A few weeks ago I wrote about Siagnos , a personal taste engine for fanfiction that learns from reading behavior instead of matching tags. I was three stages in: scraper done, schema designed, embeddings working as a proof of concept. Then I got a two-week internship window to build something deployable, and I made a call. Instead of pushing Siagnos forward stage by stage, I built Opsis : a scoped-down, content-based recommender that answers one specific question. Given a fic, what else in a real, collected corpus is closest to it in content? Opsis doesn't do taste modeling. It doesn't touch my reading behavior at all. It's the layer underneath that, and it's live right now. Why not just keep building Siagnos directly Two weeks isn't enough time to get a reading tracker, a feature pipeline, and a trained preference model all working end to end. It is enough time to take the scraper and schema I already had and turn them into something real: a working recommender, deployed, with a UI, that someone else can actually use today. So I scoped down on purpose. No personal taste model yet. No behavior tracking yet. Just: can I take one fic and find genuinely similar ones, from AO3 metadata alone, using content instead of tags? What Opsis actually does Scrapes AO3 metadata under conditions the OTW Communications Committee confirmed were acceptable before I collected anything: one persistent session, randomized delays, capped retries Cleans and validates the raw data, log-and-skip instead of all-or-nothing, so one malformed row doesn't take down a 7,000-fic load Normalizes everything into PostgreSQL: fics, six lookup tables, six join tables, idempotent upserts so re-running the loader is always safe Embeds every fic's summary with sentence-transformers/all-MiniLM-L6-v2 Ranks candidates with a blended score: 0.70 embedding cosine similarity, 0.15 fandom overlap, 0.10 relationship overlap, 0.05 popularity If you submit a fic that isn't in the database yet, Opsis scrapes it, cle

LSTMs are still the go-to architecture for a lot of time series work, but they're annoying to trust. You get a number out the other end and no real sense of why the model landed there. This tutorial walks through training an LSTM on daily temperature data, then pulling it apart with three explainability methods: permutation importance, SHAP, and Integrated Gradients. Who this is for: people who already know some Keras and want to add interpretability to a forecasting model, not a from-scratch intro to neural nets. 1. Getting the data into shape LSTMs want a 3D tensor — (samples, timesteps, features) — so before anything else we need to turn a flat column of temperatures into overlapping 7-day windows, each one paired with the value on day 8. import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler # 1. Load data df = pd . read_csv ( " weather_data.csv " ) data = df [ ' Temperature ' ]. values . reshape ( - 1 , 1 ) # 2. Scale the data for stable neural network training scaler = MinMaxScaler ( feature_range = ( 0 , 1 )) scaled_data = scaler . fit_transform ( data ) # 3. Create sequences: 7 days of lag to predict the 8th day X , y = [], [] for i in range ( 7 , len ( scaled_data )): X . append ( scaled_data [ i - 7 : i ]) y . append ( scaled_data [ i ]) X , y = np . array ( X ), np . array ( y ) print ( f " Input shape: { X . shape } " ) # Output: (Samples, 7, 1) Scaling matters more than it sounds like it should — LSTMs trained on unscaled temperature values are prone to exploding gradients, and training just falls apart. The windowing step is really the whole trick here: every prediction only ever sees the past seven days, nothing more. 2. Building the model Two stacked LSTM layers, dropout after each one, early stopping so we don't have to babysit the epoch count. from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM , Dense , Dropout , Input from tensorflow.keras.callbacks import EarlyStopping # 1. Build

I’ve always disliked writing documentation. Not because it’s hard, but because it’s repetitive. You write a function, you describe what it does, you give an example, and then you realize you need the same thing in another language because half your users don’t speak English. So I decided to automate it. The result is an API that takes source code as input and returns a clean Markdown README, API reference, or inline comments — in any of 13 languages. No templates, no manual translation. curl -X POST "https://ai-code-documentation-generator.p.rapidapi.com/demo" \ -H "x-rapidapi-host: ai-code-documentation-generator.p.rapidapi.com" \ -H "x-rapidapi-key: YOUR_KEY" \ -H "Content-Type: application/json" \ -d '{"code":"def add(a,b): return a+b","code_language":"python","doc_language":"en"}' { "success" : true , "documentation" : "# Add Utility \n\n ## Overview \n A simple function to add two numbers..." , "quality_score" : 9 , "target_language" : "en" } It auto-detects the programming language (Python, JavaScript, Go, Rust…) and spits out a polished doc. The English output is solid, but seeing it generate accurate Japanese or German READMEs from the same code still feels like magic. The Tech Behind It Backend: Python + FastAPI, hosted on Northflank. AI Model: DeepSeek (via API). The model actually understands code structure, so generated docs aren’t just generic wrappers. Language Detection: Pygments for syntax highlighting + language guessing. Caching: 24-hour cache to avoid redundant calls and save cost. Security: Sensitive strings (API keys, passwords) are automatically redacted from the output. The whole thing is open source: https://github.com/zhaochangbo888/docgen-api Why I Didn’t Just Use ChatGPT You could absolutely paste your code into ChatGPT and ask for docs. But integrating an LLM directly into a CI/CD pipeline, or a VS Code extension, or a platform that needs programmatic access gets messy with rate limits, authentication, and output consistency. This API giv

