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260319 reddit (21개)

seedling aggregate 2026-03-19

260319 reddit 모음

[r/ClaudeAI] Claude Status Update : Elevated errors on Claude.ai on 2026-03-18T1

원문

[r/ClaudeAI] Claude Status Update : Elevated errors on Claude.ai on 2026-03-18T1

This is an automatic post triggered within 2 minutes of an official Claude system status update.

Incident: Elevated errors on Claude.ai

Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/p88wl8gmb05c

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1pygdbz/usage_limits_bugs_and_performance_discussion/

출처: https://www.reddit.com/r/ClaudeAI/comments/1rx6sf3/claude_status_update_elevated_errors_on_claudeai/

[r/ClaudeAI] Claude Status Update : Elevated errors on Claude.ai on 2026-03-18T1

원문

[r/ClaudeAI] Claude Status Update : Elevated errors on Claude.ai on 2026-03-18T1

This is an automatic post triggered within 2 minutes of an official Claude system status update.

Incident: Elevated errors on Claude.ai

Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/p88wl8gmb05c

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1pygdbz/usage_limits_bugs_and_performance_discussion/

출처: https://www.reddit.com/r/ClaudeAI/comments/1rx6ouq/claude_status_update_elevated_errors_on_claudeai/

[r/OpenClaw] 3 weeks of Claw: my basic assistant set up (33↑)

원문

[r/OpenClaw] 3 weeks of Claw: my basic assistant set up (33↑)

This post was written 100% by ME. I had Claude review it for accuracy (would have forgotten to mention Telegram if not for that!) but otherwise, no LLMs have intervened in the drafting of this post.

I’ve been running OpenClaw for the past three weeks on my Mac Mini and I wanted to share my setup. Not because anything I’m doing is lighting the world on fire - quite the opposite, my config is pretty basic - but because I don’t see enough practical use cases/applications on this sub, so I figured I’d add mine.

Basic Setup

My Claw runs on a Mac mini that otherwise just runs my local NAS/DLNA server. I locked down SSH and ports on the Mini prior to install and gave my Claw its own user (without full disc access, SUDO permissions etc).

I set up everything - and make all major changes to my Openclaw config - using Claude Code. Before setting up Openclaw I downloaded all the documentation from its website and fed it into CC, having it build a plug-in set that manages, administers and troubleshoots my OpenClaw. It has SSH access to my Mac mini and is the lynchpin in making sure my Claw is running smoothly (and not burning through tokens).

Models/Tokens

After burning through \~$60-$70 in API fees in the first few days of Clawing, I did a hard audit using Claude Code. It found a bunch of poorly managed crons my Claw had set up (firing every 15 minutes using LLM calls instead of just scripts), some inefficiencies in my SOUL.md and other context docs, and we moved all basic cron jobs to Haiku. I also use Sonnet 4.6 as my primary agent, as anything that’s too complicated I already outsource to Claude Code running Opus.

Right now if I do nothing and just let my daily crons fire it’s about $.60/day and another $1-2/day interacting with my Claw as an assistant (managing calendar, notes, small tasks). Costs really starts to climb when 1) you ask your Claw to figure out large, multistep requests (sub out to Claude Code! Just give it to your Claw when it’s ready to execute), and 2) when you ask it to install a new skill itself (again, Claude Code).

What am I actually doing?

That’s my big questions with a lot of these Openclaw posts. I’m not running a multi-agent swarm of Linkedin scraping lead generators, I can tell you that much. I’ve been slowly adding skills and integrations for the last few weeks and this is what I’m currently running with:

Telegram

My main messaging platform is iMessage, with WhatsApp a close 2nd, but as all the OpenClaw install guides will tell you, Telegram is the easiest option and the one that just works basically right out of the box. I see no reason to move beyond Telegram anytime soon.

AgentMail.to

I set up my claw with a free AgentMail inbox so I can give it its own log-ins for online services, and be able to forward it emails. I don’t really use it much at this point, but it is my claw’s Dropbox login.

Dropbox (Composio)

My whole digital life lives on Dropbox, so it only makes sense for me to collaborate with my claw using the service. I set it up with a free account (using its AgentMail.to address) and we have a shared “Shared Work” folder that serves as a, well, dropbox, for documents between us. Free Dropbox tier is only 2 gb, so this isn’t necessarily a permanent solution but it works great for the time being.

Composio handles all the OAuth for Dropbox integration and makes it as easy as possible. Which brings me to...

Email & Cal (Composio)

My Google Workspaces (just email and cal for now) is also connected via Composio. Email is read-only and my claw can write to my calendar but only with explicit instructions from me.

I’ve got a few useful crons set up around my email and cal.

  1. I get a morning briefing at 7 am with the weather and if there is anything on my calendar before noon that day.
  2. At 8:30 am (after I drop my kids at school) I get a follow up message if there are any pre-noon meetings I need to be reminded of.
  3. At 9:30 am (by the time I’m at my desk) I get a summary of my emails from the last 24 hours and if there is anything outstanding that needs a reply or other action.
  4. At 2 pm daily, my claw checks if there are any outstanding calendar invites from my wife (it has her three email addresses). If there are, it auto-accepts them.
  5. I also have another email summary at 6:00, as I tend to miss a lot of emails between 4-6 pm when I’m running around dealing with my kids.
  6. A once a week email summary that looks back at the past 7 days to see if I’ve missed anything important. When this ran last week, it caught a health form for my kids school that was due - my wife was SO impressed that I remembered it before she could. :)

Whoop

I wired up my Whoop fitness tracker to be able to pull info to my claw. This was a little bit of a pain in the ass, and required my setting up a (free) developer account with Whoop, but now I get a sleep summary in my morning briefing. Nothing gamechanging, but pretty cool.

