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Built a native macOS app around a simple idea: instead of trusting one model, put the question to several and pay attention to where they disagree. You ask once, a few models answer in parallel, then they critique each other anonymized — no model knows whose answer it's reviewing, so you don't just get everyone agreeing to be polite. The app then surfaces the real fault lines and writes a synthesis. The disagreement is the interesting part — that's the whole premise. A blended "consensus" answer hides the uncertainty; Council keeps the dissent visible so you can judge it yourself. Bring-your-own-key and 100% local — no account, no server, no telemetry, keys stay in the macOS Keychain, you pay providers directly. Free and open source (MIT). Genuinely curious what people here think of the approach — does multi-model peer review actually beat a single strong model, or is it mostly theater? submitted by /u/ahumanbeingmars [link] [留言]

Claude Cowork est sorti en 2026 et le distinguer des agents cloud classiques change tout pour les equipes techniques. Deux modeles, deux philosophies Un agent cloud (ChatGPT Operator, Mistral Agents, Gemini pour Workspace) fait des appels API vers des serveurs distants. Vos donnees quittent votre machine. La session prend fin quand vous fermez le navigateur. Claude Cowork fonctionne differemment : il tourne sur votre Mac, lit votre systeme de fichiers en direct, execute des bash commands, et continue sa tache quand vous fermez le laptop. Ce que cela change pour les equipes tech Contexte reel. Cowork peut lire vos logs, vos configs, vos repos locaux directement, sans copier-coller. Execution longue distance. Vous lancez un refactoring sur 40 fichiers, vous allez en reunion. La tache continue. Impossible avec un chatbot classique. Isolation des donnees. Pour les equipes qui travaillent sur des donnees sensibles (sante, legal, finance), garder les donnees en local repond a une contrainte non negociable. Les limites a connaitre Cowork necessite un Mac recent (Apple Silicon recommande). Le context window est partage entre linterface et les fichiers lus. Pour des taches qui necessitent une recherche web temps reel, un agent cloud reste complementaire. Pattern que je recommande aux equipes Dans les formations que janime pour des equipes de 10 a 100 personnes, on structure generalement comme ca : Agent local (Cowork) pour tout ce qui touche le codebase, les fichiers, les automatisations internes. Agent cloud pour les recherches, les comparaisons marche, les taches qui ont besoin dun acces web. Un workflow clair pour decider lequel utiliser selon la nature de la tache. Le point cle : ne pas les traiter comme interchangeables. Ce sont deux outils avec des forces differentes. Ce que les chiffres montrent Dans les equipes que jai accompagnees sur 18 mois, celles qui ont adopte ce pattern produisent en moyenne 40 % de code de configuration en moins de temps, avec moins de bugs l

Most meeting tools help during a meeting, but the real challenge often starts before it. Users spend time searching for context, reviewing past interactions, and preparing discussion points. While building MeetMind, our goal was to make meeting preparation and follow-up simpler and more intuitive. As a frontend developer, I focused on designing user-friendly interfaces, building responsive components, and creating a smooth workflow from meeting preparation to post-meeting insights. In this article, I'll share the design decisions, frontend challenges, and lessons I learned while building the user experience behind MeetMind. How We Used Hindsight Memory to Make Our AI Meeting Assistant Actually Remember Things Hook I've been in too many meetings where I blanked on something a client told me weeks ago. You're sitting there, nodding, and somewhere in the back of your head you know they mentioned a budget number or a deadline — but you can't pull it up. That feeling is expensive. It erodes trust, slows decisions, and makes you look unprepared. That's the problem MeetMind was built to solve. And the hardest part of building it wasn't the AI — it was making the AI remember. What Is MeetMind — And How Does It Actually Work? MeetMind is a web application that functions as your AI-powered pre-meeting assistant. Here's the full user flow: Before a meeting: Type a contact's name, click "Get Briefing." The app retrieves everything stored about that person — notes, promises, project details — passes it to the LLM, and returns a structured briefing: a summary of past interactions, key reminders, and conversation openers grounded in your actual history with them. After a meeting: Type your notes and click "Save." The system stores them under that contact's name for next time. Under the hood: Python + Flask backend, Llama 3.3 70B on Groq's inference API, and a JSON-backed memory layer modeled on the Hindsight architecture. The interface is intentionally minimal. Two panels, two act

