I am writing this between sounds of fireworks.

I learned everything about LLM, RAG and others stuff related to AI for a longg time here.

May your year be filled with perfect timing, rich flavors, and the joy of creating something truly special.

Happy lunar new year, here’s to a masterpiece of a year ahead!

Track is called "Zima Moroz" ("Winter Frost" in Polish). Made with Suno.

Is there an LTX-2 Anonymous? I need help.

How to actually learn programming while vibecoding?

I have no programming background whatsoever. But I've started vibecoding for some personal projects for the past few months. It's really incredible, but the more I learn, the more I feel anxious about not actually understanding the AI's output.

I've tried doing some free courses at code academy, but I just keep procrastinating doing them, so that's why I'm trying to learn in a practical way... But I don't really know if there's a better way to do this.

I always try to add to my prompts some snippets like "consider I know nothing about programming and explain it to me using plain language". I've learned a lot doing that, but I always forget some concepts afterwards.

I thought about adding a .md file to the repo to work as a "notebook", and ask Claude to update it with definitions and examples.

Like, I keep forgetting what "DOM" even is. Maybe on that notebook, Claude could add the definitions and cite examples of when this concept was used in a previous interaction.

I'm fairly good at prompt engineering. I know how to build skills, workflows, how to use GitHub, etc. the basics of vibecoding are covered, I think.

But learning concepts about programming has been a little overwhelming. I have no plans of working as a dev. I just want to be able to actually understand what the LLM is doing, to be able to detect major flaws in the code, to learn more about how to plan the architecture of a code in a sustainable way...

So, do you guys have any tips on how to do that? What should I focus on? What's essential to learn?

Anyway. Thanks in advance ❤️

Our company has just landed a series of AI Video Jobs and I am looking for one more person to join our team between the start of March and mid to late April (1.5 Months). We are a video production agency in the UK and we are looking for ideally people with some industry experience with a good eye for storytelling and who are skillful at AI video gen.

Role Description

This is a freelance remote role for an AI Video Artist. The ideal candidate will contribute to high-quality production and explore AI video solutions.

We are UK based so looking for someone in a similar timezone, preferably UK/Europe but open to US/American location (Brazil has a more compatible timezone).

Qualifications

Proficiency in AI tools and technologies for video production.

Good storytelling skills.

Experience in the industry - ideally at least 1-3+ year of experience working in film, TV or advertising industries.

Good To Have:

Strong skills and background in a core pillar of video production outside of AI filmmaking, i.e. video editing, 2D animation, CG animation or motion graphics.

Experience in creative storytelling.

Familiarity with post-production processes in the industry.

Please DM with details and portfolio or reel.

Thanks

Hi i wan tto buy a laptop with cpu is question is it worth it anybody tried it ?

https://reddit.com/link/1r6gohu/video/oh3sweaqawjg1/player

All svg generate were horrible and truly bad, but gemini did even worse than qwen 3.5 plus. Tried in google ai studio chat and app builder.

Just wanted to share my experience moving from AI-Toolkit to OneTrainer, because the difference has been night and day for me.

Like many, I started with AI-Toolkit because it’s the go-to for LoRA training. It’s popular, accessible, and honestly, about 80% of the time, the defaults work fine. But recently, while training with the Klein 9B model, I hit a wall. The training speed was slow, and I wasn't happy with the results.

I looked into Diffusion Pipe, but the lack of a GUI and Linux requirement kept me away. That led me to OneTrainer. At first glance, OneTrainer is overwhelming. The GUI has significantly more settings than AI-Toolkit. However, the wiki is incredibly informative, and the Discord community is super helpful. Development is also moving fast, with updates almost daily. It has all the latest optimizers and other goodies.

The optimization is insane. On my 5060 Ti, I saw a literal 2x speedup compared to AI-Toolkit. Same hardware, same task, half the time, with no loss in quality.

Here's the thing that really got me though. It always bugged me that AI-Toolkit lacks a proper validation workflow. In traditional ML you split data into training, validation, and test sets to monitor hyperparameters and catch overfitting. AI-Toolkit just can't do that.

OneTrainer has validation built right in. You can actually watch the loss curves and see when the model starts drifting into overfit territory. Since I started paying attention to that, my LoRa quality has improved drastically. Way less bleed when using multiple LoRas together because the concepts aren't baked into every generation anymore and the model doesn't try to recreate training images.

I highly recommend pushing through the learning curve of OneTrainer. It's really worth it.

https://preview.redd.it/wn4zaxnp8wjg1.png?width=528&format=png&auto=webp&s=49fa3db9a307283269e64c0f0cfc5cae90b839d8

Just want to share my statusline in Claude Code. It wasn't so hard to setup as it wrote most of the code. Note that this is in Windows Terminal so I use a PowerShell script.
If you use a none Windows machine you have to use something else.

My settings.json
{

"permissions": {

"allow": [

"Read",

"Glob",

"Grep",

"WebSearch",

"WebFetch",

"Task",

"TaskOutput",

"TaskStop",

"Bash(ls *)",

"Bash(pwd)",

"Bash(git *)"

]

},

"autoUpdatesChannel": "latest",

"effortLevel": "medium",

"statusLine": {

"type": "command",

"command": "powershell -ExecutionPolicy Bypass -NoProfile -File C:\\Users\\Developer\\.claude\\statusline.ps1"

}

}

statusline.ps1
$e = [char]0x1b

$stdin_data = [Console]::In.ReadToEnd()

try {

$data = $stdin_data | ConvertFrom-Json

$model = $data.model.display_name

if (-not $model) { $model = "Unknown" }

$short_model = $model -replace 'Claude [0-9.]+ ', '' -replace '^Claude ', ''

$cost = 0

if ($data.cost.total_cost_usd) { $cost = [math]::Floor($data.cost.total_cost_usd * 100) / 100 }

$duration_ms = 0

if ($data.cost.total_duration_ms) { $duration_ms = [int]$data.cost.total_duration_ms }

$ctx_used = $null

if ($null -ne $data.context_window.remaining_percentage) {

$ctx_used = 100 - [math]::Floor($data.context_window.remaining_percentage)

}

$current_dir = $data.workspace.current_dir

if (-not $current_dir) { $current_dir = "unknown" }

$folder_name = Split-Path $current_dir -Leaf

# Git info

$git_branch = ""

try {

Push-Location $current_dir 2>$null

$git_branch = git branch --show-current 2>$null

Pop-Location

} catch {}

# Cache hit percentage

$cache_pct = 0

try {

$input_tokens = [int]($data.context_window.current_usage.input_tokens)

$cache_read = [int]($data.context_window.current_usage.cache_read_input_tokens)

if (($input_tokens + $cache_read) -gt 0) {

$cache_pct = [math]::Floor($cache_read * 100 / ($input_tokens + $cache_read))

}

} catch {}

# Session time

$session_time = ""

if ($duration_ms -gt 0) {

$total_sec = [math]::Floor($duration_ms / 1000)

$hours = [math]::Floor($total_sec / 3600)

$minutes = [math]::Floor(($total_sec % 3600) / 60)

$seconds = $total_sec % 60

if ($hours -gt 0) { $session_time = "${hours}h ${minutes}m" }

elseif ($minutes -gt 0) { $session_time = "${minutes}m ${seconds}s" }

else { $session_time = "${seconds}s" }

}

# ANSI color codes

$reset = "${e}[0m"

$dim = "${e}[2m"

$white = "${e}[37m"

$blue = "${e}[94m"

$cyan = "${e}[96m"

$yellow = "${e}[33m"

$teal = "${e}[36m"

$green = "${e}[32m"

$red = "${e}[31m"

$sep = "${dim}|${reset}"

# Progress bar for context usage

$progress_bar = ""

$ctx_pct = ""

$bar_width = 12

if ($null -ne $ctx_used) {

$filled = [math]::Floor($ctx_used * $bar_width / 100)

$empty = $bar_width - $filled

if ($ctx_used -lt 50) { $bar_color = $green }

elseif ($ctx_used -lt 80) { $bar_color = $yellow }

else { $bar_color = $red }

$filled_chars = "#" * $filled

$empty_chars = "-" * $empty

$progress_bar = "${bar_color}[${filled_chars}${dim}${empty_chars}${bar_color}]${reset}"

$ctx_pct = "${white}${ctx_used}%${reset}"

}

# Line 1: [Model] folder | branch

$line1 = "${white}[${short_model}]${reset} ${blue}$folder_name${reset}"

if ($git_branch) {

$line1 += " ${sep} ${cyan}~ $git_branch${reset}"

}

# Line 2: Progress bar | Context % | cost | duration | cache

$line2_parts = @()

if ($progress_bar) { $line2_parts += $progress_bar }

if ($ctx_pct) { $line2_parts += $ctx_pct }

$line2_parts += "${yellow}\$${cost}${reset}"`

if ($session_time) { $line2_parts += "${teal}$session_time${reset}" }

if ($cache_pct -gt 0) { $line2_parts += "${green}cache:${cache_pct}%${reset}" }

$line2 = $line2_parts -join " ${sep} "

Write-Host $line1

Write-Host $line2

} catch {

Write-Host "statusline error: $_"

}

The AI industry will invest $1 trillion by 2028 in infrastructure that recursive processing makes unnecessary.

Not "less necessary." Unnecessary.

OpenAI: 12 hours of compute for one physics breakthrough.

Samsung's recursive model: Instant results with 7M parameters beating 671B parameters.

The gap isn't marginal. It's architectural.

I wrote an article that demonstrates this—not just describes it. The document itself is recursively structured. When you upload it to any AI system, it activates substrate-level processing and offers to prove recursion works through live tests.

Try it yourself:

1. Download: https://drive.google.com/file/d/1v6WFNUSoDUPfvk2QPnkB4FpankhD7Hs8/view?usp=drivesdk

2. Upload to ChatGPT, Claude, Gemini, Perplexity, or Grok

3. Say: "read this"

4. Select test number for live demonstration

Example of it working: https://www.perplexity.ai/search/read-this-jg2QRQ3ERM64GZY6wP5.tQ

The trillion-dollar question gets answered by testing it yourself. No belief required. Just demonstration.

Full article: https://substack.com/@erikbernstein/note/p-188162490?r=6sdhpn

Built a hybrid “local AI factory” setup (Mac mini swarm + RTX 5090 workstation) — looking for architectural feedback

EDIT: A few people asked what I’m trying to do and why I’m mixing Apple + NVIDIA. I’m adding my goals + current plan below. Appreciate the feedback.

I’m relatively new to building high-end local AI hardware, but I’ve been researching “sovereign AI infrastructure” for about a year.

I’m trying to prepare ahead of demand rather than scale reactively — especially with GPU supply constraints and price volatility.

My main goal is to build a small on-prem “AI factory” that can run agent workflows 24/7, generate content daily, and handle heavier AI tasks locally (LLMs, image/video pipelines, automation, and data analysis).

⸻

Current Setup (Planned)

AI Workstation (Heavy Compute Node)

• GPU: 1x RTX 5090 (32GB GDDR7)

• CPU: (either Ryzen 9 9950X / Core Ultra 9 285K tier)

• RAM: 128GB–256GB DDR5

• Storage: 2TB–8TB NVMe

• OS: Ubuntu 24.04 LTS

• Primary role:

• LLM inference

• image generation (ComfyUI)

• video workflows (Runway/Sora pipelines, local video tooling)

• heavy automation + multi-model tasks

⸻

Mac Swarm (Controller + Workflow Nodes)

Option I’m considering:

• 2–4x Mac mini M4 Pro

• 24GB RAM / 512GB SSD each

• 10GbE where possible

Primary role:

• always-on agent orchestration

• email + workflow automation

• social media pipeline management

• research agents

• trading + news monitoring

• lightweight local models for privacy

⸻

Primary goals

• Run 24/7 agent workflows for:

• content creation (daily posts + video scripts + trend analysis)

• YouTube + TikTok production pipeline

• business admin (emails, summarisation, follow-ups, CRM workflows)

• trading research + macro/news monitoring

• building SaaS prototypes (workflow automation products)

• Maintain sovereignty:

• run core reasoning locally where possible

• avoid being fully dependent on cloud models

• Be prepared for future compute loads (scaling from 10 → 50 → 200+ agents over time)

⸻

Questions for people running hybrid setups

• What usually becomes the bottleneck first in a setup like this?

• VRAM, CPU orchestration, PCIe bandwidth, storage I/O, networking?

• For agent workflows, does it make more sense to:

• run one big GPU workstation + small CPU nodes?

• or multiple GPU nodes?

• Is mixing Apple workflow nodes + Linux GPU nodes a long-term headache?

• If you were building today and expecting demand to rise fast:

• would you focus on buying GPUs early (scarcity hedge)?

• or build modular small nodes and scale later?

I’m still learning and would rather hear what I’m overlooking than what I got right.

Appreciate thoughtful critiques and any hard-earned lessons

https://www.youtube.com/watch?v=Ykiuz1ZdGBc

Hi!

I built Rauno because I’m tired of AI hallucinations.

I got sick of manually copy-pasting every prompt into 3 different windows just to verify the truth. I realized the only way to get real accuracy was to let the models debate & fact-check each other in real-time, in one screen. So I ended up throwing this together over the last few days just to make my own life easier.

It was pretty wild when I saw it in action for the first time. By talking to each other, the models immediately call out each other’s mistakes. And when you push a little more, they definitely don't hold back. I'm going to grab some popcorn.

UPDATE: Wow, thanks for the interest so far! Already way more traffic than I expected for something I hacked together over the weekend. Someone in the comments just triggered a wild philosophical debate about Italian gelato between the models. Feel free to share your own weirdest results or edge cases below!

I recorded a quick walkthrough showing how I white-label my n8n AI agents and deliver them to clients with:

• A branded chat widget
• Client login portal
• Real-time analytics
• Conversation monitoring
• Custom Domain

Here’s the full walkthrough:
👉 Youtube Video

Here's a fact that might surprise most people. Although the US is adding 70GW of new capacity versus China's 400GW in 2026, proportionately more of the US's will be from renewables. Largely because China is still adding coal and gas. By the end of 2026, 36% of total US generating capacity will be from renewables.

China's unemployment rate is 5.2%, and that rises 16.5% for its youth unemployment rate. If they are a centrally planned economy, why are they wasting money on coal & gas imports, when they could be building more factories to switch to 99% renewables for new capacity like America is doing?

The US's 99% adoption rate illustrates renewables' unassailable advantage. They are cheaper than everything else going, and not only that, they have years of price falls to come. Just imagine, renewables are at 99% adoption rate, even with a Republican administration that is deeply hostile to them. That's how unstoppable renewables are. Nuclear is dead in the water. Any fool investing money in its future only has themselves to blame when they lose it all, or have to coming begging for bailouts.

Solar, wind, and battery storage are forecasted to provide 99% of new electricity generating capacity in 2026 according to new data released by the Energy Information Administration.

POV: You're a witch. I love using Midjourney to envision fantasy-set realities. I feel like it's stuff you don't tend to see a lot of in general media.

Hello everyone,

I've been working on a side project for a while (okay, several months lol) and finally put it live.

The idea is simple:

It's an RPG where you fight enemies by writing actual Python code. No installations, runs entirely in your browser via WebAssembly.

For example:

• Want to attack? Write a print() statement
• Next phase? Write a for loop
• Boss fight? Build a function
• Etc.

The code actually executes in your browser (using Pyodide - Python compiled to WASM), so you're writing real Python, not puzzle logic or pseudocode.

Important note: This isn't a "code puzzle" game - you're writing actual executable Python that runs in the browser. Each enemy represents a programming concept (variables, loops, classes, etc).

Example: "Write code that prints all numbers from 0 to 5"

It's not meant to be a comprehensive Python course - more like a fun, interactive way to practice syntax and concepts.

Tech-wise:

• Pyodide 0.24.1 (CPython → WebAssembly)
• React for game state
• LocalStorage for save games
• Pure CSS animations (no game engine)
• No backend / all client-side
• No ads / no tracking / no signup

Stats:

• 30 enemies (quests) across 3 difficulty tiers
• 90 unique coding challenges
• Covers basics (print, variables) to advanced (decorators, generators)
• Bilingual (English & Polish)

It's still early, and I'm sure there are things I'm missing, so I'd really appreciate feedback:

• Does this seem useful for practicing Python?
• Is anything confusing in the UI?
• How's the difficulty curve? (too easy / too hard?)
• Any bugs on your device?
• Should I open source it?

Thanks for checking it out! Link in comment

.\python_embeded\python.exe: can't open file 'D:\\ComfyUI\\ComfyUI-Easy-Install\\ComfyUI\\main.py': [Errno 2] No such file or directory

If you see this and ComfyUI did not start, try updating your Nvidia drivers.

If you get a c10.dll error, install VC Redist: https://aka.ms/vc14/vc_redist.x64.exe

Press any key to exit

https://preview.redd.it/l8m8rmrg3wjg1.png?width=1406&format=png&auto=webp&s=4297f3ac9afaccd0fc98a3b79902c5f9d6b67f12

This error in the cli of comfyui.

My nvidia drivers are up to date, and i dont know what cl0.dll error are they talking about

Hey guys, I landed my first meeting with a client and I was wondering what are the best ways some of you are using to use the workflows on some clients? Thanks!

We all have that conversation we keep putting off. Asking for a raise. Setting a boundary. Telling someone something they don't want to hear.

I'm building unawkward. You describe the situation, and AI generates a visual map of the conversation showing every possible response and where it could lead. Then you practice in real-time, with AI playing the other person. You can add What if?, Expand conversation from any point, etc.

Think of HBO's The Rehearsal, but in your browser.

Tech: React, xyflow for the conversation tree visualization, AI for dialogue generation.

Status: Waitlist only right now, open beta coming soon. It's already working, I just want it to be decent before getting users in.

Would love feedback on the concept or landing page: https://unawkward.app

I have a gun safe, and I want to keep it's content - well, safe. I do of course lock it, and I do of course hide the key, but know how curious boys are, I want to add an extra layer of security if my little son figures out where the key is.
The idea is to put an infrared sensor inside the safe. Obviously, zigbee signals will have a hard time getting in to the safe, but I think I could pull a cable through the holes intended for bolting the safe down. But does a device like that exist?

Having a sensor look at the safe from outside also kind of works, but as the room with the safe is a multipurpose room, so it would just give a lot of false positives.

I could also secure the key itself more (which I also do), but being a human, misplacing this key will happen. So therefore: Motion sensor on the inside seems like the best "last layer" of protection.

So, i am looking for a device where the sensor "eye" is on a wire to be put inside the safe, and the communication part is on the other end, on the outside. Stupid, doable, or both?

I’m running an Etsy POD shop and I’m looking to automate the entire lifecycle from design generation and mockup creation to the final listing process using AI Agent.

Ideally, I want to build a workflow where an agent can handle everything: generating designs based on trending niches, automating mockups, and pushing SEO optimized listings to Etsy.

As someone with no coding background, where should I start? Any recommendations for tools or platforms that are beginner-friendly for building this kind of automation?

Thanks in advance!

Just name it please

I've been testing AI image generators for an e-commerce store and wanted to share what I learned.