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

Hello! This is my first time submitting an actual conference paper (only done workshops so far). Got a 3/3/5/7 for the Position Paper Track. Reviews all seem quite addressable. Meta review also seemed kinda positive? Included wording such as "a revision should include..." followed by actionable stuff we can take. Feels like there may be a shot. My question is... what does that mean? We submit rebuttals for each reviewer. And I agree with a lot of the feedback. So thats not an issue. But what's going to happen? Do reviewers change their scores? Does the AC read each rebuttal to see if we'll make an adequate revision? How does all of this get judged? Who am I trying to convince here? And of what? And what should the wording be like in the rebuttal? More informal? Sorry if some of these questions seem redundant! submitted by /u/Empty-Avocado5927 [link] [留言]

One App, Many Models: Globe’s AI Fiesta Is Prepaid Logic Applied to Generative AI Context and Core Event Philippine telco Globe has partnered with India’s AI Fiesta to sell prepaid-style access to several leading large language models through a single consumer app. The offer, announced around mid-July 2026, packages ChatGPT, Claude, Gemini, Grok, DeepSeek and additional models behind token packs that start at ₱49. The commercial claim is straightforward: instead of juggling multiple foreign subscriptions priced near US$20 a month each, users buy a load pack, open one interface, and spend tokens across models as tasks demand. That framing matters more than the headline price. In the Philippines, prepaid mobile top-ups already define how most people buy connectivity. AI Fiesta imports the same habit into generative AI. Users are not asked to commit to a full OpenAI, Anthropic, Google, or xAI plan before they know whether a model fits their workload. They buy a small pack, try side-by-side answers, and only escalate spend if the workflow sticks. Globe’s pitch also leans on “subscription fatigue.” For students, freelancers, and micro-businesses, stacking ChatGPT Plus, Claude Pro, and Gemini Advanced is not a feature matrix problem; it is a cash-flow problem. A multi-model shell with local billing and low entry cost lowers the first-use barrier. Features reported at launch include multi-model prompting with comparative answers, Image Studio for generation and visualization, Super Fiesta Mode for automatic model routing, Deep Research for multi-step tasks, and real-time web retrieval so replies are not limited to training cutoffs. What remains thin in public materials is operational detail. Exact token counts per pack, whether unused tokens expire, which model variants are served, and whether the offer covers prepaid only or also postpaid have not been fully specified. Until those numbers land, ₱49 is an entry ticket, not a unit-economics proof. Domain Knowledge and Techn

Chrome still ships no dark mode for its built-in PDF viewer. Open a white paper at 1am and you get a flashbang. So you go to the Web Store, install the extension with the most installs, click it, and… nothing happens. The page stays white. I went and read the manifests of the top results to find out why. Two reasons, and both are boring. Reason 1: the popular ones only handle file:// The extension named "PDF Dark Mode" (about 10,000 users, rated 2.5) declares exactly this: "permissions" : [ "scripting" , "declarativeContent" ] , "host_permissions" : [ "file:///*.pdf" ] The runner-up, "PDF Dark Theme" (about 9,000 users, rated 2.9), does the same thing with a content script: "content_scripts" : [{ "matches" : [ "file://*.pdf" ], "js" : [ "content-script.js" ] }] file:///*.pdf matches a PDF you dragged in from your own disk. It does not match https://arxiv.org/pdf/1706.03762 , or the invoice your bank linked, or the syllabus on a course site. That is where almost everyone actually meets a PDF. So the extension is installed, enabled, and structurally incapable of touching the document in front of you. This is also why the reviews are full of people being told to flip "Allow access to file URLs" and reporting back that it changed nothing. It was never the missing piece. You can check any extension for this in ten seconds: chrome://extensions → Details → look at "Site access". If it says nothing beyond file URLs, that is your answer. Reason 2: the CSS target moved The other approach is a CSS filter on the viewer element: embed [ type = "application/x-google-chrome-pdf" ] { filter : invert ( 90% ) hue-rotate ( 180deg ); } That used to be right. When you navigate straight to a PDF today, the document you are styling has no <embed> in it. The viewer lives in an out-of-process child frame that your CSS cannot reach. Your selector matches zero elements and fails silently, which is the worst way for CSS to fail. What does reach it is a filter on the root element of the PDF doc