Introduction Excel is a spreadsheet application developed by Microsoft that helps users organize, analyze and visualize data. It is used by businesses, organizations, researchers and students worldwide because it makes working with data easier and more efficient. Business Decision Making One of the ways Excel is used in real-world data analysis is in supporting business decision-making. Companies collect data such as customer information, financial transactions and sales records. Excel helps in organizing and analyzing this data using tools such as formulas and PivotTables. This makes it easier to identify trends and patterns in business performance, such as which products to stock and when to restock them. For example, a supermarket can analyze the monthly sales in Excel to identify the best-selling products and ensure that they remain in stock. Marketing Performance Excel is also used to analyze marketing performance. Businesses use it to track data from marketing campaigns such as website visits, social media engagement and sales conversions. This information is organized using charts and reports, which help evaluate which strategies are producing the best results. This allows companies to allocate their resources more effectively and improve future campaigns based on data rather than assumptions. As a result, Excel plays an important role in helping businesses understand their customers and improve the effectiveness of their marketing efforts. Financial Reporting Excel is widely used in financial reporting. It helps businesses to organize and analyze financial statements such as income statements, cash flow reports and balance sheets. It is also used to record transactions, calculate totals, and generate summaries that show the financial health of the business. By using built-in formulas and functions, accountants can quickly compute profits, expenses, taxes and forecasts with a high level of accuracy. Excel also allows the creation of financial charts and dashb

Hey everyone! Our team and I (acting as a junior backend) recently finished rebuilding GlobeVM (an enterprise Cloud, IT, and Cybersecurity provider) from a traditional monolithic WordPress site into a Headless architecture. I wanted to share our entire journey, our tech stack, the massive headaches we faced, and the solutions we engineered. If you are planning a Headless WP build soon, grab a this might save you weeks of debugging! The Tech Stack Frontend: Next.js (App Router), React, deployed on Vercel. Backend: WordPress hosted on Cloudways (Purely as a headless CMS). Data Structure: Advanced Custom Fields (ACF Pro) + Custom Post Type UI (CPT UI). SEO: Yoast SEO Premium (via REST API). Caching: Vercel Edge Cache (ISR) + Redis Object Cache on Cloudways. During the development and deployment of this website we faced several issues containing the frontend stack itself and traditional features of WordPress. I tried my best to save them all and show them all up here for everyone so that we can discuss on every section of it, maybe we could reach to even something more special :) Challenge 1: The API Was a Mess ("BFF" Architecture) When we first started, we were using the default WordPress REST API. Our Next.js frontend was making 8 different fetch() calls just to build the Blog Homepage (fetching posts, authors, categories, tags, ACF fields, etc.). We were also using messy URLs like ?_fields=id,title,acf&_embed. The Solution: We built a Backend-For-Frontend (BFF). Instead of Next.js doing the heavy lifting, we wrote a custom PHP plugin in WordPress. We created a single master endpoint (/wp-json/gvm/v1/blog-home). WordPress ran all the complex database queries, bundled the Tags, Hero Article, and Categories into one beautiful JSON array, and cached it in RAM using Transients & Redis. Only one of WordPress default api was used for our categories. Result: Next.js made one fetch call. The page loads instantly.' Challenge 2: Handling Vector Icons & ACF Our design heavily re

