code
code on Beyond Market Intelligence: a running collection of 40 stories we have gathered and hand-picked because they are worth your time. Every post here touches on code in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around code, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
It's time to desk reject papers that don't include code that can reproduce the results [D]
A concerning trend is emerging from recent conference review seasons: a significant lack of reproducible code accompanying submitted papers. Across 12 reviews this year, only one provided complete, runnable code, while seven offered none at all. This severely impacts quality assurance and reproducibility, with even partial code often containing critical bugs. Incentives currently favor code concealment, but a shift towards penalties for non-disclosure is needed to ensure rigorous scientific standards.
How to Build CLI Agents with Python & Ollama
Unlock the power of local AI with this practical guide to building Command Line Interface (CLI) agents using Python and Ollama. This tutorial empowers you to create custom agents from scratch, entirely free of charge. Explore the fundamentals of agent design and implementation, leveraging the efficiency of local LLMs. For a deeper dive into the engineering layers underpinning these systems, see our article, "Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On." Discover a future-focused approach to data interaction and automation.
Deep Dive on RL and OPD for Training LLMs [D]
Recent advancements in large language model (LLM) training, exemplified by models like Kimi and Qwen, increasingly leverage policy distillation and reinforcement learning from human feedback (RLHF) techniques. To demystify these powerful methods, we’ve published a deep dive exploring the underlying mathematics and code—connecting these algorithms to pretraining and supervised fine-tuning. Discover how RL and OPD are shaping the future of LLMs. Explore the full explanation here: [https://youtu.be/MaZWafi4gYY?is=8jLkAp_Fe86abUVP](https://youtu.be/MaZWafi4gYY?is=8j
Why Reddit Data Scientists Keep Saying Not To Use Prophet
A recurring sentiment within the Reddit data science community cautions against relying on Facebook’s Prophet for time series forecasting. This post explores why, presenting initial observations and a small experiment to understand the underlying concerns. While Prophet offers accessibility, the community often finds its limitations outweigh the benefits in more complex scenarios. For those seeking robust evaluation strategies to improve forecasting workflows, our article, "Structured Evaluation Pipelines to Improve Your AI Workflows," provides deeper insights.
7 Machine Learning Algorithms That Still Matter
Before diving into the world of large language models and generative AI, ensure a solid foundation in core machine learning principles. Discover 7 essential algorithms – from linear regression to support vector machines – that remain vital for any data scientist. Each is explained simply, accompanied by practical Python code examples. Mastering these fundamentals empowers you to build robust, reliable models. For deeper insights into leveraging AI strategically, explore our article, "AI-Assisted Software Development: Team Profiles and Capabilities for Putting Research into Action."
AWS Lambda's Self-Managed Code Storage Lifts the Account Quota, Not the Function Size Limit
AWS Lambda users can now significantly expand their data processing capabilities. A recent update allows functions to reference deployment packages directly from customer-managed S3 buckets, effectively eliminating the per-region code storage quota and boosting the default managed storage from 75 GB to 300 GB. Importantly, this enhancement doesn't alter per-function package limits, and the `UpdateFunctionCode` action remains necessary after package replacements. For those building high-frequency streaming pipelines, consider exploring the normalization techniques outlined in “Avoiding Entity Key Drift in a Data Lake."
Built & Trained a Transformer from Scratch in Pure PyTorch for English-to-Tamil Machine Translation [Math + Code Breakdown] [P]
Delve into a comprehensive exploration of Transformer architecture with this practical guide. Developer ImranCoder786 has meticulously built and trained a Transformer model from scratch using pure PyTorch, mirroring the seminal "Attention Is All You Need" paper. Trained on an English-to-Tamil dataset and detailed with a step-by-step mathematical breakdown, this resource empowers users to understand and replicate the process.
Presentation: The Future of Engineering: Mindsets That Matter When Code Isn’t Enough
As AI code automation accelerates, how can software engineers not just survive, but thrive? Ben Greene, drawing on his startup experience, tackles this critical question in "The Future of Engineering: Mindsets That Matter When Code Isn’t Enough." Greene identifies key principles—starting simple, maintaining comprehension, prioritizing difficult challenges, and focusing on customer impact—highlighting why human empathy and practical problem-solving remain irreplaceable.
