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👋 Hey there, Tech Enthusiasts! I'm Sarvar, a Cloud Architect who loves turning complex tech problems...

Decode sentences directly from non-invasive brain signals Discussion | Link

Build with coding agents from anywhere Discussion | Link

Build with coding agents from anywhere Discussion | Link

I have been writing a lot about AI lately, but this one is more personal than usual. Not a tutorial,...

A personal AI agent that runs on your desktop and iPhone Discussion | Link

Claude Code & Codex in your Mac notch Discussion | Link

Build with your components, colors, fonts, and patterns Discussion | Link

Token burn and business outcomes are not correlated. More burn means more inefficiency, not more value. The electricity problem Imagine you walk into a dark room. Turning on a light helps you see. Turning on every light in the building does not help you see better. It's still the same room. Now every surface is equally lit, the contrast is gone, and you're paying for power you didn't use. Tokens work the same way. A focused prompt with clear scope is the single overhead light over your desk. A sprawling prompt with unlimited exploration is every light in the building — you're burning power, not producing insight. Tokens are electricity, not output. More throughput doesn't mean more value. I've had weeks where I burned through my allocation and looked back at the end to find nothing concrete. Code that worked but went unused. Exploratory branches that dead-ended. Agents that generated plausible-looking output that didn't survive first review. A lot of motion. Not much progress. The ceiling stops you from doing that indefinitely. It forces a moment of reflection: did this burn produce anything real? If the answer is no, more capacity isn't the fix. More discipline is. Three patterns I now use instead I started paying attention to what actually ships versus what just burns context. I gave the patterns names so I could catch myself faster: RTK — Read The Knowledgebase. A focused 15-minute read of the codebase, identifying the exact files and exact changes, saves 200K+ tokens of exploratory waste. The agent doesn't discover the shape of the task — it executes against a known one. Caveman — compress before you prompt. Strip greetings, filler words ("I think", "basically", "Let me know if that makes sense"), and closing courtesies. Every word in your prompt multiplies across every response token. Less fluff in means less fluff out. Ponytail — spec the minimum viable solution. "Robust", "scalable", "enterprise-grade", "comprehensive" — these words invite scope creep. Specif

WIRED has tested 100-plus bed-in-a-box mattresses for a week each. Our top pick, the Helix Midnight Luxe hybrid, is the best bed you can buy online.

The first app dedicated to 1980s tape-edit effects. Discussion | Link

Browser agent that’s 10x faster than Claude Discussion | Link

Social listening for the agent era Discussion | Link

Even if you only use WhatsApp sometimes, you might want to snag your username now to stop giving out your phone number.

[v0.3.0] - 2026-06-29 🚀 Added Checkpoint Mechanism — ReActCore introduces three checkpoints during streaming: content_ended (after text content), before_tool_calls (before tool calls), and after_tool_calls (after tool calls), enabling precise interception and state synchronization of the execution flow. Message Queue System — Added a new MessageQueue class in run.py , supporting async enqueue, drain, and remove operations. Users can now queue messages while the LLM is running; queued messages are sent automatically after the current task completes. The frontend introduces a QueueBar component to display queued messages, with CSS spinning animation, single-line ellipsis, and hover-to-delete functionality. Queue Message Merging — MessageQueue.drain_all() now merges consecutive messages with the same name into a single message, preventing fragmented user input when multiple queue entries share the same sender. Queue WebSocket Events — The execution event protocol introduces three new event types: message_queued , queue_drained , and queue_returned ( useRunWebSocket.ts ). The frontend processes queue state updates in real time. Stop & Queue Integration — When the user clicks Stop, pending queued messages are returned to the input box via queue_returned . Checkpoint stops cleanly clear the queue and automatically start the next message. System Notification Messages — Introduced the SystemMessage type (with notification role) to separate error messages from assistant content. Errors are now rendered as independent notification bubbles, no longer embedded within assistant message cards. tiktoken Real-Time Token Estimation — ReActCore initializes a tiktoken encoder on startup for real-time token counting during streaming. Unknown models fall back to o200k_base . 🔧 Improved Custom Model Name Auto-Complete — The model name field in ModelManager has been upgraded from Select to AutoComplete , allowing users to type custom model names not in the predefined list. Message Block T

Free font playground for live websites Discussion | Link

As a solo developer working out of an RV, I've learned to appreciate the importance of staying organized, especially when it comes to managing user data in my Chrome extension, Tab Reminder. One of the key challenges I faced was efficiently storing and retrieving user-scheduled tabs, which led me to explore the world of popular tags in browser storage. During the development of Tab Reminder, I realized that using a simple key-value pair system wasn't enough to manage the complexity of user data. I needed a way to categorize and prioritize scheduled tabs, which is where popular tags came into play. By utilizing the localStorage API, I was able to store user-defined tags and associate them with specific tabs, making it easier for users to manage their scheduled tabs. One technical insight I gained from this experience was the importance of using a robust data structure to store user data. In my case, I used a combination of arrays and objects to store tag information, which allowed me to efficiently query and update user data. For example, when a user schedules a new tab, I use the following code to store the tag information: // Store tag information in localStorage const tags = JSON . parse ( localStorage . getItem ( ' tags ' )) || {}; tags [ tabId ] = tagName ; localStorage . setItem ( ' tags ' , JSON . stringify ( tags )); One lesson I learned from this experience is that even small, useful tools like Tab Reminder require careful consideration of data management. By leveraging popular tags and a robust data structure, I was able to create a seamless user experience that allows users to efficiently manage their scheduled tabs. If you're interested in trying out Tab Reminder, you can check it out at https://go.sg1-labs.us/tab-reminder .

Flipper Device's new Busy Bar will retail for $249.

When I write code in unfamiliar territory, I write three lines, then I run it. Then I write three more lines, and I run it again. I've been doing this for twenty-four years. It's the most specific habit I have. I almost didn't write this article, because the habit feels too small to be worth describing — but then I noticed that it's the part of my way of working that I can never seem to explain to someone in real time. It needs writing down. Three principles The discipline rests on three things I believe about writing code. They're not deep. They've just stayed with me. 1. Trust nothing but your own code. If you can't trust the code you wrote yourself, what can you trust? Not a library, not a vendor's documentation, not your own assumption from yesterday. The only thing in the system whose behavior you can fully verify is the code you just typed, by running it. 2. Write in code, not in language. If you're describing what the code should do in Japanese or English, you're spending the same time you could have spent writing the code itself. By the time the code runs, the description is already done — by the code, in a more precise form than any language could give it. 3. Make three lines complete. The three lines you just wrote should be complete. Error handling included. Validation included. Logging included. Not "I'll add validation later." Not "I'll wrap it in a try-catch later." Three lines, complete, then run. (There's a small exception to this. Sometimes you do want to ignore every error and move on — for instance, when you're trying to understand whether the happy path works at all before you care about anything else. That's a different mode, used deliberately. It's not the same as "I'll handle errors later.") Why three lines Three lines is roughly the unit of thought I can hold completely. Five lines, and I start guessing what the third line did. Ten lines, and I'm reading the code as if it were someone else's. Three lines is the size that stays mine. When thre

With AI-powered noise reduction, an automatic speech-focusing system, and a simple, effective hearing test, it’s a shame these aids don't come with a better app.