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The most valuable thing Claude has done for my work is help me make better architectural decisions before I start building. When I was designing my hotel reservation system I needed to handle multi-currency pricing and timezone conversion. My first instinct was to put all of that logic directly in the booking controller alongside the reservation code. It would have worked but it would have created a mess that was hard to test and harder to extend. Before writing anything I described the problem to Claude and talked through a few approaches. The conversation helped me see that separating pricing and timezone logic into dedicated service classes would make each piece independently testable and easier to swap out later. The booking controller would just call those services without knowing how they worked internally. That conversation took maybe twenty minutes. It saved me hours of refactoring later. I still designed the system. I still made the judgment calls. But the quality of my thinking going into implementation was significantly better because I had a thinking partner to pressure test my ideas against.

When Anthropic introduced the Model Context Protocol on November 25, 2024, it got everyone's eyes on it, including Christy, who was Appwrite's Engineering Lead back then. I had just started my role as an "Engineering Intern" and had no idea what a whole new protocol meant, or why it was such a big deal. Looking at the surface, I wasn't entirely wrong. MCP is JSON-RPC with a schema and a handshake stapled on. What took us sixteen months was everything stapled around it. Streamable HTTP did not exist when MCP launched. It replaced HTTP+SSE in the 2025-03-26 revision. The stdio years Christy had a working stdio server in the repo by February 26, 2025. We already had API keys, so the wiring was simple: claude mcp add appwrite \ --env APPWRITE_PROJECT_ID = <YOUR_PROJECT_ID> \ --env APPWRITE_API_KEY = <YOUR_API_KEY> \ --env APPWRITE_ENDPOINT = https://cloud.appwrite.io/v1 \ -- uvx mcp-server-appwrite An API key is scoped to exactly one project by design, so the ceiling was baked into the credential. Switching projects meant editing your editor config. Creating a project was impossible. So was anything at the organization level. The credential is the whole difference between the two transports, and everything hard about the hosted version follows from swapping it for a token that belongs to the user instead of the project. Authorization ate the schedule By the spec, authorization is genuinely optional: Authorization is OPTIONAL for MCP implementations. [...] Implementations using an HTTP-based transport SHOULD conform to this specification. For a service where one tool call can drop a database, we weren't comfortable treating it as optional. If you use Auth0 or WorkOS, this is a config screen. Appwrite keeps everything in-house, so Matej built the authorization server itself, and I built the resource server plus whatever Cloud was still missing before real clients would work. Steps 2 through 6 are the part that makes "just paste this URL" work. Nothing is pre-provisioned.

Asking for a friend: We submitted our paper to ARR May 2026 and got decent scores from the reviewers - 2.5,3,3.5,4. The meta-reviewer gave an overall of 3.5. However, we missed the deadline to commit our work to EMNLP! On our Saturday (we live in the eastern half of the globe), we saw the EMNLP 2026 page on open review with deadline set as Aug3, 11:59PM UTC-0. Apparently, a mail had been sent by ARR on our Sunday regarding committing our work to EMNLP but we didn’t check our mail on the holiday and when we logged in to commit our work on Monday - BOOM - deadline was Aug3 11:59 AM. Yes it’s our fault that we should have checked the mailbox but at the same time...the Open Review page just switched the deadlines. We have written mails to Program Chairs and some workflow chairs. like 1 hour after the newer deadline on the Open Review page ended. I wonder whether some help will be extended because of the thousands of papers that would already be in their buckets. Does anyone have any idea about what can be done or if they faced this previously and it was resolved somehow? submitted by /u/Happy_Today_3288 [link] [留言]

Classic Machine Learning Through the Eyes of an SRE — Part 2 The second algorithm I studied broke everything I'd just learned from the first. Logistic regression taught me that training means gradient descent: guess, measure error, adjust the weights, repeat until convergence. So when I opened decision trees, I went looking for the optimizer. There wasn't one. A decision tree isn't optimized the way I expected. It's grown. At each step it finds the locally best split, commits to it, and recursively repeats the process. No backtracking. No second chances. There is optimization happening — each split minimizes impurity — but only locally, one step at a time. Finding the globally optimal tree is NP-hard, so the algorithm doesn't even try. That felt surprisingly familiar. In incident response or capacity planning, we rarely know the perfect answer. We make the best decision with the information we have, knowing a different first choice might have led somewhere else. Decision trees simply turn that idea into an algorithm. The bet a tree makes Every machine learning algorithm makes a different bet about the world. Logistic regression assumes relationships are smooth. Risk gradually increases as signals change. Decision trees make the opposite assumption. They assume the world is made of boxes. A project isn't slightly riskier because velocity drops. It's risky when several conditions happen together: a fixed-price contract, a new account manager, and a month-end delivery. Inside that box, projects fail. Outside it, they're usually fine. This is exactly how many operational systems work. Severity matrices, routing rules, escalation policies, approval workflows — they're all collections of decision boxes. That's why trees immediately felt intuitive to me. The hidden cost of flexibility Trees make very few assumptions about the data. That sounds like an advantage. The price is instability. Change a small part of the training data and the first split can change. Since every l

