Daily Editions
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  1. Page 1: Today’s IndexTLDR, TLDR AI, TLDR Web Dev, TLDR Crypto · 9 stories
  2. Page 2: TLDRTLDR · 11 stories
  3. Page 3: TLDR AITLDR AI · 5 stories
  4. Page 4: TLDR Web DevTLDR Web Dev · 10 stories
  5. Page 5: TLDR CryptoTLDR Crypto · 10 stories
  6. Page 6: Resources & ToolsTLDR, TLDR AI, TLDR Web Dev, TLDR Crypto · 8 stories
  7. Page 7: SponsoredTLDR, TLDR AI, TLDR Web Dev · 3 stories
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Daily Edition · Page 1

THE DAILY INDEX

Technology, AI, software, security, crypto and business

Across today’s newsletters

Today’s Index

THE STRANGE STORY OF THE TEENS BEHIND THE MIRAI BOTNET

The Mirai botnet, which exploited vulnerable Internet of Things devices, was put together by three teenage boys. They rented out the botnet to paying customers and used it to attack targets of their own. Its use has affected millions of people. This article looks at how Mirai was created, the market around botnet providers, the malware behind the botnet, and the events that led to the creators' arrest and subsequent sentencing.

WHY SNX HAS SURPASSED GMX IN VOLUME

Synthetix has surpassed GMX in trading volume, driven by several key factors. It lowered trading fees to as low as 0.02% for certain pairs, significantly increasing trade volume. Additionally, an airdrop program incentivizes traders with $OP tokens that essentially outpace the trading fees they incur on the protocol. Increased decentralization, with trades settled by incentivized keepers and the use of Pyth Network as a primary oracle, has given it greater trust and uptime. Further, decentralizing frontends has increased Synthetix's censorship resistance.

SEAHORSE: A TOOL FOR EVALUATING MULTILINGUAL SUMMARIZATION SYSTEMS

SEAHORSE is a dataset created to assess the quality of summarization systems in multiple languages. It contains 96,000 summaries that have been rated by humans based on six important aspects: clarity, repetition, grammar, attribution, main ideas, and conciseness. This dataset not only serves as a benchmark for evaluating the performance of automated metrics but also provides a valuable resource for training such metrics, helping researchers advance the field of multilingual summarization evaluation.

MODELS

This paper introduces meta-in-context learning, a recursive self-improvement process for large language models. By studying regression and decision-making tasks, the researchers demonstrate that meta-in-context learning enhances the models' in-context learning abilities, modifies their strategies, and achieves competitive performance on real-world problems. This work sheds light on improving language models without relying solely on traditional fine-tuning approaches.

THE DIRE DEFECT OF AI CONTENT MODERATION

The article discusses the challenges of using artificial intelligence to moderate content across multiple languages. It argues that current AI systems are not able to accurately detect harmful content in all languages, and that this can have serious consequences. It finishes by calling for more research into how to improve the accuracy of AI content moderation systems, and for more transparency from social media companies about how they are using AI to moderate content.

TLDR IndexPage 1