What it does

All text below is quoted directly from the project README.

>A skill for your coding assistant that stops it from burying the answer. Action first. Steps numbered. No "Hope this helps!"

The rules:

1. Lead with the next action.
2. Number multi-step tasks.
3. End with one concrete next step.
4. Suppress tangents.
5. Restate state every turn.
6. Specific time estimates (minutes, not "a bit").
7. Make wins visible.
8. Matter-of-fact errors.
9. Cap lists to 5 items.
10. No preamble. No recap. No closers.

Credits: Loosely based on The Adult ADHD Tool Kit by J. Russell Ramsay and Anthony L. Rostain. Adapted for how an LLM should respond, not how a human should organize their day.

>Install the i-have-adhd skill/plugin from https://github.com/ayghri/i-have-adhd, refer to the repo's AGENTS.md for instructions.

Repository

https://github.com/ayghri/i-have-adhd

Repository stats

Main contributor

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After generating thousands of images across multiple models, i realised it was becoming a pain to maintain a decent list of metadata stored in the images to be able to recreate them or re-use prompts with other models to see how the models generate images with similar prompts/seed/cfg/steps etc. I decide to build something that I can use to maintain images to make it easier to find images, copy prompts and see what metadata is embedded into the images. It's effectively an AI image manager based on how ComfyUI generates images and stores metadata.

Looking for feedback on whether this is a useful tool or if there's any feature issues that you see.

Download: Augie's AI Image Browser

Current features I've built into it:

Reading AI image info:

• Reads ComfyUI settings automatically from PNG, WebP, MP4, WebM, MOV and MKV files.
• Shows the generation details for each file: model, LoRA, sampler, scheduler, steps, CFG, seed, positive prompt, negative prompt and wildcard prompt.
• One-click copy for prompts and the full workflow.
• Raw workflow view: you can expand and copy the complete ComfyUI workflow data.
• Works with videos too, including files made with the Video Combine node.

Library and folders

• Add or remove folders for the app to scan.
• Live folder watching: new images show up on their own as ComfyUI saves them.
• Pause and resume watching from the top bar, with a status light (Watching, Indexing or Paused).
• Keeps your work when files move: if you rename or move a file, its ratings, tags and collections go with it.
• Missing file tracking: files deleted from disk are flagged, not forgotten, and you can clear them out later.
• Thumbnails for images and videos.

Viewing Images

• Built-in full-size viewer for images and videos, with zoom (mouse wheel or double-click), pan, fit-to-screen and keyboard controls.
• Open in default app: opens the file in Photos, VLC or whatever Windows uses for it (desktop app only).
• Show in folder: opens File Explorer with the file selected (desktop app only).
• Table view with sortable columns: keeper, rating, filename, model, sampler, scheduler, steps, seed and date modified.
• Paging for large libraries.

Sorting your images

• Keep or Discard with one click.
• 0–5 star ratings.
• Your own tags, with suggestions as you type.
• Collections: create, delete, add files and remove files.
• Delete from disk, with a confirmation step.

Auto-Collection Rules

• Rules that file images into collections for you based on their details.
• Rule conditions can check:
  • Folder, filename, file type, image or video, date created
  • Prompts, model, LoRAs, sampler, scheduler, steps, CFG, seed
  • Width, height, shape (portrait, landscape or square), workflow nodes
• Flexible matching: contains, equals, starts with, number ranges, "in the last N days", regex and more.
• Combine conditions with "match all" or "match any", nested up to 3 levels deep.
• Preview which files a rule would catch before saving it.
• Apply rules to images you already have, or only to new ones.
• Create a rule from an image: starts a new rule using that image's settings.
• ⚡ badge on images that a rule filed.

Clean Export - For selling and sharing

• Removes all hidden AI info and workflow data from exported files.
• Keeps transparent backgrounds for PNG and WebP, or fills them with a colour you choose for JPEG.
• Sets resolution and DPI (for example 300 DPI for print, 72 for web).
• Custom file naming using collection, number, date, original name, tag or rating. The seed and prompt can't be used in names, so they can't leak.
• Ready-made presets (Etsy PNG, Shopify WebP, Stock JPEG), and you can save your own.
• Checks every exported file afterwards to confirm nothing leaked.
• Private export log, so each exported file can be traced back to its original. Includes an export history screen.
• Download as ZIP.
• CSV or JSON export of the image details as a spreadsheet or data file.

> Did nobody bother to share how much it cost or which plan they’re on? >   > — Kompos >   >   > im on the $200 Codex plan and I use all of it every week! >   > — Spencer Sterling

Source: https://x.com/cerspense/status/2096960824535572498

Disclosure: I'm one of the people behind Aidlab.

If you're working on human activity recognition, exercise classification, or repetition counting, we've published AIDLAB-HAR on Hugging Face under CC BY 4.0.

It contains 180 chest-worn recordings and 518,300 samples at 50 Hz. The signals include 3-axis acceleration, orientation quaternions, 16 activity labels, and repetition markers.