Things

This one was also kind of a mess setting up initially through the Things CLI, but now works quite nicely. I can add, change or mark as complete items on my Things to-do lists, and add cron reminders to my existing to dos.

Plaud

I just got this one setup in the last 24 hours, using the OpenPlaud skill. Basically, any voice memo that goes into my Plaud cloud account gets pulled by an every 15 minute cron, transcribed locally by mlx-whisper, and added to my claw’s memory logs (in addition to their own transcripts folder).

Github

Last but not least, my claw is connected to Github solely for the purpose of syncing itself every night at 3 am (only if any tracked files were changed in the previous 24 hours).

That’s it, folks! I’m not running a money printer over here, but I’m also not lighting money on fire (anymore). My openclaw is not yet a can’t-live-without tool, but I am making it more useful on a daily basis.

Biggest advice I can give is to 1) lean HEAVILY on Claude Code to manage your setup and maintenance and 2) watch and audit your token counts like a hawk in your first days/week.

Hope this was helpful! Enjoy!

출처: https://www.reddit.com/r/openclaw/comments/1rx31x2/3_weeks_of_claw_my_basic_assistant_set_up/

[r/ObsidianMD] I built a media tracking vault for Obsidian and created a tempalt

원문

[r/ObsidianMD] I built a media tracking vault for Obsidian and created a tempalt

Obsidian has slowly become my single hub for everything, so i naturaly wanted to migrate from Letterbox and start to track movies, series and books in Obsidian too.

I looked around and found plenty of tutorials/ exmaples showing how to build something like this — but no ready-made vault I could just download and start using. So I wanted to share my solution.

What it does:

  • One-click media import via the Media DB plugin — search a title, it lands in your vault with metadata and poster automatically (based on obsidian-media-db-plugin)
  • Watchlist & history via Obsidian Bases — mark something as watched or add a personal rating and it moves to your history automatically
  • DataviewJS dashboard with your watchlist sorted by online rating and a top rated section
  • Movies, series and books supported out of the box

→ obsidian-media-starter-vault

If you want to see what a filled vault looks like, I also published my personal one: chronicle
(disclaimer: i just started to fill it)

Manga and games support is on the roadmap. Feedback welcome.

출처: https://www.reddit.com/r/ObsidianMD/comments/1rx5bwk/i_built_a_media_tracking_vault_for_obsidian_and/

[r/ObsidianMD] [Update] Remindian now syncs to Todoist & TickTick, plus file

원문

[r/ObsidianMD] [Update] Remindian now syncs to Todoist & TickTick, plus file

Hey r/ObsidianMD!

A while back I shared Remindian, a free macOS menu-bar app that syncs Obsidian tasks to Apple Reminders. Since then, a lot has happened thanks to feedback from this community and GitHub issues — so here's an update on where things are now.

Quick recap for those who missed it

Remindian syncs tasks from your Obsidian vault to your task manager. Your vault stays the source of truth. Completions, due dates, priority, and tags sync back with surgical edits that never touch anything they shouldn't.

What's new since v1

New destinations — You're no longer limited to Apple Reminders:

  • Things 3 (added in v3.1)
  • Todoist via REST API — just paste your API token (v4.0)
  • TickTick via OAuth — click Connect and authorize (v4.0)

Two-way sync — Completing a task in Reminders/Things/Todoist now ticks the checkbox in Obsidian. Due dates, start dates, priority, and tags all sync back too (opt-in per field).

TaskNotes support — Besides the Obsidian Tasks format, Remindian now reads TaskNotes plugin files (one YAML file per task) with fully configurable field mapping.

File-to-list mapping (v4.1) — A user asked to map entire files to specific lists instead of tagging every task. So now you can: set Projects/Work.md → "Work" and every task in that file goes there automatically.

Other highlights since v1:

  • Recurrence support (completing a recurring task creates the next occurrence)
  • Real-time file watcher sync
  • GoodTask tag writeback (Kanban board tag changes sync back)
  • Cross-file deduplication
  • Onboarding wizard
  • Auto-updater
  • Global hotkey for manual sync
  • 49 automated tests

Nearly every feature from v3.2 onward was requested by users on GitHub. You all shaped this app more than I did.

What's next

Notarization — This is the #1 request and I totally get it. The app is already sandboxed and the entitlements are ready. The blocker is the Apple Developer Program ($99/year). I've set up GitHub Sponsors (pending approval) — in the meantime, you can buy me a coffee if Remindian saves you time and you'd like to help make it installable without the Gatekeeper dance. That would mean a lot.

iOS companion app — Planned after notarization. The sync engine is protocol-based so it could be adapted for iOS with CloudKit-backed state.

Homebrew cask — Formula is ready, will ship with the next stable release.

Download

Free, open source (MIT), macOS 13.0+. Right-click → Open on first launch (not notarized yet).

Looking for beta testers for Todoist and TickTick! If you use either, I'd really appreciate feedback on how they work with your setup.

