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.
I scraped 5.94 billion TikTok videos and 3.23 billion profiles in 3 weeks. Uploaded full dataset to Hugging Face for free. Step by step tutorial and code below. [P]
A significant advancement in accessible data research has arrived. A developer has released a comprehensive dataset of 5.94 billion TikTok videos and 3.23 billion profiles, collected over three weeks and now freely available on Hugging Face. This unprecedented scale of data, alongside associated code and a detailed write-up, offers researchers a unique opportunity to explore TikTok’s ecosystem. For those interested in alternative machine learning approaches, consider “Deepity,” a C++ library demonstrating Predictive Coding Networks’ capabilities. Explore the full dataset and resources here: [https://huggingface.co/datasets/kuben-developer/tiktok-videos-4b](https://hugging
5 AI Skills That Will Keep Data Scientists Relevant in 2027
## 5 AI Skills That Will Keep Data Scientists Relevant in 2027 The data science landscape is evolving rapidly. To remain valuable through 2027, focus on these five essential AI skills: Prompt Engineering, Generative AI Model Fine-Tuning, Responsible AI Implementation, Advanced Retrieval-Augmented Generation (RAG), and AI-Powered Data Synthesis. Each addresses a critical challenge – from maximizing LLM output to ensuring ethical deployment and generating synthetic datasets. Discover runnable code examples for each skill—easily pasted into your notebook—to accelerate your learning.
Do you use a whiteboard when thinking? [D]
Many data scientists and engineers retain a fondness for the whiteboard's intuitive problem-solving power, even as their workflows shift to code and complex models. Originally shared by /u/Huge-Leek844, this post explores how professionals in DSP, data science, and ML integrate that visual thinking style into their daily work. Do you still rely on whiteboards, or do you transition directly to implementation? Explore the discussion and consider how techniques like those highlighted in "FlexGanttFX is Open Source" can complement your approach.
Why Claude Code Time Estimates Are Poor
Large language models like Claude often provide inaccurate time estimates when generating code. This discrepancy stems from their probabilistic nature and limitations in fully simulating execution environments. Consequently, relying on these estimates can lead to unrealistic project timelines and frustrated developers. Learn why Claude's code time predictions fall short and, more importantly, how to become a more effective communicator when working with LLMs for programming tasks. For a deeper dive into related AI infrastructure challenges, see our article, "Connecting My LangGraph AI Agent to Postgres."
How to Work with AI Coding Agents
AI coding agents promise better code, not just *more* code, and mastering their use is essential for modern data professionals. This practical guide explores how to effectively collaborate with these agents, maximizing their potential to streamline development and improve code quality. Discover strategies for prompting, evaluating outputs, and integrating AI assistance into your existing workflows. For a deeper understanding of the evolving roles of humans and AI in analytics, explore "Agentic AI Is Rewriting The Analytics Stack."
Reviewing 4 papers for AAAI 2027 and none have code, Reject? [D]
Reviewing submissions for AAAI 2027 presents a recurring challenge: empirical claims lacking supporting code or data. While a complete absence of reproducibility materials shouldn't trigger an automatic rejection—legitimate concerns around funding and intellectual property exist—it significantly impacts reviewer confidence. Flagging this explicitly in the review, requesting anonymized code during the rebuttal phase, is a pragmatic approach. As explored in "Millwright — experimenting with an end-to-end machine learning framework in Rust," ensuring verifiable results remains paramount for robust AI research.
Catching bugs in scikit-learn [D]
Scikit-learn users, be aware: version 1.9 includes a fix for a subtle bug in the BayesianRidge uncertainty calculation. Keen observers can now explore this firsthand through a fascinating bug-hunting exercise. The provided notebook [https://github.com/aadya940/scikit-verify/blob/master/examples/sklearn_bug_hunting.ipynb] challenges you to identify the formula change between versions 1.8 and 1.9 before revealing the solution. For those seeking to maximize their coding agent efficiency, consider "How to Effectively Solve 100+ Tasks with Claude Code" for deeper insights.
