Posts with #AI4Code

#AI4Code

Public posts on X that use #AI4Code.

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  1. Awesome Papers@awesomepapersio
    💻 AI for Code · paper of the day GLM-5: from Vibe Coding to Agentic Engineering The number that matters: Through these innovations, GLM-5… GLM-5-Team: Aohan Zeng et al. · 2026 ⭐ 6,936 GitHub stars 🔗 #AI4Code #CodeGen #SoftwareEngineering
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  2. Michele Catasta✓@pirroh
    "Small" Language Models are the sweet spot that will bring #AI4Code to every developer in the world. Low latency is paramount when it comes to UX! @amasad, @samipddd and the whole @Replit team accomplished the unthinkable today. I couldn't be more proud.
    Amjad Masad @amasad
    In 2018 we announced Multiplayer Mode. Since then, Replit became the world's leading collaborative editor. Today, we're announcing AI Mode, which infuses state-of-the-art intelligence into nearly all IDE features, the next major evolution for Replit:
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  3. Amjad Masad✓@amasad
    In 2018 we announced Multiplayer Mode. Since then, Replit became the world's leading collaborative editor. Today, we're announcing AI Mode, which infuses state-of-the-art intelligence into nearly all IDE features, the next major evolution for Replit:
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  4. Shanchao Liang@LiangShanchao
    New models, same struggle! DeepSeek-R1, GPT-4.5 Preview, and Claude 3.7 Sonnet still show low performance on realistic coding tasks (RepoCod-Lite), with Pass@1 of 4.5%, 4.0%, and 3.5%. Top models just reach 10% Pass@1. We need better approaches for code generation! #AI4Code #LLM
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  5. Aditya Kanade@adityakanade0
    Microsoft Research India is hiring for full-time positions in Bengaluru! Please reach out if you are working in #AI4Code.
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  6. Aditya Kanade@adityakanade0
    @adityakanade0
    Please review the flyer for the job description and requirements, and apply online if you feel qualified and interested. Please reach out to me directly (after applying online) only if you have an established track record in the research area of #AI4Code.
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  7. Alexey Svyatkovskiy@asvyatko
    The Data & AI team at Microsoft seeks a Principal Research Scientist to contribute to the next generation of AI-assisted developer tools 🚀 💡 Copilot model improvements 💡 Copilot AI for pull requests (code review, program repair, ...) Message me if interested #ai4code #copilots
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  8. Kaggle@kaggle
    Check our our new #ai4code competition with @google and @theteamatx! Understand the relationship between code and comments in Python notebooks:
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  9. Kevin Leneway@kleneway
    Now *this* is what the new @cursor_ai agent feature can enable! IMO there are incremental, nice-to-have coding gains from the agent screenshot @ryolu_ and @shaoruu shared. But to make a custom agent for writing? 🤯Turn off coding tools, create Editor agent…endless possibilities!
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  10. Kumar Sharma@K_Sharma__
    @kleneway
    Cursor AI’s new agent feature is next-level! 🚀 From incremental coding gains to fully custom writing agents, the flexibility here is wild. Combine that with @QodoAI for seamless PR analysis, and you’ve got a powerhouse dev setup. What’s your ideal AI agent use case? #AI4Code #DevTools
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  11. Salesforce AI Research✓@SFResearch
    @SFResearch
    (5/6) INDICT also makes LLMs more robust in open-domain generation tasks against complex red-teaming prompt engineering methods (in the HarmBench benchmark).
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  12. Salesforce AI Research✓@SFResearch
    @SFResearch
    (6/6) In summary, INDICT's cooperative critic system demonstrates potential for responsible AI for code, achieving state-of-the-art security without compromising functionality. Explore the future of safe and helpful AI-generated code today! #AI #Innovation #AI4Code
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  13. Pushmeet Kohli✓@pushmeet
    Following up on the AlphaEvolve code opt. agent, I am happy to share how our team at @GoogleDeepMind has developed the CodeMender agent to design/apply patches to fix security vulnerabilities in large scale open source projects. #AI4code Read more at:
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  14. Michele Catasta✓@pirroh
    (unordered) list of reasons why I 🖤 this post: • framing is spot on: it's all about improving developer productivity 👩‍💻🧑‍💻👨‍💻 • combination of rule-based Semantic Engines and Large Language Models is a winner → #AI4Code innovates on hybrid neural+symbolic approaches 🧠🔣 1/2 🧵
    Google AI @GoogleAI
    Check out a new hybrid approach that leverages both ML and rule-based semantic engines to build a Transformer-based semantic code completion model, which we show can improve developer efficiency. Read more ↓
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  15. Google AI✓@GoogleAI
    Check out a new hybrid approach that leverages both ML and rule-based semantic engines to build a Transformer-based semantic code completion model, which we show can improve developer efficiency. Read more ↓
