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Xiaomi is the latest manufacturer to release a short, wide foldable phone, in the same week Apple is expected to do the same. I got to try the 18 Fold behind closed doors at IFA last week, and while it looks and feels similar enough to Samsung's Galaxy Z Fold 8, superior specs make it […]

It's not surprising that software development is one of the first jobs that AI has really transformed. It's text-based, economically valuable, and generally of interest to the kinds of people inventing and training the AI. There's also a large amount of training examples available online. The job in most cases is now: You are a manager of robots who do things that used to be your job. You're responsible for what they produce and so the skill and art is in guiding and reviewing their work. It seems likely that one by one most jobs will turn into the above description, over the next year or three. Of course the domain will vary, so your knowledge and expertise used for instructing and verifying what the robots do will be what matters. This means there will still be a big difference between the jobs of e.g. an architect, surgeon, or builder. Probably the same basic kinds of robots in most cases, but requiring very different kinds of oversight. If that's the case then everyone needs to learn the job of robot management, well and quickly. I don't know how we do that, but I think that's what we need to accomplish as a society. submitted by /u/StrategicHarmony [link] [留言]

Memory is not a log. It is a story rewritten after you learn how it ended. You will remember being less certain than you were. You will remember seeing the risk you missed. That is not dishonesty. That is how remembering works. Which is why you cannot learn judgment from memory alone. The correction is unglamorous. Before the decision, write down what you expect. Not a plan. A prediction. We are choosing this because we believe that. We think it takes three weeks. We think the load arrives by March. If it does not, we were wrong about the growth, not about the design. Four lines. A date. Somewhere you will find it again. Add the number you least want to write. How sure are you. Seven out of ten is a real answer. Certain is almost never a real answer, and writing it down makes you notice how often you reach for it. Then set a time to come back. The value is not in the writing. It is in the reading, six months on, when the outcome is known and the note is not. You find out something nobody else can tell you. Whether you run early or you run late. Whether your confident calls land better than your hesitant ones. Whether you are wrong about people more often than you are wrong about systems. That is calibration. It is the whole difference between ten years of experience and one year lived ten times. It changes rooms, too. A review where somebody can produce what they actually believed in March is a different meeting from one where everyone remembers agreeing. Hindsight makes us unfair in a peculiar direction. It makes the past look obvious, and the people standing in it look careless. They were not careless. They were standing exactly where you are, with less information and no ending. So write down where you are standing. Your future self will not be able to reconstruct it, and your future self is the one who has to get better. – Serguey Asael Shinder

Introduction Implementing the Karatsuba multiplication algorithm on an 8-bit computer isn’t just an academic exercise—it’s a practical solution to a real-world problem. Resource-constrained systems, like the 8-bit TTL computer I’ve been hacking on, face inherent limitations in memory, processing power, and clock speed. These constraints force every operation to be scrutinized for efficiency. Traditional multiplication algorithms, such as the grade-school method, become bottlenecks when dealing with large numbers, as they scale quadratically with input size. Karatsuba, however, offers a theoretical edge: it reduces the number of multiplications required from n² to nlog₂3 ≈ n1.585 , making it significantly faster for sufficiently large inputs. The Challenge: Balancing Theory and Practice Theoretical efficiency is one thing; practical implementation on an 8-bit system is another. The Karatsuba algorithm relies on recursive division and addition, which introduces overhead in memory usage and control flow. On an 8-bit computer, where registers are limited to 8 bits and memory addressing is constrained, this overhead can negate the algorithm’s benefits if not carefully managed. For example, splitting a number into high and low parts requires additional memory accesses, and recursive calls can quickly exhaust the stack. The physical limitation here is the finite number of transistors and clock cycles , which directly impact how much computation can be performed within a given time frame. Why Karatsuba? A Comparative Analysis Other multiplication algorithms, like Toom-Cook or even hardware-specific optimizations, were considered. However, Karatsuba strikes a balance between simplicity and efficiency. Toom-Cook, while more efficient for very large numbers, introduces additional complexity in implementation and requires more memory for intermediate results. Hardware-specific optimizations, such as using lookup tables, are limited by the 8-bit architecture’s address space. Kar

