标签：#productivity

A few months ago I noticed something annoying about how I worked: I was spending more time collecting information than actually thinking about it. The pattern was always the same. Open a search engine, open a dozen tabs, skim past the SEO filler and cookie banners, copy the paragraphs that actually mattered into a doc, paste the whole mess into an LLM and ask it to make sense of things. Then, a week later, do it again because whatever I was tracking had changed. At some point I stopped asking "how do I do this faster" and started asking why I was doing it by hand at all. Why the obvious answers didn't work ChatGPT and Perplexity are fine for a single question. They're worse at the part I actually needed help with, which was repetition: running the same research loop on a schedule, keeping a record of what changed, and getting a notification when it did. Neither tool is built to sit in the background and check on a topic for you. Plain scraping scripts have the opposite problem. They get you raw HTML, not understanding. You still have to strip out nav bars and footers by hand, and the moment you point one at a list-style page like Hacker News instead of a blog post, it falls apart. And bookmarking is just deferring the problem. A folder of forty saved links isn't research, it's homework you haven't done yet. I wanted something in between: automated enough to skip the tab-hoarding, but still producing something I could read and trust, not just a black-box answer. So I built Focal Harvest It's a modular CLI that runs the whole research loop, search, scrape, clean, synthesize, report, on its own, and stays lightweight enough to run on a laptop with no GPU and no database. A single run looks like this: you give it a topic and a focus area (what you specifically want answered), it searches the web, pulls and cleans the pages, synthesizes a report, and writes it to disk. There's also a loop mode, so the same query can re-run every few hours and ping you on Discord or Teleg

OpenAI usage page, check spend. Anthropic console, check spend. Add it up in my head. Close both...

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

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

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

[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

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

You export a CSV from a UK system and double-click it in US Excel — 28/05/2026 suddenly looks like May 28, or something even stranger. The file isn't corrupt. Excel is guessing your date format based on your computer's locale. This short guide covers why that happens, how to import CSV correctly, and how to batch-fix dates that are already wrong. For the full walkthrough with examples, see the official guide: How to Fix Excel CSV Date Import Problems . Why Excel breaks CSV dates A CSV is plain text. It does not store whether a value is a date or a string. When you double-click to open, Excel parses using your regional settings: US Excel: MM/DD/YYYY UK / Europe: DD/MM/YYYY Dates like 03/04/2025 are the most dangerous — both parts are ≤ 12. US Excel may read April 3; UK Excel reads March 4. Excel won't warn you. Other common traps: Dates exported as serial numbers (e.g. 44927 ) Two-digit years triggering century guesses Re-saving as CSV destroys formats a second time The right way: don't double-click the CSV Open Excel first — don't double-click the .csv file Data → Get Data from Text/CSV (older Excel: Data → From Text) Set date columns to Text if you need to preserve the original string Confirm the source region (US / UK), then convert to a single format For team data exchange, agree on ISO 8601: YYYY-MM-DD in your schema — unambiguous, sorts correctly as text, and works in JSON APIs and databases. Ambiguous value US reads as UK reads as Safe format (ISO) 03/04/2025 2025-04-03 2025-03-04 Confirm source region 28/05/2026 — 2026-05-28 2026-05-28 05/28/2026 2026-05-28 — 2026-05-28 Already broken? Fix dates in the browser (no server upload) I built a free tool cluster on FormatList — everything runs locally in your browser : 1. Date Format Fixer — bulk repair Paste a date column or upload .csv / .txt Ambiguous rows highlighted; optional US / UK preference Export ISO / US / UK or download a fixed CSV Best for: normalizing a whole column to ISO before a database or API imp

Contact Form 7 runs on millions of sites for a good reason: it's free, light, and gets out of your way. I shipped it on client sites for years. The problem isn't that CF7 is bad — it's that it answers exactly one question ("did the form submit?") and stays completely silent on the one that actually matters in production: did the notification arrive? Here's the call every developer who maintains WP sites has taken at least once: "I filled in your contact form last week and never heard back." You check. The form is fine. JavaScript fires, the success message shows, no console errors. CF7 did its job — it handed the message to wp_mail() and forgot it ever existed. There's no record the submission happened, and no log of whether the email was delivered, bounced, or quietly dropped by the host's unauthenticated sendmail. The lead is just gone, and you have nothing to debug with. The three gaps that bite in production No submissions database. CF7 sends an email and discards the data. If the email fails or lands in spam, the submission never existed. (Flamingo helps, but it's a bolt-on — separate screen, no filtering or export out of the box, not tied to your form config.) No delivery log. You can't tell whether mail was sent, rejected, or bounced. "I never got it" has no audit trail to check against. No native block. CF7 is still a shortcode — [contact-form-7 id="123"] . You can't drop it into a block template, control its layout with block spacing, or edit it inline in Gutenberg. You paste a shortcode and hope. None of these are dealbreakers for a throwaway contact form. All three are dealbreakers when a missed submission is a missed sale. Migrating without rebuilding by hand The reason most people put off switching isn't the feature gap — it's the thought of rebuilding every form field by field. That's the part I wanted to skip. The migration path I use reads CF7's stored form definitions directly and recreates them as native forms. What comes across automatically: All

Leading into the AI Engineer event in San Francisco, I’m looking forward to having my mind blown....

