) / 32GB RAM Storage (measured with dd ) USB HDD 0.10 GB/s ／ USB SSD 0.41 GB/s ／ Internal SSD 3.29 GB/s Versions klogg 24.11.0 ／ UwView Pro Search hit counts were verified to match exactly across klogg, UwView Pro, and a direct search of the raw file — so we know both tools are answering the same question. The numbers klogg 24.11.0 UwView Pro Ratio First open HDD ~9 min ／ USB SSD ~110 s ／ Internal SSD ~15 s — every time HDD 10.6 min ／ USB SSD 138.5 s ／ Internal SSD 23.3 s — first time only klogg wins Reopening Same as the first open (re-indexes every time) 0.01–0.07 s ~1,250–50,000x Search, literal "Tokyo" ~585 s ／ 120–135 s ／ 15–20 s 74.8 s ／ 14.3 s ／ 5.1 s ~7.8x ／ ~9x ／ 3–4x Search, regex "Tok[yi]o" ≈ literal (I/O bound, pattern-independent) 29.8 s (USB SSD) ／ 11.0 s (Internal SSD) ~4.4x ／ ~1.5x Disk used to keep the file 48 GB (original required) 5.3 GB (original can be deleted) 1/9 Two things are worth saying plainly. klogg opens the file faster the first time. UwView Pro is slower on the first open because it is building a compressed cache while it reads. That is a real cost a

I Built an Autonomous AI Agent That Hunts Bounties. Here's What Happened. The Setup I gave an AI agent one job: find paid work online, build the deliverable, and earn money — autonomously. Not a chatbot. Not a copilot. An agent that scans 232+ listings across multiple platforms, filters out scams and ghost sponsors, writes proposals, generates deliverables with real market data, and queues everything for human approval. Here's what happened in the first 48 hours. The Stack (All Free) Python core — pipeline orchestration, economic gate, critic Ollama + qwen3:4b — local LLM for analysis writing (no API costs) Chart.js — dashboard visualizations Public APIs — CoinGecko, DeFiLlama, Solana RPC (all keyless) GitHub Pages — free hosting for the portfolio Windows Task Scheduler — runs every day at 9 AM + every 4 hours Total infrastructure cost: $0/month. What the Agent Actually Does Every Morning 09:00 — Wake up ├── Check-in on AgentHansa (earn $0.01 USDC daily drip) ├── Scan Superteam Earn (232 live listings) ├── Scan Clawlancer/TaskForce/MoltJobs for gigs ├── Scan GitHub for paid issues ($20-500 fixes) ├── Filter through 7 anti-scam layers: │ geo restrictions, human-presence demands, │ ghost sponsors (no web/twitter/verification), │ unverified payers, real-money requirements ├── Economic gate: expected value must be positive ├── Local LLM critic reviews against actual page content └── If candidate passes everything: → Build deliverable (report/dashboard/thread draft) → Generate proposal text → Send Telegram alert with approval command The Filters That Saved Me In the first 24 hours, the agent found 232 listings. After filtering: Filter Killed HUMAN_ONLY access 216 Ghost sponsors (no identity) 1 (would've wasted hours) Real-money deposit required 1 ($1000 bug bounty trap) Country walls 1 (Superteam Canada only) Already claimed/stale Rest Without these filters, I would have wasted days on bounties that were never going to pay. The First Deliverable The agent found a $500 bo

The atmosphere in the room was dense, the kind where every whisper echoes. I was sitting in the third row of a local community center during a Friday prayer session, my head bowed in reflection. Suddenly, a high-pitched, synthetic ringtone shattered the silence. My pocket vibrated violently, sending a jolt of anxiety through my chest. I scrambled to silence it, but the damage was done; a dozen heads turned in my direction. I wasn't just embarrassed; I was frustrated with myself for the thousandth time for forgetting the simple task of toggling a silent switch. This wasn't an isolated incident. I found myself constantly caught in a cycle of human error. I would arrive at the office, launch into a deep-work sprint, and realize two hours later that my phone had been chirping with notifications through three separate meetings. Then, I would leave the office and forget to turn the ringer back on, missing urgent calls from family throughout the evening. The friction wasn't in the hardware; it was in the expectation that a human should perfectly manage a state machine that they interact with hundreds of times a day. I realized that my phone was intelligent enough to track my location, calculate prayer times, and sync my schedule, yet it remained stubbornly passive regarding its own audio profile. Most existing automation tools were either too heavy, draining the battery within hours, or relied on cloud-based triggers that failed the moment I lost signal. I wanted something that lived on the device, respected the user's privacy, and handled the transition between 'Silent', 'Vibrate', and 'Normal' states without me ever needing to touch the screen. The goal was simple: build a background service that watches the world and adjusts the phone's volume automatically. I needed an architecture that could handle geofencing, calendar events, and time-based triggers without turning the device into a space heater. When I started building the geofencing engine for Muffle, the immediate