I built a compiler that turns computation graphs into the weights of a vanilla transformer — no training anywhere [P]
Explore a novel approach to transformer architecture with TorchWright, a compiler that generates transformer weights directly from Python computation graphs – eliminating the need for any training. This innovative system, detailed in a recent post on ood.dev, allows users to define algorithms independently of the learning process, producing standard Phi-3 checkpoints compatible with vanilla Hugging Face. See how this achieves expressiveness within a transformer, building upon work like RASP while prioritizing accessibility and a stock architecture.
The Fluid Simulator That Doesn’t Solve the Fluid Equations
Challenge conventional fluid dynamics with a novel simulation approach. I’ve generated a Kármán vortex street—a striking visual manifestation of fluid behavior—without resorting to solving the complex Navier-Stokes equations. This innovation leverages the Lattice Boltzmann Method, derived from first principles and implemented in C++. Running on a supercomputer, this method offers a powerful alternative for exploring fluid phenomena. For further insights into high-performance computing architectures supporting AI development, explore “KDnuggets Weekly Roundup: Week of July 20, 2026."
Edtech platform raises $4.5M to help teach students how to vibe code
Imagi, an edtech platform focused on teaching students to “vibe code,” has secured a $4.5 million seed round. Backed by investors including Brighteye Ventures, Day One Capital, and artist Will.i.am, Imagi aims to make coding accessible and engaging for a new generation. This funding underscores the growing demand for innovative approaches to tech education, particularly as AI continues to reshape the landscape. It follows similar investments in the AI space, like the recent surge in valuation for Etched, an AI chip startup.
Building an AI-text detector from scratch [P]
Delve into the intricacies of AI-native data detection with a practical tutorial from Ordinary Intelligence. This project, submitted by /u/gamedev-exe, guides you through building an AI-text detector from scratch—a valuable skill in navigating the evolving digital landscape. Explore the full tutorial and accompanying notebook on GitHub to empower your understanding of AI-driven analysis. For those interested in related explorations, consider the discussion around GPU-accelerated AI projects, highlighting the intersection of performance and learning.
Build an LLM Agent That Can Write and Run Code
Unlock the potential of AI-powered code generation and execution. This hands-on walkthrough guides you through building an LLM agent using the OpenAI Agents SDK and Docker. Learn to empower your workflows by seamlessly integrating code writing and running capabilities. We’ll demonstrate a practical approach to leveraging these tools, offering a future-focused solution for data professionals. For those interested in a deeper dive into LLM runtimes, explore "How To Build Your Own LLM Runtime From Scratch" for a comprehensive understanding of the underlying infrastructure.
Did blatant AI Slop just win a 25K USD Deepmind / Kaggle Grand Prize? [D]
A recent DeepMind/Kaggle competition, "Measuring Progress Toward AGI," has sparked considerable debate following the announcement of its results. The 25,000 USD grand prize was awarded to a submission critiqued as presenting “nonsensical number generation” and questionable methodology. The work, intended to assess LLM reasoning through viewpoint comparison, appears to have been overlooked for critical review. Explore a deeper investigation of this outcome, detailing the methodology and data—a journey that may challenge conventional understanding.
![Prism accidentally leaked [D]](https://preview.redd.it/csr59ogtwtdh1.png?width=140&height=27&auto=webp&s=d8b3c46b64b19d75c4b2b1726b0b3cbea225f38d)
Prism accidentally leaked [D]
A recent, swiftly addressed incident at Prism highlights a critical concern in the AI research space. A data leak inadvertently resulted in the compilation and distribution of another researcher's paper, a situation quickly acknowledged and rectified by Prism's team, who took their website offline within ten minutes of initial reports. While their responsiveness is commendable, the incident raises valid questions about data security and the potential for unintentional intellectual property breaches.
Article: Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture
AI accelerates initial development, but often obscures underlying architectural complexity until it presents a critical challenge. Engineering leaders must prioritize systemic comprehension over mere throughput to ensure stability. This article, "Comprehension at AI Speed," introduces a "Context Store"—a repo-bound unification of SDD, TDD, and automated fitness functions—enabling safe code evolution by both AI agents and human reviewers. Authored by Berhe, Bragner, Maran, and Jayaraman, it offers a progressive approach to managing AI-driven development.