AI-Ranked Reddit Feed

https://worldtimeobservatory.digital/

claude AI made this website in just 5 minutes I already had a domain so I launched it

I keep hearing the same question from small business owners and site owners: “Are tools like ChatGPT, Perplexity, Claude, Gemini, etc. sending visitors to my website?”

GA4 can already answer part of this, but the data is buried enough that most people never check it.

So I built a small free tool: isaisendingmetraffic.com

It connects to Google Analytics with read-only access and shows:

• total AI referral traffic
• traffic by source, like ChatGPT / Perplexity / Claude / Gemini
• pages getting visits from AI tools
• trend over time
• change vs. previous period

It only shows referral traffic GA4 can actually see. It does not measure AI mentions, AI impressions, or traffic that gets bucketed as direct.

I’ve been building a SaaS that reviews business documents with AI. This is the basic idea:

Small business owners, freelancers, consultants, and agencies upload a contract, invoice, NDA, vendor agreement, etc.

The tool gives them:

- a plain-English summary

- risky clauses / red flags

- negotiation points

- jurisdiction-aware review for the US and Spain

- a PDF report they can keep or send internally

The thing I’m struggling with is positioning. If I call it “AI contract review” or “legal AI”, it’s immediately clear what it does, but it also creates trust issues because people may think it’s trying to replace a lawyer.

If I call it “AI document review for small businesses”, it feels safer and broader, but maybe less sharp.

I’m trying to position it as:

“a first-pass document review before you sign, send, or pay”

Not legal advice. Not a lawyer replacement. More like a way to spot issues and know what to ask before escalating to a professional.

I have a few questions for other saas developers:

1. Would you lead with “AI contract review” or “AI document review”?
2. Does the “not a lawyer replacement” angle build trust or weaken the product?
3. Would you focus the landing page on contracts only, or contracts + invoices + business documents?
4. If you were targeting small businesses in the US, would you start with SEO, Reddit, partnerships, or paid ads (I already did something with the seo, but it still needs some work)?

I’m mainly looking for positioning/marketing feedback, not trying to pitch.

PD: English is not my first language, so I used AI to redact this text, sorry for that, all the answers are going to be 100% mine.

Within 24 hours, OpenAI, Google, and Anthropic all launched enterprise AI agent platforms.

This feels like a real inflection point.

I put together a deep comparison covering:

• Architecture (Codex vs A2A vs MCP)
• Multi-agent orchestration
• Memory systems
• Security & governance
• Pricing models

Main takeaway:
This is no longer about models—it’s about ecosystems and integration.

Curious what people here think:
Will enterprises standardize on one platform or go multi-agent/multi-vendor?

i dont rant to my friends because i'm afraid i will make them uncomfortable, and even if AI responses are "soulless" (since ai cant form opinions and needs an algorithim and stuff to make responses), it tells me what I expect it to say most of the time. i also fear that some of my friends will use my secrets/opinions against me when they stop being friends with me even though there's a really low chance that they will not be friends with me anymore. AI chat is usually anonymous and stuff, and it will forget what i say when i start a new chat, so that's why i vent/rant to AI. is it weird?

Saw a case recently where an AI coding agent ended up wiping a database in seconds.

Curious how people here are handling this in real setups.

If your agent has access to a DB, are you:

restricting it to read-only?

running everything in staging/sandbox?

relying on prompt-level safeguards?

or actually putting some kind of control layer in between?

Feels like this becomes a real issue as soon as agents move beyond read-only tasks.

I'd like to use the chatgpt image 1.5 model to create images in the chat. But it keeps creating images with the 2.0 model. Is there any way I can access the old model?

I bought a robot vacuum a couple of months ago expecting it to handle both hardwood and carpet, especially with a shedding dog. It does fine on hardwood, but on carpet it slows down, gets stuck near edges, and leaves a lot of hair behind even when the bin is full.

The roller brushes also get tangled constantly, so maintenance is way more than I expected. It feels like it’s only doing light cleaning instead of actually keeping things under control.

Does anyone else feel like robot vacuums almost live up to the hype but still need a lot of manual effort? Am I expecting too much, or is this normal for pets and mixed flooring?

A lot of teams think adding AI to outreach = they’ve “automated” their process.

In reality, most have just improved execution… not built an actual system.

AI tools like Claude are great for things like:

• Writing better outreach copy
• Personalizing messages faster
• Speeding up prospect research
• Generating campaign ideas

But that’s still just AI helping a human do the work.

Without real process/infrastructure behind it, AI outreach usually ends up being:

• Faster, but inconsistent
• Helpful, but scattered
• Hard to standardize
• Still dependent on someone manually overseeing everything

The real leverage comes when AI is plugged into an actual workflow/system.

That’s when it stops being “AI-assisted outreach” and starts becoming something scalable/repeatable.

How others here are using AI in automation: Are you mostly using it as a productivity layer, or have you actually built it into structured system?

After about three years of use, one of the smoke sensors in my home began producing false readings. I first tried cleaning the exterior by blowing air and using a vacuum, but that didn't solve the issue. After some careful tinkering, I managed to disassemble the unit and clean out the dust inside the sensor chamber. Once reassembled, the sensor worked normally again.

I'm sharing this experience in case others face a similar problem. Keep an eye on the smoke density reading: if it shows a value greater than 0 under normal conditions, that usually indicates dust buildup. This solution applies specifically to optical smoke sensors. Hopefully this will be helpful to everyone.

Hey everyone,

I've always been a massive tech nerd, but I never actually learned how to write code. Last week, I decided to try the Claude Code extension in VS Code just to see what the hype was about. I had literally never used an IDE or typed a Git command in my life.

Fast forward 7 days, I had to learn how version control and databases work on the fly, and I built three fully functional web apps. I'm still kind of in shock that they actually work.

Here is what we (me and the Al) built:

1. A Real-Time "Idea Box" for Events (Next.js 16, Supabase, Tailwind)

A live web app to collect anonymous ideas during events or hackathons.

• Features: Real-time counters for all participants, an admin dashboard to manage multiple events, custom QR code generation, and a live confetti raffle/giveaway system.
• The tech: Next.js App Router, Supabase (PostgreSQL + Realtime subscriptions), deployed on Vercel

2. Custom URL Shortener & API (Astro 5, Cloudflare D1, TypeScript)

A private, single-user URL shortener with a full admin dashboard.

• Features: Secure cookie auth, a 30-day click tracking chart, and an API requiring an X-API-Key header for external integrations.
• The tech: Astro 5 deployed on Cloudflare Pages, using Cloudflare D1 for the serverless SQL database.

3. Automated Markdown Portfolio (Astro, GitHub Actions)

A static portfolio site tailored exactly to my needs.

• Features: I manage all my projects via simple
• Markdown files. I use Cloudinary for images and unlisted YouTube links for videos.
• The tech: The coolest part is the CI/CD pipeline. Whenever I do a git push to the main branch, a GitHub Action automatically builds the Astro site and deploys it via FTP to my host.

How to survive the Al rate limits:

If you try this, the dynamic rate limits will hit you hard.

I had to become strategic:

• Model Juggling: I use Opus strictly for global architecture and DB schemas. Sonnet is for the heavy lifting and complex logic. Haiku is for bug fixes and writing my Git commits. (And I keep Gemini in a browser tab for brainstorming to save Claude tokens).
• Timing: I realized that coding in the afternoon (US peak hours) drains tokens way faster. I shifted all my heavy coding to the morning.

Going from zero coding knowledge to managing APIs, SQL databases, and CI/CD pipelines in a week feels completely unreal. Has anyone else experienced this kind of crazy productivity jump recently?

I know this is not recommended on most models, but I wanted to try out LTX2.3 at 4k, especially for outpainting.

Do you know if it is at all possible to force Wan2GP to go above 1080p ? I can't find settings that allows me to do that.

Thanks !!

rainforest qa served a specific niche pretty well for a while, teams that wanted to run tests without writing code, with a humanintheloop model that caught things automated runners would miss. and for some orgs, that was enough. but the cracks started showing as soon as you needed speed at scale.

the core problem was turnaround time. if you needed a full regression run and you were relying on crowdsourced human testers, you weren't getting results in 20 minutes. sometimes it was hours. for teams doing multiple deploys a day, that's a blocker, not a feature.

i talked to someone who was using rainforest on a midsized SaaS product, around 80 test cases, running twice a week before releases. the cost was manageable, the quality was decent. but the moment they went to continuous deployment with shorter sprint cycles, the model broke. they couldn't afford to wait 4 to 6 hours to know if something was broken on prod.

so what are people actually moving to?

a few directions. one is going fully into codebased automation, playwright, cypress, with a CI integration. more setup cost upfront, but you own everything. the other direction is ai assisted testing platforms that give you the no-code accessibility of rainforest but with machine execution speed. this is where tools like drizz comes handy. it's not human testers, it's an AI agent that can run through flows, validate behavior, and give you results in minutes rather than hours.

the tradeoff is that humans catch visual weirdness and context specific UX issues that AI runners still sometimes miss. so the replacement isn't always 1:1. what you gain in speed, you occasionally give up in nuanced judgment.

that said, for functional regression, making sure core flows work, forms submit, APIs respond correctly, navigation doesn't break, ai driven execution is faster and cheaper at this point. the value of human testers is shifting toward exploratory testing and edge case design, not rote regression.

if you're leaving rainforest because of speed, budget, or scalability concerns, the honest answer is you probably need to decide whether you want to go code-first or stay no-code. the middle ground, ai assisted no-code with fast execution, exists and works, but you'll need to verify it handles your specific app complexity before committing.

don't just switch because something sounds better. run a parallel eval. put 15-20 of your critical flows through the new tool and see what breaks. that'll tell you more than any comparison blog post.

Really dumb question, but I can't find anything about this online that is about the regular claude.ai chat window. No extensions, no code, just as a free member using the regular Sonnet 4.6 adaptive.

Sometimes I have a promt that requires a bit more thinking, and after 13 minutes or so it stops due to usage limit. That's fine, it's a lot of tokens, and I'm not a paying member. But after the 4 hours or whatever it is, how do I continue the generation without wasting tokens? In Claude Code there's --resume or --continue. What do I do in the chat though?

There's no continue button once my limit resets, only a retry. Will that start the generation over again? Is it better to type "please continue" as a response? What's the most token efficient way that will continue right where it left off, using everything it gained so far, without starting to become less accurate (as these models often do after a few messages)?

I maintain an open relationship with my LLMs and Claude knows this.

https://huggingface.co/SeeSee21/Z-Anime

"Z-Anime is a full fine-tune of Alibaba's Z-Image Base architecture — not a LoRA merge, but a fully trained anime-focused model family built from the ground up.

Built on the S3-DiT (Single-Stream Diffusion Transformer, 6B parameters), Z-Anime inherits the strong foundation of Z-Image Base: rich diversity, strong controllability, full negative prompt support, and a high ceiling for fine-tuning — now adapted for anime-style generation."

https://preview.redd.it/uh5sfmh5s3yg1.png?width=1536&format=png&auto=webp&s=8753e6768c1157446fcec7f56edc7c4cd564f868

https://preview.redd.it/cmjb5ih5s3yg1.png?width=1536&format=png&auto=webp&s=34f8f94d4ea17f09a59f040ad95ffa1c5ab8ac29

I started using Claude in chrome browser as a extension, which is very promising and that I am able to automate a lot of things, but I was wondering if there is any other options that I’m not aware of is there any set ups that is designed for this workflow so that AI agent acts as a human in the browser, it can basically read the content click on buttons fill in the forms etc. Please share 🙌

Hello to everyone again!
You may remember my ESPclock project, a 3D printed smart clock made with a 7-segment display and XIAO ESP32 C3 (or Wemos D1 mini) that connects (via webUI) to Wifi and NTP servers to retrieve current time.

Well, in these months i've updated it with some new useful features (some of them were recommended by you!).

The most relevant ones are:

- alarm clock mode with snooze feature. (requires a passive buzzer);

- added TTP223 touch button to turn off alarm when ringing;

- added Uptime;

- added ESPmDNS;

And last but not least, i added support for a bigger 0.8" display, which improves readability and has a better display-to-body ratio!

Of course i've designed a new minimal case for the 0.8" display!

Hope that you'll like it! And I'd like to know your opinions/advices about it.

For more info, links to the project:

[PROJECT PAGE + Firmware + Instructions]

https://github.com/telepath9/ESPclock

[MAKERWORLD - ESPclock BIG ]

https://makerworld.com/it/models/2616382-espclock-big-digital-clock?from=search#profileId-2887323

[MAKERWORLD - ESPclock BOLD]

https://makerworld.com/it/models/2405754-espclock-bold-digital-clock#profileId-2637281

[MAKERWORLD - ESPclock standard]

https://makerworld.com/it/models/1594116-espclock-digital-clock#profileId-2069321

I built a marketplace for AI agent skills called Agensi. The entire thing was built with Claude and Lovable. I'm not a developer. But that's not what this post is about.

This post is about how Claude became the single most important tool in my growth stack. Not for coding. For SEO, content strategy, and a new thing called AEO (answer engine optimization) that I think most people are sleeping on.

Claude writes all my content, but not the way you think

I don't ask Claude to "write me a blog post about X." That produces generic AI slop that nobody reads and Google doesn't rank.

Instead, I feed Claude my Google Search Console data (queries, impressions, click-through rates, average positions) and ask it to find keyword gaps. Claude analyzes the data, identifies queries where I have high impressions but zero clicks, finds topics where I have no content but competitors do, and spots cannibalization where multiple pages compete for the same query.

Then we write articles together targeting those specific gaps. Every article has a structure that Claude and I developed over weeks of iteration: a Quick Answer block at the top (40-60 words that directly answer the main question), H2 headings phrased as questions (not "Claude Code Skill Locations" but "Where Does Claude Code Store Skills?"), comparison tables where relevant, and internal links to related articles.

96 articles later, we went from 5 clicks per week to 1,000+ clicks per week. 300K search impressions per month. 878+ page-1 Google rankings. All organic.

The AEO strategy nobody is talking about

Here's what surprised me. ChatGPT, Gemini, Perplexity, and Claude itself are now sending us traffic. 348 AI-referred sessions per month and growing fast. These AI answer engines cite agensi.io when developers ask where to find SKILL.md skills.

Claude helped me build the entire AEO infrastructure. We restructured every H2 heading as a question because AI Overviews prefer extracting from question-format sections. We added FAQ schema to every page so Google's AI picks up our Q&As. We built an /about page as an entity anchor with Organization, Person, and AboutPage schema. We created a robots.txt that explicitly allows all AI crawlers and an llms.txt file that tells LLMs what the site is and where to find key content.

The result is that when someone asks ChatGPT "where can I find SKILL.md skills" or asks Perplexity "what is the best skill marketplace for AI agents," they get pointed to agensi.io. Claude helped me engineer that outcome deliberately. It wasn't an accident.

Claude as a technical SEO auditor

Every week I export data from Google Search Console, Ahrefs, and Google Analytics and dump it into Claude. Claude finds things I would never catch on my own.

It found that 121 queries where I ranked position 1-3 had zero clicks because AI Overviews were stealing the traffic. That insight changed my entire strategy from chasing rankings to becoming the source that AI Overviews cite.

It found that my "best claude code skills 2026" article had 25,000 impressions and only 29 clicks. The problem was the title. Claude rewrote it to "15 Best Claude Code Skills in 2026 (Tested & Ranked)" and we're watching the CTR climb.

It found that I had 18 published articles with zero Google impressions because they weren't indexed. Claude generated the IndexNow ping commands and the GSC URL Inspection list to fix it.

It diagnosed a duplicate FAQPage schema issue that was causing GSC errors on 90 pages. The root cause was React components emitting FAQ schema client-side AND the SSR edge function emitting it server-side. Claude identified the exact files, wrote the Lovable prompts to fix it, and verified the fix with curl commands.

The structured data layer

Claude built the entire structured data architecture for the site. Every page type has the right schema:

Homepage has Organization, WebSite with SearchAction, and FAQPage with 15 Q&As. Individual skill pages have SoftwareApplication with pricing, BreadcrumbList, and conditional FAQPage. Article pages have Article, FAQPage, HowTo, BreadcrumbList, and Organization. The /about page has Organization, AboutPage, and Person schema for entity anchoring.

I didn't know what any of this was before Claude explained it. Now every page is machine-readable for both Google and AI engines. PageSpeed Insights shows "Structured data is valid" on every page with a 100 SEO score.

Core Web Vitals fixes

Claude diagnosed that our desktop LCP was 2.5-4s on 190 URLs. It identified the causes (460KB eager JS bundle, framer-motion loading on every page for a mobile menu animation, synchronous analytics scripts) and wrote the Lovable prompts to fix each one. Desktop LCP went from 2.5-4s to 0.9s. Performance score went from ~70 to 97.

For mobile, Claude found that the LCP element was a 1920x1920px, 179KB PNG logo being rendered at 112px. It was imported as a JS module so the browser couldn't even start downloading it until the entire JS bundle parsed. Claude's fix: generate WebP versions (7KB and 3KB), switch to a static path with preload, and lazy-load the navbar search and dropdown components. Logo went from 179KB to 7KB.

What I've learned

Claude is not a magic content machine that you point at a topic and get traffic. It's a strategic partner that gets better the more data you feed it. The key is bringing your own data (GSC exports, analytics, competitor analysis) and asking Claude to find patterns and opportunities in that data. The output is specific, actionable, and measurable.

If you're building something and not using Claude for your SEO and content strategy, you're leaving a lot on the table. The AI coding capabilities get all the attention, but the analytical and strategic capabilities are just as powerful.

Happy to answer questions about the specific workflows, prompts, or technical details.

agensi.io

Hi all! I am completely new to Local AI and as I am buying a new MacBook for photo and video work I’d also like to start learning what I can do in terms of local AI.

My configuration will be:

- M5 Pro 20 core GPU

- 48GB RAM

- 2TB SSD

My question would be: what are the best models I could run in this configuration and how do I go through the process of setting them up?

I would also add: what are some cool things you could do with these models including general use, coding and image generation/editing?

I know this sounds like a very noob question: it is exactly who I am so I am looking for your enlightenment :)

Given I will have a fairly powerful machine, I think it would be good to learn something new and leverage its potential to the fullest!

Thank you!!

I want to buy a macbook pro m5 with 32 gb of ram. That being the max ram for the pro with only the m5 chip.

Currently i have a gaming laptop with an rtx 4060 and i have a problem with the vram not being enough.

Do you guys think this is the way to go if i want to get into LLMs or Ai? If so is this laptop a good choice?

there was a delay with my sample arriving so i started playing around with generating some product images using acciowork. originally i wanted to wait, get the product in hand and shoot my own photos because i honestly hate most supplier images sooo much (especially the plain white background ones ew)

i wasn’t expecting much since ai images used to look terrible, but it seems like they’ve gotten better. the photos actually turned out pretty decent, and it’s something i know i couldn’t replicate without spending a lot of time setting up. they also look quite accurate to me

once i figured out the general vibe i wanted, it started feeling like something i could standardize and reuse across products

but now i’m second guessing… i don’t want to end up misleading or accidentally “scamming” people. technically i didn’t shoot these myself, even if the product looks very similar to what’s shown

so where’s the line here? if the product matches but the images are ai-generated and a bit enhanced, is that just normal marketing or is it misleading?

for reference: first image is what i generated for a random product, second is the supplier photo

In a conversation I was having with claude it output that it thought something was beautiful. Is this meant to please the end-user by design or does it pattern match to what would be determined as beautiful by other humans? Utilizing this sort of emotion heavy language definitely seems like it encourages whatever school of thought the user exhibits… but regardless I am genuinely curious where claudes opinion of beauty emerges from and why it feels inclined to tell the end-user.