Disclaimer : Built with SwiftUI. AI (Claude) was used as a development tool — all code is reviewed and the full source is open for audit.

출처: https://www.reddit.com/r/ObsidianMD/comments/1rx38ne/update_remindian_now_syncs_to_todoist_ticktick/

[r/ObsidianMD] How to get REAL Obsidian push notifications on iPhone (10↑)

원문

[r/ObsidianMD] How to get REAL Obsidian push notifications on iPhone (10↑)

Apps you need:

- Obsidian (vault stored in iCloud)

- Scriptable

- Shortcuts

- Tasks plugin inside Obsidian

I personally use the AnuPpuccin theme which supports custom task statuses, but this works with any theme.

How to write tasks in any note:

- Due date goes after 📅 like this: 📅 2026-03-20

- Time goes after ⏰ like this: ⏰ 09:00

- You can add multiple ⏰ on the same task for multiple reminders

- Tasks marked as done with [x] are ignored and won't send a notification

Example: - [ ] Call doctor 📅 2026-03-20 ⏰ 09:00

Task statuses show up in the notification. So if you use custom checkboxes like - [?] for in progress or - [/] for partially done, that character appears at the start of the notification body so you always know the state of the task at a glance. Only [x] is excluded since that means it's done.

Priority also shows up in the notification as dots. You add it with these emojis anywhere in the task line:

- 🔺 = 4 dots (urgent)

- ⏫ = 3 dots (high)

- 🔼 = 2 dots (medium)

- 🔽 = 1 dot (low)

Steps:

  1. Open Scriptable, go to settings, tap File Bookmarks and add a new one. Name it exactly the same as your vault and point it to your vault folder inside iCloud Drive > Obsidian. My vault is called Mistico, just replace that with your vault name.

  2. At the top of the script you will see VAULT_NAME and BOOKMARK_NAME, replace Mistico with your vault name in both lines

  3. Create a new script in Scriptable, paste the code below, and save it

  4. Open Shortcuts and create a new automation triggered when you close Obsidian. Set the action to run your Scriptable script

  5. Disable the option that asks for confirmation before running, otherwise it won't fire automatically

Every time you close Obsidian, the script reads your entire vault, finds all pending tasks with a time, and schedules the notifications for the next 7 days. iOS allows a maximum of 64 pending notifications so the script handles that limit automatically.

---

// Paste this into Scriptable

const VAULT_NAME = "Mistico"; // replace with your vault name

const BOOKMARK_NAME = "Mistico"; // replace with your vault name

const fm = FileManager.iCloud();

const localFm = FileManager.local();

await Notification.removeAllPending();

const now = new Date();

const nowMs = now.getTime();

const sevenDaysLaterMs = nowMs + (7 * 24 * 60 * 60 * 1000);

function getFiles(dir) {

let results = [];

if (!fm.fileExists(dir)) return results;

let items = fm.listContents(dir);

for (let item of items) {

let path = fm.joinPath(dir, item);

if (item.toLowerCase().includes("guide") || item.endsWith(".json") || item.startsWith(".")) continue;

if (fm.isDirectory(path)) results = results.concat(getFiles(path));

else if (item.endsWith(".md")) results.push(path);

}

return results;

}

const tasksDirPath = fm.bookmarkedPath(BOOKMARK_NAME);

const allFiles = getFiles(tasksDirPath);

let pendingOverdueCount = 0;

let totalScheduled = 0;

for (let filePath of allFiles) {

await fm.downloadFileFromiCloud(filePath);

let content = fm.readString(filePath);

let fileName = filePath.split('/').pop();

let lines = content.split("\n");

for (let line of lines) {

if (!line.includes("📅") || line.match(/- \[x\]/i)) continue;

let dueMatch = line.match(/📅\s*(\d{4}-\d{2}-\d{2})/);

let timeMatches = [...line.matchAll(/⏰\s*(\d{2}:\d{2})/g)];

if (!dueMatch || timeMatches.length === 0) continue;

let timeIdx = 0;

for (let tMatch of timeMatches) {

if (totalScheduled >= 64) break;

let [y, m, d] = dueMatch[1].split("-").map(Number);

let [hh, mm] = tMatch[1].split(":").map(Number);

let fireDate = new Date(y, m - 1, d, hh, mm, 0);

let fireMs = fireDate.getTime();

if (fireMs < nowMs && line.includes("- [ ]") && dueMatch[1] === now.toISOString().split('T')[0]) {

pendingOverdueCount++;

}

if (fireMs < (nowMs - 60000) || fireMs > sevenDaysLaterMs) continue;

let cleanText = line

.replace(/- \[(.*?)\]/g, "")

.replace(/[📅⏰🛫➕⏳✅🔁🆔🔺⏫🔼🔽]/g, "")

.replace(/\d{4}-\d{2}-\d{2}/g, "")

.replace(/\d{2}:\d{2}/g, "")

