Dagens Vibes — 31. juli 2026

Armin Ronacher ⇌@mitsuhiko

A post of ours on vendor lock in and LLMs. We don't like the growing trend of AI companies quietly hiding your data while stripping away your control. We think that's bad for users and bad for the ecosystem. Here's are our thoughts: https://t.co/pM4fBX0Z1T

Addy Osmani@addyosmani

Software quality now depends on the constraints you set around your agents. When humans manually wrote most of the code we could look at the code itself for signs of quality. Is it clean? Is it thoughtful? Is it fast? Can another engineer understand it? Does it have tests? Agents can now generate more code than people can read. When code generation scales beyond review, quality - checks for one or more of correctness, maintainability, security, performance etc - increasingly has to live somewhere else. It moves into the harness, environment and operating system around the agent. This can be the tests and deterministic checks that decide what the system is allowed to do (amongst others). Your constraints are what may eventually enable loops of agents to deliver production software reliably. They can include unit tests, property tests, acceptance tests, mutation testing and quality metrics. This back-pressure lets the system resist bad work before it becomes somebody elses problem. Set your constraints. They decide whether the code your agents generate is good enough to ship.

Cursor@cursor_ai

In December, 1 in 10 of our merged PRs came from cloud agents. Today, it’s 56%, as we use cloud agents to complete longer engineering tasks from start to finish. We got here by giving agents their own cloud computers and letting them fix and improve their environments. https://t.co/HEqac7UnuC

GitHub@github

Took us a minute, but stacked PRs are now on GitHub 🥞 https://t.co/lT9GGEEDXP

0xSero@0xSero

Most slept on repo of all time, anyone can autoresearch & deeply recursively optimise anything. Do yourself a favor, get a cheap llm sub, or run something locally on some hardware, give it autoresearch and target it at something in your life. - Budgeting - Performance optimisations - Resume & job application machine - Local city/town mapping - Clear all your personal info off the web - Rebuild everything you pay for I've been doing this for months, my life is infinitely better. The concept is very very simple. 1. "Program.md": explain what it is you want, and what isn't allowed during autoresearch, especially great if u can define the output at each turn 2. "Prepare . py": a script that cleans up and enforces the experiment structure 3. Target: whatever it is you're trying to optimise, whether that's your email inbox, some mobile app/website you're trying to optimise load times for, or an llm training run. 4. Document and learn from the runs, this is practically free progress, if the run is designed as a ralph loop where the model is freshly instantiated at every turn even Qwen3.6-27B will work perfectly (heard from very trusted individuals.) This can revolutionise so many industries and lives, I fully believe this with all my heart.

Dan Peguine@danpeguine

I gave my kids a way to chat with their grandparents via WhatsApp. But without screens! This box lives in their bedroom, they press the massive blue button to record a voice message, and the message is sent to a group with both grandparents. It’s two-way so when the grandparents send a message back, there’s a ringtone (that we chose together) and the kids run to their room to listen. The grandparents even get a “listened” notification with a small headphones WhatsApp reaction. When the kids are away or asleep, the messages queue until their return. There are obviously quiet hours at night. My son and I built this over the past few weeks, iterating on the UX. The kids and grandparents absolutely love it! I love that they are in direct contact without me in the middle and without screens.