标签：#Productivity

While researching for this post, I found out something funny. Pi, the most interesting coding agent...

The concrete problem Running two or three coding-agent sessions is easy. Knowing when their work is safe to combine is not. One session changes an API while another writes regression tests against the old shape. A third investigates a production failure and quietly edits the same configuration file. Git worktrees prevent immediate filesystem collisions, but they do not explain task dependencies, transfer assumptions, or warn that two agents are solving incompatible versions of the problem. The developer becomes a human message bus: checking terminals, copying commit IDs, repeating context, and deciding which session should wait. The more capable each agent becomes, the less useful a wall of terminal panes is as a coordination interface. The current signal Claude Code now supports messaging between sessions on the same machine. Its documentation describes session discovery, plain-text messages, and a local messaging socket. Agent view separately exposes background-session state, worktrees, pull-request status, and a JSON listing suitable for scripts. Hooks can observe tool input and block a tool call before execution. That does not prove demand for a new product. It does create a concrete implementation moment: the primitives for handoffs and visibility exist, while dependency ownership and conflict negotiation remain a workflow problem. In RayTally's bounded Hacker News snapshot at August 9, 00:33 UTC, the cross-session messaging discussion had 50 points and 26 comments and ranked 18th. Those numbers describe that historical observation only; they are not user counts, market validation, or a prediction of lasting interest. A product direction: a control desk for handoffs The useful product is not another chat window. It is a small local control desk that makes each session declare four things: its goal, worktree, files it expects to touch, and the result another session is waiting for. When the API session finishes, the testing session should receive a compact hando

Qarinah compiles a compact, cited project-memory pack instead of asking every new coding-agent session to replay the entire available history. The published estimate Across six committed software-task fixtures, the full-history baseline contained 442,113 portable estimated input-context tokens . The Qarinah path used 5,682 . Every required target was still directly covered in the top five results. That is: 436,431 fewer estimated input-context tokens; 98.71% less repeated context; and a 77.81:1 baseline-to-pack ratio. The ratio is not a claim that every provider bill drops by 98.71%, or that an agent session lasts 77.81 times longer. It measures the compared input-context volume in the published six-fixture estimate. What the same token rate would cost The table applies four flat, uncached input-token rates to the same two token estimates. It is arithmetic, not a provider invoice. Flat uncached input rate Full-history baseline Qarinah pack Estimated saving $1 / million tokens $0.442113 $0.005682 $0.436431 $3 / million tokens $1.326339 $0.017046 $1.309293 $5 / million tokens $2.210565 $0.028410 $2.182155 $15 / million tokens $6.631695 $0.085230 $6.546465 The calculation is: estimated tokens / 1,000,000 x flat input rate It deliberately excludes provider-native tokenization, caching, output tokens, reasoning tokens, tool calls, retrieval, hosting, and fixed fees. Real cost depends on the provider, model, cache behavior, context composition, and how often the same history would otherwise be resent. Why the pack remains useful Compression only matters if the next task can still find its evidence. The benchmark checks both volume and retrieval coverage: every required target had to be directly present in the top five. Qarinah preserves the source event ID and content hash for selected context, so a later agent receives a bounded handoff that can be inspected instead of an opaque story. Qarinah also passed 380 of 380 deterministic file-specific exact and typo-tolerant que

After months of building with AI coding tools, I found the difference between generated code that...

Hey everyone! I'm jinyuan, an indie developer. I recently launched DevTools Box — a free online toolbox with 75+ developer tools. What's in the box? DevTools Box includes tools like: JSON Formatter — beautify and validate JSON Regex Tester — test regular expressions with live matching Base64 Encoder/Decoder — quick encoding and decoding QR Code Generator — generate QR codes instantly Hash Calculator — MD5, SHA-1, SHA-256 and more Color Picker — pick colors and convert between formats ...and 69 more tools! Why I built it I was tired of jumping between different websites for simple dev tasks. Each tool runs entirely in your browser — no login, no ads, no data sent to any server. Tech stack Next.js 14 with App Router TypeScript Tailwind CSS Static export to Cloudflare Pages Try it out Check it out at tdboxs.com . All tools are 100% free. Would love to hear your feedback! What tools would you add?