.trim();

if (cleanText === "") cleanText = "Tarea";

let taskID = `vU_${fileName}_${fireMs}_${timeIdx}_${cleanText.substring(0,5)}`;

let n = new Notification();

n.identifier = taskID;

n.threadIdentifier = fileName;

n.title = "Obsidian";

let points = "";

if (line.includes("🔺")) points = "⁾ ";

else if (line.includes("⏫")) points = "⁽ ";

else if (line.includes("🔼")) points = "∶ ";

else if (line.includes("🔽")) points = "· ";

let stMatch = line.match(/- \[(.*?)\]/);

let status = (stMatch && stMatch[1].trim() !== "") ? `${stMatch[1].trim()} ` : "";

let hashtags = (line.match(/#\w+/g) || []).join(" ");

let taskTitle = cleanText.replace(/#\w+/g, "").trim();

n.body = `${points}${status}${taskTitle}${hashtags ? " " + hashtags : ""}`;

let schedMatch = line.match(/⏳\s*(\d{4}-\d{2}-\d{2})/);

if (schedMatch) n.body += `\n⧖ ${schedMatch[1]}`;

let triggerDate = (fireMs <= nowMs) ? new Date(nowMs + 2000) : fireDate;

n.setTriggerDate(triggerDate);

n.sound = "accept";

let relativePath = filePath.replace(tasksDirPath + "/", "");

n.openURL = `obsidian://advanced-uri?vault=${encodeURIComponent(VAULT_NAME)}&filepath=${encodeURIComponent(relativePath)}&search=${encodeURIComponent(line.trim())}&highlight=true`;

n.schedule();

totalScheduled++;

timeIdx++;

}

}

}

Script.complete();

출처: https://www.reddit.com/r/ObsidianMD/comments/1rwzp7h/how_to_get_real_obsidian_push_notifications_on/

[r/ObsidianMD] Quick folder menu color customisation via CSS snippets (11↑)

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[r/ObsidianMD] Quick folder menu color customisation via CSS snippets (11↑)

Hello!

Just figured out today that Obsidian automatically adds a data-path attribute to every folder and file in the sidebar.
So for example, you could do the following to change the background colour of the "Inbox" folder to RED, or the text to RED without any background (which is the example here):

.nav-folder-title[data-path="Inbox"] {
color: red;
}

Sharing in case it wasn't widely known.

I have top level folder a specific color.
Then the next level down a lighter color.
Then the next level down a lighter color.
Then the next level down a lighter color.
But thats as deep as my folders go.

For my inbox folder, I wanted it to stand out more.
This quickly helped me change that folder only so its visibly stands out more.

Don't forget to add border-radius: 6px; (or something around that) to make it look prettier if you add a background-color.

For context - I like to keep things clean, so I use the default theme, with some minor CSS snippets to tweak a few things here and there.

출처: https://www.reddit.com/r/ObsidianMD/comments/1rwwrv9/quick_folder_menu_color_customisation_via_css/

[r/ObsidianMD] 1 Year & 500 Notes into Obsidian as a College Student, AMA! (

원문

[r/ObsidianMD] 1 Year & 500 Notes into Obsidian as a College Student, AMA! (

Hey everyone! As stated in the title, I am a college student having used Obsidian now for right at a year, having written right at 500 notes (384 in this "General" vault shown in the screenshot, and another 116 in my writing "Worldbuilding" one).

You might've seen me popping up every now and then around here or on a few other platforms, and I can't help it – Obsidian has singlehandedly changed the way I do just about everything relating to tech. I've always enjoyed that customization aspect of things, yes, but it's also nice to have a program that "just works" and allows you to build on it, making things as simple or complex as you'd like.

Since learning Markdown (and now a good amount of LaTex, specifically MathJax for Cryptography), my notetaking proficiency has only increased, and being a natural tech and teacher, I've always been on the lookout for the next best thing to improve my setup (without getting too dependent on any one tool, or, in Obsidian's case, plugins).

As of a few months ago, I've now achieved what I feel is a pretty comfortable setup that's not changed much, and have since helped over 20 people make the switch full-time for their varying use cases! Having now done this for a decent bit and purchased my Catalyst license, I feel there's no better time than now to make a proper introduction, so here I am. Now, ask me anything (though I'll get some of the big questions out of the way)!

  • Favorite Themes: Baseline (shown in screenshot), Velocity, and Border. Baseline just does everything, though the other two are nice and unique in their own right.
  • Favorite Color Palettes: Nord (I use it in everything, be it across the web with Dark Reader Plus, in code editing with VS Code, and even in Cryptography with RStudio lol). Atom is a close second.
  • Favorite Plugins: Notebook Navigator (the best!), Startpage (nice homepage), Waypoint (essential for my setups), YouVersion Linker (gotta have it)
  • Underrated Plugins: Trash Explorer, Settings Search, and LaTex Suite (not really underrated, but aweome anyway)
  • Operating Systems/Platforms: Functionally, all of them, meaning I use Obsidian to at least some extent on Android, Windows, and Linux. That said, most my time is on iOS, iPadOS, and Android.
  • Folders or Tags: Folders 100%, though I do add tags to my worldbuilding vault.
  • What I Use Obsidian For: Everything! Bible & Church Nots, College, General Ideas, Tech Findings, Script Writing, and then an entirely separate vault for Worldbuilding!
  • I could split things up into more vaults, but the plugin and theme setups would be near-identical anyway. The worldbuilding one is separate because it does use a slightly-different setup, and I don't really need it linking to general notes.
  • General Pro Tip: Pair Obsidian with a good IDE like Visual Studio Code (or another of your choice, don't flame me in the comments; I use it for class)! It can work quite nicely for cleanup and ensuring you don't accidentally lock yourself into non-open formats. MathJax, Mermaid, and even Canvases are all possible in VS Code with the right setup!
  • Other/Miscellaneous Details:
  • Not counting plugins, my entire General Notes vault – all 384 notes – only take up 4.2 MB. That jumps to 24.2 MB with plugins, where I use 33 as of now.
  • I have zero local attachments in my vault. How, you ask?
    • Resources I need for class can usually be web-embedded from Wikipedia or other sources (where I'm not too concerned about link rot as I don't usually need the images for very long)
    • I'm very organized with screenshots and other things on-device and don't need them in Obsidian,
    • And I literally started learning MathJax and even some Mermaid so that, in the rare occasion I need to take a photo of something in class because I can't write it fast enough, I go back and type it myself.