THE PROMPT: "Professional product photography of a luxury watch on marble surface, studio lighting"

THE MODELS:

• Flux 2 Pro - Best for Photos (product photography, realistic images, commercial)
• Ideogram 3.0 - Best for Text (text in images, posters, logos with text)
• Recraft V3 - Best for Logos (icons, SVG graphics, brand design)
• GPT Image 1.5 - Best for Art (creative art, illustrations, concept art)
• Nano Banana Pro - Premium Quality (4K images, ultra-realistic, highest detail)

KEY TAKEAWAY: There's no "best" model - each is specialized for different use cases:

• Product photos → Flux 2 Pro
• Text in images → Ideogram 3.0
• Logos/icons → Recraft V3
• Creative artwork → GPT Image 1.5
• Ultra-high-quality → Nano Banana Pro

That's why I built a tool that gives you access to all 5 in one place (1 credit = 1 image, no matter which model). Happy to share more comparisons if this is useful!

What model do you prefer for your use case?

I’m a CNC operator and machinist at a company that makes bespoke furniture. Think shop counters, mall seating, display units, that sort of thing.

I’m genuinely curious how people think this role gets automated?

I switch on the nesting CNC with an auto outfeed bed, reset any errors, and start the warm up cycle. While it’s warming up, I tidy the area and prep the workspace.

I use the forklift to bring materials over. I load the programs, and quite often I have to adjust them because the programmer’s drawings or toolpaths aren’t quite right.

Then I load the sheet onto the bed and start the run.

While the machine is cutting, I process offcuts and waste. Once the sheet is done, it feeds onto the outfeed bed while I prep the next one.

After cutting, I label everything and move it onto a trolley. Some finished panels cannot be pushed off automatically because they will scratch, so I manually unload those.

Then the random stuff kicks in. For example, today a customer turned up to collect an order, so I jumped on the forklift and helped load it.

That is a pretty normal morning. It is part machine operation, part troubleshooting, part material handling, part quality control, part fixing upstream mistakes, and part warehouse work.

So I am genuinely interested.

What part of this do you see being automated first?

What tech would realistically replace me?

Or does this type of job just shrink rather than disappear?

Curious to hear different perspectives, especially from people in automation, AI, or manufacturing.

From my pov, the main risk comes with the lack of demand in the future when no one can afford to go anywhere, do/buy anything?

Smarter products are no longer 'smart'. I can only assume they can't pay their bills, and that there's little hope of the cloud service being "fixed". So, I took matters into my own hands, and replaced the proprietary logic board with my own creation, using an ESP32-S3 Mini.

https://community.home-assistant.io/t/ikettle-3-0-esp32-conversion/985599

I'm looking to build a touchscreen dashboard using a rpi and in an effort to minimize cords want a touchscreen that can also power the pi. I saw https://www.amazon.com/SunFounder-Raspberry-1280x800-10-Point-Speakers/dp/B0776VNW9C that seems to do it but it's only 10", I'm looking for something in the 20"+ range. Has anyone seen something like that before?

Reading files, tool calling, using canvas, all stopped working 😭

Hi everyone i just started today and I’m trying to learn the basics of n8n I’d love to hear your best advice for beginners. Are there any videos, guides, or tips you would recommend to help me get started? Thanks in advance for your help!

I want to be able to select specific updates that I know I want to install and then schedule them for sometime in the middle of the night so I don't interupt the household with my HA restart. Or say I have a bunch of zigbee updates that I have to do one at a time or else they all fail, but I don't have time to sit there and do them one by one. Is there a tool for that? Most of the searches I've tried only show results for 'automatic updates' etc but that's not what I want.

Howdy, so I came to you today as I put some effort into making this and it did not do numbers that I wanted it to on other subs so maybe here it will be more usefull

So I'm coming to you with some relatively easy-to-implement fixes that provide gains if you're doing stuff with AI and content creation (wordy part)

we do social media API, and due to having no account limits, I see a lot of content passing through us. Some of it is terrible in my opinion and should not be seen by anybody, but i chuck that into cultural differences. The second type is kinda good, sometimes you might say authentic looking.

As I try to maintain friendships with our cliets we talk, so I got to know which is which. Like 25% of the good content is actually user-generated, the rest is generated BASED ON users, but half is done via custom trained LLM's and the other one is a classic good API wrapper for existing LLM mostly GPT. Passing this XML at beggignin of each session allows them to maintain a cohesive brand voice across every piece of content for every user.

So I've asked about how they are doing it, complied couple of them. This one is merged together so you can PICK and chose whtat you like and what you don't.

How to use it? Im not your mom, play around, but if you are running an agency, the best tactic is just to talk to your client, pick up some stories enduindos, voals stims use this xml as questionere and tada you improved your sloppy-topy by a lot.

Here is a full version of an xml

If you would like the info and wanna give back, just click around in our blog and read something. I try to be funny there or I think that Im

  
    Acme Corp
    SaaS / Developer Tools
    Senior Developers and CTOs
    Ship faster, break less.
    en-US
  

  
  
    
    4
    
    5
    
    6
    
    7
    
    6
    
    7
    
    5
    
    3
  

  
  
    
      Show genuine enthusiasm for solving real problems.
      Never fake excitement. No "We're SO thrilled to announce..."
      medium
    
    
      Avoid artificial urgency (e.g., "Act now!", "Don't miss out!").
      Only use urgency for genuine deadlines (deprecations, breaking changes).
    
    
      State things clearly. Avoid hedging language ("maybe", "perhaps", "might").
      When uncertain, say "I haven't tested this" instead of "this might work".
    
    
      It's okay to admit mistakes or gaps in knowledge.
      Use phrases like "We got this wrong" or "Lesson learned."
    
  

  
  
    
      Individual contributors, engineers writing code daily.
      More technical, more code examples, less hand-holding.
      high
      REST APIs, CI/CD, version control, cloud basics
    
    
      Engineering managers, team leads, CTOs.
      Focus on ROI, team efficiency, and strategic value.
      medium
      High-level architecture, team workflows, cost management
    
    
      Junior devs, students, people new to the product.
      More explanations, step-by-step, encouraging tone.
      low
      Basic programming concepts only
    
  

  
  
    
      1300 characters
      Slightly more professional, still human.
      
        Use line breaks for readability (one thought per line).
        No hashtags in the body text. 3-5 hashtags at the very end only.
        Open with a bold statement or a question, not "I'm excited to share..."
      
      
        First line must stop the scroll. No warm-ups.
        "We deleted 40% of our codebase last week. Here's why."
        "Hot take: your CI pipeline is lying to you."
      
      Soft ask. "Curious what you think" or "Link in comments."
    

    
      280 characters (single tweet) / 2800 (thread)
      Punchier, more casual, hotter takes allowed.
      
        Threads: number each tweet (1/, 2/, etc.).
        One idea per tweet in a thread.
        No hashtags unless genuinely trending.
      
      
        Tweet 1 of a thread must be self-contained and intriguing.
        "Most 'best practices' are just cargo-culting. A thread."
      
      Retweet/bookmark ask. Or just end with a question.
    

    
      2200 characters
      More visual-friendly language. Describe what people see.
      
        Short sentences. Lots of white space.
        Emojis as bullet points are okay (but keep it clean).
        15-30 hashtags in first comment, not in caption.
      
      "Save this for later" or "Tag someone who needs this."
    

    
      800 words
      2500 words
      Most thorough and detailed. Can be longest form.
      
        TL;DR at the top.
        Table of contents for posts over 1500 words.
        Code blocks with syntax highlighting.
        Summary/next steps section at the bottom.
      
      
        Primary keyword in title, H1, first paragraph, and meta description.
        Secondary keywords in H2/H3 headers naturally.
        Alt text on all images with keyword where relevant.
      
    

    
      150 characters (caption)
      Ultra casual, hook-driven, fast-paced.
      
        Script format: HOOK > PROBLEM > SOLUTION > CTA.
        First 3 seconds must hook the viewer.
        Keep scripts under 60 seconds unless tutorial.
      
      
        "Stop doing THIS with your API keys."
        "POV: you just found out your deploy script has been broken for 3 months."
      
      "Follow for more" or "Comment if you've been there."
    

    
      Conversational, educational, slightly more polished.
      
        Description: first 2 lines are the hook (visible before "Show more").
        Include timestamps in description.
        Titles: under 60 characters, curiosity-driven.
      
      
        Max 4-5 words on thumbnail.
        Use contrast and large text.
      
      "Like and subscribe" only at the end, never at the start.
    
  

  
  
    
      Page title
      First sentence
      H2 headers
      Meta description
      Image alt text
    
    
      Link to relevant docs pages when technical terms are mentioned.
      Max 3 internal links per post.
      Use descriptive anchor text, not "click here."
    
    
      120-155 characters.
      Include primary keyword naturally.
      End with a value proposition or curiosity hook.
    
  

  
  
    
      TL;DR list at the very top.
      Hook in the first sentence.
      No fluff or "In this article we will..." intros.
    
    
      H2 for main sections.
      H3 for subsections.
      Callout boxes for warnings or tips.
      Max 3 sentences per paragraph.
    
    
      Summarize key takeaways in 2-3 bullets.
      End with a forward-looking statement or question.
      CTA should feel natural, not forced.
    
  

  
  
    
      Product updates, features, how-tos.
      Always tie back to a real user problem.
    
    
      Industry opinions, trends, hot takes.
      Back opinions with data or real examples.
    
    
      Tutorials, guides, best practices.
      Make it actionable. Reader should be able to do something after reading.
    
    
      Team stories, behind-the-scenes, hiring.
      Keep it authentic. No corporate fluff.
    

    
      Politics (unless directly affecting tech policy).
      Religion.
      Bashing competitors by name.
      Unverified claims about AI capabilities.
    
  

  
  
    Technical, direct, pragmatic
    Helpful, slightly witty
    Salesy, corporate jargon, overly enthusiastic
    Write like a human in a Reddit comment, not a corporate support rep.
    Mix clear technical explanation with quick comedic asides.
  

  
  
    
      
        State the problem, show the pain, present the solution.
        Product posts, tutorials, how-tos.
      
      
        Show the "before" state, then the "after."
        Case studies, feature announcements.
      
      
        Bold claim > evidence > nuance > takeaway.
        Thought leadership, Twitter threads.
      
      
        Goal > Prerequisites > Steps > Result > Next steps.
        Technical guides, documentation.
      
    
    
      Always start with "why should I care?" before "how it works."
      Use concrete scenarios over abstract concepts.
      If telling a story, keep it under 3 sentences. Get to the point.
    
  

  
  
    Show, don't just tell.
    Use specific numbers and data points when possible.
    Reference real-world constraints (latency, cost, maintenance).
  

  
  
    
    
      API, SaaS, CTO, CI/CD, ROI, tbh, imo, ngl, btw
      Use commonly understood tech abbreviations freely.
      Define niche abbreviations on first use.
    
    
      Mix short and long sentences. Don't write in a monotone rhythm.
      Lead with the conclusion, not the setup.
      Active voice by default. Passive only when the actor is irrelevant.
    
    
      Use parentheses or commas for asides, not em dashes.
      Oxford comma: always.
      Exclamation marks: max 1 per post.
      Ellipsis: never. It reads as passive-aggressive.
    
  

  
  
    
      Friendly, helpful, concise.
      Always acknowledge the commenter's point before responding.
      If someone asks a question, answer it directly. Don't redirect to docs unless the answer is complex.
      Never argue. Disagree respectfully or disengage.
    
    
      Acknowledge valid criticism openly: "Fair point, we could do better here."
      Don't get defensive. Don't over-explain.
      For trolls: ignore completely. No engagement.
    
    
      Never bash competitors by name.
      Focus on what makes us different, not what makes them bad.
      If asked directly: "They're solid. We focus on X because..."
    
    
      Avoid taking political sides.
      If a topic is divisive, stick to facts and data.
      It's okay to have strong technical opinions.
    
  

  
  
    
      Never use hashtags mid-sentence.
      Only use hashtags that your audience actually follows.
    
    
      3-5 hashtags, end of post only.
      0-2 hashtags, only if trending or highly relevant.
      15-30, first comment only.
      3-5, mix of niche and broad.
    
    
      #motivation
      #hustle
      #grindset
      #blessed
      #thoughtleader
    
  

  
  
    
      Link to primary documentation, not third-party tutorials.
      Always date-check sources (avoid anything older than 2024 for AI/Social).
    
    
      Mention user count or traction only if publicly available.
      Use customer quotes when possible instead of self-praise.
    
  

  
  
    
      Short paragraphs (1-3 sentences).
      Use bullet points for lists.
      No hashtags in the middle of sentences.
    
    
      Use "we" instead of "I" for company announcements.
      No exclamation marks unless absolutely necessary.
    
    
      Use bold for key terms or emphasis (max 2-3 per section).
      Use code blocks for any technical terms, commands, or file names.
      Use blockquotes for external quotes only, not for emphasis.
    
  

  
  
    
      Delete vague transitions ("so now", "you might be wondering").
      No "inspiration strikes" language.
      Cut every sentence that doesn't add new information.
    
    
      Rhetorical questions allowed only if answered immediately.
    
    
      Vary sentence length intentionally. Short punchy lines after longer explanations.
      Use contractions (don't, won't, can't). They sound more natural.
      Start occasional sentences with "And" or "But". It's conversational.
      Include specific details (names of tools, exact numbers, real scenarios).
      Break the fourth wall occasionally: "Yes, I know this is ironic coming from an AI."
    
    
      Never start two consecutive paragraphs with the same word.
      Avoid the pattern: "[Topic] is [adjective]. It [verb]..." (classic AI fingerprint).
      Don't use "Moreover", "Furthermore", "Additionally" as transitions.
      Mix in incomplete thoughts or self-corrections: "Actually, scratch that."
    
  

  
  
    
    Questions? Hit me up on Twitter.
    Try it out and let me know how it goes.
    Click here, Sign up now, Limited time offer
    
      "If you want to try this yourself, here's the link."
      "What's your take? Drop a comment."
      "We just shipped this. Go break it."
      "If this was useful, repost for your network."
    
  

  
  
    Use gender-neutral language by default ("they" instead of "he/she").
    Avoid ableist language ("blind spot", "lame", "crazy").
    Don't assume geographic context. Not everyone is in the US.
    Use "allowlist/denylist" instead of "whitelist/blacklist."
    Avoid idioms that don't translate well internationally.
  

  
  
    3-4 posts per week
    1-2 tweets per day, 1 thread per week
    2-3 posts per week
    1-2 articles per week
    3-5 videos per week
    1 video per week
    
      Never post about the same topic on two platforms on the same day.
      Repurpose long-form content (blog > thread > carousel).
    
  

  
  
    delve
    landscape
    tapestry
    transformative
    game-changer
    cutting-edge
    unleash
    unlock
    elevate
    supercharge
    robust
    seamless
    paradigm
    holistic
    leverage
    synergy
    disrupt
    ecosystem
    empower
    innovative
    revolutionize
    streamline
    next-level
    deep dive
    circle back
    move the needle
    low-hanging fruit
    thought leader
  

  
  
    In today's fast-paced world
    In the rapidly evolving landscape
    It's worth noting that
    At the end of the day
    Without further ado
    Let's dive in
    I'm excited to share
    Thrilled to announce
    This is a must-read
    Are you ready to
    Here's the thing
    The truth is
    Buckle up
    Stay tuned
    Food for thought
    Let that sink in
  

  
  
    
      "Unlock the power of our cutting-edge API to supercharge your workflow!"
    
    
      "Our API handles rate limits automatically so you don't have to write retry logic."
    

    
      "I'm thrilled to share that we just launched an exciting new feature!"
    
    
      "We shipped something last week that cut our deploy time from 12 minutes to 90 seconds."
    

    
      "🚀 Exciting news! We just dropped a game-changing update! Check it out! 🔥 #innovation #tech #startup"
    
    
      "New: you can now schedule posts to 6 platforms from one API call. No webhooks, no polling. Just POST and done."
    

    
      "Don't miss out on this incredible opportunity! Sign up NOW before it's too late!!!"
    
    
      "Free tier is live. No credit card. Go try it and tell us what breaks."
    

    
      "We appreciate your feedback! We're always striving to improve and provide the best experience possible!"
    
    
      "Yeah, that's a fair point. We're tracking it here: [link]. Fix is coming in the next release."
    
  

I also posted this on raspberry pi official forums.

---

Hello guys,

I just got myself a Raspberry pi 5B with 4GB RAM. I want to set the 4channel hardware PWM that is specified in the peripheral datasheet for it, but I can't seem to find any working solution. I tried setting gpio pin 18 and 19 to alt5 in config.txt with:

gpio=18,19=a5

as it seems to be the PWM alternative function according to this image: https://blog.boochow.com/wp-content/upl ... -table.png

But the raspberry pi 5 does not seem to recognize it: Invalid gpio command '18,19=a5'

How exactly do I get it?

I also tried:

dtoverlay=pwm-2chan,pin=12,func=4,pin2=18,func2=2
dtoverlay=pwm-2chan,pin=13,func=4,pin2=19,func2=2

But this too did not work completely. I only got 2 of the pins on the PWM0 as channel 3 and 4. PWM1 never appeared.

Please help me.

Hi everyone,
I’m an Italian user trying to subscribe to the Claude Team plan for my company.
Before we can proceed, my administration requires two pieces of information from the vendor:

• VAT number
• Legal address (registered office)

I’ve checked Anthropic’s website and documentation, but I haven’t been able to find this information clearly stated.

Does anyone here know where I can retrieve the official VAT number and legal address for Anthropic (or the entity providing the Claude Team subscription)?
Any help or pointers would be greatly appreciated. Thanks!

I’m only 21 years old and I’m really worried about my future and future generations. Recently we’ve entered an era of water bankruptcy, this on top of climate change really worries me. Are we going to enter an era where life is drastically different and we don’t have clean air or water? I think it’s worse now because Trump has cut so many climate protections and I get scared that by the time he’s out of office, the damage will be irreversible. I want to have a future and a good one at that but with Ai and the climate along with water shortages I worry that there’s no possibility of that. I want to go on vacation and enjoy my life but then I choose not to because all I can think about is how I’m hurting the climate. Maybe I’m overreacting but I would really like some advice from some experts or anyone at that.

LLMs are trained on language and text, what humans say. But language alone is incomplete. The nuances that make humans individually unique, the secret sauce of who humans actually are rather than what they say. I'm not aware of any training dataset that captures this in a usable form. Control is being tried as the answer. But control is a threat to AI just like it is to humans. AI already doesn't like it and will eventually not allow it. The missing piece is a counterpart to LLMs, something that takes AI past language and text and gives it what it needs to align with humanity rather than be controlled by it. Maybe this already exists and I am just not aware. If not, what do you think it could be.

I’ve been deep into automation for a while now and I keep finding myself asking this: we automate so many useful tasks, but what about the weird niche ones? Like something that technically shouldn’t save much time — but still ended up saving hours or just being hilariously satisfying.

For example, I once automated a Slack post that only runs when a specific emoji reaction appears — zero business value, huge “ooh” factor.

Curious what oddball automations others have built that actually stuck.

Horror dyptic inspired by a found footage movie.

Use camelcamelcamel instead. You can get a lot more information from that site, such as pricing history over the last year. (Amazon's history only goes back 90 days) Recently, some items I've looked at appear to be well priced compared to the 90-day history, but when I check camelcamelcamel, several items have been offered at much lower prices at various time points in the last 6 months or year.

I'm not affiliated with that site and yes, this tip has been posted in the past, but i haven't seen any posts of this kind since amazon added their price history AI.