Introduction In today's fast-paced business environments, data is considered the cornerstone of decision-making, policy formulation, and other organizational needs. MS Excel is a robust spreadsheet developed by Microsoft for organizing, analyzing, and visualizing data in rows and columns. In the data science and analytics domain, MS Excel is critical for analyzing and managing data to generate insights that enhance decision-making. Excel's polarity is characterized by its ease of use, flexibility, automation, and visualization. Ways Excel Is Used in Real-World Data Analysis Across the data science and analytics domain, MS Excel is frequently employed in the following ways; a) Data Cleaning and Preprocessing At the beginning of every data science and analytics project, data cleaning is required, and MS Excel is the primary tool. Typical Excel features and functions applied during data cleaning include Text to Columns, Remove Duplicates, Find and Replace, and Power Query. b) Exploratory Data Analysis Before performing data science and analytics activities, it is crucial to understand the dataset at hand, its structure, and trends. MS Excel features Pivot Tables, Pivot Charts, and Slicers that provide instant aggregation, sorting, and visualizations. c) Data Analysis and Reporting Modern organizations and businesses operate based on insights generated from data. MS Excel features such as pivot tables, charts, and conditional formatting help data analysts analyze and visualize data for clear, actionable insights that enhance decision-making. MS Excel Features or Formulas The typical MS Excel features and formulas employed in the data science and analytics domain include the following. Data Cleaning Functions Function Purpose Example Result UPPER() Converts text to uppercase =UPPER("john") JOHN LOWER() Converts text to lowercase =LOWER("JOHN") john PROPER() Capitalizes the first letter of each word =PROPER("john doe") John Doe TRIM() Removes extra spaces from text =TRIM(

I've been building a database engine from scratch in Rust, and I recently finished the lexer. The lexer itself wasn't the most interesting part. What I found more valuable was how my design evolved as I learned more about Rust and how compilers and database systems are typically implemented. My First Approach When I started, I stored the input as a Vec<char> . It felt straightforward because I could access characters directly without worrying about UTF-8 boundaries. I also represented identifiers like this: Ident ( String ) At first glance, this seems perfectly reasonable. Every identifier token carries its own text, making it easy for the parser to consume. The Problem As the lexer grew, I started asking myself a simple question: The identifier already exists in the original SQL query. Why am I allocating another string and copying the same data into every token? For a query like: SELECT username , email FROM users ; the source text already contains: username email users Creating separate String allocations for each identifier means duplicating data that already exists. I also learned an important detail about Rust enums. The size of an enum is influenced by its largest variant. Once variants start carrying additional data, every token instance becomes larger than it otherwise needs to be. Moving to a Span-Based Design Instead of storing identifier text directly inside tokens, I switched to storing only the token kind: Ident along with source location information: Span { start , end , line , column , } Now the token only answers two questions: What is this token? Where did it come from? If the parser needs the actual identifier text, it can recover it directly from the original SQL source using the stored byte range. Replacing Vec<char> with &str The second design change was moving away from: Vec < char > and operating directly on: & str using lifetimes. Instead of creating another collection containing the entire input, the lexer now walks over borrowed source tex

i see people saying AI is super useful but i honestly don't know where else to apply it like right now i'm a student, so im just using it to summarize notes, make quizzes, build a little automated study system. that's pretty much it but i feel like there's way more to it? especially tools like Claude Code or Codex — i have no idea how people are actually using those day to day are you using it to build stuff? automate things at work? side projects? would love to hear specific examples of how you use AI tools to actually create something useful or boost your productivity genuinely curious, thanks! submitted by /u/OverHuckleberry6423 [link] [留言]

Most AI products today are wrappers. Different interfaces. Different branding. Different marketing. But underneath many of them is the same pattern: centralized models, rented intelligence, recurring dependence, and cloud-first control. The user doesn’t own the intelligence. They lease access to it. I think that creates a dangerous future. AI Is Quietly Becoming Infrastructure We’re moving toward a world where AI won’t just help write emails or generate images. It will: operate businesses, manage workflows, coordinate logistics, assist with infrastructure, analyze systems, monitor environments, and increasingly act as operational infrastructure. That changes the stakes dramatically. If AI becomes operational infrastructure, then ownership matters. Control matters. Resilience matters. And right now, most users have very little of any of those things. The Problem With Generalized Intelligence One of the biggest issues I see in modern GenAI is overgeneralization. We’re trying to build one giant intelligence that does everything: coding, marketing, legal reasoning, architecture, writing, support, psychology, operations, and research. The results can be impressive. But also unreliable. Hallucinations happen because the systems are stretched across too many domains simultaneously. The broader the intelligence becomes, the harder consistency becomes. That’s why I’ve become increasingly interested in specialized AI systems. AI Should Work Like A Workforce Instead of one giant model pretending to know everything, I believe AI should operate more like a coordinated workforce. Specialized agents. Focused responsibilities. Defined operational boundaries. For example: a development agent, an infrastructure agent, a security agent, a documentation agent, a research agent, a support agent, a creative writing agent. Each one optimized for a specific domain. Each one independently updateable. Independently replaceable. Independently trainable. Not one brain. Many experts. Local-Firs

