# Jul 2, 2026

## The Fall of the Theorem Economy
- **AI's rise threatens the traditional landscape of mathematics**, as it may redefine the discipline by automating theorem proving, potentially sidelining human mathematicians and altering the perception of mathematical creativity.

## How We Made IPFS Content Publishing 10x Faster
- **Optimistic Provide** enhances IPFS content publishing speed by reducing upload latency from over **13 seconds** to under **1 second**, significantly improving real-time content availability for developers and users.

## Is One Layer Enough? A Single Transformer Layer Matches Full-Parameter RL Train
- **Training a single transformer layer** can achieve most of the performance gains from full-parameter RL training, and in some cases, it may even exceed those gains, highlighting the efficiency of targeted updates.

## Senior SWE-Bench: open-source benchmark that assesses agents as senior engineers
- **Senior SWE-Bench** evaluates agents as senior engineers, utilizing **realistic instructions** and a **validation agent** to adapt behavioral tests for submitted solutions, enhancing the evaluation process.

## CursorBench 3.1
- **CursorBench 3.1** evaluates AI agents on **ambiguous, multi-file tasks**, revealing that **Fable 5 Max** leads with a score of **72.9%** at a cost of **$18.02** per task, showcasing its efficiency in complex coding scenarios.

## NVIDIA Unlocks AI Compute at Scale, Inviting Capital Partners to Power the AI Infrastructure Buildout
- **NVIDIA's new partnership model** enables AI clouds to deploy large-scale, multi-tenant AI factories, enhancing access to accelerated computing through a **revenue-sharing and credit-support structure** that aligns economic interests.

## Asymmetric Quantization: Near-Lossless Retrieval with 97% Storage Reduction
- **Asymmetric quantization achieves a remarkable 97% reduction in document vector storage**, compressing from 393 KiB to 12.28 KiB while maintaining retrieval quality with an NDCG@10 score of 89.65, only slightly below the baseline of 90.26.

## Launch HN: Parsewise (YC P25) – Reason Across Documents with an API
- **Parsewise** transforms unstructured data into **schema compliant** outputs, enabling tech teams to validate results quickly and efficiently across multiple documents, addressing both system limitations and human challenges in data extraction.

## Comparing Fable and 10 other LLMs on refactoring a LangGraph god node
- **Fable-5 outperformed** other models in generating architectural proposals for refactoring a complex code structure, demonstrating a clear understanding of the need to split the "god node" into manageable components for better clarity and maintainability. [Materials & reproduce this experiment](https://wtf.korridzy.com/twilight-of-the-gods/).

## Claude-real-video － any LLM can watch a video
- **`claude-real-video` enables LLMs to _actually watch_ videos by extracting meaningful frames based on scene changes, rather than fixed intervals, ensuring a more accurate representation of fast-paced content.**

## The State-Prediction Separation Hypothesis
- The **state-prediction separation hypothesis** posits that separating the roles of state storage and token prediction in Transformers enhances language modeling performance, leading to improved validation loss and efficiency.

## SentryCode: Real-time Auditor + Honeytokens for AI Coding Agents 
- **SentryCode** is an open-source, **kernel-level behavior auditing tool** designed to address privacy concerns from local AI coding agents by logging file, network, and cue activity while employing honeypot tokens for **zero-false-positive data breach detection**.

## MarketFish – Simulate a market with 128 AI consumers before you launch
- **MarketFish** is a **multi-agent market simulation engine** that utilizes **128+ AI consumers** to predict product success through simulated shopping behaviors across **30 rounds**, revealing insights into real user decisions.

## From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives
- **MAGNET** introduces a **multi-agent** framework that enhances long-form narrative generation by utilizing **persona-grounded character agents** to maintain coherence and plot consistency, addressing the limitations of large language models (LLMs) in storytelling.