How to Effectively Solve 100+ Tasks with Claude Code
Facing a deluge of coding tasks? Discover how to effectively manage 100+ tasks with Claude Code, empowering your workflow through intelligent coding agents. This post explores practical strategies for leveraging Claude’s capabilities to streamline your development process and maximize productivity. Learn to delegate, automate, and optimize your coding efforts, moving beyond the limitations of traditional methods. For deeper insights into the evolving landscape of AI agents, explore "Runable hits $21M to bet AI agents can go from building businesses to growing them."
Cursor Releases Origin as an Agent-Native Alternative to GitHub
Cursor is redefining code hosting with Origin, a git-based platform now integrated directly within its AI-powered editor. Positioned as a compelling alternative to GitHub, Origin offers teams already leveraging Cursor's AI capabilities a seamless and streamlined workflow. Currently in early beta across Pro, Teams, and Enterprise plans, Origin resides within a dedicated "Codebase" tab. This move underscores Cursor’s commitment to an agent-native development experience. For a deeper dive into related AI and coding practices, explore "10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong."
Spec-Driven Development with Claude Code: Writing Bulletproof Specs
Successfully leveraging Claude Code through spec-driven development reveals a critical nuance: even well-crafted specifications can lead to unexpected outcomes. Experience demonstrates that Claude can diligently execute plans, pass test suites, and still produce flawed code—a failure mode often overlooked. This post explores strategies for writing "bulletproof" specifications, ensuring alignment between intent and implementation. Discover how to proactively mitigate this risk and unlock the full potential of AI-assisted coding. For further insights into AI agent capabilities, see our article on Inherent’s Faraday.
Nobody Laid Out The Five Kinds Of Software You Can Make. So I Did.
The landscape of software creation is surprisingly diverse. While many assume limited options, we’ve identified five distinct categories of software you can build, ranging from utility tools to complex AI applications. Understanding these classifications is crucial for strategic development and resource allocation. This guide clarifies those categories, demystifying the possibilities and empowering you to choose the right path. For a deeper dive into the infrastructure supporting these advancements, explore our article on Relativity Networks and their innovative fiber technology.
How to Remove Claude Watermarks from Text, Code, and Files
Anthropic’s Claude now embeds watermarks in AI-generated content, presenting a new challenge for users. Understanding how these watermarks manifest—through embedded text markings, signed C2PA metadata for files, and a nuanced approach to code—is crucial. This post details methods for removing these watermarks from text, code, and supported files, empowering you to leverage Claude’s capabilities with greater flexibility. Explore the intricacies of Claude's detection methods and discover practical removal techniques.
5 Things Vibe Coding Gets Right and 5 Things It Gets Wrong
Vibe coding, the emerging practice of generating code through natural language prompts, sparks debate: is it the future or a shortcut to maintainability issues? Let’s critically examine its strengths and weaknesses. We’ll explore five areas where vibe coding excels and five where it falls short, offering a balanced perspective on its potential. Curious about the underlying technology? See our article, "How to Install Codex CLI," for a deeper dive into the tools powering this trend.
5 Python Libraries That Make Data Cleaning More Enjoyable
Data cleaning doesn’t have to be a chore. This article introduces five Python libraries designed to transform tedious data preparation into an expressive and genuinely enjoyable process. We've compiled a list of tools that empower you to streamline workflows and unlock deeper insights from your data. Discover how these libraries can simplify complex tasks and accelerate your analysis. For those working with image classification, you might find our accompanying dataset, "Starfield Fauna," a valuable resource for practical application.
Anthropic shares more details about how Claude’s new watermarks will work
Anthropic has unveiled further details regarding Claude’s new AI-powered watermarking system, designed to identify AI-generated text. The technology embeds subtle, statistically improbable patterns undetectable to the human eye, yet reliably detectable by a verification tool. While basic editing may alter the text, the watermark’s underlying structure remains intact, hindering circumvention. This system notably addresses concerns regarding code generation, ensuring provenance.
X open sources its ranking algorithm, letting users see if they’ve been ‘shadowbanned’
X is taking a significant step toward greater transparency by open-sourcing its ranking algorithm. This allows users to understand how X’s systems influence the visibility of their content—essentially, whether they’ve been affected by ranking adjustments. Alongside this release, new tools will directly show users when and how the 'For You' feed’s ranking systems have impacted their accounts or posts. This move builds on ongoing discussions about AI transparency, as highlighted in our recent review of AAAI 2027 submissions.