Introduction Telegram has become one of the most popular platforms for sharing files, videos, images, and other media. However, when using Telegram Web, I found that saving media files was not always convenient. For example: downloading videos from channels saving multiple images managing large files The process usually requires several manual steps. So I decided to build a Chrome Extension to make Telegram media downloads easier. The project is called TGVideoDown. Website: https://tgvideodown.com Why build a Chrome Extension? At first, I considered building a standalone desktop application. But I realized that many Telegram users already use Telegram Web inside their browsers. A browser extension provides a simpler workflow: Open Telegram Web ↓ Find the media file ↓ Click download ↓ Save directly Users don't need: additional software complicated setup third-party upload services Technical implementation TGVideoDown is built with Chrome Extension APIs. Main technologies include: Content Script Used to interact with Telegram Web pages. Because Telegram Web is a dynamic application, the extension needs to handle: dynamic DOM updates asynchronous loading user interactions Chrome Downloads API Used to manage browser downloads. Example: chrome.downloads.download({ url: fileUrl, filename: fileName }) Storage API Used for storing user preferences and extension settings. Features Currently TGVideoDown supports: Telegram video downloads Telegram image downloads Telegram audio downloads Telegram GIF downloads Telegram file downloads Large file downloads Batch media downloading Challenges during development Handling dynamic pages Telegram Web uses a highly dynamic frontend. Traditional HTML parsing is not enough. The extension needs to monitor page changes and react when new media elements appear. Download experience Large media files require a smoother download process. The goal was to make downloading as simple as possible: Click → Download → Save Current sta

Use metrics APIs for cron-job, API-failure, and business-event charts, then add a separate heartbeat monitor for jobs that never start. That is the smallest stack I would put on call for a small SaaS. A metrics dashboard can show success and failure counts, duration, backlog size, and error-rate trends; it cannot prove that a scheduler actually invoked a job. Healthchecks-style monitoring closes that specific gap. It still isn't full monitoring coverage, and I wouldn't describe it that way in an SLO review. The distinction matters because a failed run and a missing run leave different evidence. An API error usually increments something. A business event can be counted. A cron job that never fires may produce nothing at all — no duration, no failure, no final log line. No signal. How should a backend metrics dashboard combine cron jobs, API failures, and healthchecks? Start with the questions an operator must answer, not with a vendor menu. For cron jobs, I want a success count, a failure count, duration, and any queue backlog that can delay completion. For API failures, I want error counts and an error-rate trend beside request volume, because a raw count without a denominator can make ordinary traffic growth look like a regression. For business events, I want domain verbs: invoices issued, imports completed, or messages accepted. Those widgets belong on one dashboard because they describe the same service from different angles. Heartbeat monitoring is a separate control. A job reports a start or completion ping to Healthchecks, Cronitor, or an equivalent tool; if the expected ping doesn't arrive within its schedule and grace period, that system owns the missing-run signal. Keep that alert outside the metrics query path. Otherwise the component that failed to emit data is also the component being asked to notice its own silence. Silence counts. I've learned to write the failure matrix before drawing the dashboard. In one incident, a call returned 200, but the side e

Since Lakebase uses a decoupled storage and compute architecture, the synchronous WAL flushes have to travel over the network to the distributed storage layer. How is Databricks Lakebase avoiding the standard network round-trip penalty per transaction commit? submitted by /u/Alternative-Fig-6465 [link] [留言]

submitted by /u/Strong-Seaweed8991 [link] [留言]

When you open a long page — a news feed, an admin table, a documentation article — the browser lays...