I keep seeing the same mismatch in the screen-time category: a lot of apps are technically blockers, but they feel like parental control software. That is fine if a parent is the customer. It is a bad fit if the user is an adult trying to manage their own habits. The difference is not cosmetic. When a blocker feels like surveillance, adults bounce. They do not want an account, a dashboard, or the feeling that their phone behavior is being watched somewhere else. That is the gap I built SproutGuard for: built for adults blocking themselves , not kids runs on-device through Apple's Screen Time APIs no account no server no usage data leaving the phone App Store: https://apps.apple.com/us/app/sproutguard-screen-time-detox/id6768664921?ct=devto-adults I also put the positioning plainly on the product page: self-control, not parental control The hard lesson from launching it is that being right about the problem is not the same thing as being shareable . Privacy architecture matters, but users rarely tell friends about architecture. What they do share is something emotional or visible: a streak, a mascot, a challenge, a before/after feeling. So the current working question for me is not "how do I explain on-device privacy better?" It is: How do you make a self-control product feel human enough that people talk about it? Website: https://shantj.github.io/sproutguard/ If you've worked on consumer productivity or habit products, I'm interested in what actually made users talk about them.

If you've ever opened a PR with 47 changed files and a diff so long GitHub just gives up and shows you "Load Diff" seventeen times, this one's for you. GitHub quietly shipped what might be the biggest pull request update in years, and it's aimed squarely at that problem. Let's talk about stacked pull requests. The problem, in one sentence Big PRs are where good reviews go to die. Nobody reads a 2000 line diff carefully. Some folks reach for AI code review tools like LiveReview to take the edge off, and honestly that helps, but even the best reviewer (human or model) does a better job on a tight, focused diff than on a 2000 line wall. Smaller inputs, better reviews. That's true no matter who's doing the reviewing. Stacked PRs are GitHub's answer: break one massive change into a chain of small, dependent PRs, where each one only reviews the diff it actually introduces, not everything below it. What a stack actually is The rule is simple. You need two or more PRs in the same repo where: The bottom PR targets your trunk branch (usually main ) Every PR after that targets the PR below it, not main That's it. That's the whole trick. Foundational stuff (schemas, shared types) goes at the bottom. Stuff that depends on it (API routes, UI) goes higher up the chain. And here's the part that surprised me: if you just do this manually with plain git, by opening PR #11 against the branch for PR #10 instead of against main , GitHub now recognizes that as a stack automatically. No special tool required. It just notices the base branches form a chain and lights up a banner. Stacking isn't a git concept at all, it's purely a GitHub UI concept layered on top of branches you were already making. Let's actually build one Enough theory. I built a real stack in one of my own repos ( peektea , a terminal file browser I maintain), using a harmless scratch file so nothing real got touched. Here's the actual terminal session, copy pasted, warts and all. First I tried to be fancy and use the CL

