| I use DeepSeek every day (via VSCode Insiders and Zed Editor). It's very affordable and, while it's slightly behind Claude (not sure how far behind Fable), it suits my working style well. I'm not using unsupervised multi-agent workflows and don't need a library of skills files - I'm writing most of the code and leaning on AI to help with mundane tasks - like; - generating types for APIs - generating boilerplate based on existing code - improving existing code (adding error handling, timeouts, things like that) - Writing SQL repository boilerplate / queries - Creating implementations against hand written tests - Helping me understand and implement APIs from third party libraries - Writing documentation I've spent like $2 in the last month and have used over 100 million tokens. It's doubled my productivity and unlocked work that I could not have done before. As an Australian, I'm not sure that I care about the safety of my data when it comes to LLMs. US companies already stole scores of data to train their models on and it's hard to imagine they suddenly grew some integrity. I'll care when regulators step in, until then it's out of my control so I'll just use the best price-to-productivity product available. |
If you expanded the list to very hard research tasks, Fable was so far ahead of the others that it doesn't even deserve debate. If you are a researcher doing something involving scientific computation or mathematics that wasn't rejected by the guard rails, and you were using Fable, that week was probably your most productive week ever. A couple of my PhD students effectively finished their current projects in that period by getting Fable to chew on it for 30 hours straight (not sure how I feel about that).