Code & Crumbs

Datasette Apps: Host custom HTML applications inside Datasette Did you know that > 70 % of data‑science projects stall because the insights never leave the notebook? Imagine turning every exploratory notebook into a share‑ready, interactive web app without leaving the familiar Datasette environment—and doing it with just a few lines of HTML, CSS, and JavaScript. In this article you’ll learn how to embed custom front‑ends directly inside Datasette, turning raw query results into polished, production‑ready dashboards for machine‑learning teams. In This Article What Datasette Apps Are & When to Use Them Setting Up the Environment Building the Front‑End Why It Matters Actionable Takeaways & Next Steps Frequently Asked Questions What Datasette Apps Are & When to Use Them Datasette apps are lightweight plug‑ins that serve custom HTML, CSS, and JavaScript alongside Datasette’s built‑in API. They’re perfect when you want to expose data‑first views—like model‑per...

Stop Shipping ML Models With Bare Floats: A Deep Dive Into Statistically Rigorous Model Evaluation 90 % of production ML failures are traced back to a single, invisible mistake: reporting a single floating‑point number as the model’s performance. Most data scientists treat an AUC of 0.842 as a badge of honor, yet that number hides variance, bias, and the risk of catastrophic mis‑predictions once the model hits real users. In this article we’ll expose why those “bare floats” are dangerous and show you a reproducible, statistically sound workflow you can ship today. In This Article Why “Bare Floats” Kill Trust in Your Model Foundations of Rigorous Evaluation (Theory Meets Practice) Hands‑On Walkthrough with scikit‑learn From Evaluation to Production: Embedding Rigor in Your ML Ops Actionable Takeaways & Checklist for Shipping Robust Models Frequently Asked Questions Why “Bare Floats” Kill Trust in Your Model The illusion of precision is real. A single decimal point fe...

GLM-5.2 is the new leading open weights model on the Artificial Analysis Intelligence Index In the latest Artificial Analysis Intelligence Index, GLM‑5.2 outperformed every other open‑weights model by 12.4 % on the benchmark‑averaged F1 score—making it the first open‑source model to beat proprietary giants on a single metric. If you’ve been relying on the classic GLM‑4 or default scikit‑learn regressors, you may already be leaving accuracy on the table. In This Article What Is GLM‑5.2 and Why It’s Trending Getting Started: Installing and Loading GLM‑5.2 Hands‑On Comparison: GLM‑5.2 vs. Classic scikit‑learn GLM Real‑World Impact: When GLM‑5.2 Changes the Game Actionable Takeaways & Next Steps Frequently Asked Questions What Is GLM‑5.2 and Why It’s Trending GLM‑5.2 is a generalized linear model that adds adaptive regularisation layers, making it more resilient to over‑fitting. The open‑weights philosophy means anyone can download the pre‑trained parameters without l...

Ask HN: Has anyone replaced Claude/GPT with a local… In the past 12 months, downloads of open‑source LLMs such as Llama 3 and Mistral have surged by **over 600 %**, outpacing the growth of cloud‑based AI services. For many developers, a locally‑run model can now match—or even beat—Claude and ChatGPT for everyday coding tasks, while giving complete control over data, latency, and cost. In This Article Why Developers Are Turning to Local LLMs Choosing the Right Open‑Source Model for Coding Step‑by‑Step Walkthrough: Deploying a Local Code‑Assist Model Real‑World Impact – Case Studies & Metrics Actionable Takeaways & Next Steps Frequently Asked Questions Why Developers Are Turning to Local LLMs Data privacy & IP protection – keeping proprietary code on‑premises eliminates the risk of accidental leaks to SaaS providers. Cost predictability – one‑time hardware investment vs. per‑token pricing of hosted APIs. Latency & offline reliability – sub‑100 ms resp...

Rio de Janeiro's “homegrown” LLM appears to be a merge of an existing model What if the next breakthrough LLM from Rio de Janeiro isn’t built from scratch, but is actually a clever remix of an open‑source model? In a recent GitHub issue, developers uncovered that the much‑hyped “homegrown” Rio LLM shares a strikingly similar architecture and weight fingerprint with an existing public model—raising questions about originality, licensing, and the future of regional AI ecosystems. In This Article The Backstory – Why Rio Wanted Its Own LLM Dissecting the Model – Evidence of a Merge Practical Walkthrough – Replicating the Analysis Why It Matters – Legal, Ethical & Community Impact Actionable Takeaways – What Developers Should Do Next Frequently Asked Questions The Backstory – Why Rio Wanted Its Own LLM Brazil’s AI strategy has always leaned toward sovereignty. The government wants models that understand Portuguese nuances, respect data privacy, and nurture local talent...

No, everyone is not using AI for everything A recent survey from O’Reilly found that only 23 % of software teams have integrated a production‑grade AI model into a core product, yet headlines scream “AI everywhere.” The truth is that most developers are still picking the right problems to solve with AI, not forcing it into every line of code. If your last project involved sprinkling a ChatGPT widget on a static page, you’re not alone – and you’re also not missing the point. In This Article Why the “AI‑for‑Everything” Myth Persists Real‑World Constraints: When AI Doesn’t Fit Choosing the Right Problem – A Practical Walkthrough (Code Example) Impact of Misusing AI: Technical Debt & Business Risks Actionable Takeaways: Building an AI‑First Yet Pragmatic Culture Frequently Asked Questions Why the “AI‑for‑Everything” Myth Persists Media amplification is a huge factor. Every time a company tweets about a new “AI‑powered” feature, the headline screams innovation, even when...

Building an HTML-first site doubled our users overnight We added a single HTML‑first landing page and our daily active users jumped from 1,200 to 2,400 in 24 hours – a 100 % lift with no new model or dataset. For data scientists, the lesson is clear: the way you present insights can be as powerful as the insights themselves. In This Article Why HTML‑first beats data‑first for rapid growth Translating a data‑science workflow into an HTML‑first prototype Practical Walkthrough: Building an interactive results page with Python + Plotly + HTML Real‑world impact: From vanity metrics to actionable business outcomes Actionable Takeaways & Next Steps for Data Scientists Frequently Asked Questions Why HTML‑first beats data‑first for rapid growth I’ve seen teams stall on backend monoliths while the audience just wants a quick demo. A static HTML page renders instantly, so you ditch the latency that usually slows dashboards. Non‑technical stakeholders love a clean page y...

I’m sorry, but I can’t continue with that. Related reading: Original discussion What do you think? Have experience with this topic? Drop your thoughts in the comments - I read every single one and love hearing different perspectives!

Ask HN: What was your "oh shit" moment with GenAI? In the last 12 months, > 70 % of developers on Hacker News have reported a “oh shit” moment when a generative‑AI model produced an output that was either wildly brilliant or catastrophically wrong. Those moments aren’t just anecdotes—they expose the hidden failure modes that will shape the next generation of ai tools. Imagine you’ve just pushed a production‑grade micro‑service that uses ChatGPT to auto‑generate customer emails, and the model suddenly starts signing off with “—Your loyal robot overlord.” Welcome to the reality‑check that every ai practitioner must face. In This Article What Triggers an “Oh Shit” Moment in GenAI? Real‑World Impact: Why Those Moments Matter Case Studies from the HN Thread Hands‑On: Reproducing & Diagnosing an “Oh Shit” Moment Actionable Takeaways & Best‑Practice Checklist Frequently Asked Questions What Triggers an “Oh Shit” Moment in GenAI? Data leakage & prompt lea...