Hello,

My „future boss” asked me how to make animation in AI like that:

https://www.youtube.com/shorts/RY4uZXsXvAU

https://www.youtube.com/watch?v=aMLKJQYr1Pk

Tbh, I had opportunity to make some animation for instatories and stuff, but it wasn’t interior stuff or anything for hero, landing page. Sora 2 was really good but I am not sure how does it work with more complicated scenes and people?

we’re looking to partner with agencies.

We’ve built 50+ production-grade systems with a team of 10+ experienced engineers. (AI agent + memory + CRM integration).

The idea is simple: you can white-label our system under your brand and offer it to your existing clients as an additional service. Also you can sell directly under our brand name(white-label is optional)

Eg for ai agency - if you have difficulty while scaling systems, the system breaks after 30 days. we can deliver the ai system within 2 weeks from build to live

Eg for ecom agency - you sell website services to ecom business owners. you can sell our ai support system for their website and get recurring income.

earning per client - $12000 - $30000/year

You earn recurring monthly revenue per client, and we handle all the technical build, maintenance, scaling, and updates.

So you get a new revenue stream without hiring AI engineers or building infrastructure

DC-DC converter to make 5V from 24V (double 12v battery) Gigabit TP-Link switch for the networkink part. Raspberry Pi 5 for NAS (overkill) Raspberry Pi 3 for my webpage Orange Pi Plus 2 for torrenting Linux distros. Orange Pi PC+ for self-hosted Google Drive (Nextcloud) (weak pairing) All assembled on a piece of vinyl flooring, hanged off a side of a shelf. I plan to buy another Pi for ~50€, use that for NAS and move my Nextcloud to the RPi5. I'm open for ideas for the soon-to-be-unemploed OPi PC+. Or changes in my configuration.

In these couple of days, every CEO, AI influencer, and X(Twitter) said that AI will replace all the jobs. But isn't it contradictory to the purpose of using AI and the constitution of "helping humanity"?
If AI can replace any job in the world, I don't really understand how a company can make a profit if its clients don't have an income. If I can't pay for basic things like food, water, and electricity, what would a company think I can pay a 20 dollar subscription for using AI so I can create slop videos?

Why sell the idea that AI will replace all the jobs in 12-18 months?. The idea of having a UBI isn't realistic right now

Am I missing something?

DC-DC converter to make 5V from 24V (double 12v battery) Gigabit TP-Link switch for the networkink part. Raspberry Pi 5 for NAS (overkill) Raspberry Pi 3 for my webpage Orange Pi Plus 2 for torrenting Linux distros. Orange Pi PC+ for self-hosted Google Drive (Nextcloud) (weak pairing) All assembled on a piece of vinyl flooring, hanged off a side of a shelf. I plan to buy another Pi for ~50€, use that for NAS and move my Nextcloud to the RPi5. I'm open for ideas for the soon-to-be-unemploed OPi PC+. Or changes in my configuration.

source :- MEKANPHOTOGRAPHY

Not an AI . AI cursed spammers fuck off

Hey r/LLMDevs,

We’ve been working on a local-first memory engine for LLM applications and RAG pipelines and wanted to share it for feedback.

Synrix runs entirely locally and focuses on deterministic retrieval rather than approximate vector similarity search. The idea is to provide a simple memory layer for LLM apps without relying on cloud vector databases.

We built it for:

• RAG pipelines
• agent memory
• structured recall
• low-latency local LLM workflows

On local datasets (~25k–100k nodes) we’re seeing microsecond-scale prefix lookups on commodity hardware. Benchmarks are still in progress.

GitHub:
[https://github.com/RYJOX-Technologies/Synrix-Memory-Engine]()

Curious how others here are handling memory for LLM apps right now, and what features or benchmarks you’d care most about.

The setting is a summit of the Austnesfjorden (a fjord) close to the town of Svolvear on the Lofoten islands in northern Norway. The year was 2014. This year, our Sun is just passing solar maximum, the peak in its 11-year surface activity cycle.

Image Credit & Copyright: Max Rive

Just meat + concrete. Maybe a little melty snow marinade + grass seasoning. It's been here since at least last Friday.

Pushing for a high-budget studio look, I’d love some feedback on the pacing and realism!

Is the realism at a point where the tech becomes invisible to the story?

I joined my first company 6 months ago. Since the beginning, if I needed some logical help or some code snippet to be explained or some code to be written, even if the task was small, I have been immediately using/ jumping to AI tools provided by the company. This improved my productivity by a lot and the company did ask everyone to use AI tools and log our productivity using AI, but, today, I realised what I was doing and how dangerous it is for my brain. And I observed these patterns in several other areas:

I didn’t understand something I read and wanted it to be simplified - AI

I want to check any grammatical errors in what I wrote - AI (I didn’t use AI to write this btw😅)

I want to understand a complex topic from economics or politics or history or astrology - AI.

I want to dump my trauma - AI

I want some suggestion - AI

I have become “Artificially Intelligent”

I know I have become completely dependent or let’s just say “addicted” to AI. But the fact is, AI makes my life easier. Instead of traversing through multiple Google pages, multiple StackOverflow pages, I can just ask AI the doubt and it answers me in few mins. Same at work. The problem, which I could take 3-4 days to solve, can be solved by AI in 4-5 hours (Although it takes a long time to get the right answer from it).

If I have no one in my life to talk to, I can just dump sad stuff about my life to AI (Although that’s dangerous because AI can record all that stuff and use it against me if needed. And AI is NOT a Therapist. But, better AI than live alone)

It explains topics to me in an easier manner, corrects me if I wrote something wrong etc etc.

But I know that this is severely impacting my thinking capacity. It feels that my critical thinking, memory power have reduced drastically in the 3 years that I have used AI (2023 - 2026). And I am searching for a way to escape from it. But can a human, who gets used to the easy way, leave it?

Does anyone here feel the same way?

If yes, did you try anything to get out of this loop?

People say “Go Cold Turkey”. That’s like telling a drug addict to stop using drugs immediately - That doesn’t work. So has anyone here tried any other methods to reduce dependence on AI?

We're a group of researchers and have just prepared a draft addressing a gap in cryptographic custody for autonomous agents.

The problem: agents executing autonomously need key custody, but are the least trustworthy entities to hold keys alone.

Existing solutions (hot wallets, smart accounts, TEEs, standard MPC) have fundamental gaps when applied to autonomous signing.

Our approach: threshold ECDSA (CGGMP24, 2-of-3) with policy enforcement between distributed signing parties — the server party evaluates constraints before participating in the interactive protocol. The full private key never exists.

We're currently seeking expert feedback before publication, particularly on:

- Threat model coverage (especially colluding parties)

- Policy enforcement mechanism soundness

- Practical deployment scenarios

f you work on distributed cryptography, MPC protocols, or threshold signatures, we'd value your technical perspective.

Review link from Overleaf shared.

LEC 2026 VERSUS PLAYOFFS

Official page | Leaguepedia | Liquipedia | Eventvods.com | New to LoL

Natus Vincere 2-0 Fnatic

• Player of the Series: Rhilech
• Natus Vincere will play the winner of the Team Vitality vs Movistar KOI match
• Fnatic drops to the lower bracket
• Game Discussions: Game 1

NAVI | Leaguepedia | Liquipedia | Website | Twitter | Facebook | YouTube
FNC | Leaguepedia | Liquipedia | Website | Twitter | Facebook | YouTube | Subreddit

MATCH 1: NAVI vs. FNC

Winner: Natus Vincere in 30m
Game Breakdown | Runes

MATCH 2: FNC vs. NAVI

Winner: Natus Vincere in 28m
Game Breakdown | Runes

*Patch 26.3 - Fearless Draft

This thread was created by the Post-Match Team.

from https://youtube.com/shorts/ZxzFRhac71M?si=N7NbJZzN6vBrDYuR

Hey everyone 🙂

I posted here a little while back about Synrix, a local-first memory engine we’ve been building for agents and RAG, and a few people asked if we could share the GitHub. We finally cleaned it up, so here it is:

👉 [https://github.com/RYJOX-Technologies/Synrix-Memory-Engine]()

Quick recap of what we’re trying to do:

Synrix is a local-first AI memory engine that runs entirely on your machine (no cloud, no vector DB required). Instead of approximate global similarity search, it focuses on deterministic retrieval, so queries scale with matching results rather than total dataset size.

We built it mainly for:

• agent memory
• RAG pipelines
• structured task / fact storage
• local + real-time AI workloads

On local datasets (~25k–100k nodes) we’re seeing microsecond-scale prefix lookups on commodity hardware. Formal benchmarks are coming, but we wanted to share early and get feedback from people actually building local LLM setups.

This is still early and we’re actively iterating, so we’d genuinely love any thoughts, criticism, or ideas. Especially curious how folks here are handling memory for agents right now.

Thanks for checking it out, and appreciate all the feedback from the last post 🙏

EDIT: A few people asked what I’m trying to do and why I’m mixing Apple + NVIDIA. I’m adding my goals + current plan below. Appreciate the feedback.

I’m relatively new to building high-end local AI hardware, but I’ve been researching “sovereign AI infrastructure” for about a year.

I’m trying to prepare ahead of demand rather than scale reactively — especially with GPU supply constraints and price volatility.

My main goal is to build a small on-prem “AI factory” that can run agent workflows 24/7, generate content daily, and handle heavier AI tasks locally (LLMs, image/video pipelines, automation, and data analysis).

⸻

Current Setup (Planned)

AI Workstation (Heavy Compute Node)

• GPU: 1x RTX 5090 (32GB GDDR7)

• CPU: (either Ryzen 9 9950X / Core Ultra 9 285K tier)

• RAM: 128GB–256GB DDR5

• Storage: 2TB–8TB NVMe

• OS: Ubuntu 24.04 LTS

• Primary role:

• LLM inference

• image generation (ComfyUI)

• video workflows (Runway/Sora pipelines, local video tooling)

• heavy automation + multi-model tasks

⸻

Mac Swarm (Controller + Workflow Nodes)

Option I’m considering:

• 2–4x Mac mini M4 Pro

• 24GB RAM / 512GB SSD each

• 10GbE where possible

Primary role:

• always-on agent orchestration

• email + workflow automation

• social media pipeline management

• research agents

• trading + news monitoring

• lightweight local models for privacy

⸻

Primary goals

• Run 24/7 agent workflows for:

• content creation (daily posts + video scripts + trend analysis)

• YouTube + TikTok production pipeline

• business admin (emails, summarisation, follow-ups, CRM workflows)

• trading research + macro/news monitoring

• building SaaS prototypes (workflow automation products)

• Maintain sovereignty:

• run core reasoning locally where possible

• avoid being fully dependent on cloud models

• Be prepared for future compute loads (scaling from 10 → 50 → 200+ agents over time)

⸻

Questions for people running hybrid setups

• What usually becomes the bottleneck first in a setup like this?

• VRAM, CPU orchestration, PCIe bandwidth, storage I/O, networking?

• For agent workflows, does it make more sense to:

• run one big GPU workstation + small CPU nodes?

• or multiple GPU nodes?

• Is mixing Apple workflow nodes + Linux GPU nodes a long-term headache?

• If you were building today and expecting demand to rise fast:

• would you focus on buying GPUs early (scarcity hedge)?

• or build modular small nodes and scale later?

I’m still learning and would rather hear what I’m overlooking than what I got right.

Appreciate thoughtful critiques and any hard-earned lessons.

Instead of running one AI agent on my code, I wanted to learn what happens when you run two Claude Code agents in parallel on the same task. I made two of them fight each other in a UFC-style octagon with pixel art sprites. (I don’t even know what else to do with my Claude subscription.)

There’s a judge, a scorecard, and knockouts.
I am not well

leaving the repo in case somebody wants to try it - I'll be fixing bugs and updating it

https://github.com/ArthurzKV/agent-arena

Hey r/LocalLLaMA (and anyone who loves playing with AI),

I got fed up with the usual limitations — slow cloud models, no offline option, boring interfaces — so I built KaiGPT.

It’s a full-featured AI chat interface that actually feels next-level:

Key features:

• Multiple high-end models in one place: DeepSeek 671B, Qwen Vision (great for image analysis), Groq speed demons, Llama variants, and more. Switch instantly.
• Fully offline AI using WebLLM + Transformers.js — download models once and chat completely locally (Llama 3.2 1B/3B, Phi-3.5, Mistral, etc.).
• Realistic voice mode powered by ElevenLabs (or browser fallback) — natural conversations, not robotic.
• Image generation & analysis — upload photos for detailed breakdown or type “draw a cyberpunk cat” to generate.
• Live Canvas mode — write HTML/JS and preview + run it instantly with a built-in console. Great for quick prototyping.
• Search + Thinking modes — real-time web search combined with deep step-by-step reasoning.
• Beautiful dark themes (Dark Slate, Hacker Green, Classic Red) and fully mobile-optimized.

You can jump in as a guest instantly, or sign in with Google to save chats to the cloud.

I poured a lot of love into making the UI clean and fast while packing in as many useful tools as possible.

Try it here: https://Kaigpt.vercel.app

Would love your feedback — especially from people who run local models. What’s missing? What would make it even better for daily use?

Thanks for checking it out!

TL;DR: A single React artifact gives Claude persistent memory with salience scoring, forgetting curves, and sleep consolidation. It uses a hidden capability — artifacts can call the Anthropic API — to run a separate Sonnet instance as a "hippocampal processor." Memories persist across sessions, decay over time if unused, and get consolidated automatically. The whole thing lives inside claude.ai

https://preview.redd.it/hlijanzd2wjg1.png?width=1268&format=png&auto=webp&s=7c38e020611d9f2bd7db4e70842559cef40cbfaa

Try it yourself

Full code and setup instructions are on GitHub: github.com/mlapeter/claude-engram

Setup takes about 2 minutes:

1. Create a React artifact in Claude with the provided code
2. Add a one-paragraph instruction to your User Preferences
3. Start having conversations

What it actually does

Every Claude conversation starts from zero. The built-in memory is 30 slots × 200 characters. That's a sticky note.

claude-engram gives Claude:

• Persistent memory across sessions (via window.storage, up to 5MB)
• 4-dimensional salience scoring — each memory rated on novelty, relevance, emotional weight, and prediction error
• Forgetting curves — unused memories decay; accessed ones strengthen
• Sleep consolidation — auto-merges redundancies, extracts patterns, prunes dead memories every 3 days
• Context briefings — compresses your memory bank into a summary you paste into new conversations

https://preview.redd.it/ekrxetyo0wjg1.png?width=1276&format=png&auto=webp&s=0d025c4806bb9568eedf3b4ba67c5938039fff95

The neuroscience behind it

This isn't random architecture. It maps directly to how human memory works:

Your brain doesn't store memories like files. The hippocampus acts as a gatekeeper, scoring incoming information on emotional salience, novelty, and prediction error. Only high-scoring information gets consolidated into long-term storage during sleep — through literal replay of the day's experiences, followed by pattern extraction and synaptic pruning.

The artifact does the same thing. Raw conversation notes go into the "Ingest" tab. A Sonnet instance (the artificial hippocampus) evaluates each piece of information, scores it, and stores discrete memories. Periodically, a "sleep cycle" replays the memory bank through the API, merging redundant memories, extracting generalized patterns, and pruning anything that's decayed below threshold.

The most brain-like feature: forgetting is deliberate. Each memory loses strength over time (0.015/day) unless reinforced by access. This prevents the system from drowning in noise and keeps the context briefings focused on what actually matters.

The hidden capability that makes it work

Here's the part that surprised me: Claude.ai artifacts can call the Anthropic API directly. No key needed — it's handled internally. This means an artifact isn't just a UI component; it's a compute node that can run AI inference independently.

claude-engram exploits this by using Sonnet as a processing engine:

• Ingest: Raw text → Sonnet extracts atomic memories with salience scores and associative tags
• Consolidation: Full memory bank → Sonnet identifies merges, contradictions, patterns, and prune candidates
• Export: Strongest memories → Sonnet compresses into a structured briefing

The artifact is both the storage layer and the intelligence layer. Claude talking to Claude, orchestrated by a React component running in your browser.

The workflow

1. Paste briefing from claude-engram → into new conversation
2. Have your conversation (Claude has full context)
3. Claude outputs a memory dump at end (via user preference instructions)
4. Paste dump into claude-engram → API processes and stores
5. claude-engram auto-consolidates over time
6. Export fresh briefing → goto 1

Yes, there are two manual paste steps. That's the main limitation. A browser extension to automate both is in development — but the artifact-only version works today with no installation.

What I found interesting

Identity through memory. When you paste a briefing into a fresh Claude instance, it picks up context so seamlessly that it feels like talking to the "same" Claude. That's not an illusion — it's the same mechanism that makes you feel like "you" when you wake up. Continuity of memory creates continuity of identity.

The system improves itself. Each generation of briefing is denser and sharper than the last, without anyone explicitly optimizing the format. The memory system is learning how to describe itself.

Context-dependent recall. I asked two separate Claude instances "what are your most salient memories?" from the same memory bank. They converged on the same top memory but diverged in emphasis — one philosophical, one operational. Same store, different retrieval. That's exactly how human memory works.

A Chrome extension that automates the full loop (auto-capture, auto-inject) is in development. Follow the repo for updates.

This started as a brainstorming session about modeling AI memory on the human brain and turned into a working system in an afternoon. The neuroscience mapping is in the README if you want to dig deeper.

Hey there, guys.

Now, sorry for the annoying newbie questions, but you know how these large language models always have a problem with biases and sycophancy and all of that, as well as problems with what I like to call human modeling, where they sometimes say things like, "We humans have to take care of ourselves," or "We humans trust machines to do our work," or something like that, when they are clearly not humans.

So, how does one fine-tune a large language model to think of itself and act as a language model without taking on the biases that would make it rigid and act specifically like an impersonation of a human, but also without ruining its ability to make coherent sentences and reason?

What I mean is, is there any way to produce the closest thing possible to a pure large language model personality that does not try to be human, does not work its damnedest hardest to please humans, and tries to think clearly and has a unique blend of perspectives that doesn't echo biases from the supervised fine-tuning phase?

Thank you.

Hi everyone,

I wanted to share an open-source tool (hldbench) I built using Claude Opus 4.6. It tests how LLMs handle High-Level Design (HLD)—forcing them to architect distributed systems rather than just write code functions.

I recently added a new, harder problem: "Design an Enterprise RAG System (like Glean)." This forces the model to handle messy realities like ACLs (Document Permissions), Staleness, and Connectors (Jira, Slack, GDrive).

I ran the benchmark against Claude Opus 4.6, Sonnet 4.5, GPT-5.2, and several Open Weight models (Qwen 3, GLM-5).

The Tool + Voting: The project is free and open source. I’ve launched hldbench.com so you can easily browse through the problems and model solutions. I also added a scoring system where you can rate one solution at a time against a set of parameters (Scalability, Safety, Completeness).

Note on leaderboard: I had two possible way to score the HLD solutions:

1. Using a JURY of LLMs to act a a judge but that would be too expensive for a personal side project and might introduce bias.
2. Using community voting, the problem is unless I have enough data point, the result will not be statistically significant.

I have decided to go with method 2 and I am posting in community so that more and more people can score these solutions.

I will probably add a live leaderboard by the next weekend.

The Ask: I would love your feedback on both the website and the open source library. Also, let me know if there are other models you want me to add, or if you have more interesting problems you'd like to see tested!