Part 1 : Understanding Token Economics, Hidden Costs, and the Fundamentals Every AI Engineer Must Know Table of Contents Introduction Why Token Cost Optimization Matters More Than Ever Understanding What a Token Really Is How LLM Providers Charge for Tokens Input Tokens vs Output Tokens Why "Cheap Prompts" Can Become Expensive Hidden Sources of Token Costs The Real Cost of Production AI Systems How Token Costs Scale with Users The Cost Optimization Mindset Key Takeaways Introduction If you have ever built an AI application using GPT, Claude, Gemini, Llama, or another large language model, you've probably celebrated the moment your first prompt worked. The model answered intelligently, users loved the experience, and everything seemed perfect. Then came the cloud bill. What initially looked inexpensive suddenly became one of the largest operational costs in your application. Many developers assume AI infrastructure is expensive because of GPUs. Surprisingly, for many production applications, tokens—not GPUs—become the biggest recurring expense . Every prompt, every response, every retrieved document, every conversation history, and every AI agent interaction consumes tokens. Those tokens translate directly into cost. Imagine building an AI customer support chatbot. It serves 500 users during testing, and costs seem negligible. After launch, the application attracts 50,000 daily users. Each interaction now includes system prompts, conversation history, retrieved documents, tool outputs, and generated responses. Without careful optimization, token usage grows exponentially—and so does your bill. This is why token cost optimization is no longer just a performance concern. It has become a core engineering discipline. Just as software engineers optimize CPU and memory, AI engineers must optimize tokens. This guide is designed to help you understand the economics behind token usage before diving into optimization techniques. By mastering these fundamentals, you'll be able

A Month With Bash — Part 3: Building Projects After all the expansions and syntax, I moved on to regex in bash. It wasn't too hard since I'd already worked with regex in Python, but alongside it I learned grep , sed , and awk — tools that turned out to be extremely useful for automation. I built a few mini projects and started automating some of my small day-to-day tasks. I won't go too deep into that here, but you can check out my learning-bash GitHub repo, which has all my learning scripts. From there I covered conditionals, loops, and repetitive tasks. Finally I learned about array variables in bash and shell options, went even further testing different ways of looping, and that's when I started actual project building(I am still building ) #!/usr/bin/env bash ## looping with range functions -- somehow # python style looping {start..end} for i in { 1..10 } ; do # this uses brace expansion so using vars wont work becase of execution sequencing echo $i done clear ## c - slyle looping for (( i = 0 ; i < 10 ; i++ )) ; do # variables works here well echo "hello $i " done ## using variables to loop clear start = 1 stop = 10 step = 2 for i in $( seq $start $stop ) ; do # this uses the seq command echo "hello world" done Conclusion Spending so much time on bash wasn't a waste. Not only did it force me to learn a huge number of commands, it changed how I think about my own machine — most of what I used to do manually, I can now automate. That shift alone made the month worth it. i am still learning and trying to get the best practices and things not to do THANK YOU FOR READING THIS FAR. That is a rough summary of me writing bash for a month there is really a lot left unsaid here but still building and learning. If you are just starting out with bash or if you haven't tried it hope this helps feel free to drop questions advice and corrections

A Month With Bash — Part 2: Expansions Continuing from where I left off, the next thing I learned was special parameters in bash: "$*" $# $? $@ $N $- $0 Another important concept I picked up is how bash executes shell scripts. Bash is one of those languages that interprets each line as it goes — but it doesn't stop if a line fails. It continues on unless you explicitly set set -o pipefail (or -e , depending on what you want it to catch). Generally, the procedure looks like this: Tokenizing : splitting the line into tokens, usually split using the IFS value. Brace expansion : a mechanism by which arbitrary strings can be generated. echo file { 1,2,3 } .txt ## output: file1.txt file2.txt file3.txt Bash preserves the order from left to right. Tilde expansion : this is where expansion of special symbols takes place. ~ represents the HOME built-in variable ~+ represents PWD , the current working directory and others DIR = ~/Desktop # this is $HOME/Desktop echo " $DIR " Parameter expansion : introduced with the $ symbol. # ${} — the braces can be omitted for normal variables but not for array-type variables Command substitution : very important — it lets you assign the output of a command to a variable, and use commands inside if and for statements. Done with $(command to execute) . week_name = " $( date +%A ) " # gets the current day of the week echo " $week_name " Generally, $() spawns a new shell instance, so it's advisable to avoid it where possible, for latency reasons. Arithmetic expansion : just from the name, this allows evaluation of arithmetic expressions and substitution of the result. It starts with $(( expression )) . There are some rules — bash doesn't support floating point arithmetic natively, so you'd reach for bc if you need it. I won't go deep into that here since this isn't a full bash tutorial. Here's a simple BMI calculator I wrote while practicing this: #!/usr/bin/env bash # script calculates user's BMI and gives a recommendation set -euo pipefail #