Adding a “Control de Obra” Module to Ventas → Desarrollos (NestJS + Next.js) TL;DR: I built a brand‑new Construction feature (Control de Obra) inside the Ventas → Desarrollos flow, wiring a NestJS controller, a migration for branding_settings , and a Next.js page. While doing that I also fixed the setToken bug that stopped the BrokerDashboard from refreshing its session. The result is a clean, testable API endpoint and a functional UI component that talks to it. The Problem Our product needed a way for sales teams to track the construction status of each development (obra). The UI already had a “Desarrollos” list, but the backend had no endpoint to create, read, update, or delete construction records. At the same time the BrokerDashboard ( apps/web/src/app/portal-broker/page.tsx ) was failing to refresh the user session after a token rotation. The console showed: Error: setToken is not a function at Object.<anonymous> (src/portal-broker/page.tsx:78:15) Both issues were blockers: No API → the UI could only display static data. Stale token handling → users were logged out unexpectedly after a token refresh. What I Tried First I first tried to reuse the existing VentasPropertiesController ( apps/api/src/ventas/ventas-properties.controller.ts ). The controller was already imported in AppModule , but it was dead code (the class had no routes) and its methods lacked the AuthGuard we use across the API. I added a couple of ad‑hoc routes inside that controller, but: The routes conflicted with the existing /ventas namespace. The controller’s @UseGuards(AuthGuard) was missing, causing 401 errors in the browser. The migration for branding_settings was still out of sync, leading to a “column does not exist” error when the new endpoint tried to read branding data. After a few hours of chasing 404s and 401s, I decided the cleanest path was to create a dedicated module for construction and keep migrations in sync. The Implementation 1. Register the new controller in AppModule // a

Automating Multi‑Platform Content Publishing with a Node.js Scheduler TL;DR: I extended the content-automation repo to generate weekly newsletters, Dev.to articles, and platform‑specific markdown in a single CI run. The key was a tiny Node.js scheduler that reads a JSON manifest, writes files, and flips “generated” flags in metadata.json so downstream pipelines know what to publish. The Problem Our content pipeline had three independent manual steps: Write a weekly newsletter markdown file. Draft a Medium article. Publish a Dev.to post. Each step required copying the same body copy into a different folder ( weekly/ , content-automation/medium_* , content-automation/substack_* ) and then manually toggling flags in metadata.json . During a production run on 2026‑08‑08 the CI job failed with a cryptic log line: Error: Conn The truncated message was coming from the Prisma client that our automation script uses to fetch the latest draft from the CMS. Because the script never updated the metadata.json flags after a successful write, the next run tried to re‑process the same draft, hit a stale DB connection, and blew up. In short: the automation was not idempotent , and the state tracking was brittle. What I Tried First My first attempt was to wrap the whole generation flow in a try / catch and, on any error, abort the job without touching the manifest. I added a quick if (fs.existsSync(filePath)) return; guard to each write operation. // naive guard if ( fs . existsSync ( targetPath )) { console . log ( ` ${ targetPath } already exists – skipping` ); return ; } That prevented duplicate files, but it also silently skipped a legitimate update when we intentionally rewrote a newsletter (e.g., after a typo fix). Moreover, the guard didn’t address the stale Prisma connection, so the same Error: Conn kept surfacing in later runs. The Implementation 1. Central Manifest ( metadata.json ) The manifest now lives at content/2026/08/08/content-automation/metadata.json . I added expli

As I sit here in my RV, typing away on my latest project, I often think about the community that has formed around my indie apps. One story that stands out is when I released ShipDrop, a simple one-click hosting tool for developers. I was overwhelmed by the response from the developer community, who appreciated the ease of use and simplicity of hosting their projects. One user even hosted a website for their local animal shelter using ShipDrop, and it was amazing to see how such a small tool could make a big impact. From a technical standpoint, building ShipDrop taught me a lot about the importance of simplicity in code. When I started working on the project, I was tempted to add a lot of features and complexity, but I realized that the core value of the app lay in its ease of use. By keeping the codebase small and focused, I was able to create a seamless user experience that allowed developers to host their projects in just a few clicks. For example, using a simple drag-and-drop API, I was able to abstract away the complexities of hosting and deployment, making it accessible to a wider range of users. One lesson I've learned from building and sharing ShipDrop with the community is the importance of listening to feedback and being open to iteration. When I first released the app, I thought it was perfect, but the community quickly pointed out areas for improvement. By being receptive to their feedback and making changes accordingly, I was able to create a tool that truly met the needs of my users. This experience has taught me the value of community involvement in the development process, and I'm grateful to be a part of the DEV community, where I can share my experiences and learn from others.

I used to think AI was making me lazy. I was wrong. AI wasn't making me lazy I was using AI as an...