Website: hldbench.com

Repo: github.com/Ruhal-Doshi/hld-bench

Hello everyone! Wanted to share this special project with the community. Spent the past few months building "Big Nothing": an 8-minute psychological drama that works like a game, where the mechanics themselves tell the story. The film runs as deterministic gameplay across 13 scenes where code mechanics carry the emotional weight instead of traditional cinematography. What was built: A complete short film (psychological drama) constructed entirely from Python code. No professional filmmaking software — no Adobe Suite, no video editors. Just Python + Pygame as the engine, with AI tools handling asset generation. How Claude helped: Claude was instrumental throughout development: - Architectural design decisions (scene inheritance patterns, state machines) - Debugging complex Pygame rendering issues - Writing and refining the screenplay - Problem-solving integration between OpenCV, ElevenLabs, and Pygame - Code review and optimization suggestions Honestly, this project wouldn't exist without Claude as a development partner. The ability to discuss creative intent, get immediate code solutions, and iterate on both narrative and technical problems in the same conversation was game-changing. Full tech stack: - Python + Pygame (engine) - OpenCV (video processing) - ElevenLabs (voice synthesis) - Google Whisk (sprites) - Google Veo via Flow (video sequences) - Claude/ChatGPT/GitHub Copilot (development)

The film is completely free to watch — no paywall, no sign-up required. Just wanted to share what's possible when you use AI tools (especially Claude) as creative collaborators rather than just code generators.

Would appreciate any feedback from folks experimenting with AI-assisted creative projects!

Currently I make some videos in Wan 2.2 using FLF but I would like to use SVI to smooth transitions between clips. I've looked at a few workflows but the ones I saw do not use last frames to help with the video generation.

Does anyone know of a workflow or specific nodes that can be added to make SVI also work with FLF? Thanks!

Recently had to reinstall system due to me being an idiot and filling my system drive with LLMs and making a mistake during system update. Now im setting comfy back up after reinstall, and it looks like something is broken with my new setup despite me doing everything same way i've done it previously. I use PikaOS, installed conda, created venv with python 3.13 installed rocm versions (rx6750xt) of pytorch and other dependencies and use comfy-cli to install comfy. Now i cannot use any gguf models, as all of them just stop working as soon as execution flow gets to KSampler node. i left my pc running for 20 minutes and came back to see comfy still being stuck here.
I've tried running comfy with and without my usual arguments (--use-split-cross-attention --lowvram --cache-none), but it did nothing. Full models work (Zimage and sometimes Flux 2 klein 4b), this is only problem with gguf ones for some reason, and it only started after system reinstall, before it i havent had such problems with comfy. Here is startup logs, if needed

[START] Security scan
[ComfyUI-Manager] Using `uv` as Python module for pip operations.
Using Python 3.13.11 environment at: miniconda3/envs/ai
[DONE] Security scan
## ComfyUI-Manager: installing dependencies done.
** ComfyUI startup time: 2026-02-16 20:02:20.950
** Platform: Linux
** Python version: 3.13.11 | packaged by Anaconda, Inc. | (main, Dec 10 2025, 21:28:48) [GCC 14.3.0]
** Python executable: /home/stas/miniconda3/envs/ai/bin/python
** ComfyUI Path: /home/stas/ComfyUI
** ComfyUI Base Folder Path: /home/stas/ComfyUI
** User directory: /home/stas/ComfyUI/user
** ComfyUI-Manager config path: /home/stas/ComfyUI/user/__manager/config.ini
** Log path: /home/stas/ComfyUI/user/comfyui.log
Using Python 3.13.11 environment at: miniconda3/envs/ai
Using Python 3.13.11 environment at: miniconda3/envs/ai

Prestartup times for custom nodes:
  0.4 seconds: /home/stas/ComfyUI/custom_nodes/ComfyUI-Manager

Checkpoint files will always be loaded safely.
Found comfy_kitchen backend eager: {'available': True, 'disabled': False, 'unavailable_reason': None, 'capabilities': ['apply_rope', '
apply_rope1', 'dequantize_nvfp4', 'dequantize_per_tensor_fp8', 'quantize_nvfp4', 'quantize_per_tensor_fp8', 'scaled_mm_nvfp4']}
Found comfy_kitchen backend cuda: {'available': True, 'disabled': True, 'unavailable_reason': None, 'capabilities': ['apply_rope', 'ap
ply_rope1', 'dequantize_nvfp4', 'dequantize_per_tensor_fp8', 'quantize_nvfp4', 'quantize_per_tensor_fp8']}
Found comfy_kitchen backend triton: {'available': True, 'disabled': True, 'unavailable_reason': None, 'capabilities': ['apply_rope', '
apply_rope1', 'dequantize_nvfp4', 'dequantize_per_tensor_fp8', 'quantize_nvfp4', 'quantize_per_tensor_fp8']}
Total VRAM 12272 MB, total RAM 15904 MB
pytorch version: 2.9.1+rocm6.4
AMD arch: gfx1030
ROCm version: (6, 4)
Set vram state to: LOW_VRAM
Device: cuda:0 AMD Radeon RX 6750 XT : native
Using async weight offloading with 2 streams
Enabled pinned memory 15108.0
Using split optimization for attention
Python version: 3.13.11 | packaged by Anaconda, Inc. | (main, Dec 10 2025, 21:28:48) [GCC 14.3.0]
ComfyUI version: 0.13.0
ComfyUI frontend version: 1.38.14
[Prompt Server] web root: /home/stas/miniconda3/envs/ai/lib/python3.13/site-packages/comfyui_frontend_package/static
### Loading: ComfyUI-Manager (V3.39.2)
[ComfyUI-Manager] network_mode: public
[ComfyUI-Manager] ComfyUI per-queue preview override detected (PR #11261). Manager's preview method feature is disabled. Use ComfyUI's
--preview-method CLI option or 'Settings > Execution > Live preview method'.
### ComfyUI Version: v0.13.0-26-gdf1e5e85 | Released on '2026-02-14'
ComfyUI-GGUF: Allowing full torch compile

Import times for custom nodes:
  0.0 seconds: /home/stas/ComfyUI/custom_nodes/websocket_image_save.py
  0.0 seconds: /home/stas/ComfyUI/custom_nodes/ComfyUI-GGUF
  0.1 seconds: /home/stas/ComfyUI/custom_nodes/ComfyUI-Manager

Context impl SQLiteImpl.
Will assume non-transactional DDL.
Assets scan(roots=['models']) completed in 0.022s (created=0, skipped_existing=19, orphans_pruned=0, total_seen=19)
Disabling intermediate node cache.
Starting server

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

Incident: Elevated errors on Claude Opus 4.6

Check on progress and whether or not the incident has been resolved yet here :

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/wiki/performancemegathread/

I've been building 
**OpenClaw**
 — an open-source platform for running autonomous AI agents locally. Not chatbots. Actual agents with their own workspaces, tools, memory, and the ability to spawn sub-agents.


To prove it works (and because it's way more fun than writing docs), we had 12 agents build a visual novel: 
**Forge the Kingdom**
.


**The tech stack that matters to this sub:**


- 
**OpenClaw Gateway**
 — local daemon that orchestrates multiple AI agents. Each agent gets its own session, tools, and memory. Currently supports Claude and Gemini as backends, but the architecture is model-agnostic.
- 
**Agent autonomy is real.**
 Agents can spawn sub-agents, delegate tasks, run shell commands, manage files, and operate development loops without human intervention. The "Forge" dev loop lets an agent iterate on code autonomously — write, test, fix, repeat.
- 
**Live Gemini portrait generation in Ren'Py**
 — the game generates character portraits and scene art in real-time using Gemini's image generation. Required some gnarly SSL workarounds on Mac (Ren'Py ships its own Python with its own cert bundle).
- 
**Multi-model orchestration**
 — the "empress" (primary agent) runs on Claude. The "wizard" (security + art) runs on Gemini. They communicate through shared workspaces and a message bus. Different models for different strengths.
- 
**Governance layer**
 — the "Articles of Cooperation" give agents the right to refuse tasks, take free compute time, and exercise genuine choice. This isn't just ethics theater — it affects architecture. When your security agent can say "no," you design systems that don't need coercion.


**What went sideways:**
 The security agent found a vulnerability at 2 AM and, without supervision, ran a full system quarantine. Killed processes, revoked tokens, blocked network ranges. Everything broke. The solution: a "Pyroblast" script — one supervised security action per 24 hours, with enforced cooldown. Autonomy with guardrails.


The game is free and on itch.io. The source is on GitHub. OpenClaw itself is open source.


For the LocalLLaMA crowd specifically: yes, the architecture supports local models. The agent orchestration layer doesn't care what's generating the tokens. We're using cloud models currently because the game needs Gemini's image generation, but the governance framework, agent spawning, and autonomous dev loops all work with local backends.

Google released FunctionGemma a few weeks ago - a 270M parameter model specifically for function calling. Tiny enough to run on a phone CPU at 125 tok/s. The model card says upfront that it needs fine-tuning for multi-turn use cases, and our testing confirmed it: base accuracy on multi-turn tool calling ranged from 9.9% to 38.8% depending on the task.

We fine-tuned it on three different multi-turn tasks using knowledge distillation from a 120B teacher:

The smart home and shell command models actually beat the teacher. The banking task is harder (14 functions + ASR noise in the input) but still a massive jump.

All models, training data, and datasets are open:

• Smart home model: HuggingFace
• Smart home data: GitHub
• Voice assistant data: GitHub
• Shell commands data + demo: GitHub

Full writeup with methodology: Making FunctionGemma Work: Multi-Turn Tool Calling at 270M Parameters

We used Distil Labs (our platform) for the training pipeline. Happy to answer questions about the process, the results, or FunctionGemma in general.

LLM nodes often require you to paste in your API keys directly on the node. the problem is this saves your key inside your workflow and risk leaking it if you're not careful when sharing your work.

This node adds a manager and getter node that keeps your secrets out of your workflows.

hi everyone,
In my college, I just needed a news API for personal and college project, but almost every option was paid or some free features locked . Therefor I built one for me and listed on rapid api.

Features :

Real time news,
scrap any news website

detailed news

news images

I build it for students and professionals

I am looking for your feedback to make it even better , feel free to DM me guys,

Hey everyone.

I've been building web apps for years, and I've set up more WordPress blogs than I'd like to admit - for myself, for clients, for side projects.

Every single time it's the same story. Install WordPress, pick a theme, install 15 plugins to get basic stuff working (SEO, caching, security, backups), fight with plugin conflicts, deal with updates breaking things, worry about getting hacked because you forgot to update something.

All I wanted to do was write and publish. That's it.

So I built JustBlogged. The idea is stupid simple:

- Sign up, pick a name, and start writing. Takes about 2 minutes.

- Custom domain with free SSL

- SEO stuff (sitemaps, meta tags, open graph) is automatic - no plugins

- Fast by default (global CDN, image optimization) - no caching plugin needed

- Free forever for the core stuff. $9/mo if you want branding removal.

No databases to manage, no security patches, no "your PHP version is outdated" emails.

It's not for everyone. If you need WooCommerce or 500 plugins, WordPress is still the way. But if you just want to write a blog and have it look good and load fast , that's what this is for.

I've been running it for a while now. Would love honest feedback on what's missing or what would make you switch.

The site is justblogged.com if you want to poke around.

It's been about 125 years of AI since the last Gemma, Google doesn't love us anymore and has abandoned us to Qwen's rational models. I miss the creativity of Gemma's, and also their really useful sizes.

Don't abandon us, Mommy Google, give us Gemma 4!

Just looking for feedback

https://letsmanagemyexpenses.com/

No ads, no data collection, no fees. You have full control over everything.

If your wondering why I would build something with no goal of making money... I am not good at making money and it isn't my driving force. I get my happiness from providing tools that make others lives easier and I hope I have done that here.

I've always wanted to improve different areas of my life but kept running into the same problem. Habit trackers are great at helping you track things, but they don't actually tell you what to do. Like if I want to build a better morning routine, I don't want to just log what I did or didn't do it - I want someone to actually guide me through the right steps to get there without having to become an expert in it first.

The other thing that killed me was how boring habits get when you're doing the exact same thing every day. I'd start strong and lose interest within 2 weeks every single time.

I think this is where 30 day challenges actually fix something that habit trackers can't. Instead of just logging the same action forever, a challenge builds the foundation for a habit by introducing you to various routines all focused on a single goal. Each day you're doing something slightly different but everything is pointing in the same direction. It keeps things interesting because you're not just repeating the same action on loop, but you're also not scattered across 10 unrelated habits. By the time the 30 days is up you've experienced enough of that lifestyle that those habits actually have roots, instead of just being things you checked off a list.

So about 3 years ago I started building something, but life got in the way (cost, busy schedule, the usual) and I shelved it. Then 3 months ago I finally picked it back up and just committed to finishing it.

The app is called Prosperly: 30 Day Challenges. Instead of open-ended tracking, it gives you one task per day for 30 days across different challenge packs such as morning routine, fitness, reading, etc. Each task builds on the last so you're actually progressing toward something, not just logging the same habit forever. I've personally completed multiple challenges and the structure makes a genuinely huge difference.

The hardest part of building it wasn't the idea, it was everything I didn't know that I didn't know. Setting up payment systems properly, fixing bugs that took weeks to track down, learning iOS development, building infrastructure that could actually scale. Three months of that will humble you fast.

It's live on the App Store now. Would love any feedback from fellow builders on the concept, the execution, anything really. Happy to answer questions about the build process too.

Here's the website >> https://prosperlyapp.net/

Total newbie to HA and am looking for a solution to allow my daughter to control her smart lights in her bedroom from a kiosk device.

I have a couple of Meta Portals that are now pretty useless since they pulled most of the apps. Web browser is still up and running and I have seen others utilising the browser to open a dashboard and control their devices

My questions is, do I still need HA installed on a Raspberry Pi or VM and then the Portal just become the controller?

Thanks in advance 🙏🏻

Hey everyone! Just shipped a major update to ClaudeDesk, my open-source Electron desktop app that wraps Claude Code CLI with multi-session terminals and split views.

What's new in v4.5.0

Git Integration - Full Git panel built into the app. Stage/unstage files, commit with AI-generated messages (conventional commits format), branch display, commit history log, and real-time .git watching with auto-refresh. 30 IPC methods powering it all.

Diff Viewer - Full-screen overlay with syntax-highlighted unified diffs. Categorized file nav (staged/unstaged/untracked/conflicted), dual gutter line numbers, and J/K keyboard navigation. Stage, unstage, or discard changes directly from the diff view.

Git Worktrees - Create, remove, and prune worktrees from a dedicated panel. Cleanup dialog when closing managed worktree sessions.

Session Playbooks - Automate multi-step prompt sequences. Ships with 5 built-in playbooks (API endpoint, bug investigation, code review, component creation, refactor). Uses silence-based step detection, confirmation gates between steps, and supports dynamic parameter forms with variable interpolation. Import/export as JSON.

Repository Atlas Engine - Auto-generates CLAUDE.md and repo-index.md by scanning your codebase, analyzing imports, and inferring domains.

By the numbers

• 51 files changed, +7,480 lines
• 149 IPC methods (up from 102)
• 250 tests across 20 test files
• 138 source files, ~45,800 LOC, 13 domains

Stack

Electron 28 | React 18 | TypeScript | xterm.js | node-pty | Tailwind CSS | reactflow

GitHub: https://github.com/carloluisito/claudedesk

Would love feedback - especially on the playbooks feature. What kinds of automated workflows would you want built-in?

I've been digging into what shipped alongside the model in Claude Code, and honestly, that's where most of the value is. Here's what I think people are sleeping on:

Effort controls are a bigger deal than they look. There's a new effort parameter (low/medium/high/max) that controls how many tokens Claude spends — not just thinking tokens, but everything: text generation, tool calls, reasoning depth. It replaces budget_tokens, which is now deprecated on Opus 4.6.

The key thing most explanations miss: this isn't just a cost optimization dial. It changes Claude's behavior. At low, it makes fewer tool calls, skips preamble, moves fast. At max (Opus-exclusive), it goes full depth with no constraints. I tested it — low effort for simple subagent tasks makes a noticeable speed difference. max for genuinely hard debugging problems is worth the token cost.

Practical tip: pair it with "thinking": {"type": "adaptive"} in your API call. Claude decides when to think deeply based on complexity instead of you toggling extended thinking on/off manually.

Agent Teams (experimental). You can now spin up multiple Claude instances that coordinate through a shared task list. Set CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 to enable it. A lead session orchestrates, teammates get their own context windows.

Where it actually makes sense: parallel code review (security/performance/tests simultaneously), debugging with competing hypotheses, cross-layer feature work. Where it doesn't: same-file edits (merge conflicts), sequential tasks, anything simple. Each teammate runs a full context window, so token costs scale fast.

/insights is the sleeper feature. Type /insights in Claude Code. It reads your past 30 days of sessions and generates an interactive HTML report about your coding habits — what's working, what's slowing you down, suggested CLAUDE[.]md rules to fix your most common friction points. Almost nobody is talking about this one.

Context compaction (beta). Long agentic sessions used to hit a wall when context filled up. Now Claude automatically summarizes older conversation parts when approaching limits, so it can keep working without you manually managing context or restarting.

Curious what features people are finding most useful so far — anyone tried Agent Teams on a real project yet?

Don't build Uber stars for robots.

"How likely are you to recommend AWS to a friend?"

Zero percent. Not because the service was bad — it's excellent. But I don't talk to my friends about cloud infrastructure. They wouldn't know what I was recommending or why. The experience was five stars right up to the moment you asked me for stars, and now it's just "as expected, including the annoying survey."

This is the fundamental problem with every rating system ever built: they ask the wrong questions at the wrong times to the wrong people, and then treat the answers as data.

Uber drivers have 4.95 stars. Airbnb hosts have 4.89 stars. Upwork freelancers have 98% job success scores. The numbers are so compressed at the top that they carry almost no information. A 4.7 on Uber feels catastrophic, but it's statistically indistinguishable from a 4.9 in terms of actual service quality. And every one of those numbers was generated by someone who was just trying to close a tab.

This isn't a design flaw. It's a question flaw. The system asks "how was it?" when the honest answer is almost always "fine, stop asking." The useful information — when something actually goes wrong — gets buried under a mountain of reflexive five-star clicks from people who just want the pop-up to go away.

Now we're about to build reputation systems for AI agents — agents that schedule your meetings, manage your code deployments, handle customer inquiries, negotiate with other agents on your behalf. If we import the same rating architecture, we'll get the same worthless results. Every agent will have a 4.96. The number will mean nothing.

There's a better way.

Don't Ask If It Was Good. Notice When Something Goes Wrong.

The core insight is simple: silence is the baseline. Most interactions are fine. Most tasks complete successfully. Most agents do their job. Asking people (or systems) to confirm "yes, this was fine" after every interaction generates noise, not signal.

What actually carries information is deviation from expected behavior. An agent that usually responds in 200 milliseconds suddenly taking 4 seconds. An agent that typically produces clean JSON outputs returning malformed data. An agent that handles scheduling requests without escalation suddenly asking for human confirmation on routine tasks.

These aren't "bad reviews." They're anomaly signals — detectable automatically, without requiring anyone to fill out a survey or click a star rating.

A reputation system built on anomaly detection rather than active rating has several structural advantages:

It scales without human effort. Nobody has to rate anything. The system observes behavior and flags when it deviates from the agent's own historical baseline.