And... that is all. Thank you, u/kepano and the rest of Obsidian for making this fantastic application into what it is today!

All of this was manually typed up by me. I am a writer, a tech, a documenter, and a certified over-explainer lol; my sincerest apologies but I will not be fixing it anytime soon :)

출처: https://www.reddit.com/r/ObsidianMD/comments/1rwus39/1_year_500_notes_into_obsidian_as_a_college/

[r/ObsidianMD] obsidian-web-mcp: a sync-safe MCP server that lets Claude reach y

원문

[r/ObsidianMD] obsidian-web-mcp: a sync-safe MCP server that lets Claude reach y

I use Obsidian as my primary knowledge management system and Claude as my primary AI tool. The problem: Claude can only access your vault when running locally on the same machine. From the Claude web app or mobile app, your vault doesn't exist.

Every Obsidian MCP server I found is some form of a local stdio server. Great if you're running Claude Code in a terminal, useless from your phone.

So I built obsidian-web-mcp, a Python MCP server that runs on your machine and serves your vault over HTTPS through a Cloudflare Tunnel.

Once connected, Claude (web, desktop, or mobile) can read files, write files, search content, query frontmatter, and manage your vault from anywhere.

What it does:

  • 9 tools: read, write, search (full-text + frontmatter), list, move, delete, batch read, batch frontmatter update
  • Parses YAML frontmatter and maintains an in-memory index for fast queries
  • Full-text search uses ripgrep when available, falls back to Python
  • Soft deletes (moves to .trash/, same as Obsidian)

Why it's safe for Obsidian Sync users (like me):

  • Every write is atomic -- writes to a temp file, then os.replace() to the target. Obsidian Sync never sees a partial file.
  • .obsidian, .trash, and .git directories are excluded from all operations
  • Path sanitization blocks directory traversal, symlink escapes, and null byte injection - the server physically cannot read or write outside your vault

Security model:

  • OAuth 2.0 with PKCE for client authentication (what Claude uses when you connect)
  • Bearer token on every MCP request
  • Cloudflare Tunnel means outbound-only connections - no ports opened on your machine, no public IP exposed
  • Optional: layer Cloudflare Access on top for SSO or device-based restrictions

Setup is straightforward: install with uv, set three environment variables (vault path, auth token, OAuth secret), run the server. Connect in Claude app via Settings > Integrations. For remote access, run the included Cloudflare Tunnel setup script. Includes macOS launchd plists for always-on operation.

MIT licensed, open source. https://github.com/jimprosser/obsidian-web-mcp

Happy to answer questions about the architecture or security model.

출처: https://www.reddit.com/r/ObsidianMD/comments/1rwmuiq/obsidianwebmcp_a_syncsafe_mcp_server_that_lets/

[r/MachineLearning] [D] ICML rejects papers of reviewers who used LLMs despite a

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[r/MachineLearning] [D] ICML rejects papers of reviewers who used LLMs despite a

According to multiple posts on Twitter/X ICML has rejected all paper of reviewers who used LLMs for their reviews even though they chose the review track with no LLM use. What are your thoughts on this? Too harsh considering the limited precision of AI detection tools?

It is the first time I see a major conferences taking harsh actions on LLM-generated reviews.

https://preview.redd.it/trkb82lumspg1.png?width=1205&format=png&auto=webp&s=03953ce11b9803cf35dd7fe83428e4187f8c4092

출처: https://www.reddit.com/r/MachineLearning/comments/1rx201a/d_icml_rejects_papers_of_reviewers_who_used_llms/

[r/MachineLearning] [R] A Gradient Descent Misalignment — Causes Normalisation T

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[r/MachineLearning] [R] A Gradient Descent Misalignment — Causes Normalisation T

This paper, just accepted at ICLR's GRaM workshop, asks a simple question:

>Does gradient descent systematically take the wrong step in activation space?

It is shown:

>Parameters take the step of steepest descent; activations do not

The paper mathematically demonstrates this for simple affine layers, convolution, and attention.

The work then explores solutions to address this.

The solutions may consequently provide an alternative mechanistic explanation for why normalisation helps at all, as two structurally distinct fixes arise: existing (L2/RMS) normalisers and a new form of fully connected layer (MLP).

Derived is:

  1. A new form of affine-like layer (a.k.a. new form for fully connected/linear layer). featuring inbuilt normalisation whilst preserving DOF (unlike typical normalisers). Hence, a new alternative layer architecture for MLPs.
  2. A new family of normalisers: "PatchNorm" for convolution, opening new directions for empirical search.

