Auto-aggregated global tech articles · 1,000 posts
Hi all! I’ve been coding professionally for the last 15 years - and more and more with AI next to me. Naturally pushed by the market to produce more and more output, I now find myself hopping daily between 5 to 7 simultaneous AI sessions. I realized that the bottleneck is becoming the code review
Party Challenges is a free 18+ browser party card game with 1,000+ cards across 23+ themed decks for friends, couples and solo play. We built it at Inithouse, our product studio, and after several months of running it we started tracking where sessions break down. The short version: most card games
What Actually Happens When You Run docker run You type docker run -d --memory 512m myapp:latest and hit Enter. A second later, a container is running. It feels like one action. It is not. Behind that single command, four separate programs hand work down a chain, an image gets pulled apart into la
Benchmark scores (GPT-6 uses a harness for ARC-AGI-3, and is at about 60% without one): https://preview.redd.it/v7nik4nbtfnh1.png?width=1378&format=png&auto=webp&s=a6ec04b5b87e7f2dce748b275d878ab0243f751d https://openai.com/index/gpt-6-astra/ submitted by /u/we_are_mammals [link]
For the past year, the default approach to building AI automation has been simple but flawed: write a massive prompt, hand it to a single "god-mode" AI agent, drop it into an autonomous loop, and cross your fingers. While this loop engineering works for simple summarizations, it shatters in product
Short answer: model session creation, verification, refresh, and logout as four separately authorized, auditable state transitions; for a property-management portal, require abuse checks before creating or recovering access, keep renewal more constrained than initial login, and distinguish one-devic
A D2C technology stack is often discussed as a shopping list: commerce platform, OMS, CRM, analytics, payments, search, loyalty, customer support and increasingly AI. That framing is useful when a brand is starting. It becomes dangerous when the brand begins to scale. The reason is simple: custome
Shared AI sessions make a ton of sense. We already do a janky version of this today. I talk through something with ChatGPT, copy the useful parts to a friend, and they keep going from there. Or I share the conversation. Or two people work through the same problem together. Nothing weird about t
I've seen this happen dozens of times. An engineering team finally ships a massive, much-needed fix—something that solves a long-standing headache described in three different GitHub issues—and the resulting announcement sounds like it was written by a mid-level PR agency intern trapped in a basemen
Docker starts today. After fourteen days of Git, the track moves to containers, and it opens with the least glamorous task available: install the packages and start the service. Both halves of today turned out to be about time. When something starts, how long it takes, and what still holds after a
A pull request can pass the compiler, the formatter, and the linter and still leave behind a serious engineering problem. Maybe two independent network requests are being awaited one after another. Maybe a log line includes credentials or personal data. Maybe an error is being discarded without mak
Proving Your AI Agent Rules Hold for Every Input — Not Just the Ones You Tested Here's a question your auditors will eventually ask: "This decision the agent made — on what basis?" LLMs are probabilistic. Ask twice, get two answers. If your enterprise delegates approvals, refunds, or access decis