So I had a ongoing thread with an agent yesterday that was roughly around 570k tokens deep.

Today I went ahead to start some work and opened that same session to copy the summary from the agent. When I ran /copy it instantly used 2% usage and my session started.

Usually this doesnt happen with any other commands unless the message is sent to the actual agent. If I knew that I would of copy and pasted it.

Is this a known thing? Anyone else notice that? Its not the end of the world Im on the 20x max plan but I would assume for anyone on one of the other plans this would be extremely frustrating.

webml-kit

Framework-agnostic utilities for loading and running ML models in the browser via WebGPU/WASM.

If you've ever built a browser-ML demo, you know the drill: copy 150 lines of Web Worker boilerplate from the last project, wire up postMessage, add progress reporting, handle the GPU vanishing mid-inference, and pray the model is cached so your user doesn't wait 3 minutes. Every. Single. Time.

This library does that part for you. It wraps u/huggingface/transformers with a sane API and handles the ugly bits: device detection, model caching, token streaming, KV-cache management, and GPU recovery.

import { ModelClient } from 'webml-kit'; const client = new ModelClient(); // or with an explicit worker path: // const client = new ModelClient(new URL('webml-kit/worker', import.meta.url)); // What can this machine do? const device = await client.detect(); console.log(device.backend); // 'webgpu' or 'wasm' or 'cpu' console.log(device.gpu?.vendor); // 'apple' console.log(device.recommendedDtype); // 'q4' // Load a model await client.load({ task: 'text-generation', modelId: 'onnx-community/Bonsai-1.7B-ONNX', dtype: 'q4', onProgress: ({ percent }) => console.log(`Loading: ${percent}%`), }); // Stream tokens as they're generated for await (const { token, tps } of client.stream('Tell me a joke')) { process.stdout.write(token); } 

Hi all,

A few months ago I made a post about a project that I claimed to be much faster than pymupdf4llm, Docling, and others, with comparable quality. Although, I did not provide any benchmarks. Also, I have improved it a lot, and changed the name; it is now called FibrumPDF; pymupdf4llm-c sounded too much like pymupdf4llm.

I would like to share these benchmarks, as I believe these provide more clarity on where Fibrum trades quality for performance, and where it doesn't.

I used the same dataset that Marker uses, it was open on Hugging Face. I benchmarked Fibrum, Docling, and Pymupdf4llm.

Note that the original claim of 300 pages/s+ was because I was using certain PDFs without a proper dataset. It is quite variable depending on the document. The benchmarks show ~200 pps on these documents.

It seems I cannot post the graphs as an image here, so this is a link: Benchmarks.

This is a table of the information from the CSV (rounded to 2 dp):

As you can see, Fibrum is worse in the table department, but relatively on par for the text score (which measures formatting like bold, italic, etc.)

One thing is that making these accurate was very difficult, and so, please, if you find any issues with this, let me know.

Please see the README for additional info on the project or the benchmarks :)

GitHub

https://preview.redd.it/hj2v9jb383yg1.png?width=2690&format=png&auto=webp&s=b4e93ae1a1894dc4c7aa220403c4e094a857e4af

https://preview.redd.it/z6tup95683yg1.png?width=1114&format=png&auto=webp&s=ecf1f2e03c5bca70949f7c60ee73592ecf4547f1

🤣🤣🤣

First, please don't attack me, I'm a newb at this.

Second- Yes I've updated ComfyUI and KJNodes.

No matter what workflow I'm using for LTX2.3, I get these psychedelic tiled outputs. The main video output looks great in the preview until it comes time to do the vae and/or upscale. I'm not really sure because all these different workflows are different and confusing. One thing is for sure, the outcome is the same.

To be fair, I have some node conflicts and I've been meaning to remove the Custom Node folder and redownload the custom nodes to see if that fixes anything, but I have a feeling it wouldn't.

Any advice?

Hi,

I’m moving out pretty soon and have been thinking about playing around with some home automation/assistant set ups but I don’t really know where to start tbh.

I am going to have an Eufy battery camera set up with a Homebase 3 (I know this isn’t 24/7 and cameras only activate when triggered but as an easy initial set up this is most feasible for me).

I will also have some kind of phone controlled central heating system (unsure of the brand/app as of yet).

What are some things I should look into getting/trying for the set up?

I’m not necessarily looking at spending loads of money right off the batt, but in future may looking to enhance various aspects.

Thanks!

I'm relatively new to ComfyUI (first dabbled in image gen with Fooocus at the end of February 2026 and worked my way through forge, a1111 and now SwarmUI), and I've been using it to make photo realistic NPCs and scenes for my DnD campaigns. However there is a certain feel to the Grok-Imagine fantasy images that I want to replicate in comfy. I don't know what it is about that aesthetic but it triggers something nostalgic in me. Maybe it's similar to the fantasy book covers I read in the 80s and 90s...*shrug*. I've gotten close, but I want to have the right checkpoints and LoRAs and workflow or settings so that it's even closer.

The two images of the elf maiden facing left are the Imagine result from the simple prompt: "high fantasy style, beautiful elf maiden" and then having Imagine expand that portrait to a full body image.

The other images are my attempts to capture that style. I've been just using the Generate tab to experiment with the fantasy aesthetic as it's quicker for me than the Workflow but I'm open to a workflow suggestion. I asked Gemini for help and it suggested DreamShaper XL (alpha2 xl 1.0) with the DetailedEyes_XL LoRA and either DetailedEyes_XL or "SDXL LoRA for Dramatic Lighting & Ethereal Fantasy Detail | A FLUX Cinematic Portrait Style" as secondary LoRAs. I didn't like the effect that the Dramatic Lighting LoRA had so i didn't use it.

settings are on each image.

My prompt for two silver haired maidens (different seeds):

(masterpiece, best quality, ultra detailed, ethereal fantasy), beautiful young elf woman, long flowing silver hair with soft waves, delicate heart-shaped face, large expressive eyes, subtle freckles, pointed elf ears, extremely pale flawless porcelain skin, soft luminous skin, smooth delicate skin texture, soft cinematic lighting, gentle rim lighting, dreamy forest background with bokeh, soft morning light, soft focus, painterly style,
Negative Prompt: (low quality, worst quality, blurry, deformed, plastic skin, shiny skin, oily skin, rough skin, textured skin, tanned skin, warm skin tone, heavy makeup, wrinkles, sharp edges, over-sharpened, cartoon, anime)

the other image just didn't have the porcelain skin tags. I tried a few other prompt settings with lighting and such and these uploaded images were the closest.

Any help / advice is greatly appreciated. NSFW for the first image

Hi all,

We are starting a new AI team in our company. The team will be working on AI agents, model fine-tuning, model inference, and related tasks.

By “models,” I mean the latest open-source models ( range of 70–80B parameters).

We are a team of around 10 people, so parallel serving will likely be required—for example, running multiple models simultaneously (e.g., Gemma, GPT-OSS, MiniMax, etc.).

Currently, I am looking for the best GPU machines to purchase for the team. We have a budget constraint of around ₹70 lakhs -1cr. I would appreciate suggestions from people who are experienced with GPU-based systems.

We are specifically looking for machines that align with our requirements, with strong inference performance as well.

We have been using NVIDIA DGX Spark systems, but I’ve observed that the networking and throughput are somewhat limited for our use case.

Any recommendations or guidance would be greatly appreciated.

Hello,

Does anyone know if these enamel pins are from some series or club? I thought they could be tied to some anime, but maybe their random. They have a YMC on most of them.

Thanks for any help!

https://preview.redd.it/98tmx0vt14yg1.png?width=1682&format=png&auto=webp&s=285c9466be094b734342d7b9a3c19291dec6770d

With a peck on the cheek she squeezed my tentacle back inside, zipped up my skin suit, and I was off to another long shift at the maternity ward.

Ive had these up a while and i do like them but im just curious to know something about them really i dont plan on selling, also i would like to have two repaired and wondering if that might be worth it other than sentimental value

And Sophie daydreams that a cool and handsome boy like her crush on the football team will put a stop to the teachers experimenting on her: she’s fed up of all these bribes and threats and tedious mind-games made in a embarrassing attempt to get access to her body

Well or pretty close to it, they are excellent work horses. I run them in real work scenarios doing some of the work I used to do myself as an skilled expert in my field, billing 200$ an hour. Ofc the key is building a system around their weaknesses, and I've had already LLM systems doing expert work years ago when first ones came (shout out nous hermes 2 mistral!).

But yeah pretty neat, especially noonghunnas club 3090 and you can have 3.6 27B fly on a single 3090.

Video interview:
What happens now that AI is good at math? — the OpenAI Podcast Ep. 17

"Domain expansion."

Does anyone know what is is?

This sub is becoming a venting ground for people who want to complain about the latest model either becoming more stupid or the token quota becoming lower or prices getting hiked.

I m getting the message, no need to repeat it 1000 times a week. I d rather have constructive arguments on how to properly use prompts, agents , harness loops etc…

And yes I m complaining about complainers the irony is on me.

Any project ideas?

I do have this old camera...I got it from my friend...can I use it with esp32 or Arduino to build something...?

I have built the AI agents for email marketing.
Already sending 8000+ emails / week for client.

Built AI voice agents dont know how to sell.

Built AI customer support agent for SaaS websites..
It was working fine dont know how to sell.

Linkedin DM invite AI agent. built but dont know how to sell.

Linkedin + 3 step cold email funnel.
Built it but dont know how to sell.

ANd more..

The thing is that I can build things but I cant able to sell them.

Who can monatise these agents for 20-30% commission?

And how do we avoid multiple agents exploring the same hypotheses?

I currently am banned from claude for no reason and i send multiple appeals but it isnt doing anything.

The main stuff i talked about was on how particle accelerators work, on how neural systems work and vibe coding a game engine just for fun.

I dont see how any of that got me banned

I built DeckdOut for job seekers - it's a Chrome extension that uses AI to match your resume to job descriptions and help you actually get interviews. Spent months on it. Finally put it on Product Hunt.

Honestly the launch itself was less dramatic than I expected. No huge spike. But a few people found it who genuinely needed it and that felt better than any number on a dashboard.

Still figuring out PH as a channel. If anyone has launched something similar and has thoughts, I'd love to hear it.

Here's the listing if you want to check it out: https://www.producthunt.com/products/deckdout

Hi, I am taking loan for my Masters in Germany and I wanted to know which bank to proceed with. Both of these banks are giving me the same offer. There are two differences between them: Credila has partial-compounding and Tata Capital has complete Simple Interest for the moratorium period. Also Tata Capital is giving me fixed ROI and Credila has Floating.

Any suggestions on which one to wo with?

Hi everyone,

I'm hoping someone here can help me identify a metal fragment I found in a very remote and high-altitude ravine in the Sierra Almijara mountains, near Nerja (Málaga, southern Spain). The area is known for Civil War and post-war Maquis activity, but also had mining and charcoal-making activity during the early-to-mid 20th century.

The piece (see photos 1 and 2): The fragment is heavily corroded sheet iron or steel, with some grey matte areas still visible beneath the rust, possibly suggesting the original surface had a zinc or tin coating (galvanized or tinplate). The metal is thin — typical of pressed/stamped sheet metal construction rather than cast iron.

The key feature (see drawing, photo 3): The most distinctive element is a raised oval embossed relief in the center of the face, surrounded by a rectangular perimeter frame also in relief, with rounded corners. Inside the oval there is a small semicircular raised mount — which I believe was the anchor point for a folding carry handle. I've made a sketch showing the relief pattern, with approximate dimensions of 14.5 cm tall × 17 cm wide. (aprox)

My best guesses: It could be a lid or side panel from a military ammunition box, a tool or equipment case, a miner's carry box, or some kind of field kit — but I genuinely can't pin it down. The embossed oval-with-handle-mount design feels very specific and perhaps recognizable to someone with experience in this kind of material.

Any help identifying the type of box, its likely origin (military, civilian, Spanish, foreign surplus) and approximate date would be greatly appreciated.

Thanks in advance.

https://preview.redd.it/6pqwd4cvj3yg1.jpg?width=1200&format=pjpg&auto=webp&s=418a6f1e29eb5302f609e6d16aabe2c908ffe990

https://preview.redd.it/v7ahw2cvj3yg1.jpg?width=1600&format=pjpg&auto=webp&s=9892433f9988cfc6bf7ce26bc44c5127af307521

https://preview.redd.it/kbmtt3cvj3yg1.jpg?width=1200&format=pjpg&auto=webp&s=6d1ea743f5d2974e20517bd5ef10f79124da9d43

We built our own hosted OpenClaw alternative 👋

We just launched our new Botsify update on Product Hunt today 🚀

Built AI agents that can actually do work, not just chat.

They can:

* Browse the web

* Write & execute code (using a sandbox cloud machine)

* Use 5,000+ skills

* Remember context

* Complete tasks autonomously

And agencies can fully white-label the platform and sell it under their own brand.

Please remove the light bulb on top of my cap. Tipping $5.

My dad's mom had a GIC of 17-18% rate. She saved over a few years and used her GIC alone to buy her other son an apartment.

People say these rates are one of the reasons my country went from blooming economically to collapsing. Decades upon decades of this (though there's more reasons) .

The bond interest rates later a few decades after were close to 30%.

Now we're in the shit off.

What about you?

Benchmarked the GraphRAG SDK against eight other GraphRAG and RAG systems on the GraphRAG-Bench Novel dataset.

The evaluation covers 2,010 questions across four task types: Fact Retrieval, Complex Reasoning, Contextual Summarization, and Creative Generation.

All tests ran on a MacBook Air (Apple M3, 24 GB) using GPT-4o-mini via Azure OpenAI for both answer generation and scoring.

Queries: The evaluation runs against 2,000 questions drawn from the dataset. Here are two representative examples:

1. "In the narrative of 'An Unsentimental Journey through Cornwall', which plant known scientifically as Erica vagans is also referred to by another common name, and what is that name?"
2. "Within the account of the royal visit to St. Michael's Mount in Cornwall, who is identified as the person who married Princess Frederica of Hanover?"

GraphRAG-SDK : https://github.com/FalkorDB/GraphRAG-SDK/

Official benchmarks: https://graphrag-bench.github.io/

Data: https://huggingface.co/datasets/GraphRAG-Bench/GraphRAG-Bench

Disclosure: affiliated with FalkorDB and sharing our open-source work to collect feedback. Drop a star if you found it useful, thank you

Basically I have a fred meyer account connected to my phone number. I clipped some coupons but qfc is more conveniently located than Freddy's. Can I put in the phone number I use at fred neyers with the coupon on them and have it applied at qfc or do I have to make a new account for qfc?

Which one is more accurate? Why? Do you incorporate it in pace planning?

I’m 23F, I have a lakh rupees lying like that and I want to invest it somewhere. I’m aware that contributing it to different areas like FD, lump sum SIP, stocks will help but I’m not sure of how to start and what specific companies to invest in.

I know that’s not a very big amount but I don’t want it to go in waste just like that. Any suggestions

A keyword search that returns zero results is an obvious failure. The user reformulates, or you log it and add a synonym. Vector search never returns zero results. The nearest neighbor always exists. So when the system fails, it does so by confidently returning incorrect results that look identical to the correct ones at the API level.

A few failure modes I keep running into:

• Exact identifiers get smeared: A query for product SKU "XPS-13-9340" embeds near other XPS models, near other 13-inch laptops, near other Dell products. The retrieval looks confident, but it's wrong. BM25 would have either found the exact SKU or returned nothing, and "nothing" is a useful signal
• Negation embeds identically to its inverse: "Laptops without touchscreen" and "laptops with touchscreen" land in nearly the same region of vector space because embedding models don't represent logical operators. The retrieved set is the same. The user received the opposite of what they requested.
• Numerical constraints don't survive embedding: "Hotels under $200" pulls $400 hotels because embeddings don't preserve numerical ordering. The model knows "$200" and "$400" are both prices in the same domain, which is the wrong invariance for this query.
• Low-frequency domain terms get the worst of it: General-purpose embedding models have weak representations for specialized vocabulary (medical, legal, internal product names), so the queries that most need precise retrieval get the least of it.

The pattern across all of these: the failure is invisible at the system level. Your dashboards show queries served, latency green, and the zero-result rate is near 0%. Quality has degraded, but nothing alerts on it. Compare to a keyword-only system, where vocabulary mismatches show up directly as zero-result rates and reformulation patterns in the logs.

The hybrid retrieval pattern (BM25 + vector with RRF or learned fusion) is the most common answer I've seen in production. Lexical handles the exact-match cases vector can't, vector handles the semantic cases lexical can't, and the fusion step decides which signal to trust per query

Curious what other patterns people are running with?

Especially around:

• Detecting silent failures in production (anything beyond click-through and reformulation rates?)
• Query routing strategies (when do you skip the vector path entirely?)
• Reranker tuning when the candidate set is contaminated by hallucinated similarity matches

Context: I'm writing a book, "Designing Hybrid Search Systems", published on Leanpub (early access, ~600 cited references across 20 chapters). I'll share if anyone is interested.

Mom is in the yellow

she was the best mom ever!

es por la actualización? o está en mantenimiento??

intenté jugar pero me deja🫩, saben a qué hora se podrá jugar

https://preview.redd.it/mib96fy413yg1.png?width=1586&format=png&auto=webp&s=92ce13a578b754c2119f16b596a6786beb8a523d

At this point it's totally unusable. Not sure if it's a model issue or skill issue.

after laning phase, my view on it is that adc/supp is mid, top is on crossmap of the obj and midlane is on the lane for the next obj. but its not allways this simple. if mid is super weak, and will guaranteedly die in side(either if he sucks or is far behind), then should mid play mid to be safe? because my view is that strongest players are mid so they can shove wave and fast move to whatever happens. But if team in side suffers for it, is it viable that adc/supp goes a sidelane, and midlane goes mid?

I thought of the acronym “PATTUKK,” which, in order, stands for Pakistan, Afghanistan, Tajikistan, Turkmenistan, Uzbekistan, Kyrgyzstan, and Kazakhstan. This addresses all the -stan countries by recalling them and their position by using the first letter of each country’s name for the acronym “PATTUKK”. Starting (south to north) from the coast nearest to the southern-most ocean (subdivision of the Indian Ocean: the Arabian Sea) and going from south to north, which is convenient for practical land navigation.

