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Remember what you know about the people you manage Discussion | Link
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Remember what you know about the people you manage Discussion | Link
Lucian Ghinda published a post arguing you should tell your coding agent to write its throwaway scripts in Ruby. Here is the block he tells you to paste into your agent's instruction file, in full: ## Scripts Write throwaway and utility scripts (data munging, one-off migrations, file renames, glue code) in Ruby, even in projects written in another language. If it needs a pipe, a loop, a conditional, or more than one line, it is a script: write it in Ruby, not Python, Node, or bash. Single self-contained commands ( `grep` , `git status` ) are fine as-is. Use only the Ruby standard library. If a gem would clearly save significant effort, stop and ask before using it. Put temporary scripts in a scratch or temp directory, not the repo root, and delete them when done unless asked to keep them. I pasted it into my global CLAUDE.md the same evening. His argument is about review: he reads Ruby daily, so when the agent writes Ruby he stays a reviewer instead of nodding at a diff. He gives three reasons and not one of them is cost. So I went looking for the number he left out. "Does the rule save tokens" only means something against what the agent writes otherwise, so the first thing I had to do was take the block back out of my global config. An agent that already carries the rule cannot tell you what it would do without it. Measuring an agent without your config in the room Every arm below runs through claude --safe-mode on Claude Opus 5, which loads no CLAUDE.md , no skills, no plugins and no hooks. Two arms deliberately skip that flag, and I name them where they appear: they are the ones that measure what my own setup does to the result. It is worth knowing that my global config alone still carries a line telling the agent to write the minimum code that does the job, and another preferring bun over node. Four tasks, one for each kind of script the rule names: munge a log, rename a key across a tree of config files, renumber a pile of screenshots, turn one CSV into another
On Monday I published a piece admitting that my deploy verification tolerates sixty seconds of "not there yet" for a reason I couldn't defend. Three retries, twenty seconds apart. I picked twenty because it was the first interval where my false alarms stopped, my sample was about three deploys, and I had never once recorded how long propagation actually takes. I made three commitments in that piece. A birth certificate for the constant. A rule fixed before the run it judges. And the one that mattered most: emit the value, not just the verdict — a check that prints only pass or fail hides the exact signal that would tell me it's miscalibrated. I did the third one that afternoon. Every deploy since writes down how long it took to go green. Three samples in: Aug 03 ( 1.7, 21.7 ]s Aug 05 ( 0, 6.7 ]s Aug 05 ( 40, 70 ]s They're intervals rather than points because my poll spacing is twenty seconds. All I can honestly say is that green happened somewhere between the last failed check and the first successful one — a number I can't resolve finer than my own instrument. The third one failed Not the deploy. The check. I shipped a post, ran verification, and got a clean red: page 404, hero missing, sitemap entry absent. Three attempts, twenty seconds apart, exactly as designed. By its own rules the deploy had failed. Nothing was wrong. A longer script came back 200 on everything. Total elapsed: somewhere between forty and seventy seconds, against a tolerance of sixty. So the false alarm I widened the interval to eliminate returned on the third recorded sample, four days after I published the sentence "my sample was about three deploys." I'd like to say I predicted this. I predicted the category, not the timing, and the timing is the part that stings. The part I hadn't considered at all Here's what the red actually said, in order: attempt 1 article 404 · hero missing · list page MISSING · sitemap missing attempt 3 article 404 · hero missing · list page OK · sitemap missing The
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Persistent Memory Layer for AI Agents Discussion | Link
Building software is easy. Building something people actually want to use is the hard part. For the last few months, I've been working on CV Mimarı, a resume builder designed to make creating ATS-friendly resumes simple, fast, and accessible. 👉 https://cvimarı.xyz My goal wasn't to build "another resume builder." I wanted to remove the usual pain: confusing editors unnecessary account creation complicated formatting resumes that look good but fail ATS screening The idea was simple: Spend your time improving your experience, not fighting with Word formatting. What it currently does Today the project includes: Resume templates AI-powered resume improvements ATS score checking Resume optimization Cover letter generation Resume examples and guides PDF export Modern responsive interface I tried to keep everything clean and straightforward instead of adding dozens of unnecessary options. Sometimes software tries so hard to become "professional" that it forgets people just want to click a button and move on with their lives. Why I'm posting here I'm not looking for compliments. I'm looking for problems. Imagine you were using this to apply for your next job. I want brutally honest feedback. Things like: Is something confusing? Does the UI feel slow? What would make you leave the site? Which feature feels unnecessary? What's missing? Would you actually trust this with your resume? If something is bad... Tell me. If something is ugly... Tell me. If something makes you want to close the tab... Definitely tell me. The biggest challenge One thing I've learned is that building features is much easier than understanding users. I can spend a weekend implementing a new AI feature. But discovering why someone leaves after 20 seconds? That takes dozens of real users. That's why I'm asking for feedback before continuing to add more features. The roadmap Some ideas I'm considering: More resume templates Better AI suggestions Portfolio integration LinkedIn import Resume version history
I put handheld, wearable, and misting fans through a sweltering summer to see which ones kept me coolest.