If your pipeline isn't idempotent, it isn't production-ready; it’s just a fragile script waiting to ruin your weekend. Most engineers treat "idempotency" as an academic concept for distributed systems papers, but in the trenches of fintech and healthcare, it’s the difference between a minor blip and a regulatory filing. If you can’t run your job five times in a row with the exact same input and get the exact same state, you aren't doing data engineering—you're doing data gambling. I’ve spent six years cleaning up the messes left by "append-only" thinking. I’ve seen millions of dollars in duplicate ACH transactions and patient records corrupted by "just one more retry" logic. This guide covers the patterns I use to make sure that when the scheduler kicks off at 3 AM, I can sleep through the alarm because the system knows how to fix itself. 1. Stop relying on "Append" mode The biggest sin in data engineering is assuming that your destination table is a clean slate. When a job fails halfway through, you don't want a partial load sitting in your production warehouse. Never use INSERT INTO blindly. If you are using BigQuery, Snowflake, or Databricks, use MERGE or overwrite-on-partition. If you are using SQL-based ELT, write your transformations to stage data in a transient table before swapping it into production. Never push directly to the target. -- The wrong way: INSERT INTO target_table SELECT * FROM staging -- The right way: Use an atomic swap or a MERGE statement MERGE INTO production . transactions AS T USING staging . transactions AS S ON T . transaction_id = S . transaction_id WHEN MATCHED THEN UPDATE SET T . amount = S . amount , T . status = S . status WHEN NOT MATCHED THEN INSERT ( transaction_id , amount , status ) VALUES ( S . transaction_id , S . amount , S . status ); Photo by 🇻🇪 Jose G. Ortega Castro 🇲🇽 on Unsplash 2. Partitioning is your safety net If your pipeline runs daily, your data must be partitioned by that day. If you are loading data without a

Ninety-eight percent of the production container images I audit in financial services contain at least one critical vulnerability, and nearly half of those vulnerabilities have a fix available that the engineering team simply hasn't bothered to apply. It matters because when you’re pulling down a python:3.11-buster image, you aren't just getting an interpreter. You’re getting a Debian distribution, a shell, a package manager, and enough attack surface to keep a red team busy for a month. In a regulated environment, that’s not just tech debt; that’s a liability that will get you a stern email from compliance during your next SOC2 audit. Why I chose this topic: I spent three weeks last quarter cleaning up a Log4j-style mess that only existed because a legacy data job was pulling a bloated, unpatched base image. I’m writing this because I’m tired of seeing production clusters running bloated images that act as a buffet for bad actors. You’re currently facing a binary choice: continue to ship heavy, "convenient" images that make debugging easy but security impossible, or embrace the friction of minimal, hardened artifacts that keep you out of the headlines. The contenders Most data engineers in my circles land on one of three paths when containerizing their PySpark or Pandas workloads. First, there’s the "Standard Distro" approach. This is FROM python:3.11-slim or FROM ubuntu:22.04 . It’s familiar, it has apt , and you can pip install anything without breaking a sweat. Second, we have the "Distroless" camp. This is Google’s gcr.io/distroless/python3 . It contains absolutely nothing but your app and its runtime dependencies. No shell, no package manager, no local tools. Third, there is the "Alpine/Musl" route. This is FROM python:3.11-alpine . It’s tiny, but it swaps the standard glibc for musl, which is a recipe for disaster if your data science libraries rely on C-extensions. Photo by CHUTTERSNAP on Unsplash The hidden cost of "easy" images If you’re using python:3.11-

“Store the data” is only the beginning of the problem. The difficult questions usually come afterward: What structure does this data actually have? Which fields are missing or inconsistent? Which values are invalid? Which changes are safe to apply automatically? What exactly changed after a repair? Can the system prove that its storage and indexes are still consistent? I built Atlas to answer those questions inside the database engine itself. Atlas is a zero-dependency embedded database for semi-structured data. It stores records, builds a full-text search index, infers schema, analyzes data quality, proposes safe repairs, preserves uncertain records, and records an audit trail of applied changes. It does not use SQLite or SQL. It is not intended to replace SQLite for relational workloads. Instead, Atlas focuses on a gap that is usually handled by external scripts and tools: Data inspection, diagnosis, and safe repair as first-class database capabilities. That is the problem Atlas was built to solve. Why data quality belongs inside the database engine Most databases are very good at storing and retrieving data. That is necessary, but real-world data work rarely stops there.** Operational records, imported JSON, CSV files, event payloads, and semi-structured documents often arrive with problems: { "id" : "T-1" , "title" : " Connection timeout " , "priority" : "HIGH" } { "id" : "T-1" , "title" : "connection timeout" , "priority" : "high" } { "id" : "T-2" , "title" : "Unicode café search" , "priority" : null } These records contain several potential issues: Duplicate logical identifiers Leading or trailing whitespace Inconsistent capitalization Null-like values Missing fields Mixed data types Malformed email addresses Different date formats Inconsistent structures across records A storage engine can preserve these values perfectly while still leaving the data difficult to understand and use. The usual response is to add external tools: A schema profiler A data-quality