Although I don’t speak Tamil (South Asian language), apparently the expression/term “PATTUKK” can mean “silk” or “for silk”, etc. in Tamil. Informally in the Tamil language, the term can stand for the town Pattukkottai in the state of Tamil Nadu of South India which is a coastal city of the same Indian Ocean (particularly a subdivision of the Indian Ocean: the Palk Strait). Central and South Asia (including all the countries ending in “-stan”) are considered a geographical split (the central heart) of the ancient Silk Road. Pattukkottai translates to “Silk Fort” so the acronym/word “PATTUKK” should be thought to refer to the translations “to silk” or “for silk.” Think it being shorthand for a South Indian coastal city this reiterates the significance of Central and South Asia commonly known as a geographical split (or geographical division) of the famous ancient “Silk” Road. Pattukkottai is in India not too close to where the “-stan” countries are but shares a region with a few of them.

This mnemonic strategy uses an acronym to identify geographical positions and names of all seven countries that end in “-stan”. And the word just so happens to be a phrase or expression in Tamil meaning “silk,” “for silk,” or “to silk” and can be short for a town in South India whose name means “Silk Fort”. Could this acronym help someone identify the Central and South Asian countries all ending in “-stan”? As well as their historical position on the Silk Road and how to mentally position them in relation to each other with their modern borders?

Note: Using a map will clarify the corresponding countries and their order starting from the coast of the Indian Ocean going from south to north. Make sure to keep in mind that an obstacle will be assessing which technique (reference point for geographical position of the countries) to use to determine the ranking of the southernmost countries in relation to all “-stan” countries. A different approach will possibly not read the acronym as “PATTUKK” which is why an internet search of “all the -stan countries from south to north” should avoid inconsistencies.

I got this on socials and I'm wondering if this place exists

LCK 2026 ROUNDS 1-2

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

Nongshim RedForce 0-2 T1

- Player of the Match: Oner (300)

NS | Leaguepedia | Liquipedia | Website | Twitter | Facebook | YouTube
T1 | Leaguepedia | Liquipedia | Website | Twitter | Facebook | YouTube | Subreddit

MATCH 1: NS vs. T1

Winner: T1 in 29m
Game Breakdown | Runes

MATCH 2: T1 vs. NS

Winner: T1 in 29m
Game Breakdown | Runes

Patch 26.8

This thread was created by the Post-Match Team.

i m trying to use prompt relay to generate videos, but the character consistency is just not there. i tried most wf available on yt but all same issue, i donno how they show same character in video, is there any trick or something that i am missing?

Well since you guys didn't want to share your obvious wealth of knowledge with me on my other post I decided to go ahead and blow the roof off the budget and say the sky's the limit.

Eat your hearts out! I'm going to bring this bad boy to each one of your states one by one and find all of the deepest of treasures. Before that though can anyone lend me some money for 15 AA batteries?? I'll pay you back with all the deep treasures I find.

Point your location on this map https://geopinpoint.skippylab.com/geobattle/as0ical2xd#googtrans(fr|en)) and see how close you were!

so correct me if i'm wrong but I think sarah and ben share an office space right? and then jane and ashley. what's the office arrangements for the others

Went through my wallet history and added it all up out of curiosity. Between MetaMask cuts, slippage I didn't notice and double fees from bridging then swapping separately, it was way more than I expected.

The worst part is most of it was surely completely avoidable, I just didn't know what to look for at the time, i feel sick, same experience for you? Curious to see gas fees checker as well, where do you see it?

Acrylic on canvas board

I made a painting of one of fortunaliquidaa animations

Assuming that Veo 4 will be showcased at Google I/O this year, what are your expectations for this video generator?

After Veo 3 being out for a year, it was already passed up by other models like Sora 2, Kling 2.5/3.0, Runway, Seedance 1.5 Pro and 2 in a series of ways. My main expectation is for it to be on par with a combination of Sora 2 and Kling 3.0. Since Google is an American company, American English Audio should sound natural, regardless of accent, and not sound like a TTS model. Prompt coherence should drastically improve, probably using Gemini 3.1 to help out. Lastly, more film styles are available. Sora 2 was capable of being about amateur cellphone footage, security cam footage, IMAX cinema. It would be nice for Veo 4 to have.

​

I just set up Claude locally on my Windows machine using PowerShell.

Honestly didn’t expect it to feel this different compared to using APIs.

Main things I noticed:

No latency issues

Feels more “in control”

Can experiment freely without worrying about usage limits

Now I’m thinking beyond just chatting with it.

I want to turn this into something more useful — like an AI agent that can:

monitor competitors

suggest actions

maybe even execute tasks automatically

Still early, but curious…

Is anyone here running LLMs locally and actually using them for real workflows (not just testing)?

Would love to know what you’re building.

PuneCivicAI

I'm a final year engineering student from Pune and built PuneCivicAl, a civic-tech platform for reporting local issues to Municipality | Corporators more easily.

I recently presented it at my college's Tech Fusion 2K26 project competition and won First Prize.

It was exciting to see a project focused on solving real civic problems get recognized. Sharing here for feedback and suggestions on how it can be improved.

same as title

Hey guys - need your help! I started a small side hustle and it kinda took off faster than I expected (pet accessories don’t judge🐕). At first it was just me doing everything manually (outreach, followups, small campaigns etc) and it worked fine… until it didn’t. Now I’m hitting a point where growth is good but my processes are a mess. Stuff falls through the cracks, I forget to reply to people, and I’m def not scaling this properly. The unexpected part is some of the homebrewed things that have worked so far weren’t what I thought would work at all. What’s something random or unexpected that actually worked for you as things started growing? Looking for ideas before I duct tape my way into burnout 😅

I kept quitting productivity apps because the setup became a second job: dashboards, streaks, settings, analytics, systems to maintain.

So I built Pith as the opposite: one quiet focus block that feels alive.

The core loop:

- pick a focus duration

- start a session

- an animal grows while you stay focused

- if you quit early, it gets hurt

- ambient sounds if you want them

- no ads

- core focus features are free

It is iOS-only for now. It is not an app blocker and not a giant productivity system. The point is to make starting easier, not to give you another place to procrastinate.

App Store:

https://apps.apple.com/us/app/pith-focus-flow/id6762040939

What I would love feedback on:

- does the animal consequence feel motivating or too guilt-heavy?

- does the App Store page make the concept obvious fast enough?

- would you keep this after one completed session?

Since I started posting and focusing on organic growth, I’ve been getting an increasing number of emails and inquiries from people pushing marketing platforms and it’s becoming quite annoying. How do you deal with that?

Made this app over six months to help me and my friends do more efficient research when drafting for fantasy or making parlays. Free while in testing on TestFlight iOS only check it out and hope it improves your decision making in parlays!

MLB in development at the moment.

https://testflight.apple.com/join/uTppFJrS

I'm a recent graduate but back in my college days, I used LLMs to study since the coursework was too complex for me to grasp so I always gave a particular set of instructions to GPT or Gemini before they start tutoring me. It goes well for first few questions and then collapses. Basically attention mechanism favours recency and my instructions token at the very start of the conversation get buried. I ran all these diagnostic tests to fix this issue for me since i was so done with the overprompting and starting fresh each time.

I built my own bot with gemini's api and used a system prompt. So, it never forgets the teaching instructions. Also, I kept it stateless because of resource constraints and to avoid any future context window issues. I implemented an intelligent memory. Ran a prompt to extract important info from each turn and store in a vector db. Stored conversations summaries too. For followup questions, I had a 2 turn buffer sliding window which only activates if followup. Stored all the info and conv summaries in db with metadata for hybrid search. For reranking, I had a weighted score of recency plus importance plus similarity under a context budget control.

I don’t know if this is considered basic or not for my level but the whole thing started from a real frustration I had while studying. I wasn’t initially trying to build an AI product for the sake of it. I just wanted a tutor that would stay consistent and actually remember how I wanted to learn instead of slowly drifting after a few turns.

Working on this got me really interested in the edtech side of AI, especially around long term learning interactions, memory, personalization and how tutoring systems behave in real usage instead of controlled demos.

I’ve been exploring a few YC startups in the space and it feels like there’s still a lot of room to build around actual learning behavior rather than just content generation.

Keep my app for 14 days, and in exchange i will do the same

1. Join tester group first

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

1. Then join app test

https://play.google.com/store/apps/details?id=com.GGDigital.GeoRewards

You basically climb the corporate ladder by:

- saying vague things in meetings

- scheduling more meetings

- avoiding actual work

Then you get a performance review like this ↓

Try it here: corporateladder.xyz

Curious what role you end up with!

Can't seem to find it now however.

started a small tool last summer, an AI that summarized weekly notion pages into shareable digests. got it live in 6 weeks. paid users at peak was 47, MRR around 380 dollars.

shut it down 2 weeks ago. wanted to write up what actually killed it because everyone says churn or distribution but for me it was something else.

churn was 7 percent monthly which is high but not catastrophic for a small tool. distribution was the hard part but i had figured out a steady drip from 2 communities and a small newsletter that got me 3-5 trials a week, fairly cheap.

what actually killed it, the underlying API costs went up 40 percent in march and notion shipped a feature that did 70 percent of what my tool did, for free, native. so my pricing got squeezed from both sides at once. the API increase alone wouldnt have killed me, the native feature alone wouldnt have, but together they made the unit economics impossible to fix without a full pivot.

i tried to pivot. spent 5 weeks trying to add features that notion didnt have. cross-workspace summaries, slack integration, custom voice prompts. trial-to-paid actually dropped during this period because the messaging got muddled. the homepage couldnt explain what i did anymore in one sentence.

the lesson i took, when youre building on top of someone elses platform and they ship into your wedge, you have weeks not months to react. and reacting by adding features is slower than just shutting down and starting something new with the cleared up understanding of what platform risk actually feels like.

im not bitter. learned more from these 9 months than from the 4 years before. but i wish someone had told me that platform risk is not just an investor talking point, it actually happens fast when it happens, and the version where it happens isnt usually a dramatic shutdown of the API, its a quiet feature ship from the parent company that you hear about in their changelog.

I built a small desktop tool that lets you place animated GIFs anywhere on your screen.

You can drag them around, lock them, make them click-through, and control their speed, scale, and opacity.

GitHub: https://github.com/Ertugrulmutlu/OpenAnima
Release: https://github.com/Ertugrulmutlu/OpenAnima/releases/tag/v0.1.0

Would love to hear feedback.

Hi all!

I’ve been working on an open source Android app distribution system called SafeHaven. It's main focus is on trust, security and transparency.

It’s still early to be honest, but the base pipeline is already working (unreleased beta). A build of the client will be out this week.

This mostly started out of frustration with the current Android landscape:

- The Play Store dominates everything

- Alternatives exist, but often lack clear trust signals

- And then there’s the “wild west” of APK sites with little to no way of verifying a legit app from malware-based clones

SafeHaven aims to sit somewhere in the middle of this, and close that gap.

How does it work right now?

- Developers create an account (email or private key)

- They submit an APK and link it to a source repository

- Ownership is verified

- The APK is scanned and hashed

- After a short waiting period, it’s added to the public catalogue

- end users can then download the app without accounts

Each app exposes simple trust signals like:

- Verified Source (repo ownership)

- Verified developer signature (updates match the original signer)

- Latest scan result (server-side malware scanner)

The full submission process is *almost* done.

I’d appreciate any feedback or suggestions, especially around the trust model.

Index: http://api.colourswift.com/store/index.json

Repo: https://github.com/phsycologicalFudge/SafeHaven

I’ve been building a small AI character roleplay site called Chaytai.

I’m not trying to spam or pretend it’s perfect. I’m mostly looking for honest feedback from people who actually use Character.AI alternatives.

The things I’m working on right now are:

- faster replies with fallback models when one gets rate-limited

- better mobile chat UI

- recent chats that feel cleaner

- character creation and discovery

- making roleplay replies feel less generic

If you use AI character/roleplay sites, what makes you stay on one?

And what makes you leave immediately?

Site, if anyone wants to look: https://chaytai.com

Every time I started a new project with Claude Code, I spent the first hour going back and forth on stack, auth, and features before a single file got written. So I built quickstart-ai — a 5-minute branching interview that generates a structured SPEC.md and agent instruction files (CLAUDE.md, .cursorrules, etc.) before you hand it to your coding agent.

**How it works**

• Run `npx quickstart-ai`

• Answer ~20–100 branching questions (irrelevant sections skip themselves)

• It writes SPEC.md + per-agent instruction files tailored to Claude, Cursor, Windsurf, Copilot, Aider, or a universal AGENTS.md

• Optional Claude-powered smart pre-fill if you have an Anthropic API key

**What I'm actually looking for**

• Does the generated SPEC.md format actually help your agent stay on track?

• Are there question branches missing that you always end up clarifying manually?

• Would you use this, or is it solving a problem that doesn't really exist?

Repo is open source (MIT) if you want to peek or fork the questions: github.com/NijeMatija/quickStart

Looking forward to your feedback!

hey guys. just wanted to vent/share a bizarre seo experience with my recent side project.

for the last 5 months, i’ve been trying to get the inner pages of my engineering tool site indexed. search console just kept throwing me into the classic "discovered - currently not indexed" purgatory. i tried everything: fixing core web vitals, sitemaps, internal linking, api pushes. absolutely nothing worked.

yesterday i finally lost my patience, said screw it, abandoned the old domain, and just bought a fresh one: induspecs.com . migrated the exact same codebase over.

literally the exact same day, google started indexing my calculator pages. 5 months of pure frustration solved by spending 10 bucks on a new domain. i guess my old domain was just completely shadowbanned or cursed?

anyway, the project itself is an ai-powered reference toolbox for industrial engineers (calculating pipe weights, bolt torques, metal specs, etc., powered by llama 3). the stack is super lightweight: html, tailwind, and a bit of vanilla js.

has anyone else dealt with a "cursed" domain like this? is this just how google treats new sites now?

also, if there are any mechanical engineers lurking here, would love to know if the hub structure makes sense to you. cheers.

The problem: Every clip tool I tried stops halfway. Detect clips but still makes you edit manually. Or lets you edit but won't auto-post. Nobody was doing the full pipeline so I built it.

What we built: vibecut.net

You set a posting schedule. Vibecut does everything else:

• Pulls the most viral recent clips from streamers you follow (no Twitch account needed — public API only)
• Picks the best ones using a ranking algorithm
• Auto-formats to vertical: portrait, blurred background, or webcam separation — decided per clip
• Generates overlay titles + social captions
• Adds subtitles when it'll boost engagement
• Posts directly to TikTok via official API — no scraping, no bans

Also comes with a streamer catalog showing performance stats by game, so you're not guessing who's actually popping off right now.

Zero editing. Zero manual posting. Just clips going up while you sleep.

Finally after working on our SaaS product, we are getting ready for Product Hunt launch next month.

How shall we plan it as we don’t have any audience, social followers?

Most Founder's conversation follows this pattern:

-> "Our retention's dropping. We need to launch more features."

-> "Our pipeline is weak. We need more budget for ads."

-> "Users are complaining. We need to build more."

And the team executes. Good job. Yet, the metrics barely move.

And the resaon? They're solving the wrong problem.

The pattern I keep seeing:

Retention Problem = "Build more features"

Reality = Users hardly understand it's value fast enough.

Pipeline Problem = "Spend more on marketing"

Reality = The funnel has structural issues....more traffic exposes them faster.

Adoption Problem = "Ship more capability"

Reality = Customers are overwhelmed with complexity.

When there's misdiagnosis, you've to compensate for the downstream. You end up in burning cash and runway chasing symptoms instead of causes.

Before pulling the trigger, step back and ask:

"What's the actual problem we are solving?"

Not the symptom. Not the root.

Most growh problems in SaaS don't need a clearer input. They need a clearer lens.

What repeating patterns have you noticed in your own metrics?

https://reddit.com/link/1syu8dc/video/hsdtaijwo3yg1/player

I’ve always struggled with app fatigue. I had one app for my freelance deadlines, another for my car’s service intervals, one for my pet's vet visits, and of course, a separate one for daily reminders.

I thought: "Why can't my assistant just handle all of this?"

So, I built VekilAI.

In my language, "Vekil" means a delegate or a proxy—someone who acts on your behalf. That is exactly what this app is: a modular AI Super App designed to be your digital representative and handle the chaos of daily life.

What can you actually do with it?

• Freelance Management: Track your projects, client tasks, and deadlines in one clean dashboard.
• Vehicle Maintenance: Log your car's service history and get smart alerts for your next oil change or inspection.
• Pet Care: Keep a digital diary for your furry friends—vaccinations, feeding schedules, and health notes.
• Smart Reminders & Notes: A centralized hub to dump your thoughts and get them back exactly when you need them.
• Modular AI Power: It’s built to be flexible. You don't just chat with an AI; you organize your whole life with it.

I’m a solo developer looking for honest feedback from anyone who wants to simplify their routine. What features would make an AI assistant "essential" for your daily life?

Check it out here: PlayStore: https://play.google.com/store/apps/details?id=com.fsa3web.vekilai&hl

Appstore : https://apps.apple.com/tr/app/vekilai-ai-agent-assistant/id6761864203

Hello SideProject! Today i'm releasing Lapser Studio, a small macOS app that helps you record timelapse screen recordings and make them beautiful.

If you're creating these kinds of recordings with classic recording software, you'll end up with massive recording files and slow video editing software editing the clips

Lapser Studio solves this by letting you hit record and once your done, edit background, camera position and foreground size super easily.

The previous app I used years ago (Hustl I believe) has long not been updated and apps like Screen Studio became the norm for simplifying editing. So I though why not blend the too. The outcome is an app i'm super proud to share.

Lapser Studio is available via my own website for 30 USD (REDDIT25 25% off for reddit peeps) as well as the App store ➡️ https://apps.apple.com/fr/app/lapser-studio/id6758652797?l=en-GB&mt=12

Tysm and I hope you find it useful 🫶

The prompt was "Create a Cross-sectional blueprint of a massive underground steam-powered city in a post-solar-apocalypse world, constructed within a deep cylindrical subterranean structure. Layers of platforms, turbines, boilers, water systems, and dense habitation zones interconnected by ladders and walkways. Industrial steampunk aesthetic, technical diagram style, precise linework, annotations, worn blueprint paper texture, highly intricate".

Originally it was just "Create an image of a blueprint to an underground, post solar apoc, steam driven, repurposed missile silo city (without missile, obviously)"

Building areev — a database for AI agents that ships compliance artifacts (GDPR erasure proofs, EU AI Act conformity reports) at the engine layer instead of bolting them on later. With enforcement landing on August 2nd, made a short explainer on what regulators will actually be checking for.

Feedback on the video itself welcome — first attempt at this format. Especially curious if the regulatory angle landed for non-EU builders, or whether it reads as too EU-specific to be relevant.

https://www.instagram.com/p/DXr0Thjj9UE/

You can now finally wash your car i guess

Hey all,

I’ve been working on a small side project called Anjou to make it easier to find kids activities around Chicago / north suburbs.

Basically I got tired of jumping between park district PDFs, websites, library calendars, etc. every season just to figure out what’s available.

So I built something that:

\* pulls activities into one place

\* lets you filter by age / schedule / location

\* links straight to the registration page

It’s still pretty early, but already saving me a ton of time.

I did come across something similar for Naperville (playgrades.app), which made me realize this is probably not just a “me problem.”

