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开发者 The Verge AI

X admits its broken algorithm made the site feel like a ‘battleground’

X's head of product, Nikita Bier, admitted in a post on Monday that X's algorithm was "missing" data about surfacing posts from people who you've followed back. Now, he says a tweak will "boost visibility of your posts to your mutuals," hopefully enhancing the sense of community instead of highlighting and spreading random arguments, but […]

Stevie Bonifield 2026-07-14 23:20 2 原文
AI 资讯 Dev.to

He Built an App in 24 Hours and Made $20,378 the Next Day. Here's the Part Nobody Screenshots.

Marc Lou read a tweet, slept on it, and woke up still annoyed. The tweet, from Pieter Levels, was about all the fake revenue screenshots on X. By the next evening Lou had built a thing to fix it. By the day after that, the thing had made $20,378. That is the part everyone retweets. I want to walk you through it, and then I want to show you the line in his own year-end letter that complicates the whole legend. The setup Lou got fired by Tai Lopez in November 2021, was broke and depressed, and moved to Bali. He started shipping tiny products in public, copying the playbook of, yes, Pieter Levels. His breakout was ShipFast , a Next.js starter kit that did $40,000 in its first month in September 2023. By December 2025 he was running 15 startups generating about $84,900 a month, with cumulative revenue past $2.26 million, per his verified TrustMRR data. The reason I trust his numbers more than most is that he verifies them through Stripe on his own product, TrustMRR , which brings me to the 24-hour story. The moment something worked, absurdly fast TrustMRR exists to kill fake MRR screenshots. You connect a read-only Stripe key, and it shows your verified revenue on a public page nobody can edit. Lou built it in a day on top of his own boilerplate, which is the cheat code here. He was not starting from zero, he was starting from ShipFast. "TrustMRR is 24 hours old and was built in 24 hours." @marc_louvion on X He monetized it with sidebar ad slots. He listed them at $299 a month, then raised the price each time one sold, all the way to $1,499. In his newsletter he wrote that within three days every slot was gone and the side project had made $20,378. He called it the third fastest-growing thing he has ever built. Five days in, he posted the run-rate dream out loud. "20/20 spots filled! TrustMRR went from $0 to $18,380 MRR in 5 days. That's $220,000 ARR if I'm allowed to dream a little" @marc_louvion on X It kept going. By December 2025 TrustMRR was his single biggest inco

Agnel Nieves 2026-07-14 23:20 4 原文
AI 资讯 HackerNews

Show HN: I built a smart proxy so your coding agent can run loose

The only way to go fast is full YOLO mode in your coding agent. I've got the local sandbox figured out (pro tip: Incus VMs work great) but I wanted to keep my agents from doing things like inadvertently blowing up my cloud services or chasing a prompt to POST to some random website. I struggle most with this on my side projects where my permission model isn't quite as robust as it is at the office. I started with a firewall on the Incus container but every time the agent needed access to somethi

dandriscoll 2026-07-14 22:55 2 原文
AI 资讯 HackerNews

Show HN: Low-latency local LLM runner via OpenJDK Panama FFM (Java 22)

I wanted to run AI from inside the JVM. I started out with the standard REST sidecar, ripped that out to use Project Panama (Foreign Function & Memory API) in the new JDK versions to interface directly with llama.cpp. I still wasn't happy with how that functioned, so I built libargus.cc to get a clean ABI to expose a structured API up in the JVM landscape. It still uses Project Panama to interface directly with llama.cpp, whisper.cpp, and ggml compute graphs. I have zero-allocation on the hot pa

KingJoker 2026-07-14 22:40 0 原文
AI 资讯 HackerNews

Show HN: Oodle.ai – $10 per million agent traces

Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet bo

kirankgollu 2026-07-14 22:36 2 原文