It's said Astra is based on a newly pretrained model. Personally I hope it solves the problem that GPT speaks weirdly (which can also be spotted on Claude models after Opus 4.8 but GPT's is more severe) because IMO the way GPT couldn't get how to write good code (obviously it's trined to write code which pass benchmarks, but never code which sound good, with pursuit towards simplification and code aesthetic in mind) is extremely similar with how it couldn't get how to speak like a human.
However, it seems like OpenAI didn't pay much attention on these perspectives and I didn't find if Astra could write a more elegant code, or communicate more naturally, etc., which made me somehow a little disappointed.
They indeed mentioned the code Astra delivered is closer to production grade but production-grade code is different from what I want since there can be a kind of messy code blowing up your whole architecture design with control flows nobody truly understands but just passes all tests perfectly. There is no difficulty in maintaining this kind of code because you only need to paste the problems into Codex. And we all know this sounds incorrect. I don't know if my appetite towards a good code (no matter how) is sound but I just imagined frontier labs to give more attention on this.
Note: fwiw Fable 5.1's release page says it's better at these perspectives of coding and per my experience, yes it is.
Claude Code supports the base URL env var so you could tell it to talk with any LLM API endpoint that receives the Anthropic style request format, e.g. DeepSeek.
Come on, think how fast Grok evolves after SoaceXAI acquired Cursor. I just believe if you can't get enough real data from practice then you can't train a good model. And for Mistral collecting data is a must step no matter what approach it takes.
Something maybe unfamiliar with you: not about coding but writing. I've asked it to write an argumentative essay, which is a part of "gaokao" (China's university entrance exam), and its work is *extremely* impressive. speaks and writes like a real senior high school student, and the opinions unfold progressively with deep hierarchy. I don't know how the Gemini team reaches this because this kind of Chinese capability literally outperforms at least 2/3 Chinese students, no to mention those who speak Chinese. After all, the model speaks like a real humankind if you prompt it well. That's AGI guys
Gemini is known for good at creative writing in the Chinese writing community.
It's a bit ironic though. Google has probably the most and best code base among all tech companies but its Gemini is bad at coding. Google has no access to Chinese market but its model is incredibly good at writing in Chinese.
> Google has probably the most and best code base among all tech companies but its Gemini is bad at coding.
probably because google knows people would want to extract google's proprietary code from gemini if they use their own code base to train it! I bet they purposefully gimped it to prevent that from happening.
I think this is because the primary use case of gemini is google search overview and the gemini app. thats probably 98% of gemini tokens. they didnt predict how important agentic coding would become.
Nitpick, but in my opinion an LLM is an "it", not a "her" or "he". Using male or female pronouns risks anthropomorphizing them which can lead to unhealthy outcomes.
There’s also one specifically for God - 祂, and one for animal - 牠, the first one you can see frequently in Chinese bibles or churches, the latter is rarely used.
What about languages, such as Russian, where every single noun has a gender assigned (he, she or it) and AI is a he by default (and everything else is already using pronouns in similar way, like a car is a she, a ship is a he).
This reminds me the article above. Now people have diverse ideas on agentic coding. Some suggest human-in-the-loop while others suggest giving a detailed specification and let the agent run freely; some suggest leveraging LLM's high productivity and here we get an opinion that LLM can actually slowly write good code.
It's happy to see opinions that are more practical and variant emerging, turning LLM into literally a tool instead of something to be hated or hyped.
In my own practice, I find LLMs (SOTA ones) good at medium-level tasks, those needed to reason and plan for a while. However, the design taste on architecture is unexpectedly disgusting. Sometimes writing interfaces myself and asking LLMs to fill in implementations, alongside context-completing tools like context7, deepwiki, docs.rs MCPs, etc. and giving a escape hatch (e.g. encouraging it to use the AskUser tool in Claude Code), may be considered my best practice.
> The kid who is right now learning to code by chatting with an agent is not a worse programmer than I was at 12, hunched over Learning Perl, retyping examples that would not run because I missed a semicolon.
To be honest, I'm 17 y.o., I'm coding by chatting with an agent, but it seems like we can't tell the distinction too absolutely.
At the first time writing a React app, I forgot to name a file with a .tsx extension and I used .ts instead, then spotting ugly error lines across my JSX syntax, confusing and sharing with my friend, and laughing this little funny thing all the day.
I once spent the whole afternoon choosing a js linter, reading their docs and perceiving different tastes. In my early twelve-ties (uh this sounds funny too) I'm always arrested by configuring Windows PEs, installing different Linux distributions on my PC, etc. Today I still read tech books, alongside videos, articles and also chatbots. Chatbot is a new tool, but there's no doubt it cannot replace other media types and what they bring to us/me.
What may I express is that a natural interest in programming or computer things cannot really be overwhelmed by LLM things. I don't know how to use vim skillfully since I majorly used Windows at my early age and I'm not familiar with vim's logic, but this practically doesn't stop anything. I still found Linux's fantasy, at last. And same for LLMs.
However, it seems like OpenAI didn't pay much attention on these perspectives and I didn't find if Astra could write a more elegant code, or communicate more naturally, etc., which made me somehow a little disappointed.
They indeed mentioned the code Astra delivered is closer to production grade but production-grade code is different from what I want since there can be a kind of messy code blowing up your whole architecture design with control flows nobody truly understands but just passes all tests perfectly. There is no difficulty in maintaining this kind of code because you only need to paste the problems into Codex. And we all know this sounds incorrect. I don't know if my appetite towards a good code (no matter how) is sound but I just imagined frontier labs to give more attention on this.
Note: fwiw Fable 5.1's release page says it's better at these perspectives of coding and per my experience, yes it is.
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