Empirical results include:

  • This affine-like solution is not scale-invariant and is not a normaliser, yet it consistently matches or exceeds BatchNorm/LayerNorm in controlled MLP ablation experiments—suggesting that scale invariance is not the primary mechanism at work—but maybe this it is the misalignment.
  • The framework makes a clean, falsifiable prediction: increasing batch size should hurt performance for divergence-correcting layers. This counterintuitive effect is observed empirically and does not hold for BatchNorm or standard affine layers. Corroborating the theory.

Hope this is interesting and worth a read.

  • I've added some (hopefully) interesting intuitions scattered throughout, e.g. the consequences of reweighting LayerNorm's mean & why RMSNorm may need the sqrt-n factor & unifying normalisers and activation functions. Hopefully, all surprising fresh insights - please let me know what you think.

Happy to answer any questions :-)

[ResearchGate Alternative Link] [Peer Reviews]

출처: https://www.reddit.com/r/MachineLearning/comments/1rx1gtn/r_a_gradient_descent_misalignment_causes/

[r/MachineLearning] [R] From Garbage to Gold: A Formal Proof that GIGO Fails for

원문

[r/MachineLearning] [R] From Garbage to Gold: A Formal Proof that GIGO Fails for

Paper: https://arxiv.org/abs/2603.12288

GitHub (R simulation, Paper Summary, Audio Overview): https://github.com/tjleestjohn/from-garbage-to-gold

I'm Terry, the first author. This paper has been 2.5 years in the making and I'd genuinely welcome technical critique from this community.

The core result: We formally prove that for data generated by a latent hierarchical structure — Y ← S¹ → S² → S'² — a Breadth strategy of expanding the predictor set asymptotically dominates a Depth strategy of cleaning a fixed predictor set. The proof follows from partitioning predictor-space noise into two formally distinct components:

  • Predictor Error: Observational discrepancy between true and measured predictor values. Addressable by cleaning, repeated measurement, or expanding the predictor set with distinct proxies of S¹.
  • Structural Uncertainty: The irreducible ambiguity arising from the probabilistic S¹ → S² generative mapping — the information deficit that persists even with perfect measurement of a fixed predictor set. Only resolvable by expanding the predictor set with distinct proxies of S¹.

The distinction matters because these two noise types obey different information-theoretic limits. Cleaning strategies are provably bounded by Structural Uncertainty regardless of measurement precision. Breadth strategies are not.

The BO connection: We formally show that the primary structure Y ← S¹ → S² → S'² naturally produces low-rank-plus-diagonal covariance structure in S'² — precisely the spiked covariance prerequisite that the Benign Overfitting literature (Bartlett et al., Hastie et al., Tsigler & Bartlett) identifies as enabling interpolating classifiers to generalize. This provides a generative data-architectural explanation for why the BO conditions hold empirically rather than being imposed as abstract mathematical prerequisites.

Empirical grounding: The theory was motivated by a peer-reviewed clinical result at Cleveland Clinic Abu Dhabi — .909 AUC predicting stroke/MI in 558k patients using thousands of uncurated EHR variables with no manual cleaning, published in PLOS Digital Health — that could not be explained by existing theory.

Honest scope: The framework requires data with a latent hierarchical structure. The paper provides heuristics for assessing whether this condition holds. We are explicit that traditional DCAI's focus on outcome variable cleaning remains distinctly powerful in specific conditions — particularly where Common Method Variance is present.

The paper is long — 120 pages with 8 appendices — because GIGO is deeply entrenched and the theory is nuanced. The core proofs are in Sections 3-4. The BO connection is Section 7. Limitations are Section 15 and are extensive.

Fully annotated R simulation in the repo demonstrating Dirty Breadth vs Clean Parsimony across varying noise conditions.

Happy to engage with technical questions or pushback on the proofs.

출처: https://www.reddit.com/r/MachineLearning/comments/1rwyy9g/r_from_garbage_to_gold_a_formal_proof_that_gigo/

[r/MachineLearning] [P] Tridiagonal eigenvalue models in PyTorch: cheaper traini

원문

[r/MachineLearning] [P] Tridiagonal eigenvalue models in PyTorch: cheaper traini

This post is part of a series I'm working on with a broader goal: understand what one nonlinear "neuron" can do when the nonlinearity is a matrix eigenvalue, and whether that gives a useful middle ground between linear models that are easy to explain and larger neural networks that are more expressive but much less transparent. Something unusual, in this "attention is all you need" world :)

In this installment, I look at a cheaper variant of the model family by constraining each learned matrix to be symmetric tridiagonal instead of dense.

The model family is still f(x) = λₖ(A₀ + ∑ᵢ xᵢAᵢ), but the eigensolve becomes much cheaper. The motivation here is that diagonal structure collapses the model to something close to piecewise linear, while tridiagonal structure still keeps adjacent latent-variable interactions.

The post walks through why this structural restriction is interesting, how I wired scipy.linalg.eigh_tridiagonal into PyTorch autograd, and what happens on a few toy and tabular experiments. In my runs, the tridiagonal eigensolver was about 5x-6x faster than the dense one on 100x100 batches, which was enough to make larger experiments much cheaper to run.

If you're interested in structured spectral models, custom autograd around numerical linear algebra routines, or model families that try to sit between linear interpretability and fully opaque neural nets, the full writeup is here:

https://alexshtf.github.io/2026/03/15/Spectrum-Banded.html

This is an engineering writeup rather than a paper, so I'd read it in that spirit.