Estimated reading time: ~11 minutes. No prior experience required. Fifty adapters in a drawer Remember the era when every phone, camera, and gadget had its own special charger? A drawer full of incompatible cables, and the one you needed was never there. Then USB (and later USB-C) arrived, and suddenly one port charged everything. The magic wasn't a better cable, it was an agreed-upon standard that every device and every charger followed. AI tools were living in that pre-USB drawer. Every time you wanted an AI assistant to talk to a new system, your files, a database, a ticketing tool, someone had to hand-build a custom connector for that specific pairing. Ten AI apps times ten tools meant a hundred bespoke integrations. The Model Context Protocol (MCP) is the USB-C moment for AI: one standard so any AI app can talk to any tool. By the end of this post you'll understand what MCP is, its core parts, how a connection works, the traps to watch, and why it matters for the future of AI. What is MCP, really? One sentence: The Model Context Protocol is an open standard that defines a common way for AI applications to connect to external tools, data sources, and services, so any compliant AI app can use any compliant tool without custom glue. It was introduced to solve the "N times M" integration explosion: instead of building a custom bridge for every AI-app-to-tool pair, everyone speaks one shared language. The USB-C analogy (in full) The AI app (a chat assistant, a coding agent, an IDE) is your laptop . A tool or data source (your files, a database, a calendar, a search engine) is a peripheral , a monitor, a drive, a keyboard. MCP is the USB-C port and cable standard between them. Before USB-C, connecting a new monitor to your laptop might need a special adapter made just for that model. After USB-C, you plug in any compliant monitor and it just works. MCP does that for AI: build your tool as an "MCP server" once, and every MCP-compatible AI app can use it, no per-app wo

Everyone says the same thing: Flutter is for apps, not games. So I decided to find out where that's actually true — by building a 3D endless runner in Flutter. From scratch. No Unity, no Unreal, no game engine at all. Just Dart and Flutter's own rendering stack. It runs in your browser right now: ▶️ Play it live (desktop, keyboard controls — A / D to switch lanes, Space to jump). Here's how it works, and what building it taught me about how far Flutter can actually go. The stack: Flutter GPU + flutter_scene The whole thing sits on two pieces most Flutter developers have never touched: Flutter GPU — a low-level rendering API that talks almost directly to the GPU through Impeller (the engine that replaced Skia). This is what makes real-time 3D possible at all. flutter_scene — a higher-level 3D scene API on top of Flutter GPU. It gives you the building blocks a game needs: a scene graph of nodes , a perspective camera , meshes, and glTF model loading. You build a tree of nodes, point a camera at it, and render it every frame inside a normal Flutter widget. That last part still surprises me — the 3D world is just a CustomPaint -style surface living inside an otherwise ordinary Flutter app. Faking an infinite world with a handful of objects An "endless" runner obviously can't build an endless world — you'd run out of memory in seconds. The trick is object pooling : you keep a small pool of track segments and obstacles, and as they scroll past the camera behind the player, you recycle them back to the front with new positions. The player never actually moves forward. The world moves toward the player , and a fixed number of segments cycle forever. Same idea for obstacles and coins. It means the game runs at a constant, tiny memory footprint — which is exactly what keeps it smooth on weaker devices. The parts that were genuinely hard Collision that feels fair. Detecting a collision is easy. Making it feel right is not. Too strict and the player rages at hits that "clearly

submitted by /u/mttd [link] [留言]

Pentagon Special Ops Accelerator: Buying Speed Without Buying Tech Debt The War Department’s special operations policy office is not staging another industry day for its own sake. On July 24, 2026, the Office of the Assistant Secretary of War for Special Operations and Low-Intensity Conflict is running a one-day “accelerator” in the national capital region, inviting fifteen vendors—winnowed from nearly seven hundred white-paper submissions—to pitch solutions across nine special-operations problem sets. The event sits under the 2026 National Defense Strategy’s push to “supercharge” the defense industrial base and deliberately grow nontraditional suppliers, not merely re-rank the usual primes. What makes the format operationally interesting is the acquisition posture. Bonnie Evangelista, acquisition director for the Secretariat for Special Operations, described a deliberate inversion of the classic requirements pipeline: instead of the government spending years specifying what it thinks it needs and then waiting for industry to build it, the office wants mission need first, then commercial or near-commercial solutions that already exist. Carmella Teeter, deputy assistant secretary of war for special operations analysis, resources, and capabilities, framed the delivery model as a “critical triangle”—operators, private innovators, and acquisition professionals who can turn a demo into a fundable contract path. Traditional fielding often stretches three years or more from award to inventory. The office’s target for selected capabilities is six months or less, with contracts potentially awarded the same day. The funnel is staged, not theatrical. Day-one pitches combine oral presentation, technical brief, and government Q&A. Passing the first gate can unlock an initial $10,000 award and a second-gate invitation; clearing that gate can add another $50,000 if the technology meets mission requirements. Later gates move into prototype delivery and production. That ladder is ex

