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first_img Data: In the first half of 2026, there were only 2,932 active job openings in the cryptocurrency industry, a drop of over 97% compared to the peak in 2022

According to the latest report from Tiger Research, as of June 18, 2026, the number of active job openings in the cryptocurrency industry is only 2,932, a significant decrease of over 97% from the estimated peak of about 130,000 in 2022.The report shows that the wave of layoffs in the cryptocurrency industry continues in the first half of 2026. March was the month with the highest concentration of layoffs, with several companies including Gemini, Crypto.com, Algorand, OP Labs, PIP Labs, and Messari announcing layoffs at the same time. Some companies were acquired at low prices after multiple rounds of layoffs; for example, Messari was acquired by Blockworks for about $10 million in June 2026 after experiencing three rounds of layoffs, while its previous valuation had reached $300 million.In terms of recruitment structure, positions in centralized exchanges (CEX) account for the highest proportion, reaching 30.8% (904 positions), mainly contributed by OKX, Bybit, and Binance. The stablecoin and payment sector accounts for 13.4%, but is highly concentrated in two companies, Tether and Ripple.In addition, the demand for AI skills in job postings continues to rise, with the proportion of cryptocurrency job postings mentioning artificial intelligence skills increasing from 23% in early 2025 to 53.1% in March 2026.

Coinbase: Has reduced AI spending by nearly 50% and is trying to default to adopting open weight models

Coinbase CEO Brian Armstrong published an article introducing the company's latest progress in AI cost optimization.Armstrong stated that as the usage of AI and Token consumption continues to grow, the key to controlling costs is not to restrict employee usage or frequently send budget reminders, but to optimize default model selection, task routing mechanisms, and caching strategies.He revealed that Coinbase is trying to use open-weight models such as GLM 5.2 and Kimi 2.7 as default options through an internal LLM gateway, while still allowing engineers to choose other models based on specific task requirements. Data shows that 91% of the company's employees have never reached the AI usage quota limit, so Coinbase has not chosen to tighten quotas but instead improved overall efficiency through lower-cost model solutions.In terms of model routing, Coinbase preprocesses prompts and, combined with cache hit rates and the pricing of different models, automatically assigns tasks to the most suitable model. Armstrong believes that complex tasks such as planning and reasoning may require support from cutting-edge models, but execution tasks do not necessarily need to invoke higher-cost models. In the future, the model selection process should be more automated by AI rather than relying on manual decisions.Additionally, he pointed out that cache hit rate is one of the important factors affecting AI costs. Coinbase has incorporated a cache-aware mechanism into the request process to improve the reuse rate of historical results. For example, in the case of LibreChat, after optimizing the caching solution, its cache hit rate has increased from 5% to 60%.Armstrong also stated that the company requires engineers to keep context as concise as possible, including starting new sessions when switching tasks, narrowing the context scope of files, and closing unused tools, to reduce unnecessary Token consumption.According to him, through these measures, Coinbase has successfully reduced AI spending by nearly 50%, while Token usage continues to grow.

OpenAI has launched the next generation GPT-5.6 series models, currently available only to trusted partners using Codex and the API

According to official news, OpenAI has officially launched the preview version of the next-generation GPT-5.6 series models, including the flagship model Sol, the balanced model Terra, and the fast low-cost model Luna. GPT-5.6 introduces a brand new maximum reasoning effort and features a super strong mode that accelerates complex tasks through sub-agents.The flagship model Sol introduces the Ultra mode, which combines maximum reasoning intensity with sub-agent collaboration. In the Terminal-Bench 2.1 command line workflow test, Sol achieved a score of 88.8%, which increased to 91.9% in Ultra mode, surpassing GPT-5.5's 83.4% and Claude Fable 5's 88.0%. The mid-range model Terra performs close to GPT-5.5 while being priced at half, and the lightest model Luna is designed specifically for everyday automation tasks. Sol is priced at $5 per million input tokens and $30 for output, and it supports reducing secondary call costs by utilizing prompt caching.In terms of security, the security assessment confirmed that Sol did not exceed the critical thresholds of the Preparedness Framework cybersecurity. OpenAI has invested over 700,000 A100 equivalent GPU hours in automated red team exercises, equipping the entire series of models with a defense stack that includes rejection mechanisms, real-time abuse classifiers, and account-level audits. Although the current limited release follows the U.S. government's security framework, OpenAI emphasizes that it does not want a government-led access mechanism to become the long-term default model, as it would limit defenders' access to cutting-edge tools.
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