Safari introduces Adaptive Dark Mode in 27.3

The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer

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Stringman, an $800 open-source room scale gripper robot

Qwen 3.8 27B on my laptop can interpret model weights, too

I saw HackerTube (<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=50319510">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=50319510</a>) a few days ago, the YouTube videos HN keeps
recommending, pulled from 19 years of comments. It made me want to flip
through them instead of picking, so I built a TV for it. I have Algo fatigue.Each topic area is a channel (DEVTV, SOLDER, RETRO, ORBIT, ...), plus HN Prime
(the top 400), a music channel, and Late Night on ch 99, which only broadcasts
11pm-5am your local time and shows color bars otherwise.Similar to HackerTube it was built with Claude Code in a few hours.All credit for the data goes to HackerTube. Happy to hear channel ideas.

Show HN: HackerTV – HN's most-recommended Youtube videos as 24/7 TV channels

The Internals of PostgreSQL

Postman silently auto-installs AI plugins and attempts to share all API data

macOS 27 does not support CoreStorage volumes

Hey HN, Ayan here from Finic. We’re launching Kairos, an open source alternative to Sift, Sardine, and other real-time fraud decisioning platforms. Kairos addresses two of the biggest problems facing traditional decisioning tools: a lack of labeled data to train on and dealing with fraudsters that are constantly changing their tactics.1. Traditional fraud solutions use ML models such as XGBoost to detect fraud, but these models are dependent on quality labeled data which many businesses do not have. We address this by using “System One” decision models in additional to traditional models such as XGBoost and logistic regression. This allows you to incorporate plain language questions such as “Is this a throwaway email” into your decisioning, bypassing the need for large amounts of labeled data. This also allows you to capture aspects of fraud that are more unstructured, which is something that XGBoost models struggle with.2. Fraud teams are facing off with live players that are constantly changing their behavior to bypass the detection logic they have in place. You can train a detection model or define a ruleset once, but it will become obsolete in a matter of weeks as fraudsters figure out what your detection logic is. This means that you need a team regularly training models and updating rules based on new fraud patterns. Typically this takes a large data science&#x2F;ML team. Kairos addresses this by introducing an MCP server, so that your agent (Claude Code, Cursor, Codex, etc.) can define a decision workflow, train models on your data, and pick the variant that performs best.You can self-host Kairos with Docker or use the hosted version at <a href="https:&#x2F;&#x2F;kairos.finic.ai">https:&#x2F;&#x2F;kairos.finic.ai</a>

Show HN: Kairos – Open-source risk and fraud decisioning platform

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