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Frontier Intelligence for Long-Running Agents Discussion | Link

Highlight and summarize any page, PDF, or video in Firefox Discussion | Link

I tested the top-of-the-line Ecovacs Winbot W2S Omni window-cleaning robot on my Victorian home, and it was an unmitigated disaster.

The Dell XPS 13 and MacBook Neo are both premium-feeling laptops with only 8 GB of RAM. Which $700 laptop is the better buy?

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

AI coding agents are useful, but team collaboration around them can become messy very quickly. A common shortcut is to share an account, API key, or long-lived access token so another teammate can run a task. It may feel convenient, but it creates avoidable security, ownership, and review problems. A better approach is to separate the task from the account that executes it. The person requesting the work prepares a complete, portable task. The person running it uses their own authorized AI-agent subscription and returns the result with evidence. Here is a practical way to structure that workflow. Why shared AI accounts create problems When several people use the same AI account, it becomes difficult to answer basic operational questions: Who initiated a specific action? Which person approved the resulting changes? What project context was exposed? Who is responsible for reviewing the output? What happens when a teammate changes roles or leaves? Shared credentials also tend to spread. A password may end up in a private message, a token may be copied into a local configuration file, or a browser session may remain active on an unmanaged device. Even when everyone involved is trusted, the process itself is difficult to audit. The goal should not be to share access more efficiently. It should be to share the work without transferring the account. Treat the task as a portable unit A useful AI task should make sense outside the original conversation in which it was created. Someone receiving the task should be able to understand: the desired outcome; the relevant project context; the boundaries of the work; the evidence required for completion; the decisions that still need human review. This turns the request into a portable unit of work rather than a fragment of chat history. For example, instead of writing: Update the import flow. Write something closer to: When a user uploads a CSV containing duplicate email addresses, show a validation summary before importing any re

Run AI models on any device Discussion | Link

Skip the setup and run OpenClaw & Hermes, fully managed Discussion | Link

📈 Building the Future of Trading: An AI-Powered Platform for Smarter Investments The Introduction: Empowering Every Investor Hello, Builders and tech enthusiasts! I'm thrilled to share my journey as part of the "Meet The Builders" campaign, where innovators are leveraging Google AI to tackle real-world challenges. My project is an ambitious endeavor to democratize effective stock trading through an intuitive, AI-enabled platform. Inspired by industry leaders like Zerodha, I set out to create a comprehensive website that not only facilitates trading but also acts as a smart, AI-powered guide, helping users navigate the often-complex world of stock markets more effectively. This project is my story, a testament to how technology, especially AI, can empower individuals to make more informed investment decisions. The Deep Dive: Why Investors Need a Guiding Hand The stock market can be a daunting place. For many retail investors, it's a whirlwind of data, conflicting advice, and emotional decision-making that can lead to missed opportunities or significant losses. From understanding market trends and analyzing complex financial reports to knowing when to buy or sell, the sheer volume of information can be overwhelming. Many feel like they're trading blind, lacking the expertise and analytical tools available to professional institutions. I believe there's a significant gap here – a need for a personal, intelligent assistant that can cut through the noise, provide actionable insights, and guide users towards more strategic trading choices. This conviction fueled the inception of my project. The Solution: Stock Trading Platform – Intelligent Trading, Engineered for Success My project, Stock Trading Platform, is a robust web-based platform designed to simplify stock trading with the power of artificial intelligence. While currently in its final polishing stages on my local machine and version-controlled with Git and hosted on GitHub, the core functionality revolves around a

Most speech-to-text is benchmarked on audio that looks nothing like a WhatsApp voice note. The standard evaluation sets are read speech, broadcast news, or recorded interviews: single speaker, decent microphone, one language, quiet room, speaker aware they are being recorded. A WhatsApp voice note is close to the opposite on every axis. I have spent a while building around this, and the gap turned out to be wider than I expected. Acoustics Phone held at arm's length while walking, in a car, in a kitchen, on a street. Distance-to-mic varies wildly within a single recording , which breaks a lot of assumptions about consistent gain. Then there is the codec. Voice notes are Opus at low bitrate — efficient, but it discards exactly the high-frequency detail that helps disambiguate fricatives. /s/ versus /f/ versus /th/ get genuinely harder, and those distinctions carry real meaning. Register Conversational, not read. False starts, self-corrections, filler, trailing off mid-sentence, and long pauses that are not sentence boundaries — someone thinking, or getting distracted. Punctuation inference is much harder here than on read speech. And punctuation is most of what makes a transcript skimmable rather than a wall of text. A perfectly accurate word sequence with no paragraph breaks is close to useless if the point was to let someone read it faster than listening. Language This is the one that surprised me most. Voice notes are heavily code-switched. People drop English technical terms into Urdu, Hindi, Arabic, Spanish sentences constantly — not as an edge case, as the default register for a huge number of speakers. If you force a single language selection up front, you mangle every mixed utterance. Auto-detection is not a convenience feature in this domain. It is a correctness requirement. Length distribution Most notes are 5–45 seconds. Very little context to work with, and per-request overhead dominates if you architected for long files. Batching strategies that make sen