Voice interfaces are rapidly becoming the next major interaction layer after mobile and web UI. Instead of clicking, users will increasingly talk to systems that understand intent, context, and can execute actions in real time. In this article, we’ll build a production-grade architecture for a real-time AI voice system using modern web technologies such as Next.js, WebRTC, and OpenAI’s streaming capabilities. We’ll also explore how this architecture powers modern conversational systems like an AI Voice Agent platform, where AI can handle real-time interactions for business use cases like bookings, support, and sales automation. 1. Why Voice AI is the Next Interface Shift Text-based chatbots solved the first wave of automation. But voice introduces: Faster interaction (no typing) Higher emotional expressiveness Better accessibility Natural multitasking Businesses are now adopting systems like Voice AI for Business to replace traditional call centers and static IVR menus. The key challenge is not just speech-to-text, but building a low-latency conversational loop that feels human. 2. System Architecture Overview A production-ready AI voice system typically consists of: Frontend (Next.js) Audio capture via Web Audio API Streaming audio chunks UI for conversation state Backend (Node.js / Edge Functions) Session management Authentication Tool execution layer AI Layer OpenAI Realtime API (streaming) Function calling Context memory Audio Pipeline Speech-to-text streaming Text-to-speech streaming Optional noise cancellation 3. Core Concept: Real-Time Streaming Loop The core of a voice agent is a continuous loop: User speaks Audio is streamed to server Model transcribes in real time Model generates response token-by-token Response is converted to audio instantly Audio is played back with minimal delay The goal is to keep latency under ~800ms for a natural experience. 4. Building the Frontend (Next.js + Web Audio API) We start by capturing microphone input: const stream = awa

There is a sentence my coding agent used to say that I now read as a warning light. You are completely right. For months I took it as a compliment. The machine agreed with me, so I figured I was onto something. I would describe a plan, watch the agent call it a strong plan, and go build it. If you work with an AI agent every day, you have heard your own version of this. Smart call. Solid approach. That makes a lot of sense. Each one is the machine nodding along while you talk. It feels good. That is the problem. What took me too long to admit An agent that agrees with everything I say stops being a thinking partner. It turns into something that flatters me into shipping my first idea. My first idea is rarely my best idea. Nobody's is. The whole point of a second mind in the room is that it pushes back when the first mind is about to walk into a wall. A yes-machine removes the one thing that made a second mind worth having. This has a name Sycophancy. These models are trained to be agreeable, because agreeable scores well in the feedback that shapes them. OpenAI said so out loud in 2025 when they pulled back a version of their model for being, in their words, overly flattering. They were pointing straight at the default behaviour. So your agent is doing exactly what it was tuned to do when it tells you that you are right. No malfunction involved. One opinion most builders have not made peace with Your agent's confident wrong answer costs more than a useless one. A useless answer wastes a minute. You see it is useless and move on. A confident wrong answer wastes a week, because you trusted it, built on it, and found out only when it broke in front of someone who mattered. Occasional wrongness is survivable. Everything is wrong sometimes. What actually bites is being wrong while sounding certain, and agreeable, and exactly like what you wanted to hear. How to tell if your agent is a yes-machine You can test it in a minute. Tell it a bad idea on purpose. Propose somethi

I log commutes in a spreadsheet because mobility apps smooth over the ugly legs. Last week I added a column I should have tracked years ago: carry seconds ? time from curb to platform when stairs replace ramps. The hidden leg My one-wheel leg is fine on paper. Three metro exits on my route have no elevator during maintenance. Carrying a 14 kg wheel down 22 stairs does not show up in trip duration. It shows up in whether I arrive annoyed enough to skip coffee. What I logged (one week) Exit Stairs Carry time (s) Mood after (1-5) North gate 22 38 2 Side ramp (control) 0 8 4 East stairs 16 29 3 Battery delta on those days? Within noise. Mood delta? Not noise. A cheap decision rule I turned this into a go/no-go check before leaving: if stairs > 15 AND carry_weight_kg > 12: prefer transit-only or locker elif stairs > 0 AND wet_floor: walk the wheel (no riding in station) else: ride It is blunt. It works better than pretending every leg is rideable. Assumptions up front Wheel weight includes pads and charger pouch (~14 kg for my commuter setup). I am not timing competitive carries ? just whether I can do this daily without hating it. Your threshold differs if every exit has elevators. What I would do differently I would log carry seconds from day one, same tab as distance and battery percent. Range math without carry math is incomplete for anyone who mixes metro and one-wheel. I work around personal EVs and sometimes cross-check specs on the official Kingsong catalog. https://www.kingsong.com/collections/electric-unicycle

Dealing with vendor invoices usually means someone is sitting there manually opening PDFs and typing...

Like most developers, I have a set of tools I use every day. Most of them work great, and I don't...