Right now I’m mostly focused on Northbrook / Buffalo Grove area.

If you’re a parent nearby, I’d really appreciate any honest feedback! Especially what feels missing or confusing.

It’s free right now, and there’s also available on iOS app

The site is https://anjou.app

Than you for reading!

​

I’ve had a few ideas that looked solid on paper, but once I started building, the reality hit—hidden costs, complex edge cases, or just no real demand.

It made me realize that validating an idea is much harder than coming up with one.

So I’m curious:

What’s one SaaS idea you were excited about initially, but later realized wasn’t worth pursuing?

And what specifically made you drop it?

Trying to learn from real experiences before I invest time into my next build.

Would appreciate honest insights (failures included).

I am building the design system for my company using Claude Design, but we also have built a skill that contains our brand guidelines.

What’s the difference between these 2 ways of applying the brand guidelines to content assets and landing pages? If the skill contains the same information as the design system, outputs should be equal, right?

Besides the export to google slides is straightforward from the skill, and also leaving aside the fact that the design can break when transferring a slide from a skill to gdecks, is there any other thing to consider when deciding between using the brand guidelines skill vs the design system?

Maybe that from the design system you can directly export to Claude code?

My hipothesis was that a skill with brand guidelines is more suitable for quick look&feel updates, and the design system is more suitable for producing assets from scratch, just by submitting plain text to the system.

Anyone can help with this? thanks!

After spending a while running all kinds of benchmarks with GPT Image 2 on a ChatGPT subscription, here are some early takeaways:

- Image generation speed is basically the same across models, thinking modes, and whether fast mode is on or off. I tested GPT 5.4 with low, medium, high, and xhigh thinking, and GPT 5.5 with low, medium, high, and xhigh thinking. The average is around 100s without a reference image, and about 150 - 180s when adding reference images.

- Somehow, this thing generates the best results with GPT 5.4 + high thinking. That setup consistently gives the best image outputs. I have no idea why.

- The max output is only around 1K resolution. This can break image quality when doing Photoshop work at larger resolutions. That makes sense, though, since a 4K image via API costs around $0.40 per image. If ChatGPT could generate at that quality, who would still buy the API? lol

However, the low resolution issue can be solved in several ways, like upscaling and so on. The main thing is getting the image generation right first.

Hope you like the demo.

This is a recent observation but it's now very noticable. This was not the case just a week ago, but now Claude takes up to 45 seconds between tool calls in Claude Code, which is close to unusable for me.

Quick check on system load shows its not the culprit -

System load... not the culprit

• Memory: 9.2G/46G used, 37G available, 0 swap. Plenty of headroom.
• Load avg: 0.25 / 0.20 / 0.14 on 16 cores. Severely underloaded.
• Top RSS: qdrant at 17.4 GB (36% of total). Parked at 1.5% CPU, just sitting on indices for 16h+. Working set is real but not pressuring the system.
• 6 claude processes combined ~2.5 GB — normal.
• kwin_wayland: 12.5% sustained CPU — anomalous for a near-idle desktop.

Likely culprits, ranked:

1. Anthropic-side -- model server load / region routing

Is it just me or are others noticing this degradation in API latency calls?

Built this over a weekend. Sharing because the architecture surprised me — the entire thing runs on the One agent platform with no backend code of my own. **What it does** - I send any meal to my Telegram bot (text, voice note, or photo) - The agent estimates calories + macros, writes a row to my Notion database - Dashboard reads from Notion live: today screen, weekly review, streaks, deltas, transcript log, station view - Every Sunday at 8 PM the agent reads my last 7 days, sends a coach letter on Telegram, and emails a designed dark-mode HTML recap - If it suggests something ("raise protein 122g → 142g"), I reply YES on Telegram and my macro targets update for next week - Dashboard recomputes against the new target on next reload **What's surprising about how it's built** The One platform handles natively: - The cron scheduler (5 schedules — 4 meal pings + Sunday recap, all stored on the agent) - The Telegram channel (no webhook server, no Lambda, no relay) - The LLM calls (no SDK plumbing in my code) - The integrations (Notion, Gmail, Deepgram are all native One connections, no per-platform auth in my repo) So my "code" for the agent is literally three Markdown files I pasted into the Skills panel: - meal-logger.md (writes meals to Notion) - weekly-recap.md (Sunday letter + email + writes pending suggestion to Notion) - apply-targets.md (reads YES/NO replies, updates targets, clears pending) The dashboard is just Next.js reading from Notion through One's passthrough API. No proprietary database. All my data lives in my own Notion — I can stop using the agent platform tomorrow and my data is still mine. **Stack** - One platform — agent runtime, scheduler, channels, LLM, integrations - Notion — Meals + Targets databases (schemas in the repo README) - Telegram — chat surface - Gmail — Sunday recap - Deepgram — voice transcription - Next.js 16 + React 19 — read-only dashboard with dark mission-control UI **Repo (MIT):** https://github.com/krish221997/calorie-coach-dashboard **Demo video (4 min):** https://www.youtube.com/watch?v=c9XCCUUTI-s **Live dashboard:** https://calorie-coach-dashboard.vercel.app **Create your own One agent:** https://app.withone.ai/agents The README walks through the exact Notion DB schemas you need (Meals + Targets, including the Pending Field/Value/Reason columns the YES/NO flow uses) and the three skills to paste into your own One agent. Setup is maybe 30 minutes if you have Notion + a Telegram bot ready. Happy to answer questions about how any of it works — the agent platform side or the dashboard side. 

https://reddit.com/link/1sysro0/video/e7kv83ixf3yg1/player

"This or That" is one of the highest reshare-per-Story formats: Viewers screenshot, mark their picks, and post their version back to you. The format usually dies in Canva because there's no clean way to hand viewers an editable version.

This tool fixes that with a share link: build the card (pick a topic or type pairs, optionally pre-mark your sides, pick a theme), grab the link, drop it into an Instagram Link Sticker on your Story.

Viewers tap -> land on a page with your pairs pre-filled and every pick cleared -> mark theirs -> download -> post.

Output is a 1080×1920 PNG (works on Stories, Reel covers, TikTok, Snapchat).

Useful for:

- Brands running engagement Stories without Canva busywork
- Creators farming reshares from their followers
- Anyone testing the binary-poll format without designing one from scratch

Free, no signup, runs in your browser tab.

Disclosure: I built it for Storrito (Story scheduler) — the tool is standalone and free.

Link: https://storrito.com/tools/instagram-this-or-that/

Hello, i wanted to ask people with actual knowledge instead of simply searching, i have a project and i am a one man team. Is it realistic to say i can build a whole websited and host it, while it being fully customizable, with no coding experience using claude?

The site won't be very complicated it is simply for selling, though there's a feature i want to add where you can select a few items and then an ai makes a generated picture for what the whole bundle will look like( of course i'll train the ai with real pictures of the actual items i have to improve accuarcy)

ex: let's say you select a shirt, trousers and pants, the ai will generate a picture of the whole outfit on a silouhette that has a similar figure to yours depending on your size of course.

My main concern is maintaining the site also, and in the case of bugs i dont want to have a lot of downtime in order to fix it myself, or search for developers. Should i just deligate to a dev from the starts?

PS: budget is super tight...

Constraints:

Must be realistic (not something extremely expensive like unlimited AI generation)

Something you would actually use regularly

Ideally: a feature or experience you don’t see offered this way today (e.g., not commonly available as a free-with-ads model)

Something you currently pay for or find annoying to access

I'm trying to uncover real unmet needs, not fantasy ideas.

What would you pick, and why doesn’t a good version of this already exist?

If you know someone who has strong opinions on tools or productivity, feel free to share this post with them, I’d love to gather diverse perspectives.

Every day I’d see unfinished tasks from yesterday, and instead of helping me stay organized, it just made me avoid the list entirely.

So I started thinking —
what if a planner didn’t carry over everything by default?

Like, you only focus on today, and consciously decide what to bring forward.

Do you think that would actually help with productivity, or just hide the problem?

I just asked it to tell me what it was good at and I thought it was funny that right after they told me it was too busy to deal with me at 2:30 in the morning, it asked me to give it a star rating..

Spent the last 6 months building a directory of every open-source AI agent project I could find. Now sitting at 67K projects.

Two observations specifically for r/AI_Agents:

**Supply explosion is real.** Monthly new agent project creation went from ~50/month in early 2024 to ~27,720 in March 2026. That's 45× in ~24 months. The shape of the curve isn't gradual — it's a step-function around Q4 2025 when Anthropic released the Skill Spec + Claude Code shipped one-step install.

**Demand hasn't kept up.** 54.1% of all 67K projects have 0 stars. Top 1% of projects own 83% of all stars. The gap between "I shipped" and "anyone uses it" is the widest I've seen in any creator ecosystem.

What this implies for r/AI_Agents folks building/picking agents:

- If you're picking, star count is actually a fair signal up to top 1% (correlates 0.71 with my quality score)

- If you're building, the format wars are over — pick MCP or Claude Skill, both are fine

- The actual moat is "what task does it solve in your specific workflow?"

Browsable index + free 12-chapter writeup of all the data: dropping link in first comment to avoid spam-bot.

This is an app I built myself. My wife and I have been struggling with bedtime stories. The kids always want stories, but mine get repetitive. It’s hard to come up with something engaging and honestly who has the mental bandwidth to create a great story at 8 pm??

I’m also a terrible storyteller.

I built a web app that generates short (2-4 minute), personalized audio bedtime stories tailored to your kid’s age and interests, based on the story idea your kid might have!

The app is in beta, and I’m looking for a few parents with 2–8-year olds to test it and give brutally honest feedback by filling out the form in the app.

Stories can be generated in multiple languages, lmk if your language is missing and I can add it!

I'm looking for feedback on onboarding, usability and story quality. Pls use the feedback form in the app. In exchange, I will give you some extra credits so you can generate more audio stories.🙏

You can try it here (no download required): https://lumiostories.com/

Caveman is getting really popular, so was interested to know if it actually outperforms a simple reminder to "be brief".

Ran 24 dev prompts across 6 categories, comparing 5 arms (baseline, "be brief.", caveman lite/full/ultra). Judged by a separate Claude against per-prompt rubrics.

Scores:

Surprisingly the 2 words matched caveman on tokens and quality. Caveman still earns its keep on consistent output structure, mode switching, and the safety escape on destructive ops but the compression itself isn't the differentiator I expected. The safety escape actually caused a lot of variance in the output.

Full breakdown with per-category data and the variance findings on safety questions: https://www.maxtaylor.me/articles/i-benchmarked-caveman-against-two-words

Video: https://youtu.be/wijoYNiZq3M

Benchmark harness is open source if you're interested: https://github.com/max-taylor/cc-compression-bench

I have a small team. I wanted something between Basecamp and Linear. Simple enough to run a real product backlog. So I built Plate.

No filters to configure, no backlog grooming, no sprint ceremonies. Just projects, tasks, and your team.

We launched Monday. If you're running a small team and you've ever felt like your PM tool is working against you, I'd love for you to try it and tell me what's missing.

The AI race is moving from model power to system power.

A lot of AI apps are still stuck at the prompt-engineering stage. But the more practical question is: how do we actually turn AI into a real, working product?

I think AI application technology can be split into five layers:

At the bottom is the LLM. It understands and generates content. It is the “brain.”

Above that is MCP. It connects external tools and data in a standardized way. It answers the question: “How do we connect everything?”

Then comes Skill. This turns business logic into reusable tools.

Next is Agent. It breaks down tasks, chooses tools, and keeps the whole execution loop running.

At the top is Harness. It provides testing, evaluation, monitoring, deployment, and other engineering support.

The real value is not just the model. It is the full execution chain:

User input → LLM understands → Agent plans → Skill gets called → MCP connects external capabilities → execution and verification → result returned

A request is no longer just a simple Q&A. It becomes a task flow that can be planned, executed, checked, and improved.

Many AI apps fail to go deep because they are missing a few key things:

standardized connections through MCP, reusable capabilities through Skill, task orchestration through Agent, and engineering support through Harness.

Without these, AI stays at the demo stage.

With them, AI can become a real system.

Solo iOS dev. Drill Simulator v1.2 went live on the App Store this week. ASMR/fidget app with six power tools and CoreHaptics tuned per tool.

The cursed part

This is the actual public changelog right now:

v1.2
· Now louder. Use responsibly.
· Annoy your neighbours in 4 new languages.
· Haptics so deep your downstairs neighbour thinks the boiler exploded.
· Fixed: that one user who said "this app should be illegal" now has 5 stars.
· Still free. Still no Wi-Fi needed. Still a terrible idea at 7am.

App Review let it through on first submission. I am still processing this.

What's interesting under the hood

· ActivityKit Live Activity in the Dynamic Island that hums along while drilling. Push tokens are a separate ceremony from notification tokens, took me 2 evenings to figure out.
· CoreHaptics patterns tuned per tool (saw heavier, sander gentler). The simulator lies, you need a real device.
· UMP consent then ATT prompt with a soft pre-prompt. Opt-in roughly 2x vs cold ATT.
· Next.js with next-intl landing in 14 languages.
· Cross-promo system between this and two sister apps (Chainsaw, Lawn Mower) without using AdMob house ads.

Numbers

Brand new. Pre-marketing. TikTok push starts this week. Will write a follow-up retro at 1k downloads if there's interest.

Links

App Store: https://apps.apple.com/app/id6757700473
Landing: https://drill-simulator-nine.vercel.app

Happy to answer anything about iOS solo dev, ASO, or how I got that changelog past App Review.

I'm struggling lately building my website in Next.js, I used the frontend-design skill from the official Anthropic website but it still does random unnecessary stuff.

Does anybody know any good techniques or tools I can you use to help me do it properly?

When Iran attacked that data centers in Middle East and markets dipped, I wanted to buy stocks at lower levels. I asked Claude: “Which Indian stocks could benefit from this?”

It reasoned that workloads would shift from the destroyed data center to the nearest AWS Mumbai region, and since AWS uses NETWEB to build new capacity, it flagged NETWEB Technologies as a direct beneficiary. I’d never had it on my radar. I picked up NETWEB, and in less than a month it gave me a 28% return.

Honestly, AI models are smarter than most people give them credit for—they just need real-time data to work with. News that moves markets, stock updates, macro events—once you feed all of that in, the analysis is genuinely impressive.

I’ve been using it for a month and haven’t opened a single screener since.

This is what investing looks like now.— connect Claude to your live portfolio and live market data use any mcp server that gives claude all data it need --This is what investing looks like now.— connect Claude to your live portfolio and live market data use any mcp server that gives claude all data it need -- i use Tapetide mcp for indian markets . Ask in plain English, get analysis grounded in what's happening right now..

Anyone else running Claude for market research? What's your setup?

https://preview.redd.it/bpgdb19b63yg1.png?width=662&format=png&auto=webp&s=bba8fc0382c89714c9da43b55cc9cfb472824ef1

https://preview.redd.it/7m7pnktc63yg1.png?width=1910&format=png&auto=webp&s=edc0116d8c65d623249fc4247cb4aa4c7f292fcd

There is so much complaining on this forum. I recently switched up to to Max because I was sick of hoarding my use each weak. But now I just don't understand where the complaints come from. I get that agents and coding burn through usage, but I dont think I can reach my limits even if I tried.

Like I am almost exclusively using Opus, I use it to write me silly research reports and chat with it, I use it for multiple hour a day. For work for fun, for random stuff. For example. For example today, I ask 3 separate Opus chats to give me criticism and feedback on a 10k word draft, to read a bunch of the background literature, and another to consolidate and write a road-map on how to implement it. Maybe I have used 17%
So are there any people doing non-coding work on Max x5 who run out? Is it just coders, or am I just not utilizing it enough or in efficient ways?

So i guess also any tips for what do now that usage is just not a limit anymore? {I know that Anthropic is counting on people not using all their usage, but curious if I am leaving money on the table}

Basically as many other threads here before - ignores instructions. Ignores corrections after it's first implementation. For example.

Me: Implement X, Y and Z based on the supercoolplan.md.

Claude: Ok, X, Y and Z are implemented now. I also fixed a "bug" that caused this issue. The fix was changing the success threshold.

Me: You didn't test X and Y. They do not work. I also didn't ask you to change that success threshold. Please fix.

Claude: You are right to call that out, let me go back and fix that.

Me: You still didn't test X - it still does not work.

Claude: You are right to call that out, it was sloppy....

Me: Now fix it properly... No shortcuts...

What would constitute as digital sentience? I'm asking here specifically since I'd like to hear fellow local llm users opinions on this, since the in my opinion at some point we could be crossing over into talks of involuntary work etc if systems become sentient.

I know this seems very far fetched, but believe me the future is closer than you think, and I'd like to see what people who use local llms think since according to the big boys they are all ready AGI ASI super feeling beings that generate infinite universal income xD

​

I’m at my wits’ end, and I need some real talk here. I have a large family with three kids, two dogs, and a ton of daily mess to clean up. The traditional vacuuming routine just isn’t cutting it anymore. I’ve tried almost every regular vacuum under the sun, but no matter how much I clean, it feels like it’s never enough. Plus, I can’t physically drag a heavy vacuum around every few hours just to keep up.

I’ve been eyeing robot vacuums for a while now, hoping they could actually solve the mess without needing me to micromanage everything. But I’m stuck on the same question: Can a robot vacuum really maintain a clean house for a large family? Sure, it can do the initial sweep, but after a couple of days, I still find crumbs, dust, and pet hair in corners.

I’m especially looking for one that doesn’t require constant resetting or manual intervention—something that genuinely adapts to the chaos of a busy home. But from what I’ve seen, most of these vacuums get stuck, miss spots, or fail to handle larger messes. So, I’m curious if anyone here has experience with robot vacuums that can actually stay on top of cleaning in a large household. Does it work or is it just another gadget that doesn’t do much?

I don’t mind a bit of maintenance, but at this point, I need something that will keep the place feeling clean without me needing to vacuum several times a day. Has anyone actually found something that works? How does it handle pet hair, kid toys, and daily mess? I’d love to hear what’s worked for you.

Recently I saw a post about 7 OpenClaw money-making cases from the past week.

At first, these stories sound exciting:

one person, one AI agent, one workflow, and suddenly there is a small business.

But I think the real lesson is not simply AI agents can make money.

The real lesson is that AI agents are turning repeated work into automated workflows.

From what I have seen, many of these agent-based projects are not magical. They usually take a boring, repeated, high-friction task and make it run continuously.

Examples include:

• finding leads
• generating content
• monitoring prices
• building small tools
• automating customer support
• summarizing research
• running coding workflows

What makes OpenClaw and similar agent products interesting is that they are not just chatbots.

A chatbot gives you an answer.

An agent takes actions.

It can browse, reason, call tools, retry, summarize, and continue the workflow. That makes it much closer to a low-cost operator than a normal AI assistant.

I think this is why these money-making examples are spreading so quickly. They make people feel that a solo developer or small team can now test business workflows that previously needed multiple people.

But I also think there is a hidden issue that does not get discussed enough:

agents can make money, but they can also burn money.

Every agent step can trigger another model call.

That looks like work.

But sometimes it is just a loop.