출처: https://www.reddit.com/r/MachineLearning/comments/1rwy5ch/p_tridiagonal_eigenvalue_models_in_pytorch/

[r/MachineLearning] [P] Weight Norm Clipping Accelerates Grokking 18-66× | Zero

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[r/MachineLearning] [P] Weight Norm Clipping Accelerates Grokking 18-66× | Zero

https://preview.redd.it/9hxa34bwhopg1.png?width=3600&format=png&auto=webp&s=909e4e1ba2feebbab94651d125a5c8e7591c4ca6

Zero failures across 300 seeds. 66× speedup. 5 lines of code.

We're two independent researchers. The method: per-row ℓ₂ clipping on decoder weights after every optimizer step. No additional memory, no weight decay needed.

Results on the standard grokking benchmark (modular arithmetic, decoder-only transformer, same setup as Grokfast [2024]):

  • 2-layer (422k params): 66× over AdamW baseline with Lion+Clip
  • 8-layer (1.6M params): 18× over baseline, zero failures across 300 seeds, IQR reduction 61–72% with edge initialization

Honest scope: all experiments are modular arithmetic. We're running a 277M LLM test but it'll take weeks on our hardware and results may not transfer cleanly — we're not claiming otherwise. Happy to share progress, dataset, and full model/training parameters.

Code + PDF:
https://github.com/NiftyliuS/cliptogrok
https://github.com/NiftyliuS/cliptogrok/blob/main/cliptogrok.pdf

We're seeking arXiv endorsement (cs.LG) — DM if willing.

출처: https://www.reddit.com/r/MachineLearning/comments/1rwl1sq/p_weight_norm_clipping_accelerates_grokking_1866/

[r/MachineLearning] [P] mlx-tune – Fine-tune LLMs on Apple Silicon with MLX (SFT

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[r/MachineLearning] [P] mlx-tune – Fine-tune LLMs on Apple Silicon with MLX (SFT

Sharing mlx-tune, a Python library for fine-tuning LLMs natively on Apple Silicon using Apple's MLX framework.

It supports SFT, DPO, ORPO, GRPO, KTO, SimPO trainers with proper loss implementations, plus vision-language model fine-tuning (tested with Qwen3.5). The API mirrors Unsloth/TRL, so the same training script runs on Mac and CUDA — you only change the import line.

Built on top of mlx-lm and mlx-vlm. LoRA/QLoRA, chat templates for 15 model families, GGUF export. Runs on 8GB+ unified RAM.

Not a replacement for Unsloth on NVIDIA — this is for prototyping locally on Mac before scaling to cloud GPUs.

GitHub: https://github.com/ARahim3/mlx-tune

출처: https://www.reddit.com/r/MachineLearning/comments/1rw58ku/p_mlxtune_finetune_llms_on_apple_silicon_with_mlx/

[r/MachineLearning] [R] Attention Residuals by Kimi Team (85↑)

원문

[r/MachineLearning] [R] Attention Residuals by Kimi Team (85↑)

arXiv:2603.15031 [cs.CL]: https://arxiv.org/abs/2603.15031

Abstract: Residual connections with PreNorm are standard in modern LLMs, yet they accumulate all layer outputs with fixed unit weights. This uniform aggregation causes uncontrolled hidden-state growth with depth, progressively diluting each layer's contribution. We propose Attention Residuals (AttnRes), which replaces this fixed accumulation with softmax attention over preceding layer outputs, allowing each layer to selectively aggregate earlier representations with learned, input-dependent weights. To address the memory and communication overhead of attending over all preceding layer outputs for large-scale model training, we introduce Block AttnRes, which partitions layers into blocks and attends over block-level representations, reducing the memory footprint while preserving most of the gains of full AttnRes. Combined with cache-based pipeline communication and a two-phase computation strategy, Block AttnRes becomes a practical drop-in replacement for standard residual connections with minimal overhead.
Scaling law experiments confirm that the improvement is consistent across model sizes, and ablations validate the benefit of content-dependent depth-wise selection. We further integrate AttnRes into the Kimi Linear architecture (48B total / 3B activated parameters) and pre-train on 1.4T tokens, where AttnRes mitigates PreNorm dilution, yielding more uniform output magnitudes and gradient distribution across depth, and improves downstream performance across all evaluated tasks.

From Kimi.ai on 𝕏: https://x.com/Kimi_Moonshot/status/2033378587878072424

출처: https://www.reddit.com/r/MachineLearning/comments/1rw1eag/r_attention_residuals_by_kimi_team/

[r/MachineLearning] [N] openreview profile glitch?? (26↑)

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[r/MachineLearning] [N] openreview profile glitch?? (26↑)

my openreview profile info is looking like this. and it is same for all of my co workers as well.

https://preview.redd.it/dy7y0pkxljpg1.png?width=1245&format=png&auto=webp&s=c4131e0868919f5fef525b0cf5004aea673c676d

출처: https://www.reddit.com/r/MachineLearning/comments/1rvy3m4/n_openreview_profile_glitch/

[r/MachineLearning] [R] Genomic Large Language Models (19↑)

원문

[r/MachineLearning] [R] Genomic Large Language Models (19↑)

Can a DNA language model find what sequence alignment can't?

I've been exploring Evo2, Arc Institute's genomic foundation model trained on 9.3 trillion nucleotides, to see if its learned representations capture biological relationships beyond raw sequence similarity.