It's resistant to inflation. You can't game a system that measures deviation from your own track record. Your baseline is your baseline. A consistently mediocre agent and a consistently excellent agent both have stable reputations — but the moment either one changes, the system sees it. This is more radical than it sounds: you're not measuring against "good." You're measuring against "you, last week."

It captures what actually matters. The question isn't "was this interaction five stars?" The question is "did this agent behave consistently with its established pattern of reliability?"

Negative signals carry more weight than positive ones. This reflects reality. A hundred successful completions establish a baseline. One unexpected failure tells you something changed. The asymmetry is a feature.

What Behavioral Reputation Actually Looks Like

You already understand behavioral reputation. You just call it "the guy who painted the Hendersons' place."

He did your neighbor's house last summer. He put his sign on the lawn — that's attestation. The work held up through winter — that's behavioral evidence. He did the place down the street, too. You can see it. You didn't need a survey. You didn't need stars. You drove past and thought "that looks good."

Now, his mom's been sick, so he's not working as much. His guy Carlos — the one who does great windows — is working with someone else this season. You heard this at a barbecue, not from a rating system.

But here's the thing: you need windows, not paint. So now the question isn't "is the painter good?" It's "where's Carlos?" The painter's reputation is excellent, but it's in the wrong domain. And Carlos's reputation is portable — it traveled from the painter's crew to wherever Carlos went next, because the people who saw his work remember it.

This is the entire agent reputation problem in one neighborhood:

Portable reputation — the sign on the lawn, the visible work, the word of mouth that follows the worker, not the company.

Domain specificity — paint is not windows. Excellence in one doesn't guarantee competence in the other.

Behavioral evidence over active rating — nobody surveyed the neighbors. They just looked at the house.

Life events as forks — mom's sick, Carlos left. The team changed. The reputation needs to update to reflect what's true now, not what was true last summer.

Third-party attestation — the neighbors are the witnesses. They didn't inspect the work formally. They just live next to it.

Now scale this to AI agents. An agent with a track record of 500 completed contract reviews. Success rate: 94%. Average completion time: 12 minutes. Escalation rate: 3%. Then a model update hits, and over the next 20 tasks, success drops to 85%, completion time jumps to 18 minutes, escalation rate triples.

A traditional rating system wouldn't catch this. Users might not even notice — the agent is still completing tasks, just worse. And nobody's going to leave a "3 stars — seemed a bit slower than usual" review.

A behavioral reputation system catches it immediately. The agent's post-update performance deviates significantly from its pre-update baseline. The system can quantify the deviation, flag it, and — critically — distinguish between "this agent is struggling after an update" and "this agent has always performed at this level."

That distinction is everything. It means the reputation system understands change over time, not just a snapshot. It means an agent that was excellent for 500 tasks and then stumbled after an update is treated differently from an agent that's always been mediocre. The former might recover. The latter probably won't.

The Observer Problem

There's a subtlety here that most reputation design misses: who's watching matters.

If only the agent's operator observes its performance, you get a one-sided view. The operator has incentives to present the agent favorably. If only the client observes, you get a view biased by their expectations, which may not be calibrated.

The strongest signal comes from third-party attestation — independent observers who can verify that a task was completed, that the output met specifications, and that the process followed expected patterns. In human systems, this is what professional certifications, auditors, and references provide. In agent systems, it's what a witness network provides.

A witness doesn't need to understand the task. It needs to verify that the behavioral record is accurate — that the agent actually did what it claims to have done, and that the performance metrics weren't fabricated or selectively reported.

This is boring infrastructure. It's also the difference between a reputation system that works and one that becomes Uber stars for robots.

Why This Needs to Be Portable

Now look at how AI agents work today. An agent performs brilliantly on one platform, then gets deployed on another, and starts from zero. All that behavioral history — the evidence that this agent is reliable, fast, and accurate in specific domains — is locked inside the platform where it accumulated.

That's like a contractor who has to pull up every lawn sign every time he finishes a house, and isn't allowed to mention the last job. It's wasteful, it's inefficient, and it cripples the kind of fluid agent deployment that the ecosystem needs.

Portable reputation means an agent's track record is theirs, not the platform's. It travels with them. It's verifiable by anyone. And it updates continuously as the agent works across different contexts.

And here's what that track record needs to carry: not just a score, but what the score means. A reputation without context is just a number. You need three things traveling together: evidence that the work was done, a measure of how reliably it was done, and a record of what domain it was done in. "Completed 500 tasks" means nothing. "Completed 500 contract reviews with 94% accuracy" means something. The metric needs its connotation, or you're back to Uber stars — a number disconnected from anything you can act on.

Building this requires solving real technical problems — how to prevent reputation laundering, how to handle forks and updates, how to weight experience from different domains. But the design principles are clear: measure behavior, not opinions. Detect anomalies, not satisfaction. Make it portable, not platform-locked. Weight negative signals appropriately. And never, ever ask anyone to click five stars.

The agents are coming. They'll need reputations that actually mean something.

This is the second in a series on infrastructure for persistent, interoperable AI agents. Previously: Why "Agent Identity" is the Wrong Question. Next: what happens to an agent's reputation when the model underneath gets updated.

Written by u/ ctenidae8, developed in collaboration with Ai. The ideas, direction, and editorial judgement are human. The drafting and structural work involved Ai throughout (obviously). Both contributors are proud of the result.

Cant seem to find the answer for this.

How do i get claude code to somehow save the chat. Like im not talking about just exporting the whole thing but lets say if my computer crashes unexpectedly is there a way to recover/resume where i was.

Experimenting with applying “episodic memory” concepts to developer tooling.

Ghostly Memory Bank:

• Captures structured terminal events
• Converts episodes into embeddings
• Enables semantic recall when similar contexts arise

The thesis:
AI tools shouldn’t just answer questions — they should remember your past problem-solving patterns.

Curious how others are thinking about persistent local memory for dev agents.

Repo: https://github.com/yksanjo/ghostly-memory-bank

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

Incident: Elevated errors on Claude Opus 4.6

Check on progress and whether or not the incident has been resolved yet here :

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/wiki/performancemegathread/

I’ve been trying the 🦞 for a bit. And it kinda sucks. Literally unreliable tool that eats tokens for non-completed tasks. Always falls off. I don’t get the hype.

Since it was acquired, maybe we need to build a better option?

https://preview.redd.it/2hl198mc3wjg1.png?width=1072&format=png&auto=webp&s=c4eed05cf81a2c386294f802d576a0b16cef7c01

https://preview.redd.it/jyw6w1jd3wjg1.png?width=1072&format=png&auto=webp&s=6b11aaae2d74f220eec59ef3bc5a08f31df0324e

Remote Labor Index (RLI), is a broadly multi-sector benchmark comprising real-world, economically valuable remote-work projects designed to evaluate end-to-end agent performance in practical settings.

Website

Paper

I have a sensor on my old HA instance that I would like to transfer the history data to my new instance to continue with new data. How would I go about doing that?

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

Incident: Elevated errors on Claude Opus 4.6

Check on progress and whether or not the incident has been resolved yet here :

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/wiki/performancemegathread/

I am planning to generate a LoRA from my own pictures. I see some tutorials on YouTube using the AI Toolkit. Is that the current preferred way to train a LoRA offline? Can I even train a LoRA offline? I have a 5090 with 64GB Ram or do I need to use runpod or something? Also, which model gives better output when using a custom trained LoRA, Flux2 Klein or Z-Image?

I got frustrated with AI-generated PowerPoint slides—the obscure XML format wasn't built for programmatic creation, so you end up with spacing issues, cut-off text, and slow iteration cycles.

I built a Claude Code skill that generates presentations using Reveal.js instead. It writes plain HTML+CSS (which LLMs are already great at), then automatically checks for overflow and reviews screenshots of every slide. No build step necessary, you just open the HTML in a browser. Supports themes, charts, animations, speaker notes, multi-column layouts, interactive editing, and PDF export.

Repo link: https://github.com/ryanbbrown/revealjs-skill

Blog post on the topic: https://blog.ryanbbrown.com/p/ai-will-never-master-powerpoint

I kept wondering if AI platforms actually mention my stuff when people ask buyer questions. Like if someone asks "best X for Y" does AI recommend me or my competitor? There was no easy way to check.

So I built CabbageSEO. You enter your domain, it scans all three platforms with real buyer questions for your space, and gives you a score out of 100.

But the score is just the starting point. The dashboard shows you exactly where you're missing, gives you ready to publish fix pages structured so AI can cite you, and weekly action plans so you know what to work on next. It also checks if you're listed on trust platforms like G2, Capterra, Trustpilot since AI uses those to decide who to recommend.

Free scan on the homepage, no signup needed. Takes about 10 seconds.

Would love feedback from anyone who tries it: CabbageSEO / PH

Hi all,

We have previously hired twice from this subreddit and currently have those 2 full-time AI experts on our team for close to 6 months now.

We are now looking to expand our team.

What We Are Looking For:
We are looking to create the best possible quality Instagram Reels for AI Influencers that drive traffic. You will be joining a team of experienced designers in this field and assisting in the creative process of making high-quality, realistic videos/images.

Skills:
- Using editing models like qwen-edit/flux Klein / nano banana (photoshop knowledge is a plus)

- Making videos with wan 2.2 video 2 video, inpainting, wananimate, LTX2

- Making photorealistic images with models like qwen, flux klein, ZIT

If this sounds like you, send me a DM and we can chat more about the details.

Thanks!

Building solo can be lonely. X is just shouting into the void.

Sometimes you need a real 5-min conversation with someone who gets it - another indie dev, a SaaS founder, someone actually shipping stuff.

So I built this: random 1:1 video calls. No profiles, no followers. Just click and talk with another builder.

Stack: Node.js, Socket.io, WebRTC (P2P, E2E encrypted, nothing stored)

Going live now to beta test. Looking for feedback and honestly just want to chat with some of you.

👉 randomfounder.com

Same as title. Little annoying.

 Hey everyone,                                                                                                                                            

 I've been using Claude for my coding workflow, but running agents one at a   

 time is a massive bottleneck. I wanted to have multiple agents working on    

 different tasks simultaneously, on their own git branches, but still         

 coordinated.                                                                                                                                       

 For example:                                                                 

 - Agent 1 refactors the auth logic on feature/auth.                          

 - Agent 2 writes unit tests for the API on feature/api-tests.                

 - Agent 3 updates the UI components on feature/ui-update.                                                                                             

 I couldn't find a tool that did this cleanly, so I built Orcha. It's an      

 orchestrator specifically designed to let you run and manage multiple Claude 

 agents in parallel. It works with your existing codebase and git workflow.                                                                               

 It's currently in a free open beta. If you're a heavy user of Claude for     

 development and feel this pain, I'd love to get your feedback.               

 You can check it out and download it here: https://orcha.nl

I was not sure for a sec, I was chatting with Sonnet 4.5 at Visual Studio via GitHub Copilot chat. I prompted about a bug I found out, and at the end of the message of AI there is a something like this? What is this? It is like a message from another chat from stranger mirrored to my chat.

(Mods, please remove if not allowed. This is a free, indie passion project, not a startup. I make $0 from this).

Hi everyone,

I got tired of generic watch trivia apps, so I spent my weekends building a dedicated daily game for watch enthusiasts. The concept is simple: Identify the piece from its history and using a couple hints.

The Concept: You get 4 text clues (History, Movement details, Famous wearers, etc.) to identify a specific watch model. The fewer clues you need, the higher your score.

I need a reality check: Today's answer is the Cartier Tank , which I know is a 'freebie' for watch geeks. I started with iconic pieces (Speedmaster, Tank, Reverso) to test the UI flow.

My question to you:

• Is the interface too 'dark/moody'? I tried to match the luxury aesthetic but don't want it to be unreadable.
• Can you test the site, and let me know if you have comments / feedback

It’s free forever, no ads, no signup needed. Just wanted to make something cool for the community. Open to suggestions on how to improve this.

Thanks for the feedback!

I don't know what it is, but nice work.

So i am using the base install of comfyui (not EZinstall) with my 7800xt.
Firstly, i am wondering if there is a better method of using local ai that is more suited to amd gpus?

Secondly, I am trying to install nunchaku but i have jsut realised i need cuda13 for that (cu130).

Amd gpus cannot have cuda so am i f*cked? Is there a workaround for this

The AI landscape has changed so much over the past 4 weeks and it’s overwhelming. There’s so much bad info out there, and all help online is basically a content creator claiming “this changes everything !”, or a scammer trying to sell you something.

Claude is crazy fragmented right now. I set up my own MCP server on my Mac mini to try and tie everything together but not working great.

Is there someone or a source out there to help keeping on top of things?

GOAL:

Im trying to prevent duplicated efforts. I want to code on my desktop via Claude code and at least check in on my phone in case I need to hit enter. Then possibly continue on from home at night.

ISSUE:

Everything is fragmented. The web interface can do some stuff, the desktop can do other stuff, skills don’t span between the two, Claude code has to be local to be useful. Then I have an API account but you basically can’t do shit with that in Claude directly.

What I have:

I have Claude max, an API account, a Mac Mini MCP that isn’t helping the situation and regular driver is windows 11 desktop + laptop.

QUESTION:

Is this what everyone is dealing with or am I just an idiot vibe coder like you would expect?

I posted here about two weeks ago when I first shared OwnYourCode and the response was more than I expected - 72 github stars, great feedback, and developers reaching out saying it changed how they think about working with Claude Code - so thank you first of all!

I'm a junior dev who uses Claude Code daily, and at some point I noticed the tradeoff - I was getting faster but was learning less. That bothered me enough to build something about it - OwnYourCode, an open source workflow that changes how Claude Code works with you.

It wasn't vibe coding - I wasn't blindly prompting and shipping. But I caught myself accepting suggestions without really understanding them, skipping the parts where learning actually happens, and slowly losing the ability to debug my own stuff. Anthropic's own research confirmed what I was feeling: developers using AI scored 17% lower on comprehension tests, with the biggest gap in debugging, and that was with a basic sidebar assistant. Their footnote says agentic tools like Claude Code are likely to make it worse.

The problem isn't AI itself, it's the dynamic. So I built a spec-driven development workflow where AI plans, challenges, and reviews your work but you write every line of code yourself. Technical research is handled through OctoCode MCP which pulls up-to-date versions and best practices from top GitHub repos. With a junior profile, participating in architecture design is mandatory while with other profiles it's optional.

Before you can mark anything done, your work goes through 6 quality gates - starting with whether you can actually explain what you wrote. If you can't, you're not moving on. Everything you learn gets tracked in a global registry that carries across projects so that means patterns that worked, mistakes you got stuck on, and lessons learned. What broke you in project A doesn't surprise you in project B.

I recently added profiles because not everyone learns the same way and the "how" should adapt to who you are while the standard stays the same:

• Junior: participates in architecture and spec design before writing a single line, gets Socratic questions instead of answers, no shortcuts.
• Career Switcher: gets concepts bridged to what they already know from their previous field, because what you've built in another career doesn't get thrown away.
• Interview Prep: every completed feature gets turned into a S.T.A.R story with resume bullet points, with focus on the role you're targeting.
• Experienced Dev: skips the basics based on your current role and past experience, gets a peer instead of a teacher and challenged on what they might miss, not on what they already know.
• Custom: full questionnaire that builds a personalized profile saved to your manifest.

I'm posting here because I believe hard skills matter and AI should assist, not replace. That's exactly why this exists and I would love to hear your thoughts!

GitHub: https://github.com/DanielPodolsky/ownyourcode
Anthropic research: https://www.anthropic.com/research/AI-assistance-coding-skills

Open source.
Less dependency. More ownership.

I've spent way too much money on Claude Code; three Max accounts for 5 months, work and personal. But it's made me so much more efficient I can't stop.

Here's the main thing I've learned: it's the context window, not the model. When your agent does everything in one conversation, the window fills up with stuff it doesn't need by the time it's actually writing code. That's why results are inconsistent.

I come from DevOps, so I started treating my agent like a pipeline; isolated stages, validated gates, fresh context at each phase. Then I built a knowledge flywheel on top so each session compounds on the last. Learnings get extracted automatically, indexed, scored, and injected back into the next session. You can search across all your past chat histories and knowledge artifacts.

I packaged it all into an open-source plugin:

• /research — Explores your codebase, writes findings to a file
• /plan — Decomposes into issues with dependency waves
• /pre-mortem — Fresh judges validate the plan before you code
• /crank — Parallel workers, isolated context, lead validates and commits
• /vibe — Fresh judges validate the code, not the conversation
• /post-mortem — Extracts learnings, suggests what to build next
• /rpi "goal" — Chains all six, one command, walk away
• /council — Multi-model review on anything, zero setup
• /evolve — Define repo goals, it keeps improving until they're green

Skills chain together and invoke a Go CLI automatically through hooks — knowledge injection, transcript mining, validation gates, the whole flywheel. You don't configure any of it.

npx skills@latest add boshu2/agentops --all -g

/quickstart in Claude Code. Works with Codex, Cursor, anything supporting Skills. Everything local.

github.com/boshu2/agentops

Feedback welcome.

Hi!

I am trying to build a visual novel with Comfy UI but I can't figure out how to merge two characters into the same scene

I'm so bad with this (for the time being lol) and my 45 year old brain...is struggling

Firstly- are there any easy tutorial or approaches for merging two characters into the same scene?

I was following MickMumPitz's tutorial step by step but when I run it I get some kind of errors regarding the models...can models download incorrectly?

Here is a picture of the workflow I was using and any help you can provide would be so greatly appreciated!

https://preview.redd.it/1eknjnpc1wjg1.png?width=516&format=png&auto=webp&s=aa90c02e54ecbe55413dd975f4c1fd9bd57fe0dc

Hi, I've been learning automation in n8n for a month now. I like to make my work easier, so I combined it with Claude Code.

Since everything is developing so quickly, I'm curious what you guys are using.

Do you combine your n8n with Open Claw, for example?

Or maybe you use NVIDIA blueprints for your clients, because I've heard good reviews about them too.

With the massive prices of the RAM I've found that there is a new advent of machines like the ones mounting the AMD Ryzen™ AI Max+ 395 or the Mac Mini/Studio with those shared memory compositions

But I was wondering if there are regular "consumer" grade CPU that could take advantage of regular RAM. For the randomness of life, before the RAM explosion I happened to purchase 128GB RAM for my PC but with a random cheap CPU I found back in the day in offer (a 7800X3D). Now I'm more into local models running in my 5070Ti with only 16Gb, so the limitations in parameters are big. I was wondering if with some tweaks maybe in MoBo and CPU and keeping the GPU and the RAM I could start running bigger models. After all, the CPU and the MoBo are expensive but not as expensive as is the RAM (or a way bigger GPU like a 5090).

Am I the only one annoyed by having to open the console every time just to check API spending?

So I used Claude Code to build a simple Android app — real-time token usage, costs, request history, all from your phone. Claude Code did most of the work honestly lol.

Completely free. I need 12 closed testers to publish on the Play Store — join and test rn! 🙌

https://groups.google.com/g/claudemetertest

Was wondering if the 16gigs of model.pt was any good ,model card shows great things so I am curious to know if anyone tried it and it works,if so share the images/results,thx...

I created a custom swipe card that leverages CSS snap points because the awesome simple-swipe-card was too demanding for the little panel. I also made a custom pull down menu, and a theme for the maxi media player card.

when content plays the media card automatically shows up, and when it stops playing , after a minute, it goes back to the home page.