We packaged it into three configurations that work directly with the datasets library: recordings, signals, and annotations. The raw v3 archive, cleaning notes, and preparation script are included too.

One detail we found while preparing the release: the paper's overview mentions 15 activities, while the archive contains 16 filename labels. We documented the discrepancy and preserved the source labels.

Dataset: https://huggingface.co/datasets/aidlab-wearables/AIDLAB-HAR

Paper: https://doi.org/10.3390/s24123891

A year of running production AI on free tiers: Oracle ARM VM (4 OCPU/24GB), 20+ free LLM providers with automatic fallback (Groq, NVIDIA NIM, Gemini, Mistral, Zhipu, OpenRouter :free, local Ollama), ~107 cron jobs all wrapped in flock + timeout so nothing double-runs or hangs, SQLite with WAL, and 3 verified backup copies.

The interesting part isn't the stack — it's what broke:

- A missing import killed a lane silently for days (bare except swallowed it)

- Providers retire models with zero notice — my router now self-suspends dead lanes

- Datacenter IPs get 403'd by half the internet — residential-IP browser lanes are the answer

- A nginx MIME type (missing .mjs) broke a whole page with zero errors

Everything is open: github.com/BraxisAI/braxis-blueprint (MIT). The router, the cron discipline, the backup system, the failure classes. If you're building on free tiers, these are the walls you'll hit.

I've been experimenting with something a little different for character consistency: using structured 3D pose data as part of the dataset-generation process for LoRA training.

Instead of generating a bunch of random images and hoping the dataset covers enough variation, I've been using controlled MetaHuman facial expressions and hand poses to deliberately build variations around the same character.

The interesting part is that the 3D poses give me something I can control and reproduce:

• specific facial expressions
• specific hand positions
• repeatable poses
• combinations of face + hand poses
• consistent character geometry

I then use those references in my ComfyUI workflows to generate the 2D training images.

The goal isn't to train the LoRA to reproduce the MetaHuman itself. It's to use the controlled pose references to create a more deliberate dataset where identity stays consistent while expression and pose change.

This seems particularly useful for characters that need to remain recognizable across a large number of generated shots.

I've been using my own MetaHuman Face Pose Library and MetaHuman Hand Pose Library for this workflow.

I'm curious if anyone else has experimented with 3D pose libraries as a structured way of generating LoRA training datasets.

It seems like there could be an interesting middle ground between manually creating every training image and completely random dataset generation.

What are you guys using for pose/expression variation when building character LoRAs?

I gave the MiniMax one image and one instruction: "Act as an expert FPV director and turn this into something viral."

It analyzed the composition, built the camera logic and pacing, then generated a detailed prompt for MiniMax H3.

The output? Absolutely insane. Professional-level FPV footage.

This is the future - AI agents handle the complexity, you just direct. No more prompt engineering hell.

https://github.com/zhaoxuya520/reverse-skill

Community Overview: https://lifehubber.com/ai/resources/reverse-skill/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

New resources are added regularly — feel free to join the sub for updates.

Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 200+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

We already saw it with inworld tts, now it’s time for DeepSeek’s value capture. Thank you for all of your data and good publicity, we will now be significantly increasing cost.

I recently moved a production workflow for one of my startups from DeepSeek V4 Flash through OpenRouter to OpenAI’s Luna model.

It runs the same kind of work every day: generating structured stock briefs, enriching news, summarizing market data, and returning JSON for my app.

The difference has been kind of ridiculous:

• DeepSeek through OpenRouter: roughly $0.60/day
• Luna through OpenAI: roughly $0.14/day
• Luna is also completing the workload noticeably faster

That’s around an 80% cost reduction, or almost 6x cheaper, with comparable output quality. I expected Luna to improve reliability, but I did not expect it to cost this much less.

It isn’t a perfectly controlled benchmark because token usage, caching, and daily workloads can vary. Still, the workload is similar enough that the gap surprised me.

Has anyone else seen this after moving production workloads away from OpenRouter? Could the difference mainly come from prompt caching, routing overhead, or DeepSeek producing more reasoning tokens?

I like OpenRouter for quickly testing different models, but direct OpenAI is currently faster, cheaper, and simpler for this workload. Kinda hard to argue with $0.14 versus $0.80 lol.

https://x.com/huang_chao4969/status/2084130198531006948

https://github.com/boogu-project/Boogu-Image

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

New resources are added regularly — feel free to join the sub for updates.

Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 200+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

Hey everyone,

We’re making the new DeepSeek-V4 Flash 0731 free in Cline.

It scores 82.7 on Terminal Bench 2.1, which puts it surprisingly close to some of the strongest coding models available today.

We’ve already shared a deeper breakdown of the benchmark results, but the more interesting part now is seeing how it performs on actual codebases. We’re excited for you to try it and feel how far Flash models have come.

To use it:

1. Install Cline CLI: npm i -g cline
2. Open /settings
3. Select Cline as your provider
4. Choose deepseek-v4-flash

It’s completely free to use right now.

Curious to hear how DeepSeek-V4 Flash 0731 performs for the you all.