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Tokenmaxxing: Strategies to Control Budgets and Optimize Performance in AI Usage

From wasteful token habits across tech giants to model routing, context pruning, and caching methods that dramatically cut AI operating costs.

Tokenmaxxing: Strategies to Control Budgets and Optimize Performance in AI Usage

The Reality of Crypto-AI Inference Networks: 1% Market Share and the Token Subsidy Dilemma

A report from Blockworks Advisory analyzes 7 Web3 inference providers on OpenRouter, highlighting their modest scale and heavy reliance on token subsidies.

The reality of crypto-AI inference networks: 1% market share and the token subsidy dilemma

AI Agents and Privacy: Look at Permissions Rather Than Promises

Dots and Muse both emphasize privacy, but computer isolation, action controls, and training data usage are distinct issues. Reading the technical documentation helps distinguish current mechanisms from future promises.

AI Agents and Privacy: Look at Permissions Rather Than Promises

GSR Report: Low Float and High FDV Is a Structure Guaranteed to Cause Retail Losses

GSR analyzed every listing on major exchanges since 2013 and found that tokens with a float under 20% lost around 75% of their value after one year, while those with a 30–50% float lost only about 45%.

GSR Report: Low Float and High FDV Is a Structure Guaranteed to Cause Retail Losses

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AI Writing: Right or Wrong?

Opinion · AI

AI Writing: Right or Wrong?

In an era where artificial intelligence technology is becoming an inevitable trend, is ostracizing or criticizing AI-generated content—especially text—still justified? And what perspective and mindset should we adopt toward this issue?

DePIN Is Not Dead, It Is Retracing the Path of AWS!

Opinion · Crypto

DePIN Is Not Dead, It Is Retracing the Path of AWS!

Storj’s bankruptcy and Helium’s buyout suggest an alternative reading of decentralized infrastructure: the 'decentralized' element is yielding to 'contracted services,' and this may mark the moment the industry truly matures.

Andrew Ng shares the four most important AI Engineering skills

Opinion · AI

The Four Most Important AI Engineering Skills, According to Andrew Ng

Andrew Ng and DeepLearning.AI have released the "AI Engineering Skills Map" based on an analysis of more than 10,000 job postings and dozens of expert interviews, identifying four core skill areas: building and deploying AI applications, software engineering fundamentals, using coding agents, and product shaping.