I don't know about you, but I re-lookup cron syntax every single time. Is it 0 12 * * 1-5 ? Or */5 ? Honestly — nobody keeps this in their head. Instead of another cheat-sheet I'll forget, I built a builder: Pick day, hour, minute from dropdowns See the expression translated to plain English live Preview the next 5 runs in your timezone (this catches the classic "off by one" DST surprises) Get copy-paste snippets for Python, Node.js, Bash, Docker, GitHub Actions and n8n Free, no signup, runs fully client-side: https://cron-generator-kappa.vercel.app If you like it, the cheat-sheet guide is here: https://cron-generator-kappa.vercel.app/guides/cron-cheat-sheet

Originally published on IFEELVOID . Play the song and count the pulse for 15 seconds. Multiply that number by four. If you counted 35 beats, the song is roughly 140 BPM. That is the fastest manual way to find the beat of a song. It is also where the confusion starts. A trap record at 140 BPM can feel like 70. A drumless intro can hide the pulse completely. A sample can drift. And knowing the tempo still does not tell you the musical key you need for bass lines, vocal tuning, remixes, or harmonic mixing. This guide gives you the manual method, the DAW method, and the faster analysis workflow I use when a session cannot stop for guesswork. First: what does “beat” mean? People use “beat” to describe three different things: The pulse: the steady count you nod your head to. The BPM: how many pulses happen in one minute. The instrumental: the drums, melody, bass, and arrangement behind a vocal. If you need the tempo and key so you can work with the audio, keep going. Method 1: count the BPM manually Find the strongest repeating pulse. In most trap and hip-hop records, start with the snare or clap. Count along for 15 seconds, then multiply by four. Start the song at a section where the drums are clear. Tap your foot or nod to the main pulse. Count every pulse for exactly 15 seconds. Multiply the count by four. Repeat once to make sure your count is stable. Twenty beats in 15 seconds is 80 BPM. Thirty beats is 120 BPM. Thirty-five beats is 140 BPM. Watch for half-time and double-time A beat can be represented at two mathematically correct tempos. A dark trap record may read as 70 BPM or 140 BPM depending on whether you count the slow backbeat or the faster production grid. Neither number is automatically wrong. Use the tempo that matches your purpose. Producers usually want the grid that makes drum placement and subdivisions easy. DJs may want the value that matches the rest of their library. Method 2: use tap tempo Most DAWs, DJ applications, and metronome tools include ta

Key Takeaways Asynchronous discussions can lose momentum because participants are focused on different tasks. For complex or important topics, it’s often better to switch to synchronous communication. At least in my experience working in a Japanese-speaking organization, AI-generated messages are often still too verbose to send as-is. As writing becomes cheaper, it’s even more important to reduce the cognitive load on readers. Async-first does not mean async-only. Keeping written records while introducing short meetings when necessary can reduce the overall cost of communication. Context I currently work from Vancouver, Canada, for a fully remote and fully flexible organization based in Japan. Since everyone works on their own schedule, much of our day-to-day communication, decision-making, and discussion happens asynchronously. There are many benefits to this way of working. People can think at their own pace, and discussions naturally leave a written record. As someone who is fairly introverted, I also appreciate having time to think through my ideas before sharing them. Recently, however, I’ve started to realize that keeping every discussion asynchronous is not always the most efficient approach. Complex discussions are expensive to read When discussing multiple options, I usually start by sharing my recommendation, then document the reasoning behind it and the pros and cons of alternative approaches. The more complicated the topic becomes, the longer the document becomes. Writing requires effort, but so does reading. Someone has to understand the background, process the trade-offs, form an opinion, and respond. Lately, I’ve become more aware of the reader’s cost than the writer’s. In our company, Japanese is the shared language, and much of our written communication is now assisted by AI. While AI makes it easier to produce long documents, the resulting text can still be unnecessarily verbose or difficult to follow. AI makes writing cheaper. It does not necessar