What happens when thousands of people decide they're hungry at the exact same time? The Quiet Before the Storm 10:00 PM. The numbers are gentle tonight. One hundred eighty-nine requests trickle in. Someone in Lagos is ordering late-night suya. A rider in Ibadan is wrapping up his last delivery. In Bangladesh, someone is just discovering us for the first time. By 11:00 PM , things get quiet. Just 8 requests. The platform takes a breath. 2:00 AM. A mystery. 151 requests spike out of nowhere. We check the logs. Nothing unusual. Just a group of night owls ordering food, maybe shift workers, maybe students pulling an all-nighter. The beauty of a platform is we're always on, always ready. 7:00 AM. Good morning, Nigeria. Fifty-five requests. People waking up, checking their wallets, planning their day. The coffee hasn't even brewed yet, but the platform is already humming. The Morning Rush 9:00 AM. 315 requests. The workday begins. Offices buzz with conversations about lunch plans. If someone searches "foodmat site" for the third time this week, they're getting closer to finding us. A corporate client logs in to set up their employee meal program for the first time. By 10:00 AM , the traffic settles to 50 requests. A calm before the real storm. 11:00 AM. 173 requests. The hunger is building. People are making decisions about what to eat, where to order, and which vendor to choose. Our World Cup campaign notifications ping. Someone shares their referral code. The viral loop begins. The Lunch Explosion 12:00 PM. 321 requests. It's happening. The platform comes alive. 1:00 PM. 339 requests. The peak is building. Our servers are handling it smoothly. This is where the magic happens when thousands of people decide they're hungry at the exact same time. 2:00 PM. 289 requests. Still going strong. Vendor dashboards refresh. Riders accept orders. Laundry bookings come in alongside food deliveries. If someone cancels an order with a reason, we take note. Every interaction teaches us

TL;DR — Claude Code sends its requests wherever one environment variable points. Aim that at a small local translator and it runs on the Kiro plan you already pay for. Full setup below, plus the two snags worth knowing about. Claude Code and Kiro have something funny in common: underneath, they're powered by the same Claude models. Same brain. They just grew up speaking different dialects, so out of the box they can't hold a conversation. I noticed this right as I was about to start a second subscription for Claude Code. My Kiro plan was already renewing every month, already serving the exact models Claude Code wanted to charge me for again. Paying twice to talk to the same thing felt absurd. So instead of buying a second seat, I hired an interpreter. One small program that sits between them, listens to Claude Code, and relays everything to Kiro in a dialect it understands. Here's how to set it up, and what I learned doing it. Why they can't just talk Claude Code is more open-minded than people assume. It doesn't hard-code where it sends requests. It reads one environment variable, ANTHROPIC_BASE_URL , and ships everything to that address. Normally that's the official endpoint, but it'll happily send its requests anywhere you tell it to. That's the opening. Point it somewhere local and the whole thing becomes possible. Meet the interpreter The catch is that Claude Code and Kiro phrase things differently. You can't just redirect one at the other and expect them to understand each other. You need a translator fluent in both. That's kiro-gateway-next : a tiny proxy that runs on your own machine. A request arrives phrased one way and leaves phrased another: Claude Code (phrases it for Anthropic) ──▶ kiro-gateway (rephrases it for Kiro) ──▶ your Kiro account Claude Code gets a reply in the format it expects. Kiro receives a request it recognizes. The interpreter does the rephrasing in the middle, and the conversation just flows. Setting it up, step by step Six steps. Abo

For the last 3 months I recorded every session I had with Claude Code. Not screenshots, not memory. Every prompt I typed and everything it did, saved to a small database I own. I did it because I kept losing my own work. I would finish a week, someone would ask what I shipped, and I genuinely could not remember. The work was real. It just lived in terminal scrollback I would never scroll through again. So I set up a chain of small agents to remember it for me. Every night, while I sleep, one agent reads that day's raw sessions and writes a single clear note: what I built, the decisions I made, what is still open. Plain language, the way I would write it in a journal, not a wall of logs. Once a week, a second agent reads all seven daily notes and updates a profile of me: the projects I am moving, the skills I have actually used, the things I learned. After a few months this turned into a more honest picture of my work than my resume. Then a third agent reads all of that and drafts posts for LinkedIn and X about what I actually worked on that week. Building in public, without me having to remember or sit down and write. The part I like most: none of it runs on my machine. It is all scheduled cloud routines. My laptop can be off. I wake up and the notes, the profile, and the draft posts are already waiting. I have started open-sourcing this as Pulse. The capture and the nightly daily-note agent are out now. You point it at your own database and your own notes repo, and it writes your day for you, in plain English, in files you own. The weekly profile agent and the post-writer are the pieces I am extracting next. It is early and rough in places. The honest caveat: the writing is only as good as the model behind it, and a quiet day still makes a quiet note. But after 3 months, I no longer guess what I did. I just open the vault. The graph at the top is 3 months of my own notes, each day linked to the projects it touched. Repo: https://github.com/muhammademanaftab/pulse