A probabilistic model can describe more than the data you observe. It can also include hidden variables that capture structure you cannot observe directly. But defining that model is only the beginning. Once an observation x is available, the practical question changes: Given this x , what does the model imply about the hidden variable z ? That is the central problem of Posterior Inference . The notation is compact, but the computation is not always easy. High-dimensional latent spaces, complex posterior distributions, and interactions among hidden variables can make both the posterior itself and expectations under that posterior difficult to compute. Start with the Joint Distribution Suppose a probabilistic model contains an observed variable x and a hidden or latent variable z . The model does not treat them as unrelated quantities. Instead, it represents their probabilistic relationship through a Joint Distribution : p ( z , x ) This joint distribution describes how the observed data and the hidden variable fit together inside a single probability structure. Once x is observed, however, the question becomes conditional. We are no longer asking only how x and z relate in general. We want to know how the possible values of z are distributed given the particular observation x . That conditional distribution is the posterior. Posterior Distribution: Conditioning on Observed Data The Posterior Distribution is p ( z ∣ x ) = p ( x ) p ( z , x ) ​ The numerator p ( z , x ) contains the probabilistic relationship between the latent variable and the observation. The denominator p ( x ) normalizes those values so that the result becomes a conditional probability distribution over z . The distinction is important: The joint distribution p ( z , x ) describes the probability structure of the model. The posterior distribution p ( z ∣ x ) tells us what that structure implies about z after x has been observed. In that sense, the posterior connects the model with actual data. Pos

Hey Everyone! At Major League Hacking (MLH), our mission is to empower student developers worldwide...

The build process for iOS applications involves multiple technical stages, including source code compilation, resource packaging, code signing, and IPA generation. Xcode is the first choice for most iOS developers, but build tools are not limited to Xcode alone; choosing the right tool for different development scenarios can significantly improve efficiency. xcodebuild: The Command-Line Version of Xcode xcodebuild is a command-line build tool bundled with Xcode that compiles and packages without opening Xcode. Common commands include: xcodebuild build to build the project, xcodebuild archive to create an Archive, and xcodebuild -exportArchive to export an IPA. xcodebuild is suitable for integration into CI/CD. CI/CD platforms such as Jenkins, GitHub Actions, and GitLab CI can invoke xcodebuild to perform automated builds. Its downside is that there are many parameters, with over a dozen common parameter combinations, requiring time for initial configuration. Moreover, xcodebuild depends on the Xcode environment and can only be used when Xcode is installed on a Mac. Fastlane: An Automated Build Tool Fastlane, written in Ruby, is an automation toolchain built on top of xcodebuild. It defines the workflows for building, testing, signing, and releasing through a Fastfile configuration file. For instance, lane :release defines a release pipeline that executes operations such as incrementing the version number, compiling, packaging, and uploading to TestFlight in sequence. Fastlane's match feature manages certificates and provisioning profiles, solving the problem of certificate synchronization in team collaboration. gym encapsulates the complex parameters of xcodebuild, allowing an IPA to be generated with a single command. Fastlane also has a rich plugin ecosystem covering push notifications, screenshots, and metadata management. Project Management with CocoaPods and XcodeGen CocoaPods handles the integration of third-party dependencies. Declare the dependent libraries

submitted by /u/ThereWas [link] [留言]