A zero-dependency, single-file Go pastebin built for terminals — burn-after-read by default, two independent encryption layers, and a curl one-liner instead of a login form. I keep ending up in situations where I need to move a small piece of text — a log snippet, a password, a container's stdout — from one machine to another, and the clipboard just isn't there. SSH session on a remote box. A locked-down corporate laptop that won't let me touch the OS clipboard at all. A container with no shared volume and no browser. Slack is right there, but pasting a database password into a channel that's archived forever is a special kind of bad idea. So I built CPYNET — a paste-sharing tool with exactly one interface that matters: curl . echo "hello world" | curl --data-binary @- https://cpynet.com/ # https://cpynet.com/482913 curl https://cpynet.com/482913 # hello world That's the whole thing. No account, no API key, no clicking around. Two curl calls and you've moved text between two machines that have nothing in common except a network path. Burn-after-read, actually The paste above is gone the instant that second curl runs. Not "gone in 24 hours" — gone the moment it's read , whether that's one second later or one minute later. Read it twice (even from the same machine) and the second request gets a plain 404 . It also auto-expires on a timer (2 minutes by default) even if nobody ever reads it, so an unread secret doesn't just sit there. None of this lives on disk. It's a Go map behind a mutex, in memory, for the lifetime of one process. Restart the server and every paste that hasn't been read yet is just... gone. That's not a limitation I'm working around — it's the actual point. A "burn after read" tool that persists to disk somewhere you're not thinking about isn't really burning anything. The shell functions, if you don't want to remember the curl flags curl -s https://cpynet.com/install.sh -o install.sh && bash -n install.sh && . install.sh That wires up two functions

You Only Hold Four Thoughts Try to multiply 47 by 83 in your head. The answer is not the point. Watch what happens while you reach for it. You hold 47, you hold 83, you start on the partial products, and somewhere around the third one the first number goes soft. You reach for a pen, because the problem outgrew the place you were keeping it. That ceiling is real and it is low. The cognitive scientist Nelson Cowan spent years measuring it and put the number at about four. Not the seven you half-remember from an old paper, but three to five distinct things held in mind at once. 1 Four. That is the working capacity of the most sophisticated object in the known universe. Everything we call getting smarter has been a way around that four. The history of human intelligence is the history of putting thoughts somewhere other than the head, and it runs as a stack, each layer holding what the one below it cannot. The first rung is paper Reaching for the pen looks like a small surrender. It is the oldest cognitive upgrade there is. The moment you write 47 above 83 and start stacking partial products, you are thinking about six or seven things at once, because the paper is holding all but the one you are working on. Justin Sung, who teaches learning for a living, puts it more sharply. Writing is not the thing you do after you have reached clarity. Writing is what produces the clarity. 2 The page becomes the workspace where the thought turns real, because your four slots are freed to do the actual reasoning while the page remembers the rest. This is also why handwriting beats typing. It is far slower than thinking, and that slowness forces you to compress, to decide what is worth the stroke. The friction is not a tax on the process. The friction is the process. A page of notes you struggled to write holds more than a page you copied without resistance. The page is not a transcript of a finished thought. It is the workspace where the thought becomes possible. The rung most people

The Stable Liar The dashboard was green for eight quarters The most dangerous number on a dashboard is the one that has stayed green the longest, and the way it fails has a shape you have probably watched up close. For eight straight quarters the dashboard holds green. Revenue up and to the right. Retention flat and healthy. NPS in the fifties. Every board meeting opens on the same slide and closes on the same nod. The plan is working. Then, six months after the eighth green quarter, the business the dashboard was supposed to describe nearly falls over. Pull the post-mortem apart and the easy story is that the numbers lied. They did not. Every quarter the dashboard reports something true: customers are still paying, logins are still happening, the survey scores are still fine. All of it accurate. The failure is quieter and worse than a lie. The words behind the numbers change meaning while the numbers stand still. “Retention” still counts the same logins, but a login has stopped predicting a customer who will renew. The metric keeps its shape long after the thing it measured has walked out of the room. Anyone who has run a team has felt a smaller version of this. The number you trusted most became the number that surprised you most. You were not lied to. You were tracking something that used to mean one thing and quietly came to mean another, and the dashboard had no way to tell you the meaning had moved. This is the stable liar: a number that goes on looking right long after it stopped being right. It is a structural property of measurement under pressure, and it has a law underneath it. Why every optimised metric drifts A metric is a substitution: you replace the thing you care about with something you can count, and the gap between them is where the trouble lives. Start with the substitution. You cannot measure value, loyalty, insight, or health directly, so you pick a proxy you can count. Revenue stands in for value. NPS stands in for loyalty. Citations stand in