The setup: extract embeddings from Evo2's intermediate layers for 512bp windows across 25 human genes, then compare what the model thinks is similar against what BLAST (the standard sequence alignment tool) finds.

Most strong matches were driven by common repeat elements (especially Alu). But after stricter filtering, a clean pair remained:

A section of the VIM (vimentin, chr10) gene and a section of the DES(desmin, chr2) gene showed very high similarity (cosine = 0.948), even though they have no detectable sequence match. Both regions are active promoters in muscle and connective tissue cells, share key regulatory proteins, and come from two related genes that are often expressed together.

This suggests Evo2 is starting to learn to recognize patterns of gene regulation — not just the DNA letters themselves — even when the sequences look completely different.

That said, this kind of meaningful signal is still hard to find. It only appears after heavy filtering, and many other matches remain noisy.

Overall, Evo2 appears to capture some real biological information beyond sequence alignment, but making it practically useful will take more work.

Would be curious to hear thoughts from others in genomics and AI.

https://preview.redd.it/ya4k6xwhmipg1.png?width=2496&format=png&auto=webp&s=8e7b4c0bd8c9540b39678a9adb5ab6e0a500eac6

출처: https://www.reddit.com/r/MachineLearning/comments/1rvu5df/r_genomic_large_language_models/

[r/MachineLearning] [D] Lossless tokenizers lose nothing and add nothing — trivi

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[r/MachineLearning] [D] Lossless tokenizers lose nothing and add nothing — trivi

I wrote up a short information-theoretic argument for why lossless tokenization neither restricts the expressiveness of language models nor introduces unavoidable redundancy. The key ideas:

  • Any target distribution over strings can be exactly induced by a distribution over token sequences (via the canonical construction)
  • The canonical distribution achieves H(Q) = H(P) — no extra entropy from tokenization
  • In practice, models do leak \~0.5–2% probability onto non-canonical tokenizations (Chirkova et al., 2023), and deliberately introducing this noise via BPE-Dropout can actually help generalization

https://douglasswng.github.io/why-tokens-enough/

I'm curious whether people find this kind of formalization useful or if it's "obviously true" and not worth writing down. The practical punchline — that the theoretically optimal thing (concentrate on canonical tokenizations) isn't always best in practice (BPE-Dropout helps) — was the part I found most interesting.

출처: https://www.reddit.com/r/MachineLearning/comments/1rv7e1e/d_lossless_tokenizers_lose_nothing_and_add/

[r/MachineLearning] [D] how to parallelize optimal parameter search for DL NNs o

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[r/MachineLearning] [D] how to parallelize optimal parameter search for DL NNs o

suppose i have 5 and 6 datasets, 11 in total.

then i have a collection of 5 different deep learning networks, each having their own set of free non-DL parameters, ranging from none to 3-4.

imagine i have a list of educated guesses for each parameter (5-6 values) and i wanna try all their combinations for each DL method on each dataset. i’m okay with leaving it computing overnight. how would you approach this problem? is there a way to compute these non-sequentially/in parallel with a single GPU?

* each run has 2 phases: learning and predicting, and there’s the model checkpoint artifact that’s passed between them. i guess these have to now be assigned special suffixes so they don’t get overwritten.

* the main issue is a single GPU. i don’t think there’s a way to “split” the GPU as you can do with CPU that has logical cores. i’ve completed this task for non-DL/NN methods where each of 11 datasets occupied 1 core. seems like the GPU will become a bottleneck.

* should i also try to sweep the DL parameters like epochs, tolerance, etc?

does anyone have any advice on how to do this efficiently?

출처: https://www.reddit.com/r/MachineLearning/comments/1rv45pi/d_how_to_parallelize_optimal_parameter_search_for/

[r/MachineLearning] [P] Using residual ML correction on top of a deterministic p

원문

[r/MachineLearning] [P] Using residual ML correction on top of a deterministic p

Personal project I've been working on as a CSE student: F1Predict, a race simulation and strategy intelligence system.

Architecture overview:

- Deterministic lap time engine (tyre deg, fuel load, DRS, traffic) as the baseline

- LightGBM residual model trained on FastF1 historical telemetry to correct pace deltas — injected into driver profile generation before Monte Carlo execution

- 10,000-iteration Monte Carlo producing P10/P50/P90 distributions per driver per race

- Auxiliary safety car hazard classifier (per lap window) modulating SC probability in simulation

- Feature versioning in the pipeline: tyre age × compound, qualifying delta, sector variance, DRS activation rate, track evolution coefficient, weather delta

- Strategy optimizer runs at 400 iterations (separate from the main MC engine) to keep web response times reasonable

The ML layer degrades gracefully if no trained artifact is present, simulation falls back to the deterministic baseline cleanly. Redis caches results keyed on sha256 of the normalized request.

Current limitation: v1 residual artifact is still being trained on a broader historical dataset, so ML and deterministic paths are close in output for now. Scaffolding and governance are in place.

Stack: Python · FastAPI · LightGBM · FastF1 · Supabase · Redis · React/TypeScript

Repo: https://github.com/XVX-016/F1-PREDICT

Live: https://f1.tanmmay.me

Happy to discuss the modelling approach, feature engineering choices, or anything that looks architecturally off. This is a learning project and I'd genuinely value technical feedback.

출처: https://www.reddit.com/r/MachineLearning/comments/1ruxn9t/p_using_residual_ml_correction_on_top_of_a/

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