Compliance itself isn't the villain. The problem is a system designed for large banks being forced onto small teams who measure time in sprints, not fiscal quarters. Innovation was never meant to wait for permission. It's supposed to be fast, chaotic, and full of discovery. But for anyone who has tried to launch a startup especially in Web3, fintech, or any regulated space there is a familiar slowdown that begins the moment compliance enters the conversation. Everything feels alive until the first "verification required" email arrives. That's when the waiting begins. Compliance itself isn't the villain. It protects systems from fraud, builds trust between unknown parties, and ensures accountability. But the way compliance exists today feels like a relic a system designed for large banks, traditional corporations, and legal departments with infinite patience and paperwork. For small teams who measure time in sprints, this system simply doesn't fit. The Repetition Tax Most founders encounter the compliance disconnect the moment they try to grow. After building their MVP and testing their product, they reach the stage where partnerships or investors arrive and suddenly they're asked to complete KYB (Know Your Business) verification again and again. Each time a new partner or service provider comes aboard, the process restarts: upload documents, verify directors, prove legitimacy. It's not that startups resist compliance; they simply don't understand why they must prove the same truth multiple times to different counterparties who have no way to see each other's work. This endless repetition creates what many founders now call the repetition tax. It isn't paid in money but in time and motivation. The same hours that could have gone into improving a product or connecting with users are swallowed by forms, follow-ups, and "under review" messages. And crucially, all this duplicated effort doesn't make the system any safer it just makes it slower. The Security Paradox There

Quick answer Craigslist search pages ship two copies of every listing: a static HTML list, and a JSON-LD <script> block with images, currency, and geo-coordinates. The obvious move is to join them by ID. Don't — Craigslist's JSON-LD carries no shared identifier at all , not a bare post ID, not a URL, not a SKU. The only field both copies reliably share is the listing's title, and titles repeat. We joined by title through a per-title FIFO queue and measured it recovering 290 of 325 listings (89%) end-to-end on a captured 298-item page. That number is the ceiling of what a title-only join can do on this page shape — plan your field completeness around it, don't assume 100%. Why can't you just match the JSON-LD by ID? 🧩 When we built the Craigslist Multi-City Listings Scraper , the first design assumed what almost every JSON-LD block on almost every e-commerce-shaped site provides: a productID , a sku , a url , an @id — something that lines up a JSON entry with its DOM counterpart deterministically. Live inspection of a captured Craigslist search page found none of those. Each itemListElement entry has exactly name , image , offers , @type , and a position field that looks like it should solve the problem — until you check it against the static list past the first ~18 entries, where some static-list rows have no JSON-LD counterpart at all and the position numbering drifts out of alignment. So the join key that's actually usable, live, is title — and titles aren't unique. The fix is a FIFO queue per title: walk the static <li> list in document order, and for each title pop the next unconsumed JSON-LD entry with a matching name. # actors/craigslist-listings-scraper/src/search_parser.py def _parse_ld_json ( tree : HTMLParser ) -> dict [ str , deque [ _LdEntry ]]: by_title : dict [ str , deque [ _LdEntry ]] = defaultdict ( deque ) for list_item in data . get ( " itemListElement " , []): entry = _ld_entry_from_item ( list_item ) title = list_item . get ( " item " , {}). get

Quick answer BBB.org doesn't render business data into the HTML table it looks like it does. Both the search-results page and every business profile page embed the real data as one inline JSON blob — webDigitalData — sitting inside a <script> tag meant for analytics, not for you. The visible <dt> / <dd> list underneath it only carries the fields the analytics layer left out (accreditation date, years in business). If you scrape the DOM table and ignore the script tag, you'll get a name and maybe a phone number and nothing else. If you scrape the script tag and ignore the DOM, you'll get ratings and IDs but no address, no years-in-business, no website. You need both, merged, per business. Why does the DOM only have half the data? 🕵️ When we built the BBB Business Leads Scraper , the first pass assumed BBB profile pages worked like most directory sites: a template with labeled fields you css_first() your way through. That assumption survives for exactly four fields — address, accreditation date, years in business, and website — which live nowhere except a <dt> / <dd> definition list further down the page. Everything that actually matters for lead scoring — business name, BBB letter grade, accreditation status, phone number, the internal IDs BBB uses to build its own canonical URL — comes from a different place entirely: a webDigitalData object, wired into the page for BBB's own analytics vendor, that happens to be complete, well-formed JSON: # actors/bbb-business-leads-scraper/src/parsers/common.py WEB_DIGITAL_DATA_MARKER = " webDigitalData " def extract_web_digital_data ( html : str ) -> dict | None : marker_pos = html . find ( WEB_DIGITAL_DATA_MARKER ) if marker_pos == - 1 : return None brace_start = html . find ( " { " , marker_pos ) raw = _extract_balanced_json ( html , brace_start ) ... return json . loads ( raw ) That is not a regex grabbing {.*} between two markers — a naive non-greedy match snaps shut on the first stray } inside a nested object, which this blo