The Safe Parts of Your Job Are the First to Go A junior analyst spent two years getting good at building financial models. Last month she watched a colleague produce, in ninety seconds and a sentence of plain English, the kind of model that used to take her a careful afternoon. The output was not perfect. It was good enough to be frightening, and it raised the only question that matters: what part of this was ever mine? The question has a sharper edge. The part of your work you are proudest of may have been valuable only because it used to be hard, and the hard part just got cheap. The reflexive answers are bad ones. “Humans bring creativity.” “Humans bring the human touch.” These are comfort blankets, too vague to act on. The real answer is narrower, and it comes with a catch. Human judgment survives at five specific places, all of them sitting above the task itself, and each one can be named. Naming them is the easy half. The harder half, the part almost nobody tells you, is that the same cheap generation eating the task is thinning out how many people are left to do the part that survives. The part that stays yours Map every time the work genuinely needed a person and the same shape keeps appearing. Someone has to understand what the system is actually doing before trusting it. Someone has to choose which outputs are worth keeping. Someone has to approve the actions that cannot be taken back. Someone has to hold a decision steady while the outcome is still uncertain. And someone has to decide which problems are worth solving at all. None of those is production. Every one of them is a decision about production. The analyst’s two years went into producing the model. The part that stays hers is the judgment wrapped around it: whether the model’s assumptions survive contact with reality, whether this is even the right question, whether the number is one she will stake her name on. What survives is the deciding: whether the thing is right, whether it is worth doing, a

There are endless ways to record and transcribe your virtual meetings with AI. Here’s an option that’s free and open source.

How an alert, ten browser tabs, and a Slack ping actually get resolved when AI is in the loop — and where I still don't trust it. An alert fires. I open Grafana. Then CloudWatch. Then the logs. Then kubectl describe on the pod that's misbehaving. Then GitHub, to see what merged. Then Argo CD, to see what actually rolled out. Ten tabs in, trying to hold six timelines in my head at once, someone drops into the channel: Do we know what happened yet? That moment is the real job. Not the syntax. Not remembering the exact kubectl flag. The job is correlating scattered signals fast enough to form a hypothesis worth testing. That's the part where AI has changed how I work. It didn't take the troubleshooting away from me. I'm still doing all of it. It just shortened the gap between "something is wrong" and "this is probably where I should look." I don't use AI as a replacement for understanding Kubernetes, AWS, Terraform, Linux, networking, databases, or CI/CD. I use it as another tool in the workflow, one that helps me get from a problem to a testable hypothesis faster. My AI usage today broadly splits across three areas: ChatGPT — communication, research, reasoning, and technical analysis Claude and Claude Code — coding, Kubernetes, scripts, configurations, and troubleshooting AWS DevOps Agent — AWS infrastructure investigation, resource analysis, troubleshooting, and optimization Each tool has a slightly different role. The part that actually matters isn't having access to AI. It's knowing where it's useful, what context to give it, and when its output needs to be challenged. None of them makes a production decision for me. One habit before I get into the tools: I'm careful about what I paste into any of them. Config with real hostnames, account IDs, or anything secret-shaped stays out. ChatGPT: the part of DevOps nobody warns you about People underestimate how much of this job is communication. I'll finish a technical investigation and then have to explain it — to a deve