I've seen it happen dozens of times in my testing workflows. You give an LLM a complex set of dimensions—a wall, the area of two windows, the surface roughness, and the number of coats needed—and you ask for the paint volume. The model starts strong. It identifies the variables correctly. Then, somewhere between calculating the subtraction of the window areas and applying the texture multiplier, it hallucinates a decimal point or loses track of one of the subtractions. LLMs are incredible at reasoning through linguistics and high-level architectural patterns. They are fundamentally unreliable for deterministic arithmetic involving spatial geometry. If you're building an agent to handle real-world logistics—like construction estimation—you cannot rely on the model's internal weights to perform subtraction. You need a tool. This is why I built the Model Context Protocol (MCP) servers in Vinkius with a focus on precision tools rather than just API wrappers. The paint-coverage-calculator isn't an experiment in text generation; it's an implementation of deterministic logic exposed as an MCP server so that Claude or Cursor can execute code instead of guessing numbers. Moving from Reasoning to Execution The problem with standard prompting for estimation is the 'hidden variables.' In a real renovation project, you don't just paint a rectangle. You deal with architectural deductions (doors and windows) and surface absorption rates (smooth vs. textured). If an agent doesn't explicitly call a tool that handles these subtractions, it’s likely to over-order material. When using the paint-coverage-calculator via MCP, the workflow shifts from 'Calculate this for me' to 'Execute these specific calculation steps.' The server exposes three distinct tools designed to handle different parts of the geometric problem: calculate_wall_paint : This is specifically for vertical surfaces. It handles the logic of subtracting openings (like a 2m x 0.8m door) from the total surface area before a

DOE’s Genesis Mission Puts Fermilab at the Controls of AI-Driven Accelerators Context and Core Event Analysis On July 22, 2026, the U.S. Department of Energy named the first-phase awards under its Genesis Mission: Transforming Science and Energy with AI . Fermi National Accelerator Laboratory is not a peripheral beneficiary: it will lead one AI/ML project and contribute to eight others spanning collider data, neutrino experiments, high-performance computing workloads, and fusion-magnet digital twins. The package is less a one-off research grant than a signal that DOE wants national labs to treat AI as infrastructure for discovery, not as a side demo. The Fermilab-led effort targets a stubborn operations problem: resonance control of superconducting radio-frequency (SRF) cavities . SRF cavities are the high-efficiency resonators that transfer energy to particle beams. Because they are exquisitely sensitive, tiny vibrations and pressure fluctuations can knock them off frequency, degrading beam quality, stressing RF amplifiers, and raising operating cost. Fermilab’s plan is to build AI/ML control algorithms that keep cavities locked with higher reliability and lower cost, with partners across national labs, universities, and industry (including xLight Inc.). Lab leadership framed the work as a path to more autonomous, efficient facilities—starting with Fermilab’s own PIP-II linac and extending to machines such as SLAC’s LCLS-SC, Brookhaven’s Electron-Ion Collider, Michigan State’s FRIB, and Argonne’s ATLAS. Genesis Mission’s Phase I awards, drawn from a March Request for Applications, are deliberately foundation-building: design and demonstrate AI-integrated research workflows, then evaluate whether they actually accelerate discovery, improve prediction, or cut experimental friction. Fermilab Director Norbert Holtkamp cast the investment as strengthening next-generation technology at the frontiers of particle physics; CTO Anna Grassellino emphasized AI-driven SRF contr