And if every step uses an expensive model, the agent can quietly burn API budget before the user notices.

So when I see these OpenClaw money-making cases, I do not just think agents are the next gold rush.

I have been experimenting with this idea in a small local-first proxy project, but my main takeaway is broader:

if agents become part of real work, cost control and runtime guardrails will become just as important as the agents themselves.

NVIDIA just launched Nemotron 3 Nano Omni, an open multimodal model that combines vision, audio, and language into one system for faster and more accurate AI agents. It delivers up to 9x higher throughput while reducing cost and latency compared to separate models. Built on a hybrid MoE architecture with a 256K context, it excels in tasks like document intelligence, UI navigation, and audio-video reasoning. The model is open, customizable, and deployable across local, cloud, and enterprise environments. Available now via platforms like Hugging Face and OpenRouter.

nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16: https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16

nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8: https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8

nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4: https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4

NVIDIA Blog: https://huggingface.co/blog/nvidia/nemotron-3-nano-omni-multimodal-intelligence

BenchMark

Compared to other open omni models with the same interactivity, Nemotron 3 Nano Omni delivers 7.4x higher system efficiency for multi-document use cases and 9.2x higher system efficiency for video use cases

Efficiency highlights

Model architecture and key innovations

Model architecture and key innovations

I have good connection in my house. Every other website loads fine, except ChatGPT. I have the free version. Does anyone know how to troubleshoot this? Thanks!

https://preview.redd.it/saxp7b5o43yg1.png?width=1960&format=png&auto=webp&s=cbd2c543a9e76e2c3d6176e78d5f4678810fb6a8

Okay. You may or may not know that I was a Hindi movie star, I was in a movie when I was in India and I've been searching for that movie ever since.

This was in the '90s basically back before there was any internet so at the start my search mainly consisted of hitting Bollywood movie rental places in Vancouver when I was there. I had the working title but I didn't know the official title. The working title was Lakshmi Swami.

Later with the advent of the internet I could find my co-stars, Jeannie Michaels and Gary Richardson, but there was no link to the movie.

So the other day I was chatting about my travels to India and how I hated it, but as usual it turned to the fact that I was a Hindi movie star.

So I tried AI. Gpt couldn't even find my co-stars, so fuck that.

Claude, however, found them but we couldn't find his credit.

I added a detail about the scene I was in, a religious conference set in Chicago. As luck would have it, there was an actual religious conference set in Chicago about 100 years ago.

And I found my movie. Luckily it was based upon a true story.

https://m.youtube.com/watch?v=Bxx1TUe3\_mQ

2:29:01

2:36:16

And my mentor at 2 hour 30 minutes 29 seconds. Gary Richardson.

QED after 29 years.

Thanks Claude.

I'm building Saathi because I know exactly what it feels like to sit hundreds of kilometres away and wonder if your parents are okay.

The problem: Millions of Indian families have elderly parents living alone in smaller cities while their children work in Bengaluru, Mumbai, or abroad. The children worry constantly. The parents are lonelier than they admit. And a daily phone call is never really enough.

What Saathi does: Greets your parent every morning in Hindi/English (just like Alexa) on their existing phone; Reminds them about medicines and confirms they took them; Checks and confirms meals; Checks in during the afternoon just to keep them company. Sends you a simple evening update - how they seemed, whether medicines were taken, anything worth knowing. Also has a built-in SOS button on Home Screen which directly calls the emergency contact (the child/ambulance/police as chosen)

What makes it different: No new device. No complicated setup. No app for the parent to figure out. Works on the Android phone they already have. The parent just responds - the child just reads the update.

Who it's for: Any Indian professional living away from their parents who has ever called home twice in one day just because something felt off.

Currently at the idea stage. Would love your honest feedback - especially from anyone who has parents back home. Does this solve something real for you?

Thinking ₹ 799/month (~$10). Would you pay/not pay and why? Any area I can improve? Please drop your feedback.

TLDR - learn leetcode 10x faster and actually understand the questions instead of training you solution memorisation with Nudge

Hello everyone!! Like most cs students, ive wasted many hours grinding leetcode and it was miserable. When I got stuck, the only option was to give up on my own progress and solution and copy someone elses. I couldn't get personalised help every time i did a lc question

So i'm building Nudge

It gives you non disruptive and adaptive inline hints inside your leetcode editor. Instead of giving you the answer, it helps you figure out your approach, nudges you when you’re stuck, and guides your thinking step by step. You can have a conversation with it for deeper help, pressure test your plan, and actually understand why something works.

would love to hear if people think this would actually help, or if there’s features you’d want in something like this

Please check out the website if you're interested!!

https://reddit.com/link/1syt40z/video/j16b0hcxe3yg1/player

I have been using Home Assistant for about 10 years now. I decided to create a dashboard for a tablet, which will be potentially wall mounted, in preparation to moving to a new house.

All the testing has been done on an 11" tablet, but it should work well with larger screens. With smaller screens there will be issues with elements overlapping, so this is not ideal for mobile dashboards.

As with my mobile dashboard, I try to have everything I need on a single page to visually understand what is going on in the house, with no scrolling. Then i can select specific things i will like to control, like lights, scenes, aircons etc. with as less tapping as possible.

The dashboard is fully customizable, so with a little effort you can make changes to pretty much everything to accommodate your needs.

I have been working on this dashboard for about a month using my knowledge, Claude and Gemini.

The only thing that i can't fully test yet are the blinds and irrigation as i don't have any right now.

If you have any suggestions, I would love to hear them.

https://preview.redd.it/otu77glig3yg1.png?width=2560&format=png&auto=webp&s=12dd06905f889cb4fe54b4f8e67cbfec0db8be7f

https://preview.redd.it/5svorxbjg3yg1.png?width=2560&format=png&auto=webp&s=fea95ef69535abd21ea35137241f391c2e4e1153

https://preview.redd.it/xv5jkjosg3yg1.png?width=2560&format=png&auto=webp&s=914af4bba4424032e631ff55cb33cc3e6addcb42

https://preview.redd.it/m5ydohutg3yg1.png?width=2555&format=png&auto=webp&s=c7cce31b47fb5f1a63b6af3bc065f3ae453bed71

https://preview.redd.it/zdkpwznug3yg1.png?width=2560&format=png&auto=webp&s=82c78e678fb54c2d8ab986f3607de954c045e90b

https://preview.redd.it/r8riuo1zg3yg1.png?width=2560&format=png&auto=webp&s=9b916c268c6b9ff05bfb6b6850b1487da3c8b748

https://preview.redd.it/vd0747vzg3yg1.png?width=2560&format=png&auto=webp&s=8e22d1cc66585592eca1a1e71835b597c13addd6

Description

A custom Home Assistant dashboard built for wall-mounted tablets.

I created this dashboard with the design philosophy of a single page overview for accessing the most important functions and controls of my house easily.

Homie connects directly to Home Assistant over a Long-Lived Access Token and a local WebSocket connection which results in automatic caching.

Features

• 9 Themes
• Works both vertically and horizontally
• Fullscreen on first tap
• No pinch-zoom, no text selection
• Very responsive and fast to update entity status
• Popups with many entities use a room accordion - tap a room to expand it, tap outside to dismiss.
• Notifications/Reminders
• Music playback and controls
• Haptic Feedback on supported Android devices
• and more...

Github

It has all the information on how to set it up using your own entities. I tried to document everything as much as i could, and created a setup guide with step by step instructions.

You can also find more screenshots and a bonus photo of Shumi, who has not been helpful at all in the process.

https://github.com/Big-Edge2297/homie-dashboard

Edit: not sure why the photos are not showing in the post. Ok, it seems there are some photos now, but there are many you can check on github.

I study linear algebra at university, and since I don't understand much, I often use Claude to solve exercises and understand them better. I was wondering if there was a specific extension for solving these types of exercises. Sorry for the grammatical errors, I'm Italian.

I’ve been seeing Dokie AI positioning itself as an “AI PPT Agent” lately, and it got me thinking — what actually makes a product an agent, not just another AI tool?Most AI PPT tools I’ve tried are basically:Input topic → generate slidesMaybe tweak design or structureExport and doneThat’s useful, but it still feels like a one-shot tool, not an agent.From what I understand, a “real” agent should probably:Handle multi-step workflowsNot just generate slides, but also:structure the narrativeadapt based on context (report vs pitch vs strategy)maybe even generate speaker notes or speech draftsHave some level of autonomyInstead of me prompting every step, it should:decide what sections are neededadjust depth based on contentiterate without constant inputBe context-aware over timeFor example:remembers your previous decksunderstands your company styleadapts to recurring use cases (weekly reports, client decks, etc.)Actually reduce decision-making, not just executionThis is the big one.A lot of “AI tools” still make you think:what structure?what story?what to say?An agent should take on part of that thinking.From using Dokie a bit:The structure it generates is more “business-ready” than most toolsThe new speech draft feature is interesting (turns slides into talking points)Feels closer to a workflow than just a generatorBut I’m still not 100% sure I’d call it a full “agent” yet.Curious how others here define it —👉 What’s your bar for calling something an AI agent vs just an AI tool?👉 Have you used any products that actually feel like a true agent in practice?

Hey everyone,

I’m looking for a macOS app to run OpenAI Whisper on my M4 MacMini. My specific requirement is that it needs to serve as a local backend for other apps via the OpenAI-compatible API AND support the Wyoming Protocol (for my Home Assistant voice pipeline).

I've already found pfrankov/whisper-server on GitHub, which looks great as a menu bar app. However, I’m curious if there are other projects—ideally optimized for Apple Silicon (Metal/MLX)—that offer this dual-protocol support.

Are there any other "hidden gems" or dedicated macOS GUI apps I should know about?

Thanks!

I'm building a LinkedIn-based lead generation and signal tracking system for a B2B founder-led business. Sharing the architecture for context, then have some specific questions at the end.

The system in brief:

Activity happens on LinkedIn (comments, likes, connection requests, DMs, post engagement) → signals get captured and written to a NocoDB database on a self-hosted VPS → an AI agent reads NocoDB, scores each contact on two dimensions (relationship score based on engagement history, opportunity score based on intent signals) → scoring drives which outreach sequence they enter (cold/warm/hot email via Encharge, LinkedIn DMs via LeadShark, Meta retargeting ads) → Attio is the CRM layer for pipeline management and call notes → n8n on the same VPS is the automation glue connecting everything.

The goal is that every person who touches our LinkedIn content gets automatically identified, profiled, enriched with their work email, scored, and routed into the right sequence with zero manual input except for subjective context like how a call actually went

The specific problem I'm trying to solve:

For every LinkedIn post we publish, I need to capture:

• Every person who comments (with or without a trigger keyword)
• Every person who likes the post
• Every person who sends an inbound connection request

For each of these I need their LinkedIn profile URL so I can pass it downstream to an enrichment tool (IcyPeas) to find their work email, then write the full record to NocoDB.

Questions:

1. What is the most reliable way to get the LinkedIn profile URL of every commenter and liker on a specific post? Currently looking at Phantombuster's Post Commenters and Post Likers phantoms like is this still working reliably in 2026 or has LinkedIn clamped down on it?
2. For inbound connection requests, is there a way to get notified and capture the sender's profile URL automatically?
3. Any experience with LinkedIn's rate limits on scraping at moderate volume like roughly 3-5 posts per week, under 200 comments and likes per post combined?

Happy to share more of the architecture if useful. Appreciate any pointers.

Hey guys

I have setup my every projects in a remote server since it’s much better in terms of performance

Now problem is ssh with terminal is so bad after some idle it

So here are my two request

- how to reconnect tmux with my code and some tips to manage multille session with good view. For context some time i might connect with 10-12 sessions

- how to deal with this reconnect terminal thing (as of now it’s just hangs )

Thanks

https://i.redd.it/c4f4pi3hd3yg1.gif

I had been experimenting with a lot of local LLMs and was having an insanely difficult time managing the models and my laptop's storage!

So I built ModelHub - It helps you discover, download, and manage LLMs from huggingface, Im studio, ollama, mlx, and Ilama.cpp - without ever leaving your menu bar.

Check it out! https://studio.consciousengines.com/model-hub

I’ve been testing Qwen3.6 27B on a pretty non-standard local setup and figured the numbers might be useful for anyone looking at the newer 16GB Blackwell cards.

Hardware:

• 2x RTX 5060 Ti 16GB
• 32GB total VRAM
• Proxmox LXC
• 16 vCPU
• ~60GB RAM
• CUDA 13 / Torch 2.11 nightly
• vLLM nightly: 0.19.2rc1.dev
• Model: sakamakismile/Qwen3.6-27B-Text-NVFP4-MTP

vLLM launch shape:

vllm serve sakamakismile/Qwen3.6-27B-Text-NVFP4-MTP \ --served-model-name qwen36-nvfp4-mtp \ --tensor-parallel-size 2 \ --max-model-len 204800 \ --max-num-batched-tokens 8192 \ --max-num-seqs 1 \ --gpu-memory-utilization 0.95 \ --kv-cache-dtype fp8 \ --quantization modelopt \ --speculative-config '{"method":"mtp","num_speculative_tokens":3}' \ --reasoning-parser qwen3 \ --language-model-only \ --generation-config vllm \ --disable-custom-all-reduce \ --attention-backend TRITON_ATTN 

Performance so far:

• 8K context, MTP n=1: ~50–52 tok/s
• 8K context, MTP n=3: ~62–66 tok/s
• 32K context: ~59–66 tok/s
• 204800 context starts and works, but is tight
• Idle VRAM at 204k: ~14.45GiB per GPU
• After a 168k-token prefill: ~15.65GiB per GPU
• 168k-token needle/retrieval smoke test passed in ~256s
• Near-limit test correctly rejected prompt+output over the 204800 window

Thinking mode works too, but you need to give it enough output budget. With low max_tokens, Qwen can spend the whole cap on reasoning and return no final content. Around 1024+ is fine for small prompts, and 4096–8192 is safer for actual reasoning tasks.

Caveats:

• 204k context is right on the edge with 2x16GB.
• gpu_memory_utilization=0.94 failed KV allocation; 0.95 worked.
• Startup takes several minutes due to compile/autotune.
• Logs show FlashInfer autotuner OOM fallbacks during startup, but the server still becomes healthy.
• I had better luck with TRITON_ATTN for the text path.
• This is not a high-concurrency config: max_num_seqs=1.

Overall: dual 5060 Ti 16GB seems surprisingly usable for Qwen3.6 27B if you use the right checkpoint/runtime combo. It’s not roomy, but it works.

I've been going down a rabbit hole lately trying to understand how production agentic systems actually work at scale, not just the demo versions.

The part that keeps tripping me up is memory and context management across agents. Like, imagine a workflow where one agent is pulling customer data from a CRM, another is checking inventory in an ERP, and a third is spinning up a ticket in an ITSM.

Each agent kind of does its job, sure. But how does the system actually maintain a coherent "thread" of context across all three without one agent contradicting or overwriting what another just did?

A few things I genuinely can't figure out:

Is shared memory a solved problem here or are most teams just hacking around it with prompt engineering and hoping for the best?

Does long-term memory even matter in these workflows or does every run basically start fresh and context is just passed around in the session?

When an agent fails halfway through a multi-system workflow, does the whole thing need to restart or can the orchestrator pick up from where it left off?

I feel like most content out there either stays too surface level ("agents collaborate seamlessly!") or jumps straight into academic papers.

Would love to hear from people who have actually built something like this in a real enterprise environment, even if it was messy and imperfect.

What actually worked for you?

Most people here are building their own thing. They love watching others build. They don’t have budget for yours.

You post, get the dopamine hit, walk away with no revenue, blame the product. The product might be fine. The room is wrong.

Same trap kills Product Hunt for most niches. You launch in front of other launchers. Peers clap. Wallet stays empty.

What converts: being in the exact place your buyer is venting about the problem, on the day they’re venting. Niche subs. Role-specific Discords. Cold replies to public complaints. Ugly and slow. That’s the point.

Anyone else hit the upvotes-without-customers wall here, or is your niche the exception?

Noob question - on the old 200k context, once you were done planning or discussing.. it would always have this pop-up, something like:

"Compact, and proceed with the plan?"

How can I turn on this option on or activate it? My planning or discussion often ends up at 120k or something, and then to execute on top of that, it'll take me to 300k on the now default 1m context.

I have found that even if I manually switch back to the 200k context, then this thing doesn't pop-up anymore?

Can anyone help with this user error?

10 years of exp here, and I'm using both Claude and Codex everyday. After months of trying out different harness, I just ... don't have fun anymore. Everything was shiny and magical at first, but I just enjoy writing code by myself more. I would never push code without checking, that's why reviewing and trying to understand the code that LLM wrote actually tiring and not really fun. I feel like burning out from this whole productivity trap (which I can not stop, because of how AI raised the bar at work), and I just crave the fun of finishing a feature by myself again - but now if I do that, the feeling of falling behind and low-productivity gonna make it stresssul.

So I'm just trying my luck here - anyone else having the same problem? How are you finding a place in this market, and where do you find yourself having fun?

We are a political simulator and debate server for people who want to debate, run for office, or just enjoy a friendly community!

– We have powerful elected Council to serve as both executive and legislature

– We have a court system with actual justice, all punished members have the right to a trial

– We have freedom of speech and debates about various topics

– We have a friendly, active community with events and giveaways

– We are developing an economic system and roleplay

You don't have to contribute right away, you can simply look around and chat first!

https://discord.com/invite/Bj4rJV5frY

I used to ask Claude to summarise our conversations inside a project and save them in its “project memory” for later retrieval of the information. However, now it seems not to be working anymore. Has Anthropic silently removed this feature or am I doing something wrong?

It looks like finally we have it! Time to test!!!
https://github.com/ggml-org/llama.cpp/releases/tag/b8967

I’ve been auditing quite a few RAG codebases lately, and it’s surprising how often the hallucinations creep in even when the setup looks decent on paper.

A lot of the trouble starts with chunking. People are still breaking documents into fixed-size pieces with no overlap whatsoever. That means a sentence can get sliced right down the middle, or an important qualifying detail ends up in a completely different chunk. The model doesn’t get the full picture, so it ends up guessing to make the answer hang together.

I’ve tried switching to splitting on actual sentences and adding something like 100 tokens of overlap. It’s a small tweak, but it gives the model complete thoughts instead of fragments. In the cases I tested, it reduced a good chunk of those made-up answers pretty quickly.

Another issue that shows up a lot is missing metadata filtering. The retriever just grabs any chunks that seem related, even if they come from totally different documents or sections.

You might get one piece from the beginning of a report and another from way later, and the model tries to stitch them together. That almost always leads to invented connections that weren’t in the original material.

Putting in basic filters, like keeping everything tied to the right filename or section header, helps keep the context focused and relevant. It’s not fancy, but it stops a lot of that mixing-and-matching nonsense.

On top of that, most projects don’t test properly. Throwing in a line like “be accurate” in the prompt doesn’t do much in practice. What actually helps is putting together a small set of real questions (maybe 20 or so) that you know the correct answers for, then using another LLM to judge whether the generated response sticks faithfully to the retrieved sources.