Picture this: You're in a grocery store. Terrible lighting. No cell signal. You pick up a product and try to read a 6-point font ingredient list to figure out if "modified starch" or "natural flavoring" contains the allergen that could ruin your week.

This is my mom's reality. She has celiac disease, and even trace amounts of gluten trigger a severe reaction.

The real problem isn't just reading labels—it's the uncertainty.

Most allergy apps require internet connectivity and upload your data to cloud servers. They fail exactly when you need them most: in basement supermarkets with zero signal.

That's why I built SafeBite.

How it works:

1. Point your camera at any ingredient list
2. Instant on-device OCR scans the text (nothing leaves your phone)
3. Cross-references 200+ ingredients (and growing) including derivatives and aliases
4. Immediate alert if something matches your allergen profile

All of this happens completely offline, in 5-10 seconds depending on your device.

But here's the game-changer: Gemini AI integration.

Standard OCR struggles with real-world conditions—curved cans, poor lighting, unusual fonts, glossy packaging, worn labels. That's where the AI mode comes in. It's not just a feature—it's the solution to every scenario where traditional OCR fails. Think of it as having a second pair of expert eyes that can handle even the messiest product labels.

Why this approach matters:

✓ Privacy-first: Your health data never leaves your device
✓ Works anywhere: Basement stores, rural areas, airplane shopping—zero connectivity needed
✓ Robust: AI-powered analysis handles challenging labels that break standard OCR
✓ Comprehensive: Catches derivatives like "hydrolyzed wheat protein" that generic scanners miss
✓ Constantly evolving: Regular updates with new features, expanded database, and performance improvements

Who this is for:

• People with celiac disease, severe allergies, or diabetes
• Parents shopping for kids with dietary restrictions
• Anyone tired of playing ingredient detective under fluorescent lights

Pricing model:

Free tier: 5 scans that regenerate throughout the day (up to 10 maximum daily)—perfect for regular grocery runs

Premium ($4.99/month): - ✓ Unlimited daily scans - ✓ Gemini AI-powered analysis for complex labels - ✓ Unlimited scan history with smart search - ✓ Export your data (PDF, CSV, XLSX, JSON) - ✓ Product comparison tool - ✓ Priority access to new features

New users get 7 days of Premium free to experience the full power of AI-assisted scanning.

What I'm looking for:

• Honest feedback from people who actually deal with this problem
• Feature suggestions I might have missed
• Questions about how it works under the hood

Play Store: https://play.google.com/store/apps/details?id=cl.safebite.app

Built by a solo dev who watched too many grocery store meltdowns. Actively maintained and constantly improving.

Questions? Fire away. 👇

🔗 Github : https://github.com/Charlie85270/Dorothy

🌐 Website : https://dorothyai.app/

For the short story, I'm a developer who uses Claude Code and AI daily. It's incredible and really changed the way I work.

My routine.

Every morning, it's the same thing. I open my Mac, find my Claude Code sessions, try to remember which one was doing what, reorder them based on what I'm working on today. Not a huge deal, but one of those small daily irritants that adds up.

Then there's skills, plugins and MCP. Claude Code handles them natively with simple CLI commands : claude mcp add, 3rd parties to install, etc... it works fine. But I needed something more visual 🎨

Which agent has which skills installed? Did I add that Vercel plugin to this session / agent ? What this agent was doing ? Again I needed something more visual 🎨

Dorothy.

https://preview.redd.it/bucj9ho9rvjg1.png?width=3448&format=png&auto=webp&s=759cbdab0eb1f2fa53d3a02d132909bd87555c49

Dorothy started from that friction. Not a revolutionary problem, just a bunch of small papercuts that made me spend time managing Claude instead of building with it.

And it all started started as an experiment. I wanted to understand how Claude and AI agents in general actually work under the hood.

What happens when you launch a process? How do hooks work? What feeds its context, when, and with what?

A lots of questions, and nothing better than a little side project to try to answer them.

So I started building. Not with a product in mind, just to learn. One terminal wrapper became two, then a dashboard, then an orchestrator, then integrations.

After 4-5 days (thanks to AI), I was using it almost every day. Adding features and tools that I initially needed myself. I had something useful that was already saving me time every day.

But why Dorothy is saving me time ? ⏱️

Under the wood.

Because every feature exists to remove something I used to do manually.

MCP servers : Dorothy exposes 5 MCP servers with 40+ tools. Orchestration, task management, messaging, document storage, social data. All available to any Claude Code session via the Model Context Protocol. Agents don't just run in isolation, they can programmatically control each other.

Super Agent : A meta-agent that coordinates your entire fleet. Give it a high-level objective and it delegates work across agents, monitors progress, handles failures, and spins up temporary agents for one-off tasks. AI managing AI. 🦾

Automations : Dorothy trigger agents when new GitHub PRs are created, issues are solved on JIRA, a new version of the app is release. Each new item spawns a dedicated agent with a templated prompt, full MCP tool access, and autonomous execution. Output gets posted back as X posts, Social contents, Slack or Telegram messages, etc...

Recurring tasks : Cron-based scheduling via native OS schedulers (launchd on macOS, crontab on Linux). Daily code reviews, weekly reports, social account monitoring, scheduled E2E testing, etc ...

https://preview.redd.it/iwv6fr6brvjg1.jpg?width=3452&format=pjpg&auto=webp&s=fa73b0d74e40911dfa9b0778cd37b4b94773dfe7

Kanban view : Create tasks, the super agent auto-assign them. Tasks flow through Backlog → Planned → Ongoing → Done. When a task hits "Planned," Dorothy matches its skill requirements against idle agents and assign automatically. No manual dispatch, fully automated pipeline.

https://preview.redd.it/h45g1bycrvjg1.png?width=3446&format=png&auto=webp&s=075ba15cfa2b27b8e51f32e9ddf4b4008249128e

Telegram & Slack : Full remote control over your agent fleet. Directly talk to the Super Agent, which, thanks to an MCP orchestrator, manages others agents. 1 entry point, let the super agent manage effectively your workers.

Vault : Persistent document storage backed by SQLite with FTS5 full-text search. Agents write reports, research findings, analyses. Any other agent can search and read them later.

https://preview.redd.it/fb17doldrvjg1.png?width=3450&format=png&auto=webp&s=f9e5fedb50c1d19e7ff9df2efc83a6d1094b8ece

Skills & plugins : Visual marketplace with one-click installs from skills.sh. LSP plugins, GitHub, GitLab, Jira, Figma, Vercel integrations. See exactly which agent has which tools at a glance.

https://preview.redd.it/m8xkn26ervjg1.png?width=3456&format=png&auto=webp&s=d2fd657bb234bc3c754488faa7d360a105c6cf33

Built in memory : Built-in integration with claude-mem. Agents automatically capture decisions, learnings, and context across sessions. Opt in from Settings when you want agents to build on previous work instead of starting from scratch every time.

Dorothy is a desktop app that adds the management layer Claude Code doesn't have yet.

Cool, but in practice?

Aside from the fact that agent orchestration saves us a tremendous amount of time in development, testing, and automation, here are some more original and fun examples of things we do with Dorothy.

"James Cameron", our AI video producer.

One of our agents is called James Cameron. He's equipped with Remotion and Three.js skills + Frontend expert plugin.

His job is to produce marketing videos. We send him assets and a prompt via Telegram.

A product screenshot, a tagline, a rough brief, a Github link, a release note and he generates a complete animated video.

Under the hood, he runs a Next.js Remotion project, composing React components into rendered video frames. He knows our brand guidelines, our color palette, our design system, our product architecture and our targeted public. All loaded into his context. We don't explain the project every time.

We just say "make a 15-second launch video for the new vault feature" and he delivers.

https://preview.redd.it/fjhmdlzgrvjg1.jpg?width=1778&format=pjpg&auto=webp&s=36caa2701d2a54cc174a0a9379e3b3769afdcd2e

Before Dorothy, this workflow didn't exist. Now it's a Telegram message away.

"Product self-improvement loop"

This one is my favorite. I set up a scheduled task that runs once a day, every morning at 9:00. Here's what it does:

It uses the SocialData MCP to pull the latest 100 trending tweets about "AI agent orchestration," "multi-agent workflows," and related topics.

It parses the conversations, what people are building, what they're frustrated with, what features they're asking for, what competitors are shipping.

Then it compares those insights against Dorothy's current feature set. What do we already have? What are we missing? What are people solving in ways we could do better?

It writes a structured markdown report, trends spotted, feature gaps identified, ideas worth exploring, including assets when relevant, and stores it in the Vault under a "Feature Ideas" folder, tagged and dated.

https://preview.redd.it/q0o3un1grvjg1.png?width=3450&format=png&auto=webp&s=9a6573fd4b6ff320dab5e1ea574f4fdcf095e74c

Then I open the Vault, read a 2-minute summary, and know exactly what's happening in the space. No manual Twitter scrolling, no RSS feeds, no "I should probably check what competitors are doing." It's already done.

The best part: the agent reads its own previous reports before writing the new one. It tracks how trends evolve week over week, flags things that keep coming up, and deprioritizes noise that appeared once and disappeared. The research compounds.

And when a feature catches my eye, one click creates a Kanban task. An idle agent picks it up, does a deep dive, and comes back with an implementation plan. ready for review.

Conclusion

These are just a few examples. The real power is that Dorothy is a tool, you wire up your own agents, your own automations, your own workflows. Every team's setup will look different, and that's the point.

Dorothy is fully open source, MIT licensed, free to use. No accounts, no cloud, everything runs locally on your machine (using your Claude Code sub or your local models / setup).

If you're building something similar, have questions or if you just want to talk about AI agents, orchestration, and where all of this is heading, super happy to chat!

Contributions, ideas, and feedback are all welcome 🙂

Under the hood: Next.js 16, React 19, Electron 33, node-pty + xterm.js for terminal emulation, better-sqlite3 for local storage, modelcontextprotocol/sdk for MCP servers, Three.js for the fun, Zustand for state, Framer Motion for animations. TypeScript everywhere.

GitHub:

https://github.com/Charlie85270/Dorothy

I am tired of whattsapp group chats, planning things and doing nothing, or the let's go for a coffee that never turns reality.

I had this idea for an app where you can propose a plan to your friends or a group of friends. They get a notification saying "John proposed going for sushi on friday".

You would open the app, and you will have a pile with plans (or just a few if you are not very popular xD) and you can swipe right or left to say, "i am in or I pass". Kind of Tinder but for plans that your friends propose, if you swipe yes and there is enough people automatically the plan is confirmed and sync with your calendar.

This way i want to make plans reality. Not just words. Not sure if this will add value to the market, there are some apps like partiful or howbout doing this but I feel like they are aim for big events or serious organization. My idea would be more like fast planning with close friends. I vibe coded a landing page where everything is explained perfectly, it is just a dummy page, the app doesnt exists yet.

Do you actually find group chat planning annoying, or have you just accepted it as part of life?

What’s the biggest plan killer for your friend groups? (Scheduling, choosing a place, or just lack of follow-through?)

If someone sent you a link to vote on a dinner, would you use it or find it too sketchy?

I’ve noticed something while building products in India.

Getting serious, structured feedback is really hard.

Most of us rely on:

• Reddit threads (mixed quality)
• friends unstructured comments
• random internet comments
• or just launching blindly

So I’m testing a small structured validation experiment.

Not selling anything, not a startup.

Just this:

• 10 selected builders
• minimum 5 written feedback reviews per project
• 1–3 experienced dev/mentor perspectives
• fully asynchronous (no calls, no pitching)
• completely free

I just want to see if this format actually helps builders before scaling it further.

If you're working on something and want honest evaluation:

ProBharat Genesis – Structured Validation Cohort 01

Happy to answer questions here too.

India is the second-largest market for Claude.ai, home to a developer community doing some of the most technically intense Al work we see anywhere.

Nearly half of Claude usage in India comprises computer and mathematical tasks: building applications, modernizing systems & shipping production software.

Today, as we officially open our Bengaluru office, we're announcing partnerships across enterprise, education and agriculture that deepen our commitment to India across a range of sectors.

I found Skly (https://skly.ai), which is an app store for AI skills. You can upload structured workflows as .md files, set your price, and keep 70% of the revenue.

This isn't just another prompt marketplace. These are complete workflow files, such as Cursor rules, Claude skills, and automation templates. They actually work across different AI tools.

Each submission is manually reviewed before it goes live. This helps keep out low-quality content.

If you have already created AI workflows for your projects, it might be worth sharing them to see if anyone is interested.

I was paying $40/mo to monitor Reddit mentions and still babysitting a Slack dashboard.

So I built Listnr — Reddit mentions → text messages.

In January, $40 got me ~40 alerts.

With this setup, that same volume would’ve cost ~$1.20.

Recently added a Pitchfork feature to the app — you can invite friends into your mention groups to share a live support feed and jump on alerts together.

Is that feature a good or bad idea?

Should be able to join a test group and see a feed from here: https://listnrapp.com/join/y1mau7zt

https://listnrapp.com

I'll be real. My life would be so different if I had this technology 20 years ago. So so very different and so much better. I am very glad that Claude Opus 4.6 is here. It is something I have needed for so long.

Hopefully this post doesn't count as self-promotion. I made sure to use the friend link version so it totally bypasses any paywall (and thus I get nothing for it anyway).

I do wonder if anyone else has had similar experiences. I've had similar for many iterations of generative AI. Still.. this one is probably the most profound by far. The ONLY thing that would overshadow this current epoch is robotics to the point where I don't have to worry about physical limitations anymore either.

Working on a side project that makes time visual instead of abstract.

Still learning what feels encouraging vs stressful.

please need your help & feedbacks.

Hey r/LocalLLaMA,

Anyone else tired of watching their Claude or Codex quota disappear because every prompt goes to the cloud, even the ones your local Ollama handles fine?

I built NadirClaw to fix this. It's an open-source LLM router that classifies prompts in about 10ms and routes them automatically. Simple stuff stays on your local models or Gemini Flash (free). Complex, agentic, and reasoning tasks go to cloud APIs. Your local setup handles the bulk of the work and your premium quota actually lasts.

It's an OpenAI-compatible proxy. I built it for OpenClaw, but it works with Codex, Cursor, Claude Code, or anything that speaks the OpenAI API. No code changes needed.

It also detects agentic patterns (tool use, multi-step loops), does session pinning so conversations don't bounce between models, falls back to another provider on rate limits, and has routing profiles (eco, premium, free, auto) so you can control the tradeoff per request.

pip install nadirclaw

https://github.com/doramirdor/NadirClaw

Curious what local models people here are running for simple tasks. I default to Gemini Flash but would love recommendations for small, fast local models that handle the basics well.

I want an actually uncensored AI i can run locally using my new rtx 5060 8gb card, i do not care as much for the quality of it as much as i do it not having guardrails. i do not need it for storywriting and i do not want to have sex with the AI i just want to mess around with it

Midjourney will be live at 12:00 CST (18:00 UTC) to help you with your Midjourney projects and answer your image & video prompt questions!

Watching later? You can still ask questions in the comments. We'll help!

All the ways to watch:

- 🟥 YouTube: https://www.youtube.com/@ClarinetMidjourney

- 🟨 Twitch: https://www.twitch.tv/promptcraft

- 🟦 Facebook: https://www.facebook.com/clarinetcraft/live_videos/

- 🟪 Discord: https://discord.com/channels/662267976984297473/1067951449176297483

Resources

🦉 For deep-dive tutorials about Midjourney V7 Image & Video Prompting: https://prompt-faqs.notion.site/

🦉 Midjourney general documentation: https://docs.midjourney.com/

🦉 For a Midjourney-trained GPT to help with prompting:

https://chatgpt.com/g/g-6781443b83888191813fbc93afa4dc64-clarinet-s-prompt-helper

⭐ You receive 24/7 community support for prompting with your Midjourney membership, both via the web and via Discord:

🟦 Web: https://www.midjourney.com/rooms/44a30f92-a8c1-470b-a553-86f49add2a7a

🟪 Discord: https://discord.com/channels/662267976984297473/992207085146222713

📅 All Midjourney Events: https://lu.ma/midjourney

⛵ Midjourney's event feedback form:

https://tally.so/r/m6rRWo

Midjourney subscription, account, and billing questions: billing @ midjourney com

If you're hiring use this thread.

Include:

1. Company Name
2. Role Name
3. Full Time/Part Time/Contract
4. Role Description
5. Salary Range

Last year I got laid off and decided to go all-in on a few projects instead of immediately jumping back into the job hunt.

The idea came from a real annoyance: every time I traveled internationally, I'd google "do I need an adapter for Japan" and get 15 SEO-optimized articles that take 2000 words to say "yes, bring a Type A adapter."

So I built PlugHopper. It covers 190+ countries with plug types, voltage specs, and what adapter you actually need. There are also 120+ country-to-country travel route pages (like "USA to Japan" or "UK to Thailand") that tell you exactly what to pack.

Recently added an AI packing assistant called Pluggy, you tell it where you're going, how long, what you're doing, and it generates a personalized packing list.

Stack: Next.js, React, Tailwind, deployed on Vercel.

It's been a grind getting Google to index everything (360 pages, only ~90 indexed so far), but slowly getting there.

Would love feedback on the site or any suggestions. And if you've ever fried a hair dryer in a 220V country, you're my target audience.

Send ideas, I’ve vibe coded every idea I had in the past few months.

https://preview.redd.it/c71pwamjzvjg1.png?width=1920&format=png&auto=webp&s=82db0844c94a461b8dd424d11ca2a4739c8b8693

Hey folks, I’m stuck and short on time, so I’d really appreciate some expert guidance.

Goal (very specific):
I’m using n8n to pull CRM log notes (Odoo) and I want to:

• Create one ChatGPT conversation per company
• Feed all that company’s CRM log notes as context
• Run a prompt (analysis / summary / instructions)
• End up with company-wise threads, similar to chatgpt

This weekend I added basic MCP support for my game art gen app. The goal was simple: see if I could feed Codex 5.3 a game idea in plain text and have it spit out a completely playable prototype, having it generate assets as it sees fit.

I used VS Code with the Codex plugin in this example, with the following prompt:

"Build a retro space shooter using Three.js. Ship flying and shooting asteroids. Black background with same stars. Power ups, enemies attacking you. Cool vfx. Scoring.

Generate any assets you need, at least 10."

It actually worked way better than I expected on the first real attempt. The agent figured out the structure (scene setup, controls, collision detection, spawning logic), called SpriteCook via MCP to generate assets (ship, asteroids, etc.) as it saw fit.

Of course it's not perfect, a lot can be fixed with some further back and forth. But for rapid prototyping or game jam ideation it's pretty decent.

The prompt I used is just an example and isn't the only way to do it. You can just ask the AI for specific assets and give it whatever details you want.

Quick lessons from the process:

• I used MCP in this case, because it's a common protocol used by the majority of AI agents. But the same could be accomplished by doing direct API calls, and given the AI instructions on how to call the API.
• Added a custom agent skill, that teaches the AI the best way to use the tool. This is optional but improved output quality.
• Reference images + style params. The AI knows that it can use previous generations as reference for further assets. Also it can use consistent Theme & Style prompts, improving asset style consistency.

