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  • Sakana AI’s Agent Wins Programming Contest Against 800 Humans
    Sakana AI’s Agent Wins Programming Contest Against 800 Humans

    By

    @_yutaroyamada

    –

    pretty cool! Sakana AI (@SakanaAILabs) Our AI agent has achieved 1st place in a competitive optimization programming contest against over 800 human participants. Blog: sakana.ai/ahc058 In AtCoder Heuristic Contest 058, Sakana AI’s ALE-Agent took the top spot. For context on the difficulty of these challenges, an OpenAI agent secured 2nd place in the AHC world tournament last year. The task was a 4-hour production planning optimization challenge. While the problem setters anticipated a standard approach combining constructive heuristics and simulated annealing, our agent independently discovered a more effective strategy. It implemented a "virtual power" heuristic and a diverse neighborhood search that allowed it to escape local optima better than human experts. This was achieved through inference time scaling using multiple frontier AI models. The agent ran parallel code generation, analyzed the results, and iteratively refined its algorithms in real time. The total cost was approximately $1,300. This result suggests AI agents can now match top human experts in tasks requiring extended reasoning and original scientific discovery. Please read our blog for more details. We extend our deepest thanks to the host, @algo_artis, and @atcoder. We will continue to research AI as a partner that expands human exploration to discover solutions to complex real-world problems. — https://nitter.net/SakanaAILabs/status/2008195936917586416#m

    → View original post on X — @_yutaroyamada, 2026-01-05 22:24 UTC

  • Code as Liability: Why Generating More Code Isn’t Always Better

    By

    @fchollet

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    I tend to view code as more of a liability than an asset. In this light, making it cheaper and faster to generate a lot of code might not be an unmitigated blessing.

    → View original post on X — @fchollet

  • AI Development: 8-Step Process for Creating Real Value
    AI Development: 8-Step Process for Creating Real Value

    By

    @ingliguori

    –

    AI isn’t magic. It’s a process. 8 steps:
    define problem
    collect/prepare data
    choose model
    train
    evaluate
    fine-tune
    deploy
    ensure ethics & safety Real value comes from running this loop well. #AI #MachineLearning #DataScience #ResponsibleAI

    → View original post on X — @ingliguori

  • Building an AI to read posts; lists improve algorithm

    By

    @scobleizer

    –

    Nope which is why I am building an AI with @blevlabs to read them for me. Early results are quite amazing. Also engaging in lists dramatically helps the algorithm

    → View original post on X — @scobleizer

  • Reflecting on Past Code: A Developer’s Moment of Growth
    Reflecting on Past Code: A Developer’s Moment of Growth

    By

    @karpathy

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    Hah I was just thinking about the same analogy. How I suddenly feel about all of the code I've written so far

    → View original post on X — @karpathy

  • January 2026 AI Tools Recommendations by Abacus AI

    By

    @abacusai

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    Here are our recommendations for January 2026 video – Sora-2, Kling 2.6
    image – nano banana pro
    code – opus or sonnet 4.5
    writing – GPT 5.2
    app building – Abacus AI Deep Agent research – Gemini 3.0 pro

    → View original post on X — @abacusai

  • Free Online Modern AI Course with LLM Chatbot Building

    By

    @jeande_d

    –

    I've decided to release a minimal, free online version of my upcoming "10-202 – Intro to Modern AI" course, starting January 26: modernaicourse.org. As a brief summary, this course introduces students to the elements of modern AI systems: you'll build and train a simple LLM chatbot from scratch in PyTorch, without any pre-built layers or pre-trained models at all. More information about the course, and a link to enroll, is available on the webpage link above. In the free online version, you can watch all lecture videos and submit all the assignments (all of which are autograded in the full course anyway). There won't be quizzes/exams, and there's no credit offered via CMU; this is purely for educational purposes. The online course will begin two weeks after the CMU version, and lectures and assignments will all be released with the same two-week delay after the in-person version. Note that this is a first-time offering for this course, so I certainly expect some hiccups along the way. But I hope the material will prove useful for many people. Zico Kolter (@zicokolter) I'm teaching a new "Intro to Modern AI" course at CMU this Spring: modernaicourse.org. It's an early-undergrad course on how to build a chatbot from scratch (well, from PyTorch). The course name has bothered some people – "AI" usually means something much broader in academic contexts – but I think the time has come where the first thing that many students interested in AI should see is how the AI they are familiar with actually works (because it's really simple!) The more people who understand it the better. I'll be trying to put as much material as I can that we develop online (assignments + autograding, hopefully lecture videos), though as a first-time course there are also likely to be some bumps along the way. Hopefully it becomes a good resource over time, though. Feedback welcome. — https://nitter.net/zicokolter/status/1987938761498411376#m

    → View original post on X — @jeande_d, 2026-01-04 20:40 UTC

  • Writing Chapter 6 on RLVR with GRPO

    By

    @rasbt

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    Currently more than half way through writing chapter 6 on RLVR with GRPO. I think if you liked Build an LLM from scratch you will like it even better

    → View original post on X — @rasbt

  • FastAPI-Fullstack CLI Generator with LangGraph ReAct Agents
    FastAPI-Fullstack CLI Generator with LangGraph ReAct Agents

    By

    @langchain

    –

    fastapi-fullstack CLI Generator Made by the LangChain Community A CLI that generates production AI apps with LangChain or LangGraph. Creates FastAPI + Next.js apps with auth, WebSocket streaming, and LangSmith observability. v0.1.11 adds LangGraph ReAct agents. Get it:

    → View original post on X — @langchain

  • Build Multi-Agent Systems with LangGraph StateGraph
    Build Multi-Agent Systems with LangGraph StateGraph

    By

    @langchain

    –

    Multi-Agent Tutorial with LangGraph Made by the LangChain Community Build multi-agent systems with specialized agents. Create a Content Factory with Editor and Writer agents using LangGraph's StateGraph for shared state management, with production-ready code. Tutorial:

    → View original post on X — @langchain