Hello everyone! In this article, we will take a technical look at Tencent EdgeOne Makers , a Web and Agent development and deployment platform built on Tencent EdgeOne infrastructure. Rather than following a step-by-step tutorial, we will explore how the platform works behind the scenes and how its different technologies work together to support modern web applications. We will look at the journey from source code and build processes to deployment on the EdgeOne network. We will also explore how edge computing and caching deliver content closer to users, how Edge Functions and Cloud Functions handle server-side workloads, and how EdgeOne Makers supports AI Agents, storage, deployment environments, and developer tools . By understanding how these components interact, we can get a clearer picture of what Tencent EdgeOne Makers offers and how its architecture differs from traditional web hosting. Modern web applications are built from more than a frontend. They may combine APIs, serverless computing, storage, deployment automation, and AI services. Tencent EdgeOne Makers is a Web and Agent development and deployment platform built on Tencent EdgeOne infrastructure. It brings these capabilities into one environment, connecting application development with build processes, deployment, edge delivery, serverless execution, and AI development. EdgeOne Makers evolved from EdgeOne Pages, expanding from frontend hosting to full-stack Web and Agent development. Its architecture connects source code, deployment, and EdgeOne's global edge network. 1. From Source Code to Deployment At the center of Makers is its build and deployment workflow. A project can be connected to a Git repository, allowing Makers to detect common frameworks and apply build configurations. Developers can define parameters such as the root directory, installation command, build command, and output directory. Source Code → Git Repository → Build System → Deployment → EdgeOne Infrastructure → Users A push to

If you've ever lost 20 minutes at 2 AM debugging a submask enumeration loop, or drawn a segment tree on paper for the fifth time this month, this post is for you. I want to share a collection of 31 free, browser-based tools built specifically for competitive programming — no signup, no installs, code stays client-side. They're grouped at Utility Tools Lab's Competitive Programming category , and they cover almost every "I wish there was a tool for this" moment from contest practice. Here's a tour of what's inside. Visualizing structures you normally only imagine Half the pain in CP isn't the algorithm — it's seeing what your data structure is actually doing. Graph Visualizer — paste a CP-style edge list and get a force-directed graph you can drag around. Toggle directed/undirected and 0/1-indexing, then copy the adjacency list back out. Segment Tree Builder — feed in an array, pick sum/min/max/gcd, and watch the tree render, plus grab a full C++ class template. Sparse Table Builder — visualizes every level of the O(1) RMQ precomputation, with live queries. Union-Find (DSU) Visualizer — watch path compression and union-by-rank happen live on a forest view. Path Finder — paint walls on a grid and run BFS to see the shortest path animate, then copy the grid as a C++ 2D vector. Sieve Visualizer — step through the Sieve of Eratosthenes on a color-coded grid. Sorting Visualizer and Binary Search Visualizer — step-by-step animations with live comparison counts, or lo/hi/mid tracking on your own array. These aren't just "nice to look at" — watching the not-found path in binary search, or the moment a submask loop wraps around, is often exactly where off-by-one bugs hide. Code generators that skip the boilerplate Some things in CP are conceptually simple but easy to typo under time pressure. These tools generate ready-to-paste C++: Bitmask Planner — set N ≤ 12, visualize all 2^N states and submask iteration order, get a DP skeleton. PBDS Generator — Order Statistics Tree boi