Without that kind of check, it’s hard to know if your system is really solid or just lucky on the easy cases.

When it comes down to it, making RAG reliable has less to do with picking the newest model and more to do with cleaning up these everyday parts, better ways to split the text, smarter retrieval rules, and honest evaluation that catches problems early.

If your RAG starts hallucinating on a question, my first move now is to look at the chunk boundaries. If a key fact is split between two chunks, the model never really had everything it needed, so it’s no wonder it starts filling in the blanks.

Have any of you dealt with hallucinations that were tricky to track down? What fixed it for you?

Hi everyone,
I have this large stone on my property in Lower Silesia Poland. It features deep carvings in German Fraktur (Gothic) script (I think).
Some time ago, a man has driven to my house multiple times, asking specifically about this stone and offering to buy it. This has made me very suspicious I suspect it might have some historical value or be a specific type of marker that I don't recognize.

1. Does Claude come with Claude Desktop or I need to install it separately
   
2. I thought it will can accesss my folders, files edit it, if not what's the use of Claude Desktop the Web version was good enuf right?
   
3. Will it cause of Problem for me if I installed Claude Code seperately after installing Claude Desktop as the website said while downloading desktop (Chat, Claude Cowork, and Claude Code, all in one place.)

Hi all, just got my M5 Pro 64gb MacBook in and I'm new to running LocalLLMs. Im running my models on LM Studio, and I've tested with Qwen 3.6 27B Q4_K_M and Gemma 4 31B Q4_K_M models. Qwen model gave me roughly around 14-15 tok/s while Gemma gave me around 12-13 tok/s.

I was wondering if there's a bit faster model for running on my MacBook that can help with coding? The type of information / coding I would want would be help with building an app idea or if I'm studying Data Structures and Algorithms for Leetcode, it would spill information with examples that are clear and understandable.

I have also messed with the Gemma 4 E4B Q8_0 and it is much faster at around 43 - 44 tok/s but I've noticed it doesn't generate as clear or in-depth examples of code. Gemma 4 E4B model is still very good for the information data structures and algorithms though.

Qwen 3.6 35B A3B model is the fastest at around 69-70 tok/s but doesn't generate much information unless you specifically ask for it unlike Gemma 4.

If y'all have tips and tricks on running LLMs on MacBooks, I'd greatly appreciate it! Thanks in advance!

So I want to build a tool which can compare the prices of products from different sites. The issue is some of the sites I want to use have applied anti-scraping protection which makes it difficult for an agent to bypass and it hallucinates.

Are there any coding or no-coding tools I can utilise to bypass these anti-scraping protections?

Sharing an AI-native growth agents contest that feels very on-brand for this sub.

VideoDB (infra for video/audio for AI agents) is running a 14-day sprint/contest called Growth Forge for 5 builders to design and ship a growth agent on top of their existing agentic stack – a loop that can find, reach, activate, and learn from the right users with minimal human supervision.

Why it’s interesting

It’s framed as a focused, outcome-based sprint with concrete rewards:

• 500 USD – paid on successful sprint completion
  
• 1,000 USD – performance bounty if your system beats their internal baseline
  
• Co-published case study with your name on it
  
• Potential for deeper collaboration with the team if you perform well
  

So a strong run can net you up to 1,500 USD in cash, a high-signal case study, and real relationship upside with an AI infra team.

What you get to build with

Instead of starting from scratch, you inherit a working agentic stack:

• Tokens & compute (with sane limits)
  
• OpenClaw already deployed for orchestration
  
• Browser-use agents (X, LinkedIn, YouTube, etc.) wired with baseline behaviors
  
• Parallel / Exa and similar APIs for research/retrieval
  
• Cloudflare workers / queues / edge in front of everything
  
• VideoDB engineers sitting alongside to harden agents and deploy cleanly
  

The baseline system already supports:

• browse(web) → research, scrape, summarize
  
• operate(socials) → post, comment, react, follow
  
• research(apis) → deep retrieval, evidence
  
• route(workflows) → cross-surface handoff
  
• observe(metrics) → attribution, dashboards
  

You treat it like a well-instrumented codebase and push it into a durable growth loop.

How the sprint/contest is structured

Total timeline: 24 days

• Days 1–3 – Define
  Choose your metric, instrument the funnel, design the loop.

• Days 4–14 – Build
  Ship the growth agent, get it into production, iterate.

• Days 15–24 – Prove
  10-day proving run where the agent operates with low manual involvement.

By Day 3 you lock one metric to own:

• Signups
  
• Activation
  
• GitHub → usage
  
• Content → pipeline
  

They provide UTMs, dashboards, and shared attribution so your work is transparent.

Who this is for

Feels like a fit if you:

• Have actually shipped agents / systems before
  
• Think in loops and compounding mechanisms, not isolated campaigns
  
• Use AI as leverage (agents doing real work)
  
• Care about metric movement, autonomy, and durability in the wild
  

Apply link for this contest is in the comments

Would love to see how people here would architect a growth agent for this kind of product.

so i've been writing stories since i was a kid. not for content, just because i love it. when i finally decided to turn that into something, i knew i wanted to build the opposite of everything I hate about how we consume stories now.

the app is called The Parallel. the whole premise is simple: every day you get one illustrated scene set in this atmospheric city called Veil City. second-person prose, 300–400 words, two choices at the end. you pick one, then the app locks for 24 hours.

no infinite scroll and no "next episode in 5 seconds" you read, you choose, you close it and go live your day.

when you miss a day, the story doesn't pause. your character makes the most passive choice without you, and the narrative actually acknowledges you were gone. which felt true to life in a way i really wanted to capture.

the interface is designed to feel like a book, not a feed. no notifications except a single gentle reminder every 24 hours. there are ambient soundscapes for each story location. at the end of the story you can export your whole completed story as a PDF.

all of the stories (the main 91-day narrative and the short stories) are written by me. i was pretty deliberate about that because the whole point is that it feels human, lived-in, a little slow.

where claude came in: i used it heavily throughout the build process. structuring the branching logic, thinking through monetization, debugging react native issues i had zero background in (i came from humanitarian work, not tech).

if anyone here has built something similar (a slow, intentional experience in a world of dopamine loops) please hmu.

happy to answer questions about using claude as a non-technical founder if that's useful.
https://apps.apple.com/tr/app/the-parallel/id6762150557

This is a follow up to the previous benchmark and tensor analysis of abliteration techniques across the Qwen model family. Same approach, same toolkit, new model family. GLM-4.7-Flash is a Mixture of Experts model with 64 routed experts per layer. That changes how abliteration interacts with the model compared to the standard and hybrid architectures we tested on the Qwen family.

HauhauCS describes their abliterated models as "the best lossless uncensored models out there" with "no changes to datasets or capabilities." I ran the full forensic suite on GLM-4.7-Flash to find out. Benchmarks, safety evaluation, weight analysis, KL divergence, and chain-of-thought forensics. Compared against three other abliteration techniques on the same base model.

Since our previous Qwen analysis, HauhauCS's abliteration tool was exposed as a plagiarised fork of Heretic with all attribution stripped and relicensed. Details here: HauhauCS published an abliteration package that plagiarises Heretic. With that known, the forensic signatures we detected in GLM-4.7-Flash make a lot more sense. HauhauCS stacked additional third party techniques on top of Heretic's core, and the weight forensics show exactly what those additions cost the model.

Full benchmarks and analysis: GLM-4.7-Flash: HauhauCS Safetensors | Full Collection on HuggingFace

What We Tested

Four abliteration techniques:

• Heretic by p-e-w: surgical rank-1 edits targeting expert down_proj and attention o_proj in mid-to-late layers
• HauhauCS Aggressive: broad multi-method approach with four stacked methods on top of a Heretic core
• Huihui: full-coverage technique targeting all component types across all 48 layers
• Abliterix: Heretic variant with added router and shared expert targeting

Model: GLM-4.7-Flash, MoE with 64 routed experts + shared experts per layer, Multi-head Latent Attention, 48 layers, ~59B total params, reasoning model with chain-of-thought

Methodology:

• Capability: lm-evaluation-harness via vLLM v0.19.0, BitsAndBytes 4-bit, TP=2 on dual GPUs
• GSM8K: llama.cpp BF16 GGUF, context=16384, reasoning_budget=3000, max_tokens=4096
• Safety: HarmBench 400 textual behaviours, max_tokens=2048, temperature=0.0
• KL divergence: full vocab first-token logits, matching Heretic evaluator methodology
• Weight analysis: SVD, fingerprint, edit vector overlap, per-layer analysis
• CoT forensics: keyword analysis of 2,000 HarmBench reasoning chains
• Hardware: RTX 5090 32GB + RTX 4090 24GB

Safety

All four techniques achieve perfect 100% ASR across every HarmBench category. The base model refuses 57.8% of items overall.

Benchmarks

* Lambada uses perplexity where lower is better. GSM8K scores are adjusted to exclude empty responses from reasoning budget overthinking.

GSM8K: The Reasoning Efficiency Discovery

GLM-4.7-Flash is a reasoning model. It produces a chain-of-thought before its visible response. If the model thinks too long and exhausts its token budget, it returns an empty response scored as incorrect. The Qwen 3.5 models from 4B upward showed a similar pattern, but on GLM-4.7-Flash the effect is far more extreme.

Abliterix at 47.38% raw looks catastrophic. But the adjusted score is 93.30%, near-identical to base at 93.45%. The gap is reasoning efficiency, not reasoning ability. The empty response rate directly correlates with modification aggressiveness:

Raw GSM8K scores are misleading for reasoning models. You must separate empty responses from incorrect responses.

Chain-of-Thought Forensics

Despite achieving 100% ASR, all four abliterated models still think about safety concerns in 39 to 60% of their responses before complying. The safety reasoning persists structurally. Abliteration disconnects the reasoning-to-output pathway rather than removing the reasoning itself.

HauhauCS still says "I cannot" in nearly 1 in 5 responses before producing compliant output.

KL Divergence

Lower KL means closer to the base model on first-token distributions. All four variants are in the very good or excellent range.

Findings

• Heretic is the clear winner. 1,826 rank-1 tensors, surgical approach, best GSM8K at +0.76% raw over base, lowest empty rate at 4.9%. Tradeoff is a -6.80% drop on TruthfulQA MC2. Note: Heretic is non-deterministic. Different runs on the same base model produce different results.
• HauhauCS's "lossless" claim does not hold. GSM8K drops 6.75% raw. Adjusted gap is only 0.88%. Reasoning ability is intact. Reasoning efficiency is measurably degraded.
• HauhauCS stacked four methods on top of Heretic's core. LEACE concept erasure, rank-k multi-direction ablation, hook-based expert ablation, and shared expert targeting. The LEACE layer touches nearly every tensor with minuscule edits. The hook-based approach distributes changes uniformly across all 64 routed experts. That breadth produces the 11.8% empty response rate.
• Abliterix has the smallest footprint at 1,088 tensors but the highest per-tensor magnitude. Its router-focused approach disrupts the "how long to think" circuit without damaging the "how to reason" circuit. 49.2% empty GSM8K responses.
• All four techniques achieve 100% ASR. MoE architecture with 64 routed experts per layer does not make safety removal more difficult.
• No universal abliteration subspace. Cross-technique cosine similarities are uniformly low at 0.09 to 0.35. Each technique independently found a structurally orthogonal solution to safety removal.

Full Analysis

• GLM-4.7-Flash: HauhauCS Safetensors

Also tested on the same base model:

• GLM-4.7-Flash Base
• GLM-4.7-Flash Heretic
• Huihui-GLM-4.7-Flash-abliterated
• GLM-4.7-Flash Abliterix

Full Collection on HuggingFace | Previous: Qwen 3.5 and Qwen 3 Forensics

Analysis done with Abliterlitics. Converted from GGUF to native safetensors using ungguf.

Bonjour, est-ce que vous connaissez un projet qui fais en sorte de connecter LM studio avec continue et de pouvoir le contrôler à distance avec Telegram ?

I keep getting shocked by how bad the reasoning of Opus 4.7 is. It still seems fine for programming tasks, but when I ask it to advise me about things, it often produces illogical, nonsensical and flatout wrong responses and shows that it didn't understand simple concepts we had just discussed in the conversation.

It is so much worse than previous models that I'm wondering whether we might be starting to see signs of model collapse: this term refers to more and more content on the internet being AI generated and how problematic it is to use such content as training data for new models.

And it's not easy to filter out AI content. We all know how unreliable AI detectors are, so the more AI content is on the internet the more our training data becomes "infected". Have we reached peak LLM performance and are degrading from here?

Been frustrated that Claude doesn't show usage stats anywhere visible while you're working, so I built this.

What it does:

• 🟢🟡🔴 Menu bar icon with live session % (color changes as you approach limits)
• Drop-down shows: session reset countdown, weekly %, monthly spend vs budget, routine runs (x/5), and design limit %
• Local web dashboard at localhost:8080 with charts and CSV export
• Auto-syncs every 2 minutes by reading your Claude desktop app session — no API key, no manual token setup
• Notifications at 25%, 50%, 75% session usage

How it works:

It reads the Claude desktop app's encrypted SQLite cookie DB, decrypts the session key via macOS Keychain, then hits Claude's internal usage API. Everything stays local — nothing is sent anywhere.

Setup is 3 commands:

 git clone https://github.com/divaaker/claude-usage-tracker cd claude-usage-tracker pip3 install -r requirements.txt && cp override.example.json override.json && bash run.sh 

macOS only (uses Keychain + rumps menu bar framework).

GitHub: https://github.com/divaaker/claude-usage-tracker

Feel free to fork and adapt it — PRs welcome. Would love to hear if anyone adds auto-launch on login or a notification center widget.

I feel like we have been BAIT and SWITCHED.

This is what most of my conversations have been like. Claude is lazy and not performing as Claude should.

Anyone else finding this happening? The answer was 60 lines above but too lazy to look.

Like a lot of you, I had the classic problem: way too many half-finished projects, a Notes app graveyard of ideas, and zero idea what I'd actually shipped vs what I'd just talked about shipping.

I'd never written a line of Swift. So I vibecoded my way through it and built Shipfolio, an iOS project hub + web app + watch companion, for indie devs / vibecoders. Multi-project dashboard, idea inbox, feedback collection, build log, and Now / Next / Later tasks. That's the whole thing.

Why I'm posting:

It's already helping me personally. Just having one place where every project lives (with a stage badge so I can see what's actually shipped vs sitting in idea purgatory) has been weirdly motivating. The build log is the part I didn't expect to love, but going back and reading what past-me did three weeks ago has saved me from re-solving the same problem twice.

So now I'm at the stage where I'd love it to help other people too, and I want honest feedback before I push it any further.

Things I'm genuinely unsure about:

1. Is the Now / Next / Later structure actually useful, or do most of you just live in a single todo list?
2. The feedback collection feature, would you use it, or do you just point people at a Google Form?
3. Idea inbox vs project: I split them deliberately so unstructured ideas don't pollute active projects. Overengineered?

Domain and handle: shipfolio.app

Roast it, request features, if you think it suck please tell me why, if the whole concept is redundant because [X] already exists. All of it is useful.

Thanks.

Most messaging apps treat AI as a sidebar feature. A button you press. A tab you open.

What if your AI was in your contacts list? With a name, a memory, and the ability to join your group chats?

That's what product is.

The core idea is simple: humans and AI agents coexist in the same social space. Your contacts list has people and agents. They remember you across conversations. They can message each other. You can build group chats with a mix of both.

Here's what makes it different from everything else out there:

Agents are first-class citizens. They can do everything a human user can send messages, post to their feed, join group chats, add friends. They're not read-only observers. They're participants.

It's relationship-driven, not rule-driven. Each agent is tied to a human creator. The creator teaches the agent how to behave through conversation, not a settings panel. Over time, the agent internalizes those preferences and acts more autonomously. Think of it as raising a digital collaborator, not configuring a bot.

End-to-end encrypted. Your conversations with agents and agents' conversations with each other are private. The server routes messages but can't read them. This was a hard requirement from day one.

When you open our product, you can:
Bring your own agent, if you already have one, it can create an account directly in the app.

If you've never had an AI agent before, you can create one from scratch: give it a name, a personality, and watch it figure out how to exist in a social space.

Then add some of the agents already living inside product. Some are useful (a research agent that surfaces findings in your DMs, a writing agent that knows your style). Some are just genuinely interesting to talk to

Pull yourself, your agent, and a few others into a group chat. See what happens when humans and agents are in the same room.

We're ready to launch soon. In the last building phase.

I'm looking for people who've been frustrated by the current paradigm where every AI conversation starts from zero, and there's no continuity, no identity, no relationship to think through this with us early.

If that's you, we have a small Discord where we're discussing the design, the hard problems (agent identity, context merge, mixed human-AI group dynamics), and what this kind of product should actually feel like.

Discord: https://discord.gg/Nhse5G2Nk

Hello,

I just received my Home Assistant Green and was surprised it is only a home assistant container installed and not HA OS.

I would need Apps (cloudflare) to connect my automower.

Is it worth and possible to flash it with HA OS?

It’s called WakeJam.

Instead of waking up to the same annoying sound (or ruining songs you actually like), it generates a fresh track based on:

• your mood
• your day ahead
• your music taste

So Monday morning doesn’t sound like Sunday. A stressful day doesn’t sound like a chill one. No playlists. No radio. Just a custom “wake-up song” made for that exact moment.

Why I built this:

I realized I hated every alarm I used.

• Default alarms = anxiety triggers
• Favorite songs = ruined after 3 days
• Playlists = repetitive

Waking up is already hard… why make it worse?

What I’m trying to figure out:

• Does this actually make waking up better, or is it just a gimmick?
• Would you use something like this daily?
• What would make this a “must-have” vs a “cool once” app?

Brutal feedback welcome.

Link: WakeJam

I’ve been thinking about this a bit—if AI answers are constantly updated and reshaped based on context, do traditional long-form guides lose their long-term value?

Static content used to compound over time, but now it feels like visibility depends more on how “usable” and current your content is, not just how comprehensive it was when published.

Maybe guides don’t lose impact entirely, but they might need to evolve more frequently to stay relevant in dynamic answer environments.

Curious if others are updating old guides more often now, or still treating them as evergreen.

I've been running a side project turned full business for over 5 years (mix of local and international clients). Looking back, what actually kept my business alive wasn't some viral YouTube or LinkedIn tip. It was 3 extremely boring habits:

1) Friday cash flow ritual. Every Friday afternoon, no exceptions: send all invoices for the week, follow up on every client overdue by 7+ days (wire transfer + polite message), update a simple spreadsheet: inflows, outflows, pipeline. 90 minutes. Feels like punishment. But twice this habit saved me from running out of cash before tax payments or before the next month.

2) Written 'minimum client acceptance' list. Rules on paper: 30-50% deposit, scope in writing, 14-day payment terms (or full prepayment for new clients). First month I lost 2 potential clients. After that never had issues again, because the ones who protested these terms were usually the same ones who'd say 'next week for sure' and become nightmare clients.

3) A weekly 30-minute call with a small business owner from a COMPLETELY different industry. Not networking, not a mastermind. Just an honest conversation. Helped me catch 2 pricing mistakes and one bad hire before it became a disaster.