While designing the database and Product Requirements Document (PRD) for an online school project, I ran into a problem I was not expecting. The school had multiple subscription plans. For simplicity, imagine: Live Class Plan:₦50,000 per term Video On Demand Plan: ₦30,000 per term Hybrid Plan (Live Classes + Video On Demand):₦70,000 per term. Initially this looked simple. Students subscribe. System charges them. Done. Then I asked: What happens if somebody changes plans halfway through the term? Suppose: A student already paid: Live Class Plan ₦50,000 Two months later: They decide: Upgrade to Hybrid Plan Do we charge: ₦70,000 again? That would be unfair. Do we charge: ₦20,000 difference? Maybe. But what if they already used most of their subscription period? This question led me to something called: Proration What Is Proration? Proration simply means: Charging customers only for the portion they actually use. Instead of pretending subscriptions always begin and end perfectly. Proration tries to answer: "How much value remains in the current subscription?" and "How much should the customer pay for the new one?" Simple Example Assume: Term Length: 100 Days Student buys: Live Plan ₦50,000 After: 40 Days they upgrade. This means: Used: 40 Days Remaining: 60 Days Value remaining: Remaining Value = Remaining Days / Total Days = 60 / 100 = 60% Remaining credit: 60% × ₦50,000 = ₦30,000 Hybrid costs: ₦70,000 Therefore: Amount to bill = New Plan Price − Remaining Credit = ₦70,000 − ₦30,000 = ₦40,000 Student pays: ₦40,000 instead of: ₦70,000 This feels fairer. Downgrades Are More Complicated What if: Hybrid user: ₦70,000 moves to: ₦30,000 plan Should the system: Refund money? Create account credits? Apply discount later? Ignore downgrades until renewal? This is where: Proration Rules become important. What Are Proration Rules? Proration calculations are useless without rules. The business must decide: Rule 1: How Is Remaining Value Calculated? Options: Daily basis Weekly basis

Forms are everywhere in mobile apps - authentication flows, data entry, support requests, onboarding... If your app has a login screen, a form is likely the first thing a new user interacts with. That makes accessibility here not just a nice-to-have, but a first impression. The problem is that forms are consistently one of the most broken areas for assistive technology users. Missing labels, keyboard traps, silent validation errors, focus going nowhere after submission - these are issues that make an app unusable for a significant portion of your users. This guide is a complete reference for building forms that work for everyone in React Native, whether users are navigating with their fingers, an external keyboard, a screen reader or voice input. Code examples throughout show both what to do and why . A fully working demo repo is available to fork and test on a real device - check it out at rn-accessible-form-demo . Labels Every form field needs a label, whether a text input, checkbox or radio/submit buttons. No exceptions. Don't rely on placeholders Placeholder text disappears the moment a user starts typing. Screen readers will read it initially, but once it's gone, there's no way for them to recall what the field was for without clearing their input. Placeholders are useful as hints, not as labels. Use a visual label + accessibilityLabel To avoid screen readers announcing the same information twice (once for the visual label, once for the input), hide the visual label from assistive technology and put the full label on the input itself. < Text importantForAccessibility = "no" accessibilityElementsHidden > Email address* </ Text > < TextInput accessibilityLabel = "Email address, required" /> importantForAccessibility="no" handles Android, and accessibilityElementsHidden handles iOS. Together they tell assistive technology to skip the visual label entirely - the accessibilityLabel on the TextInput is the single source of truth for screen readers. Required fields De