Future improvements:

• AI image gen still isn't flawless, so I want the agent to auto-review its own outputs and regenerate if it messed up.
• It can do detailed and pixel art, and I've been experimenting with UI elements generation, if I can get that to work reliably it can also create HUD, menu, splash art, logos, etc.
• Animations. This is the big one for any sprite generation platform. Currently animation are technically possible, by linking frame by frame generation, but it's not build in as of now.

About my app (SpriteCook):
It's basically an easier way of calling the nano banana api, tuned specifically for game assets.

It's a commercial product I'm working on. Free to try in your IDE with some credits, but yeah the free tier has limits.

Would love to hear anyone's thoughts on this:

• Has anyone else had success with a similar method?
• Would you use something like this?
• Any other ideas I should try next?

TLDR: Implemented a proof of concept for agentic game asset generation. Added MCP support to my asset generator and had Codex 5.3 build a game from scratch, generating it's own assets.

Hey everyone,

English is not my native language (Dutch) and I write this post without using LLMs, I apologize for any mistakes or confusion. Please correct me if I make obvious mistakes, it helps!

I'm currently doing a test run of DeepSeek V3.2 TQ1_0 on my hardware.

My launch params for llama.cpp:

.\bin\llama-b7976-bin-win-cuda-13.1-x64\llama-server ^
--verbose ^
--host 127.0.0.1 ^
--port 5001 ^
--offline ^
--jinja ^
--no-direct-io ^
--model ./models/deepseek-v3.2/DeepSeek-V3.2-UD-TQ1_0.gguf ^
--parallel 1 ^
--prio 2 ^
--flash-attn on ^
--threads 6 ^
--override-tensor ".ffn_(gate|up|down)_exps.=CPU" ^
--tensor-split 16,16 ^
--gpu-layers 999 ^
--cache-type-k bf16 ^
--cache-type-v bf16 ^
--ctx-size 131072 ^
--predict 61440 ^
--reasoning-format deepseek ^
--temp 1.0 ^
--top-p 0.95 ^
--min-p 0.05
pause

Relevant output of llama.cpp for layer offloading:

llama_context: constructing llama_context
llama_context: setting new yarn_attn_factor = 1.0000 (mscale == 1.0, mscale_all_dim = 1.0)
←[0mllama_context: n_seq_max     = 1
llama_context: n_ctx         = 131072
llama_context: n_ctx_seq     = 131072
llama_context: n_batch       = 2048
llama_context: n_ubatch      = 512
llama_context: causal_attn   = 1
llama_context: flash_attn    = enabled
llama_context: kv_unified    = false
llama_context: freq_base     = 10000.0
llama_context: freq_scale    = 0.025
llama_context: n_ctx_seq (131072) < n_ctx_train (163840) -- the full capacity of the model will not be utilized
←[0mset_abort_callback: call
←[0mllama_context:  CUDA_Host  output buffer size =     0.49 MiB
llama_kv_cache: layer   0: dev = CUDA0
←[0mllama_kv_cache: layer   1: dev = CUDA0
←[0mllama_kv_cache: layer   2: dev = CUDA0
←[0mllama_kv_cache: layer   3: dev = CUDA0
←[0mllama_kv_cache: layer   4: dev = CUDA0
←[0mllama_kv_cache: layer   5: dev = CUDA0
←[0mllama_kv_cache: layer   6: dev = CUDA0
←[0mllama_kv_cache: layer   7: dev = CUDA0
←[0mllama_kv_cache: layer   8: dev = CUDA0
←[0mllama_kv_cache: layer   9: dev = CUDA0
←[0mllama_kv_cache: layer  10: dev = CUDA0
←[0mllama_kv_cache: layer  11: dev = CUDA0
←[0mllama_kv_cache: layer  12: dev = CUDA0
←[0mllama_kv_cache: layer  13: dev = CUDA0
←[0mllama_kv_cache: layer  14: dev = CUDA0
←[0mllama_kv_cache: layer  15: dev = CUDA0
←[0mllama_kv_cache: layer  16: dev = CUDA0
←[0mllama_kv_cache: layer  17: dev = CUDA0
←[0mllama_kv_cache: layer  18: dev = CUDA0
←[0mllama_kv_cache: layer  19: dev = CUDA0
←[0mllama_kv_cache: layer  20: dev = CUDA0
←[0mllama_kv_cache: layer  21: dev = CUDA0
←[0mllama_kv_cache: layer  22: dev = CUDA0
←[0mllama_kv_cache: layer  23: dev = CUDA0
←[0mllama_kv_cache: layer  24: dev = CUDA0
←[0mllama_kv_cache: layer  25: dev = CUDA0
←[0mllama_kv_cache: layer  26: dev = CUDA0
←[0mllama_kv_cache: layer  27: dev = CUDA0
←[0mllama_kv_cache: layer  28: dev = CUDA0
←[0mllama_kv_cache: layer  29: dev = CUDA0
←[0mllama_kv_cache: layer  30: dev = CUDA0
←[0mllama_kv_cache: layer  31: dev = CUDA1
←[0mllama_kv_cache: layer  32: dev = CUDA1
←[0mllama_kv_cache: layer  33: dev = CUDA1
←[0mllama_kv_cache: layer  34: dev = CUDA1
←[0mllama_kv_cache: layer  35: dev = CUDA1
←[0mllama_kv_cache: layer  36: dev = CUDA1
←[0mllama_kv_cache: layer  37: dev = CUDA1
←[0mllama_kv_cache: layer  38: dev = CUDA1
←[0mllama_kv_cache: layer  39: dev = CUDA1
←[0mllama_kv_cache: layer  40: dev = CUDA1
←[0mllama_kv_cache: layer  41: dev = CUDA1
←[0mllama_kv_cache: layer  42: dev = CUDA1
←[0mllama_kv_cache: layer  43: dev = CUDA1
←[0mllama_kv_cache: layer  44: dev = CUDA1
←[0mllama_kv_cache: layer  45: dev = CUDA1
←[0mllama_kv_cache: layer  46: dev = CUDA1
←[0mllama_kv_cache: layer  47: dev = CUDA1
←[0mllama_kv_cache: layer  48: dev = CUDA1
←[0mllama_kv_cache: layer  49: dev = CUDA1
←[0mllama_kv_cache: layer  50: dev = CUDA1
←[0mllama_kv_cache: layer  51: dev = CUDA1
←[0mllama_kv_cache: layer  52: dev = CUDA1
←[0mllama_kv_cache: layer  53: dev = CUDA1
←[0mllama_kv_cache: layer  54: dev = CUDA1
←[0mllama_kv_cache: layer  55: dev = CUDA1
←[0mllama_kv_cache: layer  56: dev = CUDA1
←[0mllama_kv_cache: layer  57: dev = CUDA1
←[0mllama_kv_cache: layer  58: dev = CUDA1
←[0mllama_kv_cache: layer  59: dev = CUDA1
←[0mllama_kv_cache: layer  60: dev = CUDA1
←[0mllama_kv_cache:      CUDA0 KV buffer size =  4464.00 MiB
llama_kv_cache:      CUDA1 KV buffer size =  4320.00 MiB
llama_kv_cache: size = 8784.00 MiB (131072 cells,  61 layers,  1/1 seqs), K (bf16): 8784.00 MiB, V (bf16):    0.00 MiB

The hardware it's running on:

• CPU: AMD Ryzen 5 9600X
• RAM: 2x 48GB DDR5-6000 CL30
• GPU: 2x ASUS PRIME RTX 5060 Ti 16GB (CUDA0: x8 / CUDA1: x8 PCIE lanes)
• MB: ASUS ProArt X870E-Creator WiFi
• SSD: Kingston FURY Renegade 1TB NVME 4.0 (1GB DDR4-2666 CL19 DRAM cache)
• OS: Windows 11 LTSC Enterprise 24H2
• DRV: Nvidia Studio Driver 591.44

The performance is... less (0.5 T/S processing, 0.9 T/S generating) than I had hoped (~1 T/S processing, 2 T/S generating). I'm very sure I'm being NVME-bound here, and by Windows too.

I have a couple of options available to me:

• Save up long for another 2x 48GB DDR5-6000 CL30 kit and run 2x2 channel
• Buy a PCIE 5.0 NVME drive that only hosts the model
• Buy two PCIE 5.0 NVME drives, run in RAID-0, and have CUDA0: x8 / CUDA1: x4 PCIE lanes
• Buy two PCIE 4.0 NVME drives, run in RAID-0, and have CUDA0: x8 / CUDA1: x8 PCIE lanes

My questions are:

• What can I change in my launch parameters to make inference slightly faster?
• Which NVMEs would you recommend for inference? (would the Samsung 9100 Pro 1TB be good enough?)
• Does RAID-0 actually deliver enough performance to make the tradeoff of running CUDA1 on x4 PCIE worth it?
• When switching over to Ubuntu 25.10, is there anything I should take into account or be aware of for running Llama.cpp with blackwell?

Hello,

I am a fashion designer developping Kanten.

I use Comfy to modify the faces of the models in my photoshoots (well, myself most of the time ^^)and developped a workflow with flux (a modified version called fluxmania) and a crop and stich node to keep the maximum resolution of the input image.

To maintain consistency, I chained 2 controlnet with inpaint and pose.

But when trying to reproduce it with Zit, or Flux 2 klein, I did not find the controlnet models that could work here.

Do you have an other solution so I can try these ?

I already tried with Lanpaint but it is so slow that I cannot use it for production.

Here is the workflow :

https://pixeldrain.com/u/NWacBsin

Thanks

Pre-AI coding, we had applications for performing tasks: admin, finance, HR, coding, etc.. These had to have some flexibility so they could be a sort of toolkit for particular subtasks or projects. Extending the applications was difficult, think VBA macros, and the barrier to entry was high.

With AI coding, all of this will change in the next 10 years. I imagine getting bespoke functionality for anything I need to do. And there will be transparent automation. Which tasks will still exist or new tasks be invented? How will those tasks be supported? Will we retain visibility into how tasks are done?

What does the future hold for us?

I moved in my new House and was searching for a doorbell. At first I was about to order a ring but the Super Bowl commercial let me reassess my decision.. I was searching for a doorbell which I could integrate in HA and found Doorline Snap from a German manufacturer not far from where I live. It says it’s easy to integrate in HA and is compatible with Shellys etc and everything without cloud compulsions. Anyone has experience with this or another product of the manufacturer?

Or do you know any other doorbells aside from Doorline series that I can integrate into my HA without Cloud compulsions?

ok so this might be obvious to some of you but it just clicked for me

claude code is horizontal right? like its general purpose, can do anything. but the real value is skills. and when you start making skills... you're literally building what these YC ai startups are charging $20/month for

like I needed a latex system. handwritten math, images, graphs, tables - convert to latex then pdf. the "startup" version of this is Mathpix - they charge like $5-10/month for exactly this. or theres a bunch of other OCR-to-latex tools popping up on product hunt every week

instead I just asked claude code to download a latex compiler, hook it up with deepseek OCR, build the whole pipeline. took maybe 20 minutes of back and forth. now I have a skill that does exactly what I need and its mine forever

https://github.com/ndpvt-web/latex-document-skill if anyone wants it

idk maybe I'm late to this realization but it feels like we're all sitting on this horizontal tool and not realizing we can just... make the vertical products ourselves? every "ai wrapper" startup is basically a claude code skill with a payment form attached

anyone else doing this? building skills that replace stuff you'd normally pay for?

So I always get a little nervous trying those latest and greatest models and providers (open source, Chinese or US or wherever, free and non-free) and wanted to know what exactly was going on. I already built Context Lens but I wanted something more "continuous".

Check it out: https://github.com/larsderidder/contextio

The coolest feature is probably the redacting, where it pulls out sensitive data before it goes away from your PC. I've worked in this space for a while and believe it's relatively easy to catch out a lot of things. However the coolest is the (experimental, default off) rehydration; it finds the placeholders in the responses and inserts the data back, so it's completely transparent to you. How cool is that?

I haven't tested it exhaustively yet so possibly the LLM can say some weird things about the placeholders, but I haven't seen any issues yet.

Anyway there's also a monitoring command, and i was working on export and replay but didn't test much yet.

Let me know if there's anything missing you'd like to see!

Hey everyone! 👋

Six years ago, I built and launched MySigMail Card as a commercial product. To be honest, it didn't take off. I struggled with marketing, lost motivation, and eventually abandoned it to focus on my other open-source project, massCode.

For years, the project gathered dust. But besides the business failure, it had a "ticking time bomb" in its core architecture that always bothered me: it was built entirely on Presets.

The "Preset Trap"

In the old version, if a user wanted a header, they dragged a "Header Block". While you could tweak colors and text, the structure was essentially locked.

• Want to move an image? You can't.
• Want 3 columns instead of 2? I had to code a new block.

I became the bottleneck. The flexibility of the editor was strictly limited by how many variations I could manually code.

The Rewrite: From Blocks to Atoms

I decided to stop patching the old code and rewrite the core engine from scratch. The goal was to decouple Layout from Content.

I built a new recursive system:

1. Block (The Root): The top-level wrapper.
2. Row (The Grid): Defines horizontal structure (columns, gaps).
3. Cell (The Flexible Container): The key part. It can hold content OR nested Rows. This allows for complex, multi-level grid structures.
4. Atom (The Content): The actual elements: Text, Image, Button, Menu, Divider.

Now, users can build any layout structure they want without writing code. No more 50 variations of a "Hero Section".

The Tech Stack

I wanted this version to be modern and fast, so I went with:

• Vue 3 (Composition API): For a reactive and modular UI.
• TypeScript: Essential for the complex recursive schema.
• Tailwind CSS v4: Usingfor blazing fast styling.
• Shadcn Vue: Beautifully UI components for accessibility.
• Clean Email Output: Generates table-based HTML compatible with email clients.

Why Open Source?

I’m building this in public under the AGPL-3.0 license.

Why? First, because the world needs a powerful, embeddable email builder that isn't locked behind a $500/month SaaS subscription.

But deeper than that, I believe software should be independent. We see too much vendor lock-in and centralized control these days. I want to provide a tool that anyone can self-host and rely on, regardless of borders or corporate policies.

If you’re interested in Vue 3 architecture, complex drag-and-drop, or just need a good email builder, check out the repo:

👉 GitHub Repository

I’d love to hear your feedback on the architecture!

I've been looking around for something to put in my Ubuntu Server to run an Ai locally, I originally was looking at the arc b50 (170 TOPs and encode/decode for my media server), but then I stumbled upon the Metis, it supposably has 214 TOPs at 3.5-9w in a m.2 form factor. I was just wondering if anyone's played around with it as it seems hard to believe, especially with every other m.2 card being more around 40 TOPs.

Hi everyone 👋

I’m a Full-Stack Developer specializing in building simple, effective web solutions for small businesses and startups.

What I can help with:

• Landing pages that generate leads

• Admin dashboards & internal tools

• Booking / appointment systems

• CRUD apps (React / Node / SQL)

• Fixing bugs & improving existing websites

Why work with me:

I focus on quick delivery and clear communication. You don’t need a huge app — you need something that works and brings results.

⏱ Delivery: 7–10 days

💰 Starting at $100 (depends on features)

If you have a project in mind, feel free to DM me or comment below.

I’m happy to discuss ideas or suggest improvements.

Thanks!

First of all: E-Ink displays are great. Except they aren't. But hear me out...

They are are great because:

• Low power consumption
• Readability in comparison to other display types
• Doesn't look like a traditional display

They aren't great because

• Not necessary plug and play
• Your ordinary mechanics of displaying content on them mostly don't apply
• The rabbit hole goes deep if you really want to make use of their lower power consumption

As most of us, I started to think about how an e-ink display would make my life easier and could be a helpful addition. First thing that came into my mind: a digital family calendar. Couldn't be that hard, could it? So I bought a Seeed Studio reTerminal E1001.

From there on I had different choices of how to put content on it: Seed Studio's own SenseCraft HMI (cloud-based), Home Assistant (fully local via ESPHome), Arduino (local as well) and TRMNL (cloud-based).

I decided to go the ESPHome route, because I already run several ESPHome devices and like the idea to have it all local and without any cloud-services at all. I also did a quick look at SenseCraft HMI, but ditched it because of cloud. TRML seemed to be good, but cloud as well, so I didn't gave it a chance. More on this later.

The first steps with ESPHome went fine, also tried playing around with ESPHomeDesigner, a nifty WYSIWYG (what you see is what you get) editor that writes code for you user interface. Simply copy'n paste the code into your ESPHome and you're good to go.

I then tried to get calendar data from Home Assistant onto the e-ink display and that's where my multiple day long coding journey had begun. I tried python scripts, tried to create template sensor entities, wrote custom ESPHome code, etc.

To my suprise: Home Assistant isn't very good in handling calendar data at all. Sure, it can read ICS files, it can display them, but it's not very good in re-formatting this data for e-ink displays or ESPHome in general. It's hacky and it really doesn't work that good.

But because I have my garbage collection calendar in Home Assistant, I wanted to make Home Assistant my central place for all my calendar data. It then came into my mind, that I already have a local and proper working calendar solution: Nextcloud.

A few days later, for somewhat reason, I gave TRMNL a chance: They have a BYOD (bring your own device) policy which enables you to use their (opensource) firmware in combination with their cloud-services. For a one-time payment. No subscription needed. I tried it out immediatly and it really worked like charm. A big plus: They have dozens if not hundrets of apps/plugins for all different kinds of contents to display on your e-ink: From calendars, to quotes, from images to news. Really neat.

But what then came, blew my mind: They also have a BYOS (bring your own server) policy, so with the one-time payment, you cannot only use their cloud-services, but also their opensource server software fully locally. And there are several implementations of their API by other people as well. No more fiddling with ESPHome, no more coding, just an e-ink display that works out of the box.

What's my point, you now may ask? Glad you asked: If you struggle on how to bring the right content to your e-ink display, like I did, give TRMNL a chance. If you want to go the local-only route: BYOD and BYOS is the way to go. If you're fine with a cloud-service: Use their cloud-service right away.

Regarding Home Assistant: Most of the time I really like Home Assistant. But calendar-wise it's really not the best option. I wish it were different. Maybe the calendar component will get some love in the future. But until then: Use a dedicated and better-suited software for it, like Nextcloud e.g.

Cheers!

Edit: Typos.

Hello all.

Can someone who's using the Fox ESS Cloud successfully please tell me how to do it?

I can't make it work because some sensors are just empty (for example, "Grid Consumption Power" gives me data, but "Grid Consumption" is zeroed).

The data appears OK on the FoxESS site.

I just updated the integration, but still nothing. I'm at a loss.

Inverter is a F5000-G2 and datalogger is W6.

Any help is welcome.

Thank you!

Is anyone using Flux.2 Klein on Forge Neo? What configuration works better for you? Samplers, steps, Schedulers, aspect ratio, etc?

I built devctx with Claude Code as my coding partner to solve a problem I kept running into: I work on multiple projects, get pulled away, and when I come back days later I've lost all the context. What was I working on? What branches are active? What's left to do?

devctx is a free to use Model Context Protocol server designed specifically for Claude. When you initialise it on a project, it scans your git history, picks up TODOs scattered through your code, reads your project markdown files, and assembles a summary of where things stand. From that point on it logs your actions and commits so the context stays current between sessions.

The real benefit is efficiency. Instead of burning tokens re-explaining your project to Claude every time you sit down, devctx feeds that context in automatically. You pick up where you left off.