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  16. Yasharth Bajpai✓@imYbajpai
    After 36 hours of travel, finally at Porto de Galinhas, Brazil to attend @AIwareConf @FSEconf . I'll be here throughout the conference and would love to discuss #AI4Code & #AI4SE in general. Also, share our learnings from building GitHub Copilot inside VS. DMs are open.
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  17. Baishakhi Ray@baishakhir
    Updating real-world large legacy projects like binutils? Meet ( C2SaferRust: leveraging program analysis & LLMs to create idiomatic, safer Rust with (↓38%) raw pointers & (↓28%) unsafe code while preserving functionality 🚀 #rustlang #AI4code #AIAgent
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  18. Theo X. Olausson@theo_olausson
    #ai4code peeps at #ICLR2024: come chat self-repair with me at 10:45 AM on Friday, May 10! I'll be presenting the camera-ready version of this fan favorite from last year... now with even more data! Keep reading for a sneak peek of the results 👇
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    AK @_akhaliq
    Demystifying GPT Self-Repair for Code Generation paper page: Large Language Models (LLMs) have shown remarkable aptitude in code generation but still struggle on challenging programming tasks. Self-repair -- in which the model debugs and fixes mistakes in its own code -- has recently become a popular way to boost performance in these settings. However, only very limited studies on how and when self-repair works effectively exist in the literature, and one might wonder to what extent a model is really capable of providing accurate feedback on why the code is wrong when that code was generated by the same model. In this paper, we analyze GPT-3.5 and GPT-4's ability to perform self-repair on APPS, a challenging dataset consisting of diverse coding challenges. To do so, we first establish a new evaluation strategy dubbed pass@t that measures the pass rate of the tasks against the total number of tokens sampled from the model, enabling a fair comparison to purely sampling-based approaches. With this evaluation strategy, we find that the effectiveness of self-repair is only seen in GPT-4. We also observe that self-repair is bottlenecked by the feedback stage; using GPT-4 to give feedback on the programs generated by GPT-3.5 and using expert human programmers to give feedback on the programs generated by GPT-4, we unlock significant performance gains.
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  19. AK✓@_akhaliq
    Demystifying GPT Self-Repair for Code Generation paper page: Large Language Models (LLMs) have shown remarkable aptitude in code generation but still struggle on challenging programming tasks. Self-repair -- in which the model debugs and fixes mistakes in its own code -- has recently become a popular way to boost performance in these settings. However, only very limited studies on how and when self-repair works effectively exist in the literature, and one might wonder to what extent a model is really capable of providing accurate feedback on why the code is wrong when that code was generated by the same model. In this paper, we analyze GPT-3.5 and GPT-4's ability to perform self-repair on APPS, a challenging dataset consisting of diverse coding challenges. To do so, we first establish a new evaluation strategy dubbed pass@t that measures the pass rate of the tasks against the total number of tokens sampled from the model, enabling a fair comparison to purely sampling-based approaches. With this evaluation strategy, we find that the effectiveness of self-repair is only seen in GPT-4. We also observe that self-repair is bottlenecked by the feedback stage; using GPT-4 to give feedback on the programs generated by GPT-3.5 and using expert human programmers to give feedback on the programs generated by GPT-4, we unlock significant performance gains.
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  20. Tao Yu@taoyds
    #NLProc #AI4Code GPT-3 Codex is possible to generate INTERACTIVE multi-vis interfaces📈 (not just static simple plots!) from natural language queries! Check out our demo below! Work led by @Yiru__Chen @sirrice. Stay tuned for fancier ones!
    Yiru Chen @Yiru__Chen
    No programming, No learning curve! We can now generate INTERACTIVE multi-vis interfaces from NL queries! Yes! Directly from NL! Check this demo below. I will also give a talk on this next Sat in the NLVIS workshop @IEEEVIS. Paper:
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  21. Yiru Chen@Yiru__Chen
    No programming, No learning curve! We can now generate INTERACTIVE multi-vis interfaces from NL queries! Yes! Directly from NL! Check this demo below. I will also give a talk on this next Sat in the NLVIS workshop @IEEEVIS. Paper:
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  22. Antonio Alegria@antonioalegria
    For the past few months, I've seen some of the best people in the field of #AI4Code moving to Google. I'm expecting great things. If it's true this is being done within X, then I'm even more bullish.
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  23. Theo X. Olausson@theo_olausson
    By popular request, the submission deadline for the #VerifAI workshop at @iclr_conf has been extend to Sunday, Feb 8 (AoE) 🎉🎉 🎉 But don't leave it until the last minute - submit your papers on RLVR, AI+verification, #ai4code and #ai4math now! 👉
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