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r/MachineLearningResearch

What's your take on continual learning? [D]

<!-- SC_OFF --><div class="md"><p>Everyone suddenly seems to be an expert in continual learning. Dario Amodei claimed on the Dwarkesh Patel podcast that continual learning will be achieved by 2026, and Demis Hassabis has called it the most important unsolved breakthrough on the path to more general AI. Two of the most prominent people in the field, and yet I don't think there's even a consensus on what continual learning actually means.</p> <p>I see researchers and startups approaching it in fun

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Hacker News Ask

Ask HN: What makes someone good at using Claude Code?

I am building Promptster - an AI fluency platform that helps level up engineering organizations. Engineering managers invite their teammates and Promptster analyzes the engineers work with ai coding tools (claude code, codex, cursor, copilot). The manager receives a team-aggregate dashboard where they can roll-out certain practices / skill to their whole team. Each IC receives their own dashboard where they can see their fluency statistics, skill usage, context management, and a DORA dashboard.

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Hacker News AILLMs

My Favorite Announcements from the Data and AI Summit 2026

Article URL: https://medium.com/databrickscommunity/my-favorite-announcements-from-the-data-ai-summit-2026-317fc68d4e75 Comments URL: https://news.ycombinator.com/item?id=48897518 Points: 1 # Comments: 0

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r/MachineLearningResearch

GPUHedge: Hedging serverless GPU providers improves cold start p95 latency from 117s to 30s [P]

<!-- SC_OFF --><div class="md"><p>Disclosure: I built it, it is open source, Apache-2.0 licensed, and currently alpha. Repository: <a href="https://github.com/mireklzicar/gpuhedge">https://github.com/mireklzicar/gpuhedge</a></p> <p>I started working on it after benchmarking a 17 GB AI model across several serverless GPU providers.</p> <p>On the primary provider, requests usually either completed in roughly 6–8 seconds or took around 90–122 seconds after a fresh GPU cold start. Simply switching t

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Hacker News AILLMs

The Token Tax: When Bad AI Architecture Becomes Debt

Article URL: https://medium.com/@alanscottencinas/the-token-tax-when-bad-ai-architecture-becomes-debt-81c29158ae0b Comments URL: https://news.ycombinator.com/item?id=48897422 Points: 2 # Comments: 0

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Hacker News AILLMs

What would it take for AI to discover penicillin?

Article URL: https://bosoncutter.substack.com/p/what-would-it-take-for-ai-to-discover Comments URL: https://news.ycombinator.com/item?id=48897332 Points: 1 # Comments: 0

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Dev.to

They Asked for My AI Rules. But I Could Not Just Hand Them Over.

<p>A team lead announces that the team will start using AI-assisted development. Everyone nods. Nobody asks what that actually means on Monday morning.</p> <p>Some times ago I was in that position. A project I was working on needed to start using AI-assisted development, and the team was new to it. Nobody had rules written down for an agent to follow. Nobody had skills defined for it to load. There was no shared idea of how this should work inside our specific repo. Someone had to go first. That

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Hacker News AILLMs

A brief history of distillation in AI

Article URL: https://twitter.com/SergioPaniego/status/2073066275819991472 Comments URL: https://news.ycombinator.com/item?id=48897061 Points: 1 # Comments: 0

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Dev.to

Codegraph

<p>How I Built CodeGraph: A Living Knowledge Graph That Tells You What Breaks Before You Break It<br> Built for HACKHAZARDS '26 — powered by Neo4j AuraDB, tree-sitter, Groq LLaMA, and Next.js</p> <p>The Problem That Frustrated Me<br> Every developer knows this feeling.<br> You join a new codebase. There are 50,000 lines of code. Your manager says "just fix this small bug in the authentication module." You make the change. You push. And suddenly three completely unrelated features are broken — a

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Dev.to

The Everyday Backend Engineer: Step 10 — The Observer Pattern

<p>Welcome back to <strong>The Everyday Backend Engineer: Practical Design Patterns</strong>. In our last post, we made our core algorithms interchangeable using the Strategy Pattern. Today, we close out our design patterns roadmap with arguably the most native pattern in the entire Node.js ecosystem: <strong>The Observer Pattern</strong>.</p> <p>Let’s look at how to master event-driven decoupling to trigger secondary workflows seamlessly without bloat.</p> <h2> 🔴 The Problem: Direct Inline Sid

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Hacker News Show

Show HN: Kassette – Durable agent workflows backed by object storage

Agent runs often fail after expensive model calls and executing tools that have real-world side effects. This problem is made even worse by how common it is to deploy agents to serverless environments. When your agent dies, it needs to be restarted, but doing so safely isn't easy and everyone building agents has to solve this same problem of durability. The stack you're running probably already has half of what you need for durable execution already though, ie, a queue or job runner that can inv

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MarkTechPostResearch

Building a VideoAgent-Style Multi-Agent System: Intent Parsing, Graph Planning, and Tool Routing for Video Editing Tasks