كل مقال رئيسي عن إطلاق Claude Opus 5 في 24 يوليو 2026 ذكر الميزة نفسها: التبديل بين التكلفة والقدرة. لكن معظم التغطية لم تشرح ما هي المستويات، أو ما الذي يتغير عند تبديلها، أو أثرها على الفاتورة. جرّب Apidog اليوم الميزة هي معلمة طلب باسم effort تضم خمسة مستويات في Opus 5، وقيمتها الافتراضية هي high . أعادت Anthropic معايرة هذه المستويات لهذا النموذج، لذلك لا تنقل إعدادات Opus 4.8 كما هي. كذلك، تؤدي مجموعة محددة من الإعدادات إلى خطأ 400 شائع أثناء الترحيل. 💡 إذا أردت اختبار المستويات مقابل نقطة نهاية حقيقية، استخدم Apidog لإرسال الطلب نفسه بخمسة إعدادات مختلفة ومقارنة النتائج. ما هي معلمة الجهد ( effort )؟ توجد effort داخل كائن output_config في طلب Messages API: { "model" : "claude-opus-5" , "max_tokens" : 8192 , "output_config" : { "effort" : "high" }, "messages" : [ { "role" : "user" , "content" : "Refactor this module and explain the tradeoffs." } ] } تتحكم المعلمة في مقدار التفكير الداخلي الذي يجريه النموذج قبل إنشاء الإجابة. يعمل Opus 5 بالتفكير التكيفي افتراضيًا، وتحدد effort حجم ميزانية التفكير: جهد أعلى: رموز تفكير أكثر، تكلفة أعلى، وزمن استجابة أطول. جهد أقل: رموز تفكير أقل، تكلفة أقل، وزمن استجابة أقصر. تعرض واجهات المستخدم هذه الفكرة كمحدد للجهد، لكن عند استخدام API فإن output_config.effort هي القيمة التي تتحكم بها فعليًا. راجع دليل واجهة برمجة تطبيقات Opus 5 للحصول على شكل الطلب الكامل، وراجع نظرة Anthropic العامة على النماذج للمرجع الرسمي للمعلمات. ما الذي لا تتحكم به effort ؟ لا تتحكم effort في إسهاب الإجابة أو طول النص المرئي. وفق دليل توجيه Anthropic لـ Opus 5 ، خفض الجهد يقلل التفكير الداخلي، وليس طول الإجابة. إذا أردت استجابة أقصر، اطلب ذلك صراحةً في التوجيه: أجب في خمس نقاط فقط، ولا تضف مقدمة أو شرحًا إضافيًا. لن يؤدي ضبط effort على low وحده إلى تقصير الإجابة. المستويات الخمسة المستوى ماذا يفعل الاستخدام النموذجي low أدنى مقدار من التفكير قبل الإجابة التصنيف واسع النطاق، الاستخراج، التوجيه، الملخصات القصيرة medium تفكير معتدل أسئلة وأجوبة مع سياق مسترجع، تعديلات ملف واحد، تحويلات منظمة high القيمة الافتراضية. تفكير كبير مهام عامة عندما لم تجرِ قيا

Todo artigo principal sobre o lançamento do Claude Opus 5 em 24 de julho de 2026 destacou a mesma funcionalidade: uma forma de alternar entre custo e capacidade. Mas poucos explicaram o que ela controla, quais níveis existem, como afeta a requisição ou o impacto na conta. Experimente o Apidog hoje Essa funcionalidade é o parâmetro effort . No Opus 5, ele tem cinco níveis e o padrão é high . A Anthropic recalibrou esses níveis para o modelo, então configurações ajustadas no Opus 4.8 não devem ser reutilizadas sem avaliação. Além disso, uma combinação específica de parâmetros gera erro 400 durante migrações. 💡 Para comparar os cinco níveis contra um endpoint real, envie a mesma requisição com valores diferentes de effort e registre custo, latência e qualidade da resposta. O que o parâmetro effort realmente faz O effort fica dentro de output_config em uma requisição para a API de Mensagens: { "model" : "claude-opus-5" , "max_tokens" : 8192 , "output_config" : { "effort" : "high" }, "messages" : [ { "role" : "user" , "content" : "Refatorar este módulo e explicar os trade-offs." } ] } Ele controla quanto raciocínio interno o modelo executa antes de responder. No Opus 5, o pensamento adaptativo está ativado por padrão. O valor de effort define o orçamento usado nesse raciocínio: effort mais alto: mais tokens de raciocínio, maior custo e maior latência. effort mais baixo: menos tokens de raciocínio, menor custo e menor latência. Nas interfaces de consumidor, a mesma opção pode aparecer como um seletor entre custo e capacidade. Na API, o controle efetivo é o campo output_config.effort . Consulte o formato completo da requisição no guia da API do Opus 5 e a documentação da Anthropic na visão geral de modelos . effort não controla verbosidade effort não é um controle de tamanho da resposta. Segundo o guia de prompting da Anthropic para o Opus 5 , reduzir o effort diminui o raciocínio interno, não o comprimento do texto visível. Se você precisa de respostas curtas, inclua essa