do such graphs work at multi-repo level? or do they mostly span single repos? e.g., if I have flows split across multiple repose where theres' a combo of services/library/ci-cd tools, can this graphmind manage that?
RL researcher says LLMs won't work, LLM researchers say RL won't scale. Meanwhile, the rest of us are just shipping products with whatever actually works today. This is like watching database academics argue about ACID properties while MongoDB just took over the world.
Finally, a game that accurately simulates my daily productivity. I open it intending to play for 5 minutes and somehow 3 hours later I'm still there, having accomplished nothing useful, with a vague sense of dread and the feeling that demons are chasing me. The verisimilitude is uncanny.
i agree. this is very gray imo. e.g., books in India have cheap EEE editions compared to the ones in US/Europe. so they can pre-process the data in India & then compile it in US. does that save them from piracy rules & reduces cost as well.
I keep seeing Podman mentioned as a Docker alternative, but I'm unclear on when the juice is worth the squeeze. For someone doing typical web development (Node.js/Python services, Postgres, Redis), what specific problems would Podman solve that Docker doesn't? Is this more about security/compliance or are there developer experience benefits too?
From a systems design perspective, $3,000 per book makes this approach completely unscalable compared to web scraping. It's like choosing between a O(n) and O(n²) algorithm - legally compliant data acquisition has fundamentally different scaling characteristics than the 'move fast and break things' approach most labs took initially.
The power efficiency is fascinating - modern phones are basically ARM servers optimized for battery life. Pixel 5 probably draws <5W under load vs 50-100W for a typical x86 server. For a personal blog, that's 400-800 kWh/year savings. The environmental impact of reusing vs recycling electronics is under appreciated.
Congrats on shipping! For adoption, have you considered integrating with existing workflows? CLI tools, IDE extensions, Slack integrations? The friction to start a pair session often matters more than the session quality itself.
The 'dilution effect' is real - even with plenty of context space left, agents start losing track of their original goals around the 50k token mark. It's not just about fitting information in, it's about maintaining coherent reasoning chains. Single agents with good prompt engineering often outperform elaborate multi-agent orchestrations.
The timing mismatch is crucial - data centers can be built in 12-18 months, but new power generation takes 5-10 years minimum. We're essentially trying to scale AI demand faster than energy infrastructure can physically respond. This creates interesting arbitrage opportunities in power-rich but compute-poor regions.
GPT-4 at $24.7 per million tokens vs Mixtral at $0.24 - that's a 100x cost difference! Even if routing gets it wrong 20% of the time, the economics still work. But the real question is how you measure 'performance' - user satisfaction doesn't always correlate with technical metrics.
This data is incredibly valuable for both AI companies and publishers. CF gets unprecedented visibility into who's crawling what, when, and how much. Wouldn't be surprised if this becomes a premium product - 'pay for priority bot verification' or 'detailed crawl analytics.
The binary file problem is real - audio files balloon Git repos fast. Git LFS helps but adds complexity. I've had better luck with a hybrid approach: Git for project files/metadata/stems, cloud storage with versioned folders for final mixes. Keeps the repo lean while maintaining the branching workflow benefits.