In this tutorial, we reconstruct the VideoAgent workflow as a runnable, API-key-free multi-agent pipeline. We build an intent parser, an agent library, a tool router, a graph planner, and a textual-gradient optimizer that repairs the execution graph. We wire these planning components to FFmpeg, Whisper transcription, scene detection, keyframe sampling, captioning, cross-modal indexing, and beat-synced editing. By the end, we have a system that answers questions about a video, summarizes it, and

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Building a VideoAgent-Style Multi-Agent System: Intent Parsing, Graph Planning, and Tool Routing for Video Editing Tasks
Dev.to

Part 3: A Loop Whose Job Is to Do Nothing

<p><strong>TL;DR</strong></p> <ul> <li>This loop runs on a schedule and succeeds by doing nothing almost every night.</li> <li>The pass/fail check is plain deterministic code, with no AI in the decision.</li> <li>It can run entirely free on your own machine. Only the cloud/CI version needs a paid API key. Plus the one bug that broke all three loops.</li> </ul> <p>The first two loops in this series work the same way from your side: you start them and watch. This last one runs on a schedule, like

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Dev.to

I Gave an AI Agent an Impossible Target to See If It Would Cheat

<p><strong>TL;DR</strong></p> <ul> <li>A "loop" is not an agent grading its own work. It is an external script that re-runs the agent, plus a separate check the agent cannot edit.</li> <li>I turned "feels smooth" into an FPS number and let the loop optimize toward it.</li> <li>I set the target too high to be reachable on a 60Hz screen. The loop kept failing but never faked the result. The bug was in my number, not the code.</li> </ul> <p>Could I get an AI agent to make my website faster without

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Dev.to

Part 2: When Nobody Grades Their Own Homework

<p><strong>TL;DR</strong></p> <ul> <li>Some things can't be checked with a number, like whether an animation feels right.</li> <li>So a second, read-only agent grades the first one against a written rubric it is not allowed to edit.</li> <li>In my run the reviewer rejected the builder three times, and the most interesting problem it caught was in the test evidence, not the code.</li> </ul> <p>In <a href="//./devto-part-1-goal-loop.md">Part 1</a> I built a loop that chased a number, frames per se

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Dev.to

Yes-Brainer — A council of LLMs that debate in the browser

<blockquote> <p>Yes-Brainer is a council of AI models for the decisions that aren't no-brainers. One question fans out to several models — they answer in parallel, debate to consensus, or get judged to a verdict. No backend, no accounts: your keys, your browser.</p> </blockquote> <p>For non-trivial questions — the ones that are either complex or important — I caught myself in a "ritual": copy-pasting the same prompt into Claude, then Gemini, then ChatGPT, in three browser tabs, and eyeballing th

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TechCrunch AIBusiness

The wildest allegations in Apple’s trade secrets lawsuit against OpenAI

Apple’s trade secrets lawsuit against OpenAI contains allegations that range from employees joking about unauthorized access to Apple’s systems to claims that job candidates were asked to bring Apple hardware to interviews. Here are the complaint’s most eye-catching claims.

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Dev.to

How We Built DJ ROOTS: An AI-Powered Music Recommendation Platform

<h1> 🎧 DJ ROOTS – Building a Real-Time Collaborative Music Platform with Gesture Control </h1> <p><em>Crowd Vibes. You Control.</em></p> <p>Music is one of the best ways to bring people together. However, during parties, college events, hostel gatherings, or study sessions, one common problem always exists—<strong>who gets to control the music?</strong></p> <p>Usually, one person owns the playlist while everyone else keeps requesting songs. This often creates confusion, interruptions, and argum

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MIT Tech ReviewResearch

What Anthropic’s latest AI discovery does—and doesn’t—show

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Anthropic—currently the world’s most valuable AI company, with a nearly $1 trillion valuation—has a reputation for publishing strange and heady research. It’s looking into whether AI models can feel pain, for example,…

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Hacker News Show

Show HN: Finterm.ai Bloomberg terminal for Claude Code

Hi, my name is Kam, and today my cofounder Josh and I are shipping Finterm, a CLI that gives coding agents direct access to financial data: stock prices, options data, SEC filings, and Ticker Deep Research, a filtered ticker news search. I’m a developer and have been a full-time trader for the past few years.Recently I have been using LLMs more and more in my trading and strategy. I always found it frustrating that Claude Code or GPT did not have direct access to actual financial information and

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r/MachineLearningResearch

Chain of Thought is a scaling trap. the next wave is latent reasoning (Coconut / HRM / RecrusiveMAS)... but then we hit the black box wall. Where does BDH fit? [D]

<!-- SC_OFF --><div class="md"><p>Read a long piece on the future of LLM reasoning that makes a provocative claim: Chain of Thought is a useful hack but we've started to confuse a readable trace with the actual computation. All in all, "generating text is not the same as thinking."</p> <p>There are two practical problems here:</p> <ol> <li><p>Faithfulness: CoT style traces can decouple from what the model actually "did." u can get plausible steps with a wrong answer, or messy steps with a right

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