A green test suite is not real evidence. It is often a closed argument loop. The same agent wrote both code and checks. Freeze an oracle before any agent run. Then let every patch fail in public. Cheap tokens do not weaken this rule. Take a side Stop treating generated tests as quality control. A model that authors both sides grades itself. That process is narrative, not verification. Retry-heavy coding loops make the narrative cheaper. They also make the story smoother. Smooth output is the actual danger here. You need a human-owned expected result file. Put that file in git today. Deny the agent write access during runs. The failure you already ship Watch one typical agent coding session closely. The first implementation is simply wrong. The tests fail, then the tests change. You merge a green build anyway. The bug is now official behavior. Reviewers see passing CI and move on. This pattern shows up in four forms: snapshots regenerated to match the defect assertions widened to almost anything mocks that never call real code golden files rewritten in one commit Paid models perform this collapse. Free models perform this collapse. Loop cost is not the core issue. An editable answer key is the issue. Generated tests feel productive because they compile. They also encode whatever the model just invented. That is circular proof wearing a CI badge. Oracle versus suite A test suite is still code. Agents write code without shame. So agents rewrite suites to survive. An oracle is data plus one tiny grader. You write both artifacts yourself. The agent never touches them beside production edits. Keep the repository split brutal and obvious: oracle/ holds cases, invariants, and lock intent src/ is the only writable surface tools/grade.py reads oracle and executes src tools/freeze_check.py blocks dirty frozen paths The grader is the contract you enforce. The agent is only a patch factory. Prompts cannot replace that split. Repository layout refund-service/ oracle/ cases.json i

Messy modules rarely break because a pure helper returns the wrong integer on a tidy fixture. They break because three functions share a temporary CSV path, an environment flag, and a cache nobody named. A coding agent then proposes a cleanup that deletes dead branches, renames locals, and still satisfies every existing assertion. The next production export fails because the implicit file layout moved while the return payload stayed identical. That failure mode is the reason this workflow exists, and it is not a style problem. The first commit should freeze a ledger of hidden couplings and store a hash beside it. Only after that hash is in source control should you allow one structural change. The cleanup is legitimate only when the recorded hash remains identical. Cleanup diffs fail differently than feature diffs Feature work usually changes an observable on purpose, so reviewers know which assertions must move. Cleanup work is sold as behavior-preserving, which trains people to trust deletions and rename-only hunks. Coding agents amplify that bias because they optimize for shorter files, conventional names, and green unit tests. Reviewers then accept large deletions that would look suspicious inside a feature pull request. Return-value tests are the wrong gate for that class of change. The public function can still return {"ok": true, "rows": 12} while the working directory quietly shifts. Downstream jobs that glob files or catch a named exception will fail after merge. Those hidden couplings remain part of the contract even when no unit test mentions them. Build a side-effect ledger instead of another unit test Treat the messy module as a black box that emits more than a return value. A ledger is a canonical JSONL file with one record per fixture and fully sorted keys. Side-effect entries need stable ordering so the serialized bytes stay deterministic across reruns. The SHA-256 digest of that file is the only number that must remain constant. Each record should c

I have no idea what I'm f*cking doing. Something I keep questioning: Why the hell am I building an operating system for this? Seriously. Every couple of weeks I look at ShrekOS, look at the amount of work involved in building an actual Linux distribution, and have basically the same reaction. This is f*cking ridiculous. I wanted a safer way to run AI agents on my computer. Somehow that turned into an immutable Debian system with isolated workloads, capability grants, controlled egress, verified updates, a desktop policy layer, an installer, and enough architecture documents to make me question every decision that led me here. There has to be an easier answer. There has to be some tool I missed. Run the agents in Docker. Use Podman. Use a VM. Use a better agent harness. Install some security middleware. Find a desktop application that manages all of this. Anything other than: Build a f*cking operating system. So I keep trying to prove that ShrekOS does not need to exist. And the annoying part is that every time I do, I eventually end up back at the same problem. I already know containers exist I already wrote the technical version of this question in Why I'm Building ShrekOS When Containers Already Exist . I am not going to repeat that whole argument here. Containers are useful. I use them. The Bench system in ShrekOS literally runs on rootless container technology. I did not invent a magical new isolation primitive because AI showed up. Namespaces exist. Seccomp exists. Landlock exists. Containers exist. Virtual machines exist. Linux already has an absurd number of ways to restrict a process. That is not the thing I keep getting stuck on. The thing I cannot seem to find is the user space around all of it . Not userspace in the kernel terminology sense. I mean the actual space where the human uses the computer. The desktop. The workflow. The place where I can run several autonomous things on my machine and understand, at a glance, what each one is allowed to do. I wa

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