home.social

#codegeneration — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #codegeneration, aggregated by home.social.

  1. I understand that #AI companies are shifting from monthly fee to token based pricing. In fact there is no way someone/something will write you dozens thousands lines of somehow usable code for lower dozens of dollars monthly.

    So here are my toughs:
    1) they made people addicted/dependent on AI for “free” and now they start milking, do I see here paralel with drug dealers?
    2) their management is really utterly dumb, in general I do not believe that.
    3) they have been for sure subsiding costs till now and hoped they will sort the operational costs and improve efficiency meanwhile, well either point 2 applies or something went really south.
    4) companies like Microsoft can afford to some degree burn money like this in huge pit, but it will be popcorn sitcom to see how OpenAI and friends will deal with this, as they did not produced any income cash flow and investors will start now asking viability of such companies.
    5) AI will stay with us, but in different shape, I believe it will be pushed to the edge and enduser computing with smaller specialized models.
    6) if 5 applies and I believe so, OpenAI knew tat and they intentionally screwed RAM supply chain to make this impossible/horribly expensive and unaffordable.
    7) lets say it will stay in cloud, people play for tokens, it is Q of time they will start challenging why they shall pay for tokens burned by AI debugging its own crappy code, producing even more crappy test and ending in broken record cycle. They will draw parallel with human, if human screw programming and spend more time on delivery, it is hard for him to charge you more for his mistakes. Why this shall be accepted from AI companies. And yes this mostly applies for vibe coding.

    #codegeneration #LLM #codeassistant #sustainability

  2. I understand that #AI companies are shifting from monthly fee to token based pricing. In fact there is no way someone/something will write you dozens thousands lines of somehow usable code for lower dozens of dollars monthly.

    So here are my toughs:
    1) they made people addicted/dependent on AI for “free” and now they start milking, do I see here paralel with drug dealers?
    2) their management is really utterly dumb, in general I do not believe that.
    3) they have been for sure subsiding costs till now and hoped they will sort the operational costs and improve efficiency meanwhile, well either point 2 applies or something went really south.
    4) companies like Microsoft can afford to some degree burn money like this in huge pit, but it will be popcorn sitcom to see how OpenAI and friends will deal with this, as they did not produced any income cash flow and investors will start now asking viability of such companies.
    5) AI will stay with us, but in different shape, I believe it will be pushed to the edge and enduser computing with smaller specialized models.
    6) if 5 applies and I believe so, OpenAI knew tat and they intentionally screwed RAM supply chain to make this impossible/horribly expensive and unaffordable.
    7) lets say it will stay in cloud, people play for tokens, it is Q of time they will start challenging why they shall pay for tokens burned by AI debugging its own crappy code, producing even more crappy test and ending in broken record cycle. They will draw parallel with human, if human screw programming and spend more time on delivery, it is hard for him to charge you more for his mistakes. Why this shall be accepted from AI companies. And yes this mostly applies for vibe coding.

    #codegeneration #LLM #codeassistant #sustainability

  3. I understand that #AI companies are shifting from monthly fee to token based pricing. In fact there is no way someone/something will write you dozens thousands lines of somehow usable code for lower dozens of dollars monthly.

    So here are my toughs:
    1) they made people addicted/dependent on AI for “free” and now they start milking, do I see here paralel with drug dealers?
    2) their management is really utterly dumb, in general I do not believe that.
    3) they have been for sure subsiding costs till now and hoped they will sort the operational costs and improve efficiency meanwhile, well either point 2 applies or something went really south.
    4) companies like Microsoft can afford to some degree burn money like this in huge pit, but it will be popcorn sitcom to see how OpenAI and friends will deal with this, as they did not produced any income cash flow and investors will start now asking viability of such companies.
    5) AI will stay with us, but in different shape, I believe it will be pushed to the edge and enduser computing with smaller specialized models.
    6) if 5 applies and I believe so, OpenAI knew tat and they intentionally screwed RAM supply chain to make this impossible/horribly expensive and unaffordable.
    7) lets say it will stay in cloud, people play for tokens, it is Q of time they will start challenging why they shall pay for tokens burned by AI debugging its own crappy code, producing even more crappy test and ending in broken record cycle. They will draw parallel with human, if human screw programming and spend more time on delivery, it is hard for him to charge you more for his mistakes. Why this shall be accepted from AI companies. And yes this mostly applies for vibe coding.