One tiny real-life quest a day. Then it tells you to leave. Discussion | Link

WhatsApp Sidekick for busy professionals Discussion | Link

You budgeted six months. It took fourteen. You wanted one thing; you got three things that almost do it. And somewhere between the first sprint and the final invoice, you stopped understanding what you were even paying for. If that sounds familiar, this is the breakdown no one gave you before you started. What custom software development actually means Custom software development is building software from the ground up for your specific business, not configuring Salesforce, not installing a plugin. You're solving a problem your operations have, the way your operations actually work. What trips people up: "custom" doesn't mean "built entirely from scratch." Good dev teams use frameworks, libraries, and third-party services. What's custom is the logic of how your data flows, how business rules are enforced, how users interact. Scope range is huge: Custom dev covers everything from a lightweight internal dashboard to a full-scale multi-tenant SaaS platform. This is why cost estimates vary so wildly. 3 things nobody tells you before you sign 1. Scope creep is almost always the client's fault "Users should be able to manage their accounts" sounds simple. It actually contains dozens of decisions: can they change their email? What verification is required? Can they delete their account? Each one is a feature. Each feature has a cost. The fix: Run a discovery phase (2–4 weeks) before writing a single line of production code. It costs money upfront. It saves far more mid-project. 2. The cheapest bid rarely wins long-term A $40k quote and a $180k quote for the same project both happen. The $40k team isn't lying; they're optimistic, underbidding to win work, or scoping something different. What actually happens: you hit $40k, and you're 40% done. Higher bids from experienced teams often include architecture planning, documentation, testing infrastructure, and post-launch support things the cheap bid omitted. These aren't extras. They're what make the software maintainable in t

Unified API & Infra for AI Meeting Agents Discussion | Link

Run your parallel coding agents from your phone Discussion | Link

The ownership layer for fashion, shopping, and your wardrobe Discussion | Link

The ElevenLabs alternative with unlimited generation Discussion | Link

If you have ever copy-pasted a table out of a PDF, you already know what happens. Rows collapse into one long line of text. Columns interleave. Numbers land in the wrong cell, or no cell at all. The table on the page looks perfectly structured, but a PDF has no real concept of "table." It only knows where individual characters sit on a page. Every extraction tool, ours included, has to reconstruct the table from scratch, using nothing but the position of each word. That gap between "looks like a table" and "is structured data" is where almost every free PDF tool falls apart. Here is how we handle it, what actually works, and where it still doesn't. Two different jobs, two different tools PDFHaul splits this into two separate tools because they solve different problems. PDF to Excel rebuilds the whole document as a single spreadsheet, in the order it appears on the page: form labels, key-value pairs, section titles, and tables all together. It is for documents where you want the full content, not just the numbers, things like invoices, time sheets, and reports. Extract Tables does the opposite. It ignores everything that isn't a table and hands back one clean sheet per table, nothing else. It is for people who want structured data out, ready to sum, sort, and filter, not a copy of the document. Both tools share the same underlying geometry engine. The difference is what each one keeps and what it throws away. How Extract Tables actually decides what's a table The core problem with table extraction is that "looks tabular" and "is tabular" are not the same thing. A vector chart's axis box, a form's outlined signature field, and a two-column list of allergen names all produce something that a naive extractor will happily read as a grid. None of them are tables. Our pipeline handles this in four phases, all before anything is written to a spreadsheet: Phase 1: classify the page. Every page is scored as bordered (has ruled lines or filled-rectangle grid lines), stream (no

Weird, playful desktop app for building with AI agents Discussion | Link

ChatGPT Ads is expanding to 31 European markets. Learn how advertisers can reach people as they explore, compare options, and make decisions.