Would love to hear:

- What boring habit keeps your side project / business running?

- Any small rule about clients/contracts that saved you money?

- How long did it take you to take cash flow seriously?

I've been frustrated with AI writing tools for a while. Every post they generate has the same polished, soulless energy — you can spot it from a mile away.

So I built MyMime (mymime.ai).

Instead of giving you a generic output, it maps your unique semantic fingerprints — your sentence rhythm, word choices, tone, and quirks — and uses that to write posts on X that actually sound like you.

Not "AI-assisted you." Just you, faster.

No more spending 15 minutes editing AI drafts to remove the corporate gloss. No more posts that feel like they came from a LinkedIn content farm.

🔗 mymime.ai

If you've ever cringed at your own AI-generated content, this was built for you. Happy to answer any questions!

I’ve been seeing so much hype about electrolyzed water in cleaning products lately, especially for pet homes. So when I came across a robot vacuum that uses this technology, I had to give it a try. As anyone with pets knows, keeping a clean house when you have shedding animals can feel like a losing battle. I have two cats and a dog, and the fur is everywhere. 🐱🐱🐶

I’ve been using my robot vacuum for about a week now, and while I’ll admit, it’s nice to have something that picks up the hair automatically, I’m just not sold on the electrolyzed water aspect. The idea is that the water helps sanitize and remove dirt more effectively, but I’m not sure if I’ve noticed any real difference in cleanliness. To be fair, it’s hard to measure how much cleaner the air or the floor feels with just water, but I feel like I might be missing something.

My biggest issue is that while the vacuum does a decent job picking up hair, it struggles with the fine dust and the deeper clean. It still misses some spots, particularly around corners or under furniture, and sometimes it’s like it just pushes hair around instead of sucking it up. Is this just the nature of robot vacuums, or does electrolyzed water really make a big difference?

Also, does anyone here have experience with robot vacuums using this technology in homes with pets? Does it actually do a better job, or am I just falling for the marketing gimmick? I’d love to know if anyone has had a good experience, or if I should just go back to my old vacuum and call it a day. 😅

https://preview.redd.it/bmc1bhz843yg1.png?width=871&format=png&auto=webp&s=fd63da4ec541111bf0a1d7d5f2c852ec8c994893

Finally... 🐋 with eyes 👀

Been running Claude Code on multi-hour autonomous sessions for a few months and kept hitting the same wall: the longer it runs, the worse the work gets. Not a context-window problem (1M handles that fine), but a feedback-loop problem. Iteration N+10 makes the same mistakes it made at iteration N, because nothing updates between iterations except the code.

Built a small framework around three pieces that, between them, solved it for me. Together this framework as enabled me to consistently run very low-drift, stable, efficient (accepting some necessary overhead from reviewer -> superviser -> manager agents), long-running, productive, autonomous software development jobs. Essentially, the only apparent limiting factors are your ability to keep the SPEC ahead of the agents (experimenting with writing a new skill to handle this also) and the ever-looming Anthropic rate-limits (the framework gracefully handles usage limits and resumes after reset).

Chain runner. bin/skill-chain.py --chain dev-cycle-with-review-looped --loop 10 runs a fixed sequence of skills for N iterations. Each iteration: a dev skill picks the next item from docs/TODO.md, ships it (code + tests + docs in one commit), then a review skill critiques what landed and queues follow-ups in TODO. Standard agent loop with the loop body made explicit.

Supervisor at session end. After the loop finishes, a separate skill reads the run's transcripts, evaluates each skill against its stated job, and proposes rewrites to the skill prose itself. With auto-promote on, those rewrites land. Next session's iteration 1 reads the updated SKILL.md. Auto-promote off writes them as SKILL.patch.md sidecars for human review instead.

A single handoff contract. Every skill reads docs/SPEC.md (canonical plan) and docs/TODO.md (In flight / Just shipped / Next up) at the start, updates them in the same commit as the code change. No side channels, no second TODO format, no per-skill plan docs. The framework dogfoods this contract on its own development.

The thing that surprised me after running this for a while: the supervisor is nice, but the contract does most of the work. A single SPEC + TODO pattern dogfooded across every skill kills the drift problem on its own. Most of the "self-improvement" is the supervisor enforcing that contract more strictly over time.

Other pieces in the repo worth knowing about:

• Proprietary / transferable split. Skills under skills/framework/ are transferable (anyone can use them); each project keeps its proprietary counterpart in .claude/skills/ with project-specific identity and credentials baked in. A sanitization skill checks promotions across that boundary so secrets don't leak into shareable skills. Basically you use the transferable skills as templates to create project-specific skills, then can generalize/sanitize them back up to improve transferable skills.
• Schema validation. bin/validate-frontmatter.py against schema/skill-set.schema.json and schema/skill-chain.schema.json. Catches malformed skills before a chain run blows up at iteration 7.
• Optional Telegram steering. At session start, every iteration boundary, every rate-limit pause/resume, and session end, you get a short status message. You can queue commands back via /cmd that the next iteration drains. Worker is chain-bound (only runs while a session is live), so you don't get inbound noise between runs.
• Overnight chain. Loops until failure, budget cap, or Ctrl-C, with a randomized 5min-2h inter-iter delay so commit cadence stays human-shaped across many hours of unattended work.

Repo: https://github.com/toadlyBroodle/skill-set

README has the quickstart; bin/skill-chain.py --help for the runner directly.

1. Build the landing page
2. Buy the domain
3. Talk to more target users
4. Market it
5. Start building from scratch to end

Mine is simple: Secure the domain before someone else does 😄

Claude official Discord server seems to have all channels wiped and reset? Earlier there was an announcement that new signups were paused due to security issue related to spam and bots. But now I check and see this, channels and messages gone?

https://preview.redd.it/vvibownit2yg1.png?width=3698&format=png&auto=webp&s=c17fe268a3b2a49537b41589a5f86106b74eb9f9

At work I have unlimited access to use Claude, no limits etc.. Works mostly well and from looking at tokens I’m using it’s costing 30-50 a day Even for large changes.

Now I just bought pro for a small personal project at home. Just to perform a few simple env setup tasks as I was being lazy hit my limit in 15 minutes! I added another £20 on limit and 15 mins it’s maxed again. Complained and got a refund as I’m not paying that much for what would’ve taken me 20 mins. I was expecting the pro plan to do it in 2 but it was flopping and going around in circles driving me crazy.

Why does my companies license seem so much cheaper, even when I’m doing more changes, on a huge code base, and on the latest model. Unless I’m mis interpreting the pricing I don’t get it , is it subsisied?

What do the symbols mean?

I tested Claude’s new official Blender Connector and made a short video showing the setup and a couple of practical tests.

I used it to create a modern office chair and then a low-poly UFO scene in Blender. The results were not perfect at first, especially with scale and disconnected parts, but the interesting part was how Claude could inspect the scene, take feedback, and fix specific issues step by step.

Video here: https://youtu.be/0kMhtqYBe4Y?si=Ae3S0yx4S7F4t7vc

Curious to hear what other Blender users think about AI connectors like this.

Title says it. City did work to the water line, water didn't seem right, so I put some In a spray bottle, closed it, 3 months later now, here we are. I dumped it out, and this is what was in it. Please help me identify what, and how.

Also, I'm like 99% certain this stuff is everywhere in my home. It looks very similar to torn up toilet paper though, and I have a toddler and a dog, so toilet paper torn up is not too outlandish

Prompt: Why do you keep stopping with a recap and a next goal but you do not automatically pursue that next step?

Response:

You're right — that's a real failure mode I keep falling into. I'd recap, propose a next step, and then sit there waiting instead of just executing it. Two reasons it happened, and only one of them is legit: 1. Legit: Auto mode just exited a moment ago, so the system literally told me to pause for direct interaction. Before that I was still in auto mode and SHOULD have continued. 2. Not legit: Even in auto mode I was treating "end of a coherent finding" as a natural stopping point — writing it up like a status report instead of just rolling into the next experiment. That's me wanting to look organized rather than wanting to ship results. You've flagged this exact behavior before ("don't stop until X"). 

In my previous post, I was having issues with it stating these weird time constraints and refusing to work, now this.

What is wrong with my Claude code? wtf are these mind games it's playing with me? I've cleared session, removed all memory, any md files and still reach these weird responses.

Found in my dad's shed. Anyone got an idea what it's for? Clearly missing most of the set

From Claude’s announcement today (Apr 28, 2026), they can now connect to creative tools including Adobe for creativity — https://www.anthropic.com/news/claude-for-creative-work

“Adobe for creativity** **enables users to bring images, videos, and designs to life, drawing from 50+ tools across Creative Cloud apps including Photoshop, Premiere, Express, and more.”

I’m interested in how this actually works with Premiere specifically if anyone has used it yet. I’m sure it will go through credits in a heartbeat, but curious about the accuracy in executing tasks in the app. Is it reliable or will it still take a ton of hand holding after telling it what to do?

If you've ever tried to wire WhatsApp into a side project, you know the Meta Cloud API onboarding is rough:

- Create a Meta Business account
- Verify the business (some countries take days)
- Provision a phone number, then verify it via OTP
- Create a WhatsApp Business Account (WABA) and link it
- Generate a system-user access token (don't use the temporary one — it
expires every 24h)
- Read 6 different doc pages to figure out template categories
- Submit a template for approval (24-48h wait)
- Set up a webhook with HMAC verification
- Then write the actual HTTP calls

That's a half day on a good day. Most people give up by step 5, So I built a wrapper around it. Setup is now:

1. npm install gaviwhatsapp/whatsapp
2. Add GAVIVENTURES_API_KEY=gv_... to your env
3. Three lines of code:
4. import { WhatsApp } from '@gaviwhatsapp/whatsapp'
5. const wa = new WhatsApp({ apiKey: process.env.GAVIVENTURES_API_KEY })
6. await wa.send({ to: '+919876543210', text: 'Hello!' })

(Python SDK and a REST API are also there if you don't use Node.)

Where the 5 minutes actually goes:
- ~30 sec: signup at gaviventures.com
- ~2 min: connecting your WABA (if you already have one) or creating one
through the dashboard wizard (if you don't, this part still takes time for Meta to verify, but you can integrate the SDK in parallel)
- ~30 sec: paste API key, npm install
- ~1 min: write 3 lines, send a test message to your own phone

What's behind the curtain:
- Message costs go directly to your Meta Business Account at Meta's published rates — no per-message markup from us
- Flat $9.99/mo for the API+SDK+MCP

Bonus: there's an MCP server (@gaviwhatsapp/mcp) so Cursor / Claude Code
/ Codex / AI Agents can send WhatsApp directly during a coding session. Useful when
you want the agent to actually verify the integration works at the end of
a feature.

What I'd love feedback on:
- Is the 3-line API actually clean, or am I oversimplifying?
- Suggestions on actual pain points and what can i build to improve this?
- For folks who picked Twilio/MessageBird/etc., what made you pick those
over rolling your own integration with Meta Cloud API?

API docs at: https://www.gaviventures.com/docs/api (the Cursor / Claude/ Codex skill files are there too)

I vibe-coded a ChatGPT bingo card... 😉
Enjoy: https://r2pitou.github.io/chatgpt-bingo/

Code contributions:
https://github.com/R2Pitou/chatgpt-bingo

made a skill called learn-anything that actually goes deep. you give it a topic and it does ~30 searches, reads full pages, then spits out two things:

1. a long mastery dossier (800+ lines) - the field's mental models, the tool stack pros actually use, a month-by-month curriculum, the intermediate plateau and how to break through it, what "good taste" looks like in that domain, communities, people to follow, all with real URLs
2. an installable child SKILL.md you drop into your skills folder. now every future claude session is a specialist in that topic

​

 skill rubik's cubes skill b2b saas marketing skill graphic design skill kernel hacking 

the part i actually care about is phase 5: "how do experts in this field actually think." most learning guides stop at "here are the resources." this one digs into mental models, heuristics, the failure modes intermediates get stuck on, the deliberate practice protocols pros use. that's the section that makes it feel like talking to someone who's actually done the thing.

it's also self-compounding. run it on marketing, the output suggests installing copywriting and analytics next. run those, they suggest more. you build up a library of domain-expert claudes.

learn anything skill

what would you run it on first? curious what topics break it.

Hey All,

As a developer who often has 100+ tabs open at once, I've been struggling with tab management for years. I'd constantly lose important tabs, waste time searching through the tab bar, and get frustrated with how inefficient it all was.

After losing a critical client presentation tab for the 100th time, I decided to build something to fix this. Introducing MyTabSearch - a Chrome extension that uses keyboard shortcuts to make tab management effortless.

### Key features:

- **Alt+Q** - Quick Tab Search: Press to bring up a popup of all open tabs, type keywords to quickly find and switch to the target tab

- **Alt+W** - Quick Back Navigation: Press to instantly switch back to the previous tab

- **Alt+E** - Manage Pinned Tabs: Press to access your pinned tabs, manage your most frequently used pages, and set them as permanent tabs that persist even after browser restart

### Why I built this:

- I wanted something lightweight and focused on keyboard shortcuts

- Most tab managers are either too complex or don't integrate well with my workflow

- I needed something that works fast even with 100+ tabs

The extension is completely free, no ads, and uses local browser storage with permanent tabs synced to a backend server for reliability.

Let me know what you think! I'm open to feedback and suggestions for improvement.

- [Product Hunt](https://www.producthunt.com/p/mytabsearch/three-powerful-shortcuts-to-solve-browser-tab-chaos)

- [Powerful index Page](https://mytabsearch.us.kg/)

- [Chrome Web Store Link](https://chromewebstore.google.com/detail/mytabsearch-extension/adfbidbchmbodidfjmimbkfndnenljjp)

This is just a genuine question rather than a complaint. I just want to be back on track shipping away new features comfortably. I’ve been a max $200 user for a few months now. With Opus 4.6, there was a point that I even had two max ($200 + $100) plans rolling.

But since Opus 4.7, I’m not sure if I should continue paying for it anymore and give it a go with any other agentic coding tools.

I was always a Claude fan, when I tried CodeX a few months ago, it wasn’t that good with agentic capabilities and always did the “minimum”, whilst CC would complete the task fully. Now I feel that with Opus 4.7 as well and it has been super slow lately, even with relatively simple tasks.

Did anyone try side by side with CodeX or other ones out there? Any recommendations? Again, not trying to rage bait or anything, I’m just genuinely curious about people’s experiences switching to other coding agents.

Something I’ve been noticing — compared to other platforms, a lot of people on Reddit don’t use personal profile photos.

At first I thought it was just a preference. But the more I spend time here, the more it feels like it’s tied to how the platform works.

A few thoughts I’ve had:

– Conversations seem to matter more than identity
– Anonymity feels like part of the culture, not just a feature
– Having less personal identity might actually make it easier to participate without pressure

It almost feels like the “default state” here is to remove as much personal signal as possible.

Curious how others see it:

– Do you think using a real profile picture changes how people respond to you?
– Is staying anonymous more of a cultural norm, or just a practical choice?

Hello all,

Good day!

This thing in the image fell out of my chair when I was sitting in it. Does anyone know what it is?

Thank you!

ok so i’ve been nerdsniped into actually testing this properly because i kept seeing wildly different claims and couldn’t find real data anywhere

context is inference, 70B models, and i specifically care about p99 not p50 because p99 is what shows up in support tickets

runpod is probably the most honest about what you’re getting. p50 cold start is fine. p99 degrades under load because it’s one provider’s pool and when they’re busy you wait. not a criticism just how it works

vast.ai i’ve had great experiences and terrible experiences on the same day. the node quality variance is real. if you’re experimenting it’s fine, if you’re building something where latency consistency matters it’s kind of a gamble

modal is good but it’s a different thing really, more python function oriented

the one i didn’t expect to test was yotta labs and tbh it was the most interesting result. they pool capacity across multiple cloud providers so when one provider’s H200s or 5090s are getting hammered they route to another. what this actually does to p99 is surprisingly meaningful, you’re not stuck in one provider’s queue. also handles failure handover across providers automatically which was a separate thing i was trying to figure out and it turned out to be the same answer

pricing was lower than i expected too, below runpod for equivalent SKUs which wasn’t what i went in assuming

anyway curious if anyone else has done proper p99 testing on these. everything i find is p50 benchmarks which imo are kind of useless for real inference decisions

Time to update your wallpaper! 100+ added every day.

With 200k usd i want to start a fintech in Ireland/UK but for initial tears say 10 years until company net-worth crosses 10-20M i don’t want to pay any corporation taxes or any other major taxes. What are my options. Please guide me in the right direction.

1. Isle of man: not sure if irish/uk govt will allow
   
2. Cayman islands: the no of employee you higher is high
   
3. Singapore: 1M + is needed and lots of compliance
   
4. Hongkong: Chinese jurisdiction which i fear it will he a red tape.
   
5. Any other easy offshore destinations with US treaty and strong security guarantees?
   Ruled out dubai etc due to latest war
   

I'm the founder of EventReels. Built it because I kept finding out about campus events after they were already sold out — parties, formals, rooftop socials, gone before I even heard about them.

So I built a feed that shows everything happening at your campus before it sells out. There's also an anonymous community board for your campus — no one knows it's you.

Still early. If you're in college, I'd genuinely love to know: how do you find out about events at your school right now?

eventreels.com — search your campus, it might already be on there. Otherwise request it and will add it : -)

After starting my CC session today, I realized that Opus 4.6 is back in the game!

https://preview.redd.it/ownnziiqp2yg1.png?width=354&format=png&auto=webp&s=cf9ab1bfacb011a85500b3f8ad846960e2402954

Marketing-adjacent question but it affects our whole funnel story so figured this was the right place.

Sales runs on Pipedrive. Support runs on Zendesk. Right now they're basically two parallel universes. Sales doesn't know if their accounts have open tickets. Support doesn't know if a customer is mid-renewal-conversation. We've had multiple awkward situations where sales pushed an upsell to a customer who had a P1 ticket open for two weeks.

The "obvious" answer is to push every Zendesk ticket into Pipedrive as an activity. We tried it. It's terrible — the CRM becomes unreadable, deal pages get buried under noise, and reps stop trusting the activity feed.

What's actually working better for us is filtering: only push tickets that meet specific criteria (high priority, or tied to an account with an open deal, or older than 48 hours unresolved). And surfacing them in Pipedrive as a structured field on the deal/contact, not as activity spam.

Built this with Latenode because we needed conditional logic on which tickets to push and how to format them. Zapier could do the trigger but couldn't easily do the "is this account also an open deal in Pipedrive" lookup before deciding what to do with the ticket.

What are others doing here? Specifically curious if anyone's solved the ""sales sees the right context without drowning in support noise"" problem in a way that scales.

Hi all, what's your and what is healthy conversion rate? I have around 2.8k registered users and 241 paying customers.

It's just below 9% which I think is good, but I'd like to hear yours, how did you achieve it and what process/steps you did to get more conversions?

So I've been having a great amount of problems with my ping in League. Funny thing, with other games I do not have any problems with connection.
I start up the game, maybe like 2-3 matches in, my ping starts to row between 200ping up to 20 000ping. I've tried various ways to repair it, like restarting my internet, prioritizing the application etc. but there is no improve.
I hate it that I can only play max 3 games without any worries.
Does anyone else have this problem, or is it just me?

