~hackernoon | Bookmarks (2054)

Carl “Moon” Runefelt warned of a market crash in January 2025—and it’s happening now. Using trends in the federal funds rate, he predicted the downturn and called Bitcoin a safe haven. As stocks and crypto fall, his insights are being revisited as a timely forecast rather than mere speculation.

Each week, the HackerNoon team highlights standout Startups from our Startups of The Year database. Every featured startup has been nominated as the best in their respective technology category or region. This week, we present: Neo Auth, Peris.ai, and Midas Analytics.

This paper presents a new AI-powered knowledge graph that organizes real-world materials science research into an accessible, searchable database.

This paper presents a new AI-powered knowledge graph that organizes real-world materials science research into an accessible, searchable database.

Meet Albert Lie, Co-Founder & CTO of Forward Labs, in his interview with HackerNoon Writers Spotlight, talking about writing amid the Noise of AI.

This paper presents a new AI-powered knowledge graph that organizes real-world materials science research into an accessible, searchable database.

This paper presents a new AI-powered knowledge graph that organizes real-world materials science research into an accessible, searchable database.

This paper presents a new AI-powered knowledge graph that organizes real-world materials science research into an accessible, searchable database.

The Kindle store is flooded with auto-generated e-books written by AI authors. The content is hollow. It's dry. It’s robotic. You can practically hear the metallic voice in your head as you read. Amazon doesn’t care if your e-book was written by a toaster as long as it gets clicks.

This paper presents a new AI-powered knowledge graph that organizes real-world materials science research into an accessible, searchable database.

This paper presents a new AI-powered knowledge graph that organizes real-world materials science research into an accessible, searchable database.

This paper presents a new AI-powered knowledge graph that organizes real-world materials science research into an accessible, searchable database.

This paper presents a new AI-powered knowledge graph that organizes real-world materials science research into an accessible, searchable database to speed up discovery across scientific fields.

A fictional startup’s MongoDB bill spiked 20% when Atlas auto‑scaled to an M60. By profiling slow ops, fixing N + 1 queries with $lookup, capping/TTL’ing unbounded queries, refactoring jumbo docs, and reordering indexes over a 48‑hour sprint, they slashed their monthly cost from $15,284 to $3,210 (‑79%) and improved p95 latency from 1.9 s to 140 ms—no sharding required.

When building an API for a tiny startup on a shoestring budget, the author compares raw Node.js, Express, and NestJS. Despite NestJS’ heavier footprint, it saves time on validation, documentation, and onboarding—ultimately winning out thanks to dev-friendly features and long-term maintainability.

How are you, hacker? 🪐Want to know what's trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. How GitHub Copilot Enhances Developer Productivity by Preeti Verma By @rsystems [ 4 Min read ] In this winning article by P...

This paper presents TnT-LLM, a framework leveraging LLMs to automate large-scale text analysis, including label generation and efficient classifier training.

We begin with a high-level overview of TnT-LLM, our proposed two-phase framework for 1) LLM-powered taxonomy generation and 2) LLM-augmented text classification.

This section outlines the Batched Prompting approach used to annotate preferences efficiently with GPT-4, where all candidate responses are evaluated in a single batch rather than individually. The cost analysis of scaling up the experiment to 600k training inputs reveals that sampling was the most time-consuming and expensive step, costing about $6,000 per iteration. Annotation was the highest cost, amounting to $34,000 per iteration...

Gran ==Turismo== 7 (==GT7==) game data can now be accessed with standard networking protocols. Telemetry data needed for racing lights, analog gauges, butt-kickers, and even motion are now available to console users. This article guides you through the process of using ==SimHub== to capture and display ==GT7’s== data on your computer.

In this section, we provide detailed theoretical proofs supporting the Direct Nash Optimization (DNO) framework. The proof of Theorem 2 involves a two-step procedure, beginning with regression using logarithmic loss and leading to a squared error bound. The definitions and assumptions draw heavily on concentrability from reinforcement learning theory (specifically the works of Xie et al., 2021, 2023). While the section simplifies some concepts...

Elon claimed Grok 3 was the world's best AI. Two months later, how does it really stack up against GPT-4o, Claude 3.7 & Gemini 2.5?

This section presents an extension of the Direct Nash Optimization (DNO) framework for handling regularized preferences. The key difference between SPO and Nash-MD lies in the use of smoothed policies for the latter, which helps obtain a late-iteration guarantee. The section introduces a new version of DNO, designed to converge to a Nash equilibrium using KL-regularization. The algorithm (Algorithm 3) works iteratively, adjusting the...

This section details Direct Nash Optimization (DNO), a method designed to optimize LLMs using Nash equilibrium principles, addressing challenges faced by traditional soft policy iteration. DNO replaces unstable, complex on-policy updates with a regression-based contrastive objective for stable, batch training. The approach enjoys monotonic improvements and converges to the Nash equilibrium. A 7B parameter LLM trained with DNO outperforms Mistral Large and earlier versions...