A tiny crew of monsters for your Claude Code agents Discussion | Link
30-60% faster than Claude Code and Codex Discussion | Link
title: How to Convert Images to Buildable Minecraft Pixel Art with Exact Materials published: true tags: minecraft, tutorial, gaming, opensource Originally published at blockartlab.com Disclosure: I built BlockArtLab, the free browser tool used in this guide. Most image-to-pixel-art tools stop at a preview. That is useful for seeing the idea, but it leaves the difficult questions unanswered: How large should the build be? Which real blocks should I collect? How many stacks of each color do I need? This tutorial covers the complete workflow from source image to a blueprint you can construct. 1. Pick an image that survives low resolution Minecraft pixel art works best when the source has one recognizable subject, a clear silhouette, and strong contrast. Logos, flags, game characters, and illustrated portraits usually survive conversion better than photographs with a busy background. Before uploading the image: Crop unused space around the subject Remove distracting background objects Make important features such as eyes or lettering larger Increase contrast if the subject blends into the background ## 2. Choose dimensions by material cost One converted pixel equals one placed block. The total block count is: width × height = total blocks | Size | Total blocks | Best use | |------|-------------|----------| | 16 × 16 | 256 | simple symbols and prototypes | | 32 × 32 | 1,024 | small survival logos and characters | | 64 × 64 | 4,096 | portraits, shading, and medium text | | 128 × 128 | 16,384 | a classic single-map-sized canvas | Doubling both sides multiplies the material count by four. For a first wall build, start between 32 and 64 blocks wide. ## 3. Choose a practical block palette I use three simple palette strategies: Concrete only for logos, flags, cartoons, and saturated colors Survival friendly for accessible concrete, wood, stone, sandstone, moss, and similar materials Full palette when a closer color match matters more than collection cost ## 4. Decide between
delivered tomorrow morning at a random time between 8 -10 AM Discussion | Link
A practice app for learning to play music with your hands. Discussion | Link
A 0.3-second disagreement between two sources of truth made my first paying customer fail four times. The browser preview stored the project duration rounded to a whole second: 3983s. The worker that processed the audio measured the real media: 3982.699–3982.788s. Cue generation ran against the rounded number. Delivery certification ran against the trusted measurement. Any candidate built on the rounded boundary exceeded the certified boundary by 212–301ms — so the final cue failed, deterministically, every single time. That customer ended up with four projects and three distinct audio files — four identical failures, each one blocked by the same gate. No subtitle asset, no explanation, no path forward. No alert fired. No complaint had come in. I found it because I was looking. Here is the part worth writing down: the quality gate did exactly what it was designed to do. It rejected every unsafe result before it could reach the customer. And the customer still lost. Four failures, and not one of them was a gate that misbehaved — they were four places where quality had been treated as a check instead of a product decision. A fail-closed gate is an engineering floor, not a product. Quality is not the final check that rejects bad output; it is the input boundary you commit to, the authority you give each fact, the failure states you design for, and the meaning you attach to your own scores. What follows is the postmortem as an engineering story: four deterministic failures, each one a missing product decision, and the contract I now think every pipeline like this should carry. One fact, two authorities The whole incident starts with a single number. The project duration existed twice: The browser preview rounded it to 3983s . The funded worker measured the actual media as 3982.699–3982.788s . Cue generation used the rounded value. Delivery certification used the trusted measurement. The result: the last cue always ended 212–301ms past the certified boundary, and the fin
An RSVP reader for books, PDFs, articles & webpages Discussion | Link
The fastest way to send anything to your iPhone Discussion | Link