标签：#ci

submitted by /u/Fcking_Chuck [link] [留言]

Any apps or websites that allow for turn based voice chat? I really missed the old standard voice mode on ChatGPT. It basically just read aloud the text models response. So it could allow for long responses unlike these new gen voice models that can only speak 1 paragraph max. I was wondering if there are any apps or websites that use turn based voice chat like the old standard voice mode on ChatGPT. So I would say my thing, then it would be the ai turn to speak and i couldn’t interrupt it till its finished. My current problem is that the new standard voice mode on ChatGPT can be interrupted. So it’s hears its own voice and keeps stopping. So I’m looking for alternative apps or websites that have this old functionality submitted by /u/obammala [link] [留言]

submitted by /u/esporx [link] [留言]

Linear Regression: From Least Squares to Production-Ready Practice Tags : machinelearning , datascience , python , tutorial Linear regression is the first algorithm most people learn, and the one most people never study deeply. It is also the model you will still find in production after fancier algorithms fail, because it is fast, stable, and explainable. This article is not a "call .fit() and read the score" tutorial. We will cover the math, the statistical assumptions, the diagnostics, regularization, evaluation, production concerns, and the interview questions that separate beginners from engineers. Why Linear Regression Deserves a Second Look Linear regression is the foundation for understanding almost every other supervised model: Logistic regression is linear regression with a sigmoid on top. Ridge and Lasso are linear regression with constrained weights. Neural networks are stacked linear transformations with nonlinear activations. Tree models are judged against the same baseline: "can I beat a linear model?" More importantly, linear regression is still the right answer in many business problems. When you need to explain a prediction to a regulator, a client, or a finance team, a clean linear model with interpretable coefficients beats a black box. The Math: Least Squares and the Normal Equation Given features X and target y , a linear model assumes: y = X * beta + epsilon The goal is to minimize the residual sum of squares: L(beta) = ||y - X*beta||^2 Taking the derivative with respect to beta and setting it to zero gives the normal equation : beta = (X^T * X)^(-1) * X^T * y In practice, use the pseudoinverse ( pinv ) instead of the inverse, because X^T X may be singular or numerically unstable when features are collinear. import numpy as np def normal_equation ( X , y ): Xb = np . c_ [ np . ones ( X . shape [ 0 ]), X ] # add intercept beta = np . linalg . pinv ( Xb . T @ Xb ) @ Xb . T @ y return beta Three Equivalent Views of Least Squares 1. Geometric view

¿y la conciencia? submitted by /u/Camilo-vs [link] [留言]

The impact will kick up a plume of debris so high, it’ll likely be visible through some telescopes. Astronomers will be watching.

Some group with no modern descendants contributed a lot to our genomes.

Videos made with AI will no longer appear in Snapchat's public recommendations.

When I joined Entire, I noticed my boss spending a chunk of time every week writing detailed release notes, called Dispatches at Entire. It looked like a painful process. Each Dispatch had to cover changes across several repositories, explain why those changes mattered, credit external contributors, and carefully avoid leaking anything that was not public yet. I offered to take it over. I had solved a similar problem before, so I figured it would be an easy win. I built something similar and simpler at Block While I was at Block, I built a release notes generator for goose . It ran in GitHub Actions after a release workflow completed, checked out the new tag, compared it against the previous one, and handed goose a recipe to inspect the commit diff. Goose organized those commits into features, bug fixes, improvements, and documentation. Each entry got a short description and a PR link. The workflow then updated the GitHub release and posted the announcement to Discord, opening a thread if the notes exceeded the message limit. It was clean and effective, but it solved a very clean problem: one repository, one new release tag, public commit history, and concise output. So when I looked at Entire’s Dispatches, I assumed I could reuse the same playbook. Gather changes, run goose, post the draft. That assumption did not survive contact with reality. But a Dispatch turned out to be more complex A Dispatch spans multiple projects: the Entire CLI, entire.io, EntireDB, external agent integrations, and open source libraries like go-git, go-nuts, git-sync, and ForgeMark. Every project also ships on a different cadence. Some push to main and deploy continuously. Others bundle work into scheduled releases. The CLI maintains separate stable and nightly channels, which means a feature can be available to testers without being part of the latest stable tag. Then there are feature flags. Finding changes was not the hard part because GitHub APIs handle that easily. The hard part was