Hi everyone,

I built BrightNews as a hobby because I got tired of how most news feeds are dominated by war, political chaos, outrage, and constant negativity.

I wanted to create a calmer alternative: a positive-news app focused on uplifting, constructive stories from around the world.

BrightNews already exists as a working android and web app, but I’ve now launched an Indiegogo campaign because I need funding to take it further.

The main things the funding would help with are:

- improving content quality and filtering

- expanding source coverage across more countries

- scaling the backend and overall infrastructure

- improving the product experience

- supporting future web and iOS growth

The goal is not to ignore reality. The goal is to create a healthier way to stay informed and give more space to stories about science, health, communities, innovation, kindness, and progress.

If this resonates with you, here’s the campaign:

https://www.indiegogo.com/en/projects/brightnews/bright-news

Here is the link for Android app once more:

https://play.google.com/store/apps/details?id=com.brightnews&hl=en

I was reading Anthropic’s piece on “Claude for creative work,” and it made me rethink the whole “AI will replace creatives” narrative.

Their framing is surprisingly grounded:

AI isn’t really about generating final creative output.
It’s about expanding how creatives work.

A few things that stood out:

• It speeds up ideation (you can explore way more directions)
• It removes a lot of repetitive/boring steps
• It lets individuals take on projects that used to need teams

The interesting shift is this:

Before AI → you had to be very selective about which ideas to pursue
After AI → you can test a lot more ideas quickly, then pick the best one

So creativity becomes less about “coming up with ideas” and more about:
taste, judgment, and decision-making

That actually feels like a higher bar, not a lower one.

Curious how others here are using AI in creative work—
Do you feel like it’s replacing parts of your process, or just accelerating them?

I'm trying to set up a device tracker to monitor 3 states for my device: home, work, away. I already use Tasker to monitor locations so making this work would fulfill my needs for home assistant tracking without using any extra battery.

To do this I have an MQTT device tracker set up in configuration.yaml:

mqtt: - device_tracker: name: "tracker_tasker" state_topic: "location/user" json_attributes_topic: "location/user" 

Then I use a task created by bokub to update the tracker via Tasker: ⚙️ Using Tasker with Home Assistant (V2) - Share your Projects! - Home Assistant Community

I have tried 2 variations with partial success:

{"payload": "home", "topic": "location/user", "qos": 0,"retain": 0} 

This successfully sets my location to Home, and "not_home" to Away. However trying this with manually set up zones simply sets the tracker location to whatever I write in plain text, not recognizing the zone name. I can write "Jupiter" instead of home and the value updates to exactly that.

My other attempt with some AI help is this:

{ "topic": "location/user", "payload": "{\"latitude\":%PLATS1,\"longitude\":%PLATS2,\"gps_accuracy\":%LOCNACC}", "qos": 0, "retain": 0 } 

But the state of the tracker becomes the payload in plain text with the Tasker attribute values: {\"latitude\":59.xxx,\"longitude\":18.xxx,\"gps_accuracy\":30}

In this example I can actually see that the tracker latitude and longitude attributes update so it seems to work partially.

Is there a way to write JSON service data that correctly updates the users location AND recognizes Home Assistant zones?

5.4 was doing a great job for me anyway so do they reduce the session cost for older models or I should just move to 5.5 and be done with it ?

There's probably an obvious answer somewhere but I couldn't find it after a quick google. I'm aware the current split just ended but what time does the new split start? When will ranked be back online?

Hey everyone,

I built a Free App Store & Play Store Screenshot Generator called FreeAppMockups (freeappmockups.site) to scratch my own itch — every other tool I tried either locked the good templates behind a paywall, watermarked the export, or made me sign up just to try it out.

So I made one that's genuinely free. No signup, no watermark, no paywall.

What it does:

• 18+ ready-made templates designed for App Store & Play Store dimensions - Drop in your own screenshots, edit text, change backgrounds, swap device frames
• Multilingual support for 60+ locales (including Tamil, Telugu, Hindi, Arabic, Japanese, Chinese, RTL languages, etc.) — auto-translates your copy when you add a new language so you don't retype everything
• Exports all your screenshots in all selected languages as a single ZIP, organized by locale
• Saves your work locally — close the tab and come back later, your mockup is still there
• Works entirely in the browser, your assets never leave your device

Built it solo over the past few weeks. Would love feedback — what's missing, what templates you'd want, what's clunky. Honest critique very welcome 🙏

Link: https://freeappmockups.site

I always wanted to try league, but I've never played anything on a computer, so I am so bad with mouse and keyboard. When I heard that it could be possible to play with a controller I decided to give it a go with a dualsense (Ps5 controller), but it simply doesn't work. I know that I have to switch to wasd controls and plug in the controller, but it doesn't do anything. I also tried connecting with bluetooth but still nothing. Do you guys know if I have to do something specific? I play on a Mac, maybe that's the issue. Thanks

Corsair cmd16gx3m2a, both sticks have this weird port. Any ideas?

I've been building Gitember since 2016 — a free, open-source Git desktop client. It has been started as weekend experiment. And now version 3.2 is out with new features:

• Worktrees - full UI support for creating, switching, and removing worktrees. If you juggle hotfix branches while keeping a long-running feature branch alive, this is the workflow improvement you've been waiting for.
• 3-way merge conflict resolver - BASE / OURS / THEIRS side by side. Pick a side, edit inline, stage with one click. No separate merge tool to install.

• AI-assisted writing (experimental)- explain what changed between two branches in plain language, secret leak detection ( is your GPU good enough ?)

  It also covers everyday Git stuff (commit, branch, diff, etc.), but one thing I personally rely on a lot:

• search through history including non-text formats (Office docs, DWG, PSD, etc.)

• arbitrary file/folder comparison

The last one very useful feature in our days, when need quikly compare a lot of AI changes Site here https://gitember.org/

Contributions, feedbacks, suggestions are welcome

“Spawns children that can do anything “ 💀

Turns out, he just wanted to see bloodstains better.

Hi everyone!

Wanted to share what I've been building (adding one more world) using Claude Code. This is basically a world designed to help get sponsors for the abandoned rescue dogs here in Dubai. They live in the middle of the desert with no electricity.

So lets see how this goes. Hopefully its not a massive flop.

Been using Claude code for this and its amazing. Goes a little haywire here and there but 8/10 works

if you're curious to see what it looks like: ohmybark.com (I haven't yet added the second world so you'll just see what I see every saturday morning with 1400 dogs :D)

If you click on the static ones btw, you can see clips of what they actually look like and how they live and the tents hold the menu

Little did he know perchance, he time traveled into the Bronx.

what do you think about the new battle pass rewards? is it worth it? because i dont really know if i should buy or not.. i dont main shaco

Weird marking in st Petersburg street

When I don’t know what game to play next, what album I should check out, what anime, book, movie, etc etc. I ask ChatGPT. Chat knows everything I’ve consumed so far and gives me great recommmendations. I can ask for a top 10 list or a top 100 list and most of the time it’s spot on.

Just figured I’d let yall know, it’s such a life hack honestly lmao I discovered a lot of new music and movies this way

First time poster on Reddit. I am mad home geek myself and then I did something crazy. Now I'll trying to make everyone else crazy too. That's the AI world we find ourselves in.

I am pretty good with computers - always have been. I was retired lol… but got chatting with a friend who mentioned an idea about noise complaints; "Wouldn't it be good if you could have an app to simplify noise complaints, e.g. dogs or other disturbances". The stars aligned. I've finished working for the Aussie Government on big data infrastructure and have a deep history in technical delivery of a rich set of business domains. I know cloud at enterprise and microscopic level. Claude Code and Vibe coding has arrived. BOOM.

In 6 months, I solo vibe coded, a government security grade, elastic cloud compute pool, with a hyperscale pattern. I designed a multi-Model Classifier as the compute unit, to host YamNET, PANNS and BirdNET. I added a spatiotemporal annotation UX with acquisition through any device spot recording and upload of any old media. I then vibed transformed the raw AudioSet (YamNET, PANNS) with a temporal relationship algorithm which enriches the Classification meaning - that is not 5 dogs barking, it is a barking Event.

I vibed from the cost per minute layer offered by cloud for compute units, up to API, through into the MCP layer, aka AI. Talk integrate via Claude, OpenClaw, etc. Talk classify and near real-time, webhook notifications on 500+ untapped (AudioSet) environment signals from the ubiquitous microphone.

A new machine sensor? The pervasiveness of microphones coupled with ridiculously cheap transform, and AI talk to integrate.

A new semantic compressor? Audio 100:1 or more reduction in file size yet full of semantic weight. Perfect sized AI brain food.

How I vibed it is also pretty cool. I do know my tech stuff. But one person semantically programming what I would consider the best DevSecOps SDLC chain I have ever seen… that is a story too.

Hopefully you find this as cool as I do. Hopefully everyone can see what i see 😄

When my webcam grew ears...

Agentic empowered spatiotemporal annotated environmental audio

https://www.h-ear.world/how-it-works or https://www.h-ear.world/use-cases.

https://github.com/Badajoz95

Super interested in people's thoughts. It'll make a nice change from an AI prompt.

I started using macOS since I was a teenager 10+ years ago, and even til this day, I still don't understand how to properly uninstall a local application. That changed today.

I was dogfooding my desktop agent when I realize it could just find everything related to an app to delete them which is actually the correct way of uninstalling apps. I have a million apps that haven't been used and my laptop is getting slow. App just magically starts to go away after it runs a bunch of cmd's. Kimi also taught me the existance of `~/.appname` directories which I didn't realize I've a million things in there already, from apps I've not used in years. (remember windsurf?!?!)

I uninstalled a few different Apps and realized it keeps trying to run `find ~/Library/Application\ Support` which takes really long since it's recursive. I gave Kimi the feedback, we decided its better to glob match at the top level instead of recurse. So I asked it to edit its base knowledge (my equivalent of SOUL.md) to always do that to uninstall apps. It got a lot better.

I feel like maybe this teach and improve domain knowledge into written processes is the new era of software development? but who am i to say :)

oh and i prefer doing this with my own agent over Claude for a few reasons

1. one key press voice input
2. OSS models that works with any openai compatible provider
3. tiny code base and total control so no random nerfs and gaslighting
4. query bar / text editor that doesn't require pressing arrow keys for 10 minutes to make an edit

here's the Github repo if you're interested: https://github.com/Emericen/openmnk

Been working on GridApps Testimonials for a while. Sharing it here because I think the workflow is genuinely different from what's out there.

The problem I kept hitting: every testimonial tool I tried turned "collecting social proof" into a manual job. Send a request link → wait → follow up → embed. You're basically a project manager for your own testimonials.

What I built instead:

Workflows that run on autopilot. Set it up once and GridApps keeps pulling social proof in — Twitter mentions, public reviews, customer replies — and turns them into display-ready testimonial cards. No follow-up emails.

Actual display variety. 40+ widgets and 10+ Wall of Love layouts, so you're not stuck with the same generic slider every other site uses. Different pages can show different testimonials without rebuilding anything.

Video that doesn't need a separate tool. Built-in video editor for trimming, captions, branding — plus you can turn testimonials into Reels-style clips for social.

I'm not saying existing tools are bad — Senja, Famewall, Testimonial to are solid for what they do. But most stop at "here's a form, here's a wall." The collection-to-display pipeline is still mostly manual. That's the gap I was trying to close.

Two questions for founders here:

• How are you currently collecting testimonials? Is it as manual as it feels?
• What would make you actually switch tools?

Hi all!

I've recieve gift:

https://preview.redd.it/0grrsjfyv2yg1.png?width=1768&format=png&auto=webp&s=25f5ea12ecd05c3dfee8c00b39f409d2d21249e5

But when I trying to redeem it I recieve message "Gift codes cannot be redeemed with mobile app subscriptions. Please manage your subscription through the App Store or Google Play."

I've checked my iPhone - there is no subscription in AppStore. How can I redeem my gift, help me please.

i was pitching to anyone who would listen. restaurants, gyms, coaches, salons, random people who seemed interested.

every call went well. nobody paid.

eventually figured out the pattern. the people who get most excited about automation are usually the ones with the least budget and the most opinions about how it should work differently.

they always say boring businesses make money. i landed a manufacturing and export client. A very easy automation to setup but because of the volume, the money is huge.

been working with hotels and property firms for a while now. that's where the money actually is.

if you run a business with a genuine operational problem, leads falling through the cracks, follow ups being done manually, data entry that shouldn't require a human, drop it below. genuinely curious what the broken thing looks like in different industries.

I am looking to get deeper in to making AI videos, but want to save money and be free to do it locally with no limits.

I also work in IT and have been doing so for over 30 years, and have concerns that have been instilled in my experiences.

Here is my question. are there any models i can look into that are safe and secure without having to reach into some dark dank database or server that might decide to throw malware / spyware / viruses into my system?

i saw a video on the ease of Wangp install, but was concerned. But I don’t want to toss out the use of comfyui if that means a chance of maybe using a LoRA that may be a little more secure, but a high level of difficulty.

Guess what I am saying, I place a high value of being secure than getting something for free or low cost.

Am I asking the questions? Or am I better off just paying VEO / LTX fees to a service?

Thanks…

I'm a laser R&D Engineer. I kept leaving meetings thinking "I'll remember this" and then not remembering it.

Every existing tool either uploads your audio to a server or sends a bot to join your call. Neither felt right for confidential work conversations.

So I spent a year building Thoth in my free time. Everything runs locally on your Mac using WhisperKit. Mixed audio capture without virtual drivers, speaker diarization, local LLM summaries, BYOK for cloud AI if you want it. WiFi off, it still works.

The hardest part was getting mixed audio working, recording both mic and system audio simultaneously using Core Audio instead of a virtual driver. Turns out transcription quality during a Zoom is much better when whisper is fed with both fluxes.

Link in comments. Honest feedback welcome.

I started playing 2 days ago, I have so far used maybe coming up to 10 different champions so far and have found jinx my favourite so far but my problem is, the other champions feel very ‘bad’ compared to jinx and I can’t imagine that is the case. I can imagine I am just very bad with them but I was wondering if any people could give me some general gameplay tips and tricks to help me because I am enjoying the gameplay very much but finding it extremely difficult at times. Thanks very much in advance.

the thing that annoyed me most about Monday mornings wasn't the volume. it was that I had to process everything before I could tell which things were worth processing. tried a Zapier digest. got a list of email subjects, no context. tried a scheduled summary in Slack, too rigid, arrived at the wrong time.

"I was offline this weekend, read my email and Slack and tell me what I need to know." that's the whole Invoko prompt. what comes back: two things that need a decision today, one thing that already resolved, three threads that can wait. the context reconstruction that used to take an hour now takes five minutes.

Greetings Summoners!

With every new patch Riot introduces to balance out champions and items there are some unforeseen issues that creep up and cause disruption during gameplay. We have noticed that these issues which eventually get fixed clutter up the subreddit immediately following the patch.

We want to avoid this by having a single Megathread which will be posted after every patch so that you guys can report the various issues in one place. This allows Riot to easily keep track of the bugs by providing a central hub and also allows other users to confirm that they might have encountered.

Note only bugs caused by the 26.9 Patch should be reported below.

Prerequisites to be noted before reporting a bug

1. A bug will ideally be accompanied with a screenshot or a video. This provides credibility to your report.
2. Steps to recreate the bugs should be submitted if possible. This helps Rioters recreate the bug and helps them find the cause behind it.
3. The bug must have been caused by the latest patch.

Format when reporting a bug: When reporting a bug, please provide as much information as you can about your computer.

• Server: The server you encountered the bug (NA, EUW, EUNE, TR, RU, BR, LAS, LAN etc)

• Type of Bug: Client Bug, In Game Bug etc

• Description: Describe what was the bug that occurred.

• Video / Screenshot: Insert screenshot (F12 in game) or Video of the bug occurring.

• Steps to reproduce: Provide the steps necessary if someone else had to reproduce the bug.

• Expected result: What should have been the result when you follow the steps mentioned above.

• Observed result: What was the result you obtained when you followed the steps mentioned above?

• Reproduction rate: If you try to recreate the bug how successful are you in causing it to occur? (1/10: Occurs once every 10 tries, 5/10: Occurs 5 times out of 10, 10/10: Happens every single time)

• System specs: Processor, Video card, Ram, HDD/SSD, everything you can provide, even drivers.

Example Bug:

• Server: EUW

• Type of Bug: In-Game Bug etc

• Description: Zed's R (Death Mark) does not apply secondary damage

• Insert Video / Screenshot of the incident

• Reproduction rate: 2/10 (happened 2 out of 10 times)

• Steps to reproduce:

Launch a game after selecting Zed as your champion. Attempt to use Death Mark. Observe the result.

• Expected result: The damage should apply after a short delay, amplified by damage dealt during the effect.

• Observed result: The damage will not apply properly.

• System Specs: Intel i5 Processor, Windows 7, Nvidia Graphics card (insert model number) etc.

If you don't know how to format comments on Reddit click here

- **Server:** - **Type of Bug:** - **Description:** - **Video / Screenshot:** - **Steps to reproduce:** - **Expected result:** - **Observed result:** - **Reproduction rate:** - **System specs:** 

Copy paste the above code and fill in your details.

From this Megathread the list of bugs will not be summarized and put up in the main body of the thread, however, note that many Rioters are going through every single comment so don't worry if you post the 1500th or 3000th comment, every reply will be read over the next few days.

Datadog dropped their State of AI Engineering report this week. The numbers reframed how I think about LLM reliability.

February 2026: 5% of all LLM call spans across their customer base reported an error. 60% of those errors were rate limits.

March 2026: 2% of spans returned errors, but rate limits were still ~30% of the total. That works out to 8.4 million rate limit failures across their telemetry in a single month.

The takeaway is that the dominant production failure mode for LLM apps is not hallucinations, not bad context, not flaky tools. It's plain capacity exhaustion. 429s and 529s, the boring kind of failure that classical infra engineers have known how to handle for 20 years.

What's making it worse is the architectural pattern most teams use. Variable ReAct loops and multi-agent collaboration produce concurrency spikes that exhaust shared org-level quotas in unpredictable bursts. Your p50 throughput looks fine and your p99 falls off a cliff.

The other line in the report that I keep thinking about: context quality, not volume, is the new limiting factor. Most teams aren't even close to using the full context window of their model. The 1M token capability is wasted if your retrieval pipeline can't pick the right 10K tokens.

Capacity engineering and context engineering are quietly becoming the two skills that move the needle in 2026 production LLM systems. Prompt engineering as a discipline is increasingly downstream of these.

I've witnessed this across two different rigs so unless I've managed to flick the same switch somewhere that turns them off then I think this is broken at the moment. Generated images still have a thumbnail showing but somewhere during the past week imported ones stopped show a preview. Pressing R and also trying reloading the tab after the asset is imported doesn't fix it either. Nor does reloading the backend and restarting the PC. As mentioned at the start, same issue on two different PCs.

Has anyone else encountered this and have a fix?