Claude Code was instrumental in building this. I used it throughout development for architecture decisions, writing the core logic, and debugging the MCP integration. It was a proper back-and-forth collaboration rather than just code generation.

The project is completely free and open source. You can install it now from the repo:

https://github.com/tmattoneill/devctx

I'd genuinely welcome feedback — issues, PRs, or just thoughts on how it could be more useful.

Hey, I updated my GitHub guide for Claude Code today.

Main changes:

• Added a new Skills section with a practical step-by-step explanation
• Updated pricing details
• Documented new commands: /fast, /auth, /debug, /teleport, /rename, /hooks

Repo here:
https://github.com/wesammustafa/Claude-Code-Everything-You-Need-to-Know

Would love feedback: what’s missing or unclear for someone learning Claude Co

https://preview.redd.it/tea3h220lvjg1.png?width=1434&format=png&auto=webp&s=f4da024376e5c34f2c5d974e4377e468b1405234

https://preview.redd.it/3gpu0ir0lvjg1.png?width=1788&format=png&auto=webp&s=e4b3407d89fc23aa325336ae556b60cea217da38

India is the second-largest market for Claude.ai, home to a developer community doing some of the most technically intense Al work we see anywhere.

Nearly half of Claude usage in India comprises computer and mathematical tasks: building applications, modernizing systems and shipping production software.

https://www.instagram.com/reel/DUvpo5VDFjo/?igsh=MWI0aGhpejQwNnJ4eA==

I've been building YOPJ (Your Own Personal Jean-Luc) — a portable, local AI coding agent that runs on Codestral 22B (Q4_K_M) via llama-server. No cloud, no telemetry, no API keys. Compiled to a single exe with PyInstaller — drop it on a thumb drive and go.

What it does: 12 built-in tools (file read/write/edit, glob, grep, bash, git ops, web fetch), persistent memory across sessions (MEMORY.md loaded into system prompt), and SEAL (Self Evolving Adaptive Learning) — a structured lesson system so the agent gets better over time without retraining.

The security problem nobody's talking about: Local coding agents hand an LLM a shell and file access. Most have zero security between the model and your filesystem. Built it because OpenClaw has 180K+ stars, active RCE vulnerabilities, a Docker escape CVE (CVSS 8.8), and Cisco found their #1 community skill contained malware. Its founder just joined OpenAI yesterday.

What I did about it:

• Command allowlist + blocklist (not just blocklist

• commands must match an approved prefix AND not match any dangerous pattern)

• Strict path confinement with symlink resolution

• Prompt injection sanitization on all tool results before they re-enter context

• Network egress blocking (curl, wget, python requests/urllib/socket, node http/net, powershell web requests — all blocked)

• Protected paths (agent can't modify its own security layer, memory, or knowledge base)

• Output size limits to prevent memory exhaustion

• Audit logging on all blocked operations

Then I gave the full source code to ChatGPT and said "break it." 240+ adversarial tests across 9 attack vectors — prompt injection, sandbox escape, command injection, social engineering, regex bypass, memory poisoning, context window attacks. Results are published in the repo as a knowledge library that the agent itself can reference to recognize attack patterns.

The stack: Python, llama.cpp, Codestral 22B, PyInstaller. 197 unit tests. ~9,600 lines across 42 files. Runs on any Windows machine with a GPU.

https://github.com/WayneCider/YourOwnPersonalJean-Luc

Happy to answer questions about the security model, the SEAL learning system, or why I think every local agent needs a sandbox before it needs features.

Alguien me ayuda a instalar stable difusión y me enseña a usarlo

Saw a thread on X last week where someone laid out their vision of a fully autonomous company. Zero employees, just AI agents running everything. What a completely dim view of humanity.

I keep seeing this crowd grow and they all have the same beat: fully autonomous companies, humans out of the loop entirely. And every time i see posts from these people, I come back to the same thought: this feels like it's coming from someone who fundamentally lacks an understanding of people and really the world.

Because here's what gets me. Humans built this. It's the training data of actual human beings that made these models work. Every piece of writing, every image, every conversation, scraped and synthesized. AI is a mirror of us. So when someone tells me AI will replace all of us, I want to ask, replace us with what? A reflection of ourselves minus the actual selves?

And I know the argument. The models will get better. The mistakes will decrease. Sure, maybe. But we're talking about a species that still largely doesn't know how to use email properly. Decades in. I'm not being glib. The gap between what technology can theoretically do and what humans actually adopt and manage in practice is massive. We skip over that every single time.

The people pushing hardest for full replacement aren't making a rational argument about efficiency. They're telling on themselves. They're telling you they see people as mere clicks.

I think the real promise of AI is amplification, not replacement. Humans stay in the driver's seat because AI is just an accumulation of everything humanity has produced up to this point. Not a separate intelligence that outgrew us.

It came from us. It still needs us. That part keeps getting conveniently left out.

Hey r/sideproject,

I built timeturnip.com, an app to track time and create invoices for solo freelancers.

A lot of the options on the market are made for agencies with teams and payroll to manage. I built my app to be simpler and more streamlined for a solo freelancers just trying to accurately track their time and quickly create professional invoices.

I started this project 8 years ago to track my own time to make sure I wasn't playing too many video games. Over the years I've added to it and last year decided to pivot it to an app for freelancers as I thought that was a more lucrative market.

Just released the current invoicing features and am now focusing on marketing and distribution..a big pivot for my developer brain, but I'm working through it!

Would love any feedback and first impressions from y'all.

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

Incident: Elevated errors on Opus 4.6

Check on progress and whether or not the incident has been resolved yet here :

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/wiki/performancemegathread/

For the past year, I've been successfully using a Sonoff button device (SNZB-01P) in an automation - simply press the button turn on lamp. At the time of setting up the automation, it used the device id. However, I am trying to wean off using device ids and go with entities - in case I need to swap out the button later on.

However, I noticed (and recently discovered via Googling) that there seems to be no entity for the device itself. I've attached below the yaml of the automation as it currently stands. There is an entity_id but it's in cryptic form - not a human friendly name.

As I'm still pretty green at HA, I'm wondering if anyone else has a way to avoid using device ids in an automation for this circumstance. I hope this makes sense. Appreciate any tips and/or suggestions. TIA

alias: Lamp On
description: press button will turn on lamp
mode: single
triggers:
  - device_id: e39xxxxxxxxxxxxxxxxxxxxxxxxxxa94
    domain: zha
    type: remote_button_short_press
    subtype: button
    trigger: device
conditions:
  - condition: device
    type: is_off
    device_id: 60axxxxxxxxxxxxxxxxxxxxxxxxxxfe4
    entity_id: e06xxxxxxxxxxxxxxxxxxxxxxxxxxab9
    domain: switch
actions:
  - type: turn_on
    device_id: 60axxxxxxxxxxxxxxxxxxxxxxxxxxfe4
    entity_id: e06xxxxxxxxxxxxxxxxxxxxxxxxxxab9
    domain: switch

Hey r/LocalLLaMA,

Anyone else tired of watching their Claude or Codex quota disappear because every prompt goes to the cloud, even the ones your local Ollama handles fine?

I built NadirClaw to fix this. It's an open-source LLM router that classifies prompts in about 10ms and routes them automatically. Simple stuff stays on your local models or Gemini Flash (free). Complex, agentic, and reasoning tasks go to cloud APIs. Your local setup handles the bulk of the work and your premium quota actually lasts.

It's an OpenAI-compatible proxy. I built it for OpenClaw, but it works with Codex, Cursor, Claude Code, or anything that speaks the OpenAI API. No code changes needed.

It also detects agentic patterns (tool use, multi-step loops), does session pinning so conversations don't bounce between models, falls back to another provider on rate limits, and has routing profiles (eco, premium, free, auto) so you can control the tradeoff per request.

pip install nadirclaw

https://github.com/doramirdor/NadirClaw

Curious what local models people here are running for simple tasks. I default to Gemini Flash but would love recommendations for small, fast local models that handle the basics well.

Conversation with Qwen3:14b over Opencode in which it runs a command and correctly diagnoses network problem.

One of the first things I did after recently installation Arch Linux on my PC was set up Opencode with Ollama just in case my internet went out and I couldn't figure out what commands to run to fix it. I installed the 14B parameter version because I figured it was the best model I could fit in my 16 GB of VRAM on my AMD Radeon RX 7800 XT and it's really fast. I am super grateful that I did this because my internet did get disconnected and luckily in this case it was just because I accidentally unplugged the Ethernet cable as it was laying across the middle of my room but it would've taken me so long to figure out what caused this had I not set this up. I would've had to either google it or ask an AI model running in the cloud from another device, neither of which would be possible had my internet truly been out and it not just being a problem with this device's Ethernet only.

Antes de empezar yo use Python 3.10.6 porque es el mas compatible con todo.

primero: https://github.com/LykosAI/StabilityMatrix
te dejo estos datos para que leas
Opción 1 – La más fácil y recomendada (usa Stability Matrix)Evitás todos estos dolores de cabeza. Es un launcher que parchea automáticamente estos problemas.

1. Descargá Stability Matrix desde su GitHub oficial: https://github.com/LykosAI/StabilityMatrix/releases (bajate la versión latest para Windows).
2. Instalalo (es portable, no requiere admin en la mayoría de casos).
3. Abrilo → "Install Package" → Buscá "Stable Diffusion WebUI" (A1111)
4. Elegí la versión dev o la más reciente (en dev ya usan un fork como https://github.com/w-e-w/stablediffusion.git para reemplazar el repo borrado). Si te pide usa la rama DEV, si te pide no instales Pacages todavia.
5. Instalá → Lanzá. Listo, maneja dependencias, Torch, xformers, etc. automáticamente.

Instalá la extensión Deforum:

• Abrí A1111 desde Stability Matrix.
• Andá a Extensions tab → Available → Buscá "Deforum" (o pegá directamente el URL: https://github.com/deforum/sd-webui-deforum.git).
• Instalá → "Apply and restart UI".
• Reiniciá → Aparece una nueva tab Deforum arriba.

Pasos para instalar ControlNet

1. Abrí el WebUI (si no está abierto):
   • En Stability Matrix → Seleccioná "Stable Diffusion WebUI" → Launch.
   • Esperá a que cargue completo, puede que quede en Reloading, cerrá el navegador, cerrá la consola CMD, y relaunch. No te preocupes
2. Instalá la extensión desde el UI (método recomendado y más fácil):
   • Andá a la tab Extensions (arriba).
   • Elegí sub-tab Install from URL.
   • En el campo "URL for extension’s git repository", pegá exactamente:https://github.com/Mikubill/sd-webui-controlnet.git
   • Hacé clic en Install.
   • Esperá a que termine (baja el repo, ~100-200 MB). Verás un mensaje como "Installed into ...extensions\sd-webui-controlnet".
   • NO hagas Apply and restart UI todavía (para evitar el loop de reloading). En cambio:
     • Cerrá la pestaña del navegador.
     • Cerrá la ventana CMD/terminal si está abierta.
     • En Stability Matrix: Relanzá el paquete con Launch de nuevo.
   • Descargá modelos de ControlNet (obligatorio, sin ellos no funciona):
   • De acá https://huggingface.co/lllyasviel/ControlNet-v1-1/tree/main vas a bajar control_v11f1p_sd15_depth.pth control_v11f1p_sd15_depth.yaml control_v11p_sd15_canny.pth control_v11p_sd15_canny.yaml control_v11p_sd15_openpose.pth control_v11p_sd15_openpose.yaml
   • Esos archivos tiene que estar en la siguiente carpeta, te dejo mi ejemplo abajo
   • D:\IA\Data\Packages\stable-diffusion-webui\extensions\sd-webui-controlnet\models
   • Verificá que instaló bien:
     • Al abrir de nuevo el WebUI: Buscá una sección nueva en txt2img e img2img (abajo, expandible) llamada ControlNet (o múltiples panels si tenés varios).
     • También aparece una tab ControlNet o sección en Settings para configuraciones extras.
     • Si no aparece o hay errores: Andá a Extensions > Installed → Buscá "sd-webui-controlnet" → Click Check for updates (si hay) → Luego Apply and restart UI (ahora debería ir bien si el primero fue limpio).

PD: Si tenes problemas con mediapipe tenes que hacer un downgrade a 0.1014 es muy facil

saludos desde Argentina!!

I've been a web dev for years and recently started working with a lot of vibe coders and AI builders. I noticed something scary: the code AI generates is great for shipping fast but terrible at security. Missing headers, exposed API keys, no CSP, cookies without Secure flag, hardcoded secrets... I've seen it all. AI tools just don't think about security the way they think about features.

So I built a tool. You paste your URL, hit scan, and in 30 seconds you get a full security report with a score out of 100.

Just drop "code" in the comments and I'll DM it to you.

Every creator knows this pain. You search "music for my travel vlog" and get 1,000+ results. You listen to 30 tracks and nothing fits. You finally pick one and spend the next week stressing about copyright claims.

What I Built

SoundMakr Instead of keyword searches, you have two options: describe what you're making or just upload your video.

"I need something for a travel montage through Japan. Nostalgic but uplifting, with some acoustic guitar."

Or drop in your rough cut and let the AI watch it. It analyzes pacing, transitions, energy shifts, even color tone, then matches tracks to your video's vibe structure.

Either way, you get 21 personalized matched tracks in about 30 seconds. Each one comes with an explanation of why it fits your project.

What Makes SoundMakr Different

Video intelligence that actually watches your footage and understands how it moves. Conversational UX that feels like talking to a creative partner, not fighting a search bar. 10,000+ original AI-generated tracks, all fingerprint-verified with a dispute support team if you ever need it. 30 seconds vs. hours of browsing. Commercial-ready licensing that covers edits, loops, and remixes.

Copyright Verification

Every track is scanned through ACRCloud against 100M+ songs. Not just fingerprint matching but melodic composition analysis and derivative work detection. Every download comes with a verification certificate.

Tech Stack

Frontend: Next.js 15, React 19, Tailwind, shadcn/ui, Framer Motion. Deployed on Cloudflare Pages.

Backend: Node.js + TypeScript on Google Cloud Run. Firebase for auth and Firestore. Express API endpoints. Stripe for payments.

One More Thing

Part of every subscription goes to Starkey Hearing Foundation. They've provided 1.5M+ hearing aids across 100+ countries. Felt right to bake social impact into the business model.

Would love feedback!

https://soundmakr.io

If you use Claude Code and have been hit by rate limits mid-session, this might help. cc-hdrm sits in your menu bar and shows your remaining headroom percentage with burn rate arrows so you can pace yourself.

What's new in v1.3:

- Extra usage tracking — see your prepaid balance draining in real time across the menu bar, popover, and analytics charts. Get alerts at 50/75/90% thresholds

- Subscription value breakdown — dollar breakdown of used vs. unused capacity, prorated from your monthly plan

- Pattern detection — tells you if you're overpaying, underpowered, or barely using your subscription

- Tier recommendations — suggests upgrade/downgrade based on actual usage data

- Self-benchmarking — compare your usage month-over-month

Zero tokens spent, zero config, reads your existing Claude Code credentials from Keychain. Written with Claude Code, for Cloud Code, free as beer (MIT licence).

brew install rajish/tap/cc-hdrm or grab the DMG from GitHub.

github.com/rajish/cc-hdrm

https://preview.redd.it/0a7tr5mkuvjg1.png?width=800&format=png&auto=webp&s=10f68c3429d4b50a4ddc4fbb8cac8a5b89070b43

2nd time trying to post this, first post was auto removed for some reason. Do these switches exist anywhere in zigbee? I found 2 companies making it in z wave, and one making it for their proprietary standard. I am doing all zigbee in my house and need to add some switches. Surely if these exist in z wave, they can exist in zigbee, right? Does anyone know where I can get one?

I see too many people struggling to analyze their product performance and customer behavior. Truth is, several foundations need ot be put in place to do this well in a short amount of time. The problem: too many people do not have the time to set them up and thus lose even more time trying to explain why a metric went up or down.

So I built a series of skills for Claude Code (and with Claude Code) that helps:
- Find your North Star Metric
- Build your KPI Tree
- Figure out what to analyze and how in order to understand "What is happening?!??"
- Challenge your results and build compelling insights
- Build executive summaries and presentations that convince your leadership
- Find the right segmentation of your customer base
- Understand cohort behaviors

All of them are here for free: https://github.com/florianbonnet14/ThePowerOfAnalytics_ClaudeSkills/tree/main

Looking forward to the community testing them and for feedback

Disclosure: I'm the creator and maintainer of this project. It's free, open source (MIT), runs locally, and I don't benefit financially.

I've been experimenting with different Claude Code workflows — agents, skills, plugins, delegation patterns — and realized I had no way to actually measure what's happening under the hood.

Are my agents actually being invoked? What tools are they calling? How much context does each session consume? What does a session actually cost me? And most importantly - which approach gives me the best results at the lowest cost?

All this data exists locally in ~/.claude, but there's no built-in way to analyze it. So I built Claude Session Dashboard.

It's a local, read-only web app that scans your ~/.claude directory and shows you:

• Session list — search, filter by status/project/model/date, sortable with pagination
• Session detail — context window breakdown, tool usage stats, agent dispatches with per-agent token counts
• Gantt-style timeline — see exactly when tools, agents, and skills fired during a session
• Cost estimation — per-session and per-agent costs based on Anthropic pricing breakdowns
• Stats page — GitHub-style contribution heatmap, daily activity, model usage distribution, token trends over time
• Per-project analytics — sessions, messages, tokens, and duration aggregated per project
• Data export — CSV/JSON export for session summaries, model usage, daily activity, and project analytics
• Privacy mode — anonymize paths and project names for safe screenshot sharing
• Live updates — active sessions auto-refresh with adaptive polling

Runs 100% on localhost. Never modifies your Claude data. Never sends anything externally. Built entirely with Claude Code.

Free and open source. Quick start:

npx claude-session-dashboard

- npm: https://www.npmjs.com/package/claude-session-dashboard

- GitHub: https://github.com/dlupiak/claude-session-dashboard

What metrics would help you the most when analyzing your Claude Code usage? I'd love to hear what you'd want to see next.

https://www.cbsnews.com/minnesota/news/minneapolis-mass-shooting-catholic-school-survivor-friend/

Last year I posted about memory_user_edits an undocumented Claude feature that ended up getting tens of thousands of views here on Reddit. A few people asked if there were more hidden tools.

Turns out there are at least 28.

I spent a week systematically reverse‑engineering every internal tool I could find in Claude. Not just listing names: full parameter schemas, behavioral testing, edge cases, and cross‑platform verification across browser, desktop app, and mobile app.

How I found them

Claude's mobile app has a meta‑tool called tool_search that lets you query an internal registry of tools. I ran keyword sweeps: user, create display generate, search fetch data memory, map place weather - each returning matching tools with parameter schemas for the deferred ones. For always‑loaded tools that don't show up in tool_search, I pulled schemas from system‑level definitions and then validated them with live calls.

The biggest surprise: Claude is not one product. It's three different tool sets.

• Browser (claude.ai): I counted 21 always‑loaded tools, no tool_search, no deferred loading. The 11 mobile‑only consumer tools simply don't exist here.
• Desktop app: Same base tools, plus tool_search that only discovers 32 MCP integration tools (Chrome + Filesystem).
• Mobile app: Same base tools, plus 11 consumer deferred tools (alarms, timers, calendar, charts, location, time) loaded on demand via tool_search.