Most web scraping projects are not unique snowflakes. Track competitor prices. Enrich a list of leads. Audit a site for SEO. Pull training data for a model. It is the same handful of recipes, over and over. A web scraping template is one of those recipes, pre-wired: a ready-to-use JSON config that chains the right tools in the right order, so you copy it, point it at your targets, and run. CrawlForge ships 24 of them in the templates gallery . This guide is about using them well — not just copy-paste, but read, adapt, and cost them out before you scale. TL;DR: A CrawlForge template is a copy-paste JSON config that chains multiple MCP tools into one workflow (price monitoring, lead enrichment, SEO audits, market research, AI training data). There are 24 across 9 categories, each costing 3–19 credits per run. Run them from Claude/Cursor, the crawlforge CLI, or the REST API. Free tier = 1,000 credits, no credit card. Table of Contents What Is a Web Scraping Template? Templates Gallery vs the scrape_template Tool How to Use a Template the Right Way 8 Templates Worth Copying First The Other 16 Templates Customizing or Building Your Own FAQ What Is a Web Scraping Template? A template is a saved configuration that orchestrates two or three CrawlForge tools into one workflow with a business outcome attached. Instead of wiring search_web then scrape_structured then analyze_content yourself — and guessing every parameter — you copy a config that already does it. Each template in the gallery carries: A category — E-commerce, Research, Data Collection, Monitoring, AI & LLM, Sales, SEO, Content, or Advanced Scraping (nine in total). A difficulty — beginner, intermediate, or advanced. The tool chain it runs and a fixed credit cost per run (3–19 credits). A copy-paste JSON config with sensible default parameters. You run that config from any MCP client (Claude, Cursor, Windsurf), the crawlforge CLI, or the REST API. Same config, same shape of result. Templates Gallery vs the scrap

A proposed $2 billion data center has become a political flashpoint in the small city of Shelbyville, Indiana. And the controversy has only grown more intense after the mayor, Scott Furgeson, was caught on camera saying of the "No Data Center" signs going up that, "I've seen a lot of these all over town, but […]

Benn Jordan may have initially gained notoriety for his music as Flashbulb and later, reviewing synths and effects pedals on YouTube under Benn and Gear. But about five years ago, Benn decided to take his YouTube channel in a different direction. He didn't stop covering music gear overnight, but as time progressed, his channel became […]

These newer social apps offer alternatives to Big Tech’s feeds, focusing on interests, creativity, and community.

82-0 marries the stat nerd fun of fantasy basketball with instant gratification and a bit of dumb luck. The goal is to draft a team of players that could (theoretically) have a perfect 82-0 season. Obviously, if you just had free rein to pick whoever you wanted from throughout history, there would be little challenge. […]

submitted by /u/whenyoupeeupsidedown [link] [留言]

Back in '24, there was a story about GPT-2 being run on excel https://arstechnica.com/information-technology/2024/03/once-too-scary-to-release-gpt-2-gets-squeezed-into-an-excel-spreadsheet/ How hard/$/time would it be to recreate GPT-4 (or equivalent)? GPT-4 was released in '23, since then there have been more/better chips, etc. Is this something a competent S&P500 company could do on its own? submitted by /u/tjdogger [link] [留言]

Now I myself have not used AI a ton beyond making a funny picture or two on ChatGPT/Gemini and maybe asking it a few things on the fly if I need a second opinion on something - and sometimes it's been helpful. The biggest thing I hear from the "Fuck AI" crowd is that it ruins the creative circles like artists, authors, etc. because it copies their work. I sympathize with their hate, but I've heard an argument that it's not doing anything different than what we do when/if AI didn't play a role in anything: look at other people's work for inspiration then create something. Like we can't create a song in a vacuum, we need to learn and be exposed to music theory, notes, other styles of music, instruments, etc. So someone starting a band didn't make something brand new, it took pieces from other artists. And the part that makes me sing AIs praises, so to speak, is its use in the medical field. Doctor Mike posted a video about a year ago talking about this. Like, if it's improving healthcare to the point that it's detecting life threatening things to help doctors treat and cure us more effectively and efficiently, why are we trying to get rid of it? Maybe that's not what people are saying when they want AI gone or saying how 'awful' it is, but I just hope we don't end up throwing the baby out with the bathwater with AI because I genuinely think it's an astonishing thing that's clearly helpful in certain circles. submitted by /u/SeaGlass_7 [link] [留言]