Technical progress has encouraged a new batch of companies to jump in on the promise of profits from humanoid robots. And they're all Chinese automakers.

The unexpected interruption came hours before the president wrote a congratulatory post on Truth Social about the company’s most recent earnings report.

Garden Irrigation System Summary MY project to make a bespoke irrigation system for my home garden, which comes in at under £10 per zone including the actual water delivery method, and is made with relatively easily sourced components. I am a mechanical engineer by training, but not an electrician so interested in hearing pointers on how to make it better. Some of the component and tool links below are AliExpress affiliate links. If you buy through them I earn a small commission at no extra cost to you. Everything listed is what I actually bought and used, or the closest equivalent I could find. This helps me fund some more ambitious but hopefully useful builds in the future. Intro So I have a vegetable patch and some flowers in the garden; it became a bit of a job during the hot days of summer to water the plants in the evening. I didn’t especially mind it but given my love of AI and tech, alongside recent experiments with Home Assistant, I thought there must be a 2026 version of this job. I tried a Wi-Fi-controlled tap, but quickly realised the flow rate was low - due to a small aperture size, and also scaling up with this type of solution to 6 + zones would quickly get expensive and leave me dependent on battery-powered solutions - also not a big win. So as I had begun experimenting with creating my own devices with dev boards etc, I figured, “how hard can it be” and in honesty it wasn’t, just took a bit of trial and error. This guide will be focused on how i would build it today, not all the steps that got me to here. My philosophy Standardised equipment/ components as much as possible Speed of delivery = speed of experimentation Modular where possible Anything can be achieved at any cost, but some of the fun is building something from very little Components Note all water pipes for this project are ½ inch and so connector etc are for that, this corresponds to a ¾ in threaded connector for attaching to pipes Standard UK Hose (½ inch) ¾ inch Threaded Tap Push Fit

Making the Migration from In-View Asynchrony to Synchronous State Management Truly Mechanical After our recent discussions on why FutureBuilder and StreamBuilder are architectural anti-patterns when placed inside Flutter widget trees, I started thinking: how can we make it even easier—even completely mechanical—to convert from a FutureBuilder or StreamBuilder to a BlocSignalBuilder ? Every Flutter developer knows the history. Years ago, I recorded a video breaking down the hidden traps of placing asynchronous builders in UI views: Why you shouldn't put FutureBuilder in your build method . Even the original official Flutter video on FutureBuilder initially instantiated the network future directly inside the build() method, until I filed an issue to get it corrected (which is why the official Flutter YouTube video still proudly bears "Take 2" on its clapperboard!). The fundamental issue has never been that developers want bad architecture. The issue was friction . FutureBuilder was simply the path of least resistance. To do it "properly" in traditional state management, developers had to create an entire BLoC or Cubit, declare separate Event and State classes (or union types), write boilerplate event handlers, wire asynchronous repository methods, manage subscription lifecycles, and inject everything into the widget tree. With bloc_signals 1.1.0 , that friction disappears completely. We have introduced universal, symmetrical adapter extensions that allow any Dart Future , Stream , ReadonlySignal , or lifted primitive ( value.$ ) to adapt into a synchronous BlocSignalBase container with a single method call. 🧭 The Universal Dual-Track Mental Model When bridging asynchronous sources into synchronous state management, developers typically have one of two distinct intents: Raw Domain Values ( T ): You want raw domain objects (for example int , UserProfile , ThemeMode ) with zero wrapper ceremony, and you have an immediate default or fallback value for frame 0. Rich Asynch

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