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LLMs are cheap(snellman.net)
293 points by Bogdanp | 271 comments
Tokens are getting more expensive(ethanding.substack.com)
341 points by admp | 230 comments
Substack essay by Ethan Ding arguing that AI token usage costs are rising and cheaper models won't save consumer AI subscription margins, citing Claude Code and Windsurf.
Measuring Claude 4.7's tokenizer costs(claudecodecamp.com)
678 points by aray07 | 474 comments
Blog post measuring Claude Opus 4.7's new tokenizer, finding about 1.47x more tokens than 4.6 on real content versus the documented 1.0–1.35x, and the resulting cost impact.
Related stories
Tokens are getting more expensive(ethanding.substack.com)
341 points by admp | 230 comments
Substack essay by Ethan Ding arguing that AI token usage costs are rising and cheaper models won't save consumer AI subscription margins, citing Claude Code and Windsurf.
Measuring Claude 4.7's tokenizer costs(claudecodecamp.com)
678 points by aray07 | 474 comments
Blog post measuring Claude Opus 4.7's new tokenizer, finding about 1.47x more tokens than 4.6 on real content versus the documented 1.0–1.35x, and the resulting cost impact.
The path to ubiquitous AI (17k tokens/sec)(taalas.com)
804 points by sidnarsipur | 436 comments
Taalas blog post by Ljubisa Bajic arguing that latency and cost limit AI adoption and presenting its approach to very fast (17k tokens/sec) inference hardware.
Why I'm still bearish on LLMs after Navier-Stokes(dank.systems)
426 points by jaykru | 552 comments
Personal blog essay by Jay Kruer arguing LLMs remain overhyped despite headline results like Navier-Stokes, citing poor generalization, reward hacking, and specification costs.
LLM-powered tools amplify developer capabilities rather than replacing them(matthewsinclair.com)
323 points by matthewsinclair | 214 comments
Blog post by Matthew Sinclair arguing that LLM-powered programming tools act like a mech suit amplifying developers rather than replacing them.
Why your local LLM feels dumber than it is(forum.level1techs.com)
417 points by felineflock | 171 comments
A Level1Techs forum post presenting technical experiments on how quantization and implementation differences make locally run LLMs perform worse than reference versions.