In an era where privacy is the ultimate luxury, our most sensitive data—heart rates, sleep cycles, and activity levels—is often shipped off to black-box cloud servers for "analysis." But what if you could keep that data strictly on your local machine? Today, we are building a Private Health Brain . By leveraging the MLX framework (Apple's dedicated machine learning library) and Llama-3 , we will transform raw XML exports from Apple HealthKit into actionable health insights—all running locally on your MacBook. We’ll cover everything from parsing messy XML with Pandas to running high-performance local AI inference without an internet connection. If you are interested in privacy-preserving AI , Edge computing , or just want to squeeze every bit of power out of your Apple Silicon chip, this guide is for you. The Architecture: Local Data Flow To ensure 100% privacy, the data never leaves your local environment. Here is how the pipeline works: graph TD A[Apple Health Export.zip] -->|Extract| B(export.xml) B -->|Python + Pandas| C{Data Cleaning} C -->|Structured JSON/CSV| D[Local Context Window] E[MLX Framework] -->|Load Weights| F[Llama-3 Model] D -->|RAG / Prompt Injection| G[Inference Engine] F --> G G -->|Result| H[Private Health Insights] style H fill:#f96,stroke:#333,stroke-width:2px Prerequisites 🛠️ Before we dive in, ensure you have an Apple Silicon (M1/M2/M3) Mac . MLX : Apple’s framework for machine learning on Apple Silicon. Llama-3 : We’ll use the 8B-Instruct version for a balance of speed and intelligence. Python 3.10+ Pandas : For data manipulation. Install the necessary libraries: pip install mlx-lm pandas lxml Step 1: Parsing the HealthKit XML Monster Apple Health exports data in a massive export.xml file. It’s nested, verbose, and a nightmare to read manually. We’ll use Python to extract specific metrics like Step Count or Heart Rate Variablity (HRV) . import pandas as pd import xml.etree.ElementTree as ET def parse_health_data ( xml_path ): print ( " 🚀 Pa

Every once in a while an AI announcement lands that isn't about a chat UI or a new benchmark, but about the actual substance of what these systems can now do. OpenAI's announcement of ten new results in mathematics and theoretical computer science — produced by an internal version of Astra, their next major model — is one of those moments. Here's what happened, why it matters beyond the math community, and where the honest caveats are. The results The ten problems span high-dimensional geometry, coding theory, arithmetic circuit complexity, group theory, operator algebras, quantum complexity, lattice cryptography, and extremal combinatorics. Highlights include: Non-sofic groups — a construction establishing their existence, addressing a central open question in group theory. Connes's rigidity conjecture — a disproof of a longstanding conjecture about von Neumann algebras. Quantum parallel repetition — an exponential parallel repetition theorem for general two-player quantum games. Multicolor Ramsey numbers — a superexponential lower bound, resolving Erdős problem 183. Closest vector problem — polynomial-factor hardness of approximation, a foundational lattice question tied to post-quantum cryptography. Each argument was prepared into a manuscript by humans working with the model, then formalized by the model into a Lean certificate (the proofs are public on GitHub). OpenAI also released the model's narration of its own thinking process for each solution. The price tag that reframes everything The most striking number in the announcement isn't the math — it's the cost. The total tokens needed to find these solutions would cost roughly $2,000 at Sol API rates . Think about that for a second. Two thousand dollars of compute to resolve open problems that mathematicians have worked on for decades. Some of these (like non-sofic groups) have been open for over a decade of intense effort. We're not talking about a moonshot lab budget — we're talking about the price of a mid

You have to imagine that the team at Game Freak is bursting with ideas. The studio puts out new mainline Pokémon adventures with a machinelike precision. But every so often it launches a curious experiment, whether it's an action title about a militarized elephant or turning horse races into a card game, showing that it's […]

Not everyone who doubts themselves is suffering from impostor syndrome. Sometimes the real problem...

And of course, Henry Cavill is involved.

Fenix Flexin’s hit song “Rubberz” has hip-hop fans arguing over whether it was generated by AI. Some say they have proof it’s machine-made, but will anyone care?

$100 million deal gives 50,000 Ukrainian drones US-developed AI capabilities.

OpenAI addresses Apple’s baseless lawsuit, corrects claims about its employees, and shares messages documenting what happened.