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We tested over a hundred wireless earbuds—these models from Apple, Bose, Beats, and Samsung stood out from the rest.

Turn natural language into browser tests from your terminal. Discussion | Link

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

World's First AI Copilot for Android. You talk. It acts. Discussion | Link

The Supply Chain of Intelligence framework: The best structural answer to the question every...

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

Premium seats are coming to ChatGPT Business. Sign up by August 20 to get $100 in workspace credits and unlock higher usage for your team's most demanding work.

The investment platform for trading cards Discussion | Link

Bringing intelligence to your cursor Discussion | Link

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

Coder à la voix avec ChatGPT, ça marche. Le hic tient en une ligne : chaque mot que tu dictes part chez OpenAI. Depuis le 23 juillet 2026, Codex se pilote à la voix — il ouvre une pull request, cherche l'origine d'un bug, tout ça dans une phrase. Pratique pour un side-project. Rédhibitoire quand le code appartient à un client. On voulait le même confort sans la fuite. Le résultat est un pipeline 100 % local : faster-whisper pour la transcription, Claude Code et sa commande /voice pour l'agent. Rien ne sort de la machine — ni la voix, ni le contexte, ni le code. Voici la config exacte, la latence qu'on mesure sur un M2, et les deux bugs qui nous ont coûté une demi-journée. Pourquoi pas simplement Codex vocal ? Parce que « coder à la voix » cache deux choses qu'on confond tout le temps. Le mode vocal de ChatGPT est fait pour converser : il répond, il temporise, il reformule. Dicter du code, c'est l'inverse — tu veux une transcription fidèle et muette, qui ne discute pas, ne reformule pas et n'ajoute rien à ce que tu dis. Deux gestes opposés. Le vrai stack n'est donc jamais « ChatGPT vocal seul ». C'est un outil de dictée précis d'un côté, un agent de code de l'autre. Codex vocal fait les deux dans le cloud pour 20 €/mois ; un setup local sépare les deux briques et garde tout sur ta machine. Le tour d'horizon complet — prix, outils, cas d'usage — est dans le guide de référence ; ici, on reste sur le terrain technique. Le chemin le plus court : /voice Depuis mars 2026, Claude Code embarque un mode vocal. Tu tapes /voice dans le terminal, tu tiens la barre d'espace, tu parles, tu relâches. La transcription passe par un Whisper local, pas par une API distante. > /voice [hold space to talk · release to send] Pour 90 % des cas, ça suffit. Tu dictes une intention, l'agent écrit le code, tu relis. Si tu veux garder la main sur le modèle, la langue et le vocabulaire technique, il faut descendre d'un cran et brancher ta propre transcription. Le pipeline DIY, brique par brique T

Close your MacBook. Keep the work running. Discussion | Link

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

Whether you’re training hard, traveling often, or dealing with poor circulation, these are the best compression boots for anyone looking for better muscle recovery.

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

TCL’s hybrid tablet-digital notebook tries to be the best of both worlds, and it mostly succeeds.