AAAI 2027 Review: No code submission? [D]
AAAI 2027 paper reviews have revealed a concerning trend: a surprisingly low number of submissions include accompanying code. This deviates from AAAI's explicit emphasis on reproducibility and raises questions about the rigor of some submissions. While initial scoring will reflect this omission, we seek community input. Providing code fosters transparency and allows for validation – a practice we strongly advocate, as evidenced by our own consistent code sharing on ArXiv.
Three OpenAI Engineers Shipped A Million Lines. Your Ten-Hour Agent Run Starts Here.
Three OpenAI engineers recently achieved a significant milestone: shipping a million lines of code, paving the way for extended agent runs—now available for you. This marks a pivotal shift towards more autonomous and capable AI workflows. Explore the possibilities of ten-hour agent executions, designed to tackle complex tasks with unprecedented efficiency. For deeper insights into the challenges of automated evaluation, consider our article, "Why You Shouldn’t Always Trust LLMs as Judges," available on our site. Discover how this advancement empowers your data journey.
Why You Shouldn’t Always Trust LLMs as Judges: Understanding Bias in Automated Evaluation
The increasing adoption of Large Language Models (LLMs) for automated evaluation—from assessing code to ranking research—presents a critical challenge. While their speed and scalability are compelling, relying on LLMs as impartial judges demands careful consideration. As highlighted by Bhaskarjit Sarmah at DHS 2026, inherent biases within these models can skew results, undermining the fairness of automated assessments. Explore the nuances of this issue and discover how to navigate this evolving landscape responsibly.
The Ultimate Guide to Contributing to Open Source Projects
Ready to contribute to open source but unsure where to start? This Ultimate Guide demystifies the process, covering everything from identifying responsive projects to mastering essential Git mechanics. We'll walk you through the realities of open-source contribution, ensuring your efforts are productive and welcomed. Discover how to effectively engage, navigate workflows, and make a meaningful impact. For a deeper dive into leveraging AI coding assistants, see our article, "How to Effectively Deploy Code With Claude Code."
How to Effectively Deploy Code With Claude Code
Optimizing your CI/CD pipeline for coding agents like Claude Code is critical for efficient development workflows. This post details proven strategies for effective code deployment, moving beyond traditional methods to leverage the power of AI-assisted coding. Discover practical techniques to streamline your processes and maximize productivity. If you're seeking a deeper understanding of foundational concepts, consider “I never understood positional encoding until I read this article,” for valuable insights into related AI principles.
Anthropic is turning Claude Code’s auto mode on by default
Anthropic is streamlining programming with Claude Code, now activating auto mode by default. This shift significantly reduces the need for manual oversight, empowering developers to work more efficiently. Expect a more intuitive and fluid coding experience as Claude Code anticipates your needs and completes tasks with greater autonomy. This represents a key step forward in accessible AI-assisted development. For further insights into the broader AI investment landscape, explore our article on Situational Awareness's recent $400M investment in Source Foundry.
Presentation: Rewriting All of Spotify's Code Base, All the Time
Spotify undertook a monumental task: rewriting its entire codebase, continuously. This presentation, delivered by Jo Kelly-Fenton and Aleksandar Mitic, details the creation of "Honk," an AI coding agent designed to manage this complex fleet-wide migration. Learn key architectural insights, including decoupling CI verification and addressing automated pull request bottlenecks. The team drove aggressive standardization across thousands of repositories, demonstrating a future-focused approach to data management. For further exploration of AI's impact on software development, see our article on "Top 5 Claude Skills for Writing."
Ponytail Agent Skill Corrects Its Own Benchmark After Contributor Challenge
Ponytail Agent Skill, a rapidly growing open-source project focused on streamlining coding agents, recently recalibrated its headline claim after a community challenge. Initially boasting an 80-94% reduction in code, the maintainer revised the benchmark to a more accurate 54% following feedback from a contributor. This adjustment, made transparently, highlights the project's commitment to rigorous validation.