    #codegeneration #LLM #codeassistant #sustainability

  4. #Kimi K2.6, the latest #opensource model, showcases advancements in #coding, #longhorizon execution, and #agentswarm capabilities. It excels in complex #engineering tasks, demonstrating significant improvements in #codegeneration accuracy, #longcontext stability, and #toolinvocation success rate compared to its predecessor. kimi.com/blog/kimi-k2-6?eicker #tech #media #news

  5. #Kimi K2.6, the latest #opensource model, showcases advancements in #coding, #longhorizon execution, and #agentswarm capabilities. It excels in complex #engineering tasks, demonstrating significant improvements in #codegeneration accuracy, #longcontext stability, and #toolinvocation success rate compared to its predecessor. kimi.com/blog/kimi-k2-6?eicker #tech #media #news

  6. #Kimi K2.6, the latest #opensource model, showcases advancements in #coding, #longhorizon execution, and #agentswarm capabilities. It excels in complex #engineering tasks, demonstrating significant improvements in #codegeneration accuracy, #longcontext stability, and #toolinvocation success rate compared to its predecessor. kimi.com/blog/kimi-k2-6?eicker #tech #media #news

  7. A study by Xue Jiang's group demonstrates that convergence in AI code generation is achieved through flexible natural language semantics rather than discrete logic.

    The proposed method, using the < think> token to explicitly express complex sections, significantly improves benchmark performance.

    arxiv.org/pdf/2603.29957

    #ai #softwareengineering #codegeneration #aiperformance #llm

  8. A study by Xue Jiang's group demonstrates that convergence in AI code generation is achieved through flexible natural language semantics rather than discrete logic.

    The proposed method, using the < think> token to explicitly express complex sections, significantly improves benchmark performance.

    arxiv.org/pdf/2603.29957

    #ai #softwareengineering #codegeneration #aiperformance #llm

  9. A study by Xue Jiang's group demonstrates that convergence in AI code generation is achieved through flexible natural language semantics rather than discrete logic.

    The proposed method, using the < think> token to explicitly express complex sections, significantly improves benchmark performance.

    arxiv.org/pdf/2603.29957

    #ai #softwareengineering #codegeneration #aiperformance #llm

  10. A study by Xue Jiang's group demonstrates that convergence in AI code generation is achieved through flexible natural language semantics rather than discrete logic.

    The proposed method, using the < think> token to explicitly express complex sections, significantly improves benchmark performance.

    arxiv.org/pdf/2603.29957

    #ai #softwareengineering #codegeneration #aiperformance #llm

  11. A study by Xue Jiang's group demonstrates that convergence in AI code generation is achieved through flexible natural language semantics rather than discrete logic.

    The proposed method, using the < think> token to explicitly express complex sections, significantly improves benchmark performance.

    arxiv.org/pdf/2603.29957

    #ai #softwareengineering #codegeneration #aiperformance #llm

  12. #Anthropic has introduced a new Code Review feature for Claude Code, adding an agent-based pull request review system that analyzes code changes using multiple AI reviewers.

    Dive deeper on #InfoQbit.ly/3QbwdHA

    #AI #CodeReviews #LLMs #Claude #CodeGeneration

  13. #Anthropic has introduced a new Code Review feature for Claude Code, adding an agent-based pull request review system that analyzes code changes using multiple AI reviewers.

    Dive deeper on #InfoQbit.ly/3QbwdHA

    #AI #CodeReviews #LLMs #Claude #CodeGeneration

  14. #Anthropic has introduced a new Code Review feature for Claude Code, adding an agent-based pull request review system that analyzes code changes using multiple AI reviewers.

    Dive deeper on #InfoQbit.ly/3QbwdHA

    #AI #CodeReviews #LLMs #Claude #CodeGeneration

  15. #Anthropic has introduced a new Code Review feature for Claude Code, adding an agent-based pull request review system that analyzes code changes using multiple AI reviewers.

    Dive deeper on #InfoQbit.ly/3QbwdHA

    #AI #CodeReviews #LLMs #Claude #CodeGeneration