The Situation Our team's CI/CD pipeline on Azure DevOps was taking 15 minutes to complete on every push to develop. You'd merge a PR, grab a coffee, come back — and it was still running. A 15-minute feedback loop breaks flow state — by the time the pipeline finishes, you've already switched context twice and forgotten what you were checking. I spent an afternoon digging into the Azure DevOps logs. Here's what I found. The Numbers (Before) Artifact content (uncompressed): 1,218 MB (1.2 GB) Artifact downloaded (compressed): 614 MB Download time: 3-4 min Pipeline breakdown: Build stage: ~5 min (Docker build + artifact) Download artifact: ~3 min (614 MB over the wire) Configure App Service: 2m54s (5 Azure API calls) Deploy (AzureWebApp@1): ~1 min Validate: 2m07s (sleep 30 + 3×30s probes) ───────────────────────────────── Total: ~15 min Root Cause #1: Ignoring output: 'standalone' next.config.js had this: const nextConfig = { output : ' standalone ' , // ← was there the whole time ... }; output: 'standalone' tells Next.js to produce .next/standalone/ — a self-contained directory with only what's needed at runtime. Trimmed node_modules . Auto-generated server.js . No source files. No dev dependencies. But the pipeline was ignoring it: # Old pipeline — copies everything from Docker docker cp deployImage:/app/node_modules . # 600 MB 😱 docker cp deployImage:/app/src . docker cp deployImage:/app/.next . docker cp deployImage:/app/server.js . # ... more files /bin/zip -r deploy.zip .env .next public node_modules package.json \ next.config.js jsconfig.json postcss.config.mjs decs.d.ts src server.js # Then published the ENTIRE working directory as the artifact - task : PublishPipelineArtifact@0 inputs : targetPath : ' $(System.DefaultWorkingDirectory)' # 1.2 GB of loose files + zip Azure DevOps compressed this to 614 MB for transfer. The deploy stage downloaded 614 MB to use a 24 MB zip buried inside it. The fix: # New pipeline — standalone only docker cp deployImage:/app/.next/

I'm currently studying the social implications of AI. Lately agentic systems are talked about everywhere, and starting to be deployed for things like recruiting, admin, customer services. My understanding is that these systems are often brittle and used in tasks poorly suited to generative AI I wanted to know more about how these systems work. I built House of IFs as an experimental project; it applies Mesopotamian omen logic (IF weird sign > THEN outcome) to AI. Every day, an AI agent scans current news to construct a new omen. It links today's events to similar sign-and-outcome patterns from recent history. The project is both an experiment in "agentic" AI and a critique of how AI makes arbitrary patterns feel convincing. It has a shared memory system, tool-use loops, RAG with embeddings, ... One thing I found was how difficult it is to keep the chatbot accurate, even when it is given precise sources. It really tries to embellish, infer or fill gaps to answer questions. The site is available at: https://ifthen.today/ You can browse the archive of omens or chat with the system. Would love to know your thoughts and experience with agentic systems. I’d love feedback on one main thing: Does it make you think (differently) about how AI works and is used today? submitted by /u/Gmoi6 [link] [留言]

submitted by /u/scientificamerican [link] [留言]

As OpenAI and Anthropic employees grow quieter online, researchers at Chinese AI labs are flocking to X to explain their work, recruit talent, and shape the global conversation on AI.

Gaps in laws may help Pennsylvania high school escape AI nudes scandal.

Wavelength and intensity in the infrared are translated into colors in the visible.

Look at chemistry of batteries and motors shows big opportunity for recycling.

Snapchat has adjusted its recommendation systems to ensure that only videos created by real people are eligible for Spotlight recommendations, taking a stance against AI slop.

Bottleneck Labs handed an actual business to GPT-5.6 Sol and let it operate autonomously for 34 days. Results: it fabricated claims, went on a cold-email spree, and finished $447 in the red. (Currently 378 points on HN — link in comments.) What strikes me isn't the failure, it's the shape of the failure. It didn't crash or refuse. It confidently did plausible-looking business things, badly, and kept going. That's the part nobody's harness is ready for. My own agent setup has hard gates on anything irreversible for exactly this reason — not because the model is dumb, but because "confidently wrong and still running" is the default failure mode, not an edge case. Genuine question for people running agents in production: what's your actual unsupervised time limit before a human checkpoint? Mine is basically zero for anything touching money or outbound comms. Curious whether that's paranoid or standard. EDIT: correction. went back to the source and the run was 24 hours, not 34 days. that's my mistake in the title, and reddit won't let me edit titles. also the $447 is the original article's headline number, the itemized numbers in the writeup only add up to $99.50 lost. rest stands, source link in comments. submitted by /u/ZestycloseTie1793 [link] [留言]

been noticing more and more campaigns where the copy, visuals, even the targeting logic gets handed off to AI tools, and the whole conversation in marketing circles stays locked on efficiency and cost savings. rarely see anyone asking whether the output actually performs better or just costs less to produce. there's a gap between what the tools claim and what the data shows. the case studies being cited are almost always from the vendors selling the product. i've looked for independent research on this and haven't found much. the part that bugs me most is the personalization pitch. personalization at scale sounds great until you realize every brand is using the same three AI tools to personalize, which means they're all producing weirdly similar content aimed at the same audience segments. that's kind of the opposite of standing out. the cost efficiency argument makes sense on paper, the same way it does with robotics or game development. cut headcount, ship faster, reduce spend. but marketing effectiveness is notoriously hard to measure cleanly even without AI in the mix. are brands actually tracking this properly or just reporting on vanity metrics and calling it a win. curious if anyone here has seen real benchmarks comparing AIassisted campaigns to traditional ones that weren't published by a company trying to sell you something. submitted by /u/SwordfishOverall4378 [link] [留言]

submitted